Brief Reports

In the United States, candida now accounts for between 8% and 12% of all catheter‐associated blood stream infections (BSIs).1 Additionally, crude mortality rates in candidemia exceed 40%, and a recent systematic review demonstrated that the attributable mortality of candidemia ranges from 5% to 71%.2 Candidal BSIs also affect resource utilization. These infections independently increase length of stay and result in substantial excess costs.3 Most cases of candidemia arise in noncritically ill patients and thus may be managed by hospitalists.

Historically, the majority of candidal BSIs were caused by C. albicans. Presently, C. albicans accounts for only half of all yeast BSIs, and approximately 20% of these infections are caused by organisms such as C. glabrata and C. krusei.4 These 2 organisms have either variable or no susceptibility to agents, such as fluconazole, empirically employed against yeast. Parallel with the evolution in microbiology of candidemia has been recognition that inappropriate treatment of these infections independently increases mortality.5 These factors underscore the need for the clinician to treat suspected candidal BSIs aggressively in order to avoid the risks associated with inappropriate treatment.

Efforts to enhance rates of initial appropriate therapy for bacterial infections have encompassed the realization that health care‐associated infections (HAIs) represent a distinct syndrome.6, 7 Traditionally, infections were considered either community‐acquired or nosocomial in origin. However, with the spread of health care delivery beyond the hospital, multiple studies indicate that patients may now present to the emergency department with infections caused by pathogens such as Methicillin‐resistant Staphylococcus aureus (MRSA) and P. aeruginosaorganisms that were previously thought limited to hospital‐acquired processes.69 Furthermore, hospitalists often encounter subjects presenting to the hospital with suspected BSIs who have an active and ongoing interaction with the healthcare system.

The importance of candida as a health care‐associated pathogen in BSI remains unclear. We hypothesized that health care‐associated candidemia (HCAC) represented a distinct clinical entity. In order to confirm our theory, we conducted a retrospective analysis of all cases of candidal BSI at our institution over a 3‐year period.

Methods

We reviewed the records of all patients diagnosed with candidemia at our hospital between January 1, 2004 and December 31, 2006. Our institutional review board approved this study. We included adult patients diagnosed with candidemia. The diagnosis of candidemia was based on the isolation of yeast from the blood in at least one blood culture. We employ the BACTEC 9240 blood Culture System (Becton Dickinson Microbiology Systems, Sparks, MD). We excluded subjects who were admitted to the hospital within one month of a known diagnosis of candidemia.

We defined a nosocomial candidal BSI as the diagnosis of candidemia based on cultures drawn after the patient had been hospitalized for >48 hours. We considered HCAC to be present based on previously employed criteria for identifying HAI.69 Specifically, for patients with candidemia based on blood cultures obtained within 48 hours of hospitalization, a patient had to meet at least 1 of the following criteria: (1) receipt of intravenous therapy outside the hospital, (2) end stage renal disease necessitating hemodialysis (ESRD requiring HD), (3) hospitalization within previous 30 days, (4) residence in a nursing home or long term care facility, or (5) underwent an invasive procedure as an outpatient within 30 days of presentation. Community‐acquired candidemia was restricted to patients whose index culture was drawn within 48 hours of admission but who failed to meet the definition for HCAC.

The prevalence of the various forms of candidemia served as our primary endpoint. In addition, we compared patients with respect to demographic factors, comorbidities, and severity of illness. Severity of illness was calculated based on the Acute Physiology and Chronic Health Evaluation (APACHE) II score. We further noted rates of immune suppression in the cohort and defined this as treatment with corticosteroids (10 mg of prednisone or equivalent daily for more than 30 consecutive days), other immunosuppressants (eg, methotrexate), or chemotherapy. Those with acquired immune deficiency syndrome (AIDS) or another immunodeficiency syndrome were defined as immunosuppressed as well. We examined the distribution of yeast species across the 3 forms of candidemia. Finally, we assessed the prevalence of fluconazole resistance. Fluconazole susceptibilities were determined based on Etest (AB BIODISK, Solna, Sweden). An isolate was considered resistant to fluconazole if the minimum inhibitory concentration was >64 g/mL.

We compared categorical variables with the Fisher's exact test. Continuous variables were analyzed with either the Student's t‐test or a Mann‐Whitney test, as appropriate. All tests were 2 tailed and a P value of 0.05 was assumed to represent statistical significance. Analyses were performed with Stata 9.1 (Stata Corp., College Station, TX).

Results

The final cohort included 223 subjects. The mean age of the patients was 59.6 15.7 years and 49% were male. Nearly one quarter (n = 55) fulfilled our criteria for HCAC. The remainder met the definition for nosocomial candidemia. We observed no cases of community‐acquired candidemia. Most (n = 33) patients with HCAC had exposure to more than 1 health care‐related source and many were initially admitted to the medicine/hospitalist service as opposed to the intensive care unit (ICU). The most common criteria leading to categorization as HCAC was recent hospitalization (n = 30, 54.5% of all HCAC). The median time from recent hospitalization to admission was 17 days (Range: 5‐28 days). Other common reasons for classification as HCAC included ESRD requiring HD (30.9%), residence in a nursing home (25.5%), and undergoing an invasive outpatient procedure (16.4%). More than 75% of subjects with HCAC (n = 42) had central venous catheters in place at presentation. Between 2004 and 2006, the proportion of all candidemia due to HCAC increased from 20.9% to 26.9%, but this difference was not statistically significant.

Patients with HCAC were similar to those with nosocomial candidemia (Table 1). There was no difference in either severity of illness or the frequency of neutropenia. The prevalence of most comorbidities did not differ between those with nosocomial candidemia and persons with HCAC. However, immunosuppression was more prevalent among patients with HCAC (prevalence ratio, 1.67; 95% CI, 1.13‐3.08; P = 0.004). In part this finding is expected given that our definition of HCAC includes exposure to agents which may lead to immunosuppression, such as chemotherapy. Of patients with HCAC, the majority (n = 38, 69.1%) were initially admitted to the general medicine service and not to the ICU.

A multitude of various yeast species were recovered (Figure 1). Overall, nonalbicans candida were responsible for nearly 60% of all infections. Nonalbicans yeast were as likely to be recovered in HCAC as in nosocomial yeast infection. Among both types of Candidemia, C. krusei was a rare culprit accounting for fewer than 2% of infections. C. glabrata, however, occurred more often in HCAC. Specifically, C. glabrata represented 1 in 5 cases of HCAC as opposed to approximately 10% of all nosocomial yeast BSIs (P = 0.05). In part reflecting this, fluconazole resistance was noted more often in HCAC (18.2% of patients vs. 7.7% among nosocomial candidemia, P = 0.036). There was no difference in the eventual diagnosis of deep‐seeded yeast infections (ie, endocarditis, endopthlamitis, or osteomyelitis) between those with HCAC and persons with nosocomial candidemia (3 cases in each group).

Figure 1

Discussion

This analysis demonstrates that HCAC accounts for approximately a quarter of all candidemia. Our findings underscore that candidemia can present to the emergency department as an HAI and may potentially be initially cared for by a hospitalist. In addition, patients with HCAC and nosocomial candidemia share many attributes. Furthermore, nonalbicans yeast are as prevalent in HCAC as in nosocomial candidal infection. Nonetheless, there appear to be important differences in these syndromes. Immunosuppression appears to be more common in HCAC as does infection due to C. glabrata.

Others have explored the concept of HCAC. Kung et al.10 described community‐onset candidemia at a single center over a 10‐year period. They described 56 patients and noted that the majority had been recently hospitalized or had ongoing interaction with the healthcare system. Sofair et al.11 followed subjects presenting to emergency departments with candidemia. Overall, more than one‐third met criteria for community‐onset infection. In this analysis, though, Sofair et al.11 did not distinguish between community‐acquired processes and HCAC. From a population perspective, Chen et al.12 explored candidemia in Australia. Among over 1000 patients, the noted that 11.6% represented HCAC and, as we note, that select nonalbicans yeast occurred more often in HCAC than in nosocomial candidemia. Our project builds on and adds to these earlier efforts. First, we confirm the general observation that candidemia is no longer solely a nosocomial pathogen. Second, unlike several of these earlier reports we examined a larger cohort of candidemia. Third, beyond the observations of Chen et al.,12 we note that currently, the proportion of Candidal BSI classified as HACA relative to nosocomial candidemia seems larger than reported in the past. Finally, a unique aspect of our report is that we employed express criteria to define HAI.

Our findings have several implications. First, hospitalists and emergency department physicians, along with others, must remain vigilant when approaching patients presenting to the hospital with signs and symptoms of BSI and multiple risk factors for candidal BSI. The fact that the patient has not been hospitalized should not preclude consideration of and treatment for candidemia. The current evidence does not support broad, empiric use of antifungal agents, as this would lead to excessive costs and potentially expose many patients to unnecessary antifungal coverage. On the other hand, given the association between delayed antifungal therapy and the risk for death in candidemia, failure to consider this infection in at‐risk subjects may have adverse consequences. Second, our observations emphasize the need for clinical risk stratification schemes and rapid diagnostic modalities. Such tools are urgently needed if physicians hope to target antifungal therapies more appropriately. Third, if the clinician opts to initiate therapy for possible HCAC, reliance on fluconazole alone may prove inadequate. As the generalizability of our conclusions is necessarily limited, we recommend that infection control practitioners review local epidemiologic data regarding HCAC so that physicians can have the best available guidance.

Our study has several important limitations. Its retrospective nature exposes it to several forms of bias. The single center design limits the generalizability of our findings. Prospective, multicenter studies are needed to validate our results. Additionally, no universally accepted criteria exist to define HAI syndromes. Nonetheless, the criteria we employed have been used by others. We also lacked data on exposure to recent broad spectrum antimicrobials. Selection pressure via exposure to such agents is a risk factor for candidemia and without this data we cannot gauge the impact of this on our findings. Finally, we cannot control for the possibility that some patients were miscategorized. This could have arisen because of: (1) either limitations inherent in the definition of HCAC or (2) because the clinician delayed the decision to obtain blood cultures. Some patients classified as nosocomial may actually have had HCAC or community‐acquired diseasebut for some reason blood cultures were not drawn at time of admission but were deferred until later. Although a difficult issue to address in any study of the epidemiology of infection, the significance of this misclassification bias must be considered a significant concern.

In summary, Candidemia can be the cause of BSI presenting to the hospital. Moreover, HCAC represents a significant proportion of all Candidemia. Although patients with HCAC and nosocomial candidemia share select characteristics, there appear to be some differences in the microbiology of these syndromes.

In the United States, candida now accounts for between 8% and 12% of all catheter‐associated blood stream infections (BSIs).1 Additionally, crude mortality rates in candidemia exceed 40%, and a recent systematic review demonstrated that the attributable mortality of candidemia ranges from 5% to 71%.2 Candidal BSIs also affect resource utilization. These infections independently increase length of stay and result in substantial excess costs.3 Most cases of candidemia arise in noncritically ill patients and thus may be managed by hospitalists.

Historically, the majority of candidal BSIs were caused by C. albicans. Presently, C. albicans accounts for only half of all yeast BSIs, and approximately 20% of these infections are caused by organisms such as C. glabrata and C. krusei.4 These 2 organisms have either variable or no susceptibility to agents, such as fluconazole, empirically employed against yeast. Parallel with the evolution in microbiology of candidemia has been recognition that inappropriate treatment of these infections independently increases mortality.5 These factors underscore the need for the clinician to treat suspected candidal BSIs aggressively in order to avoid the risks associated with inappropriate treatment.

Efforts to enhance rates of initial appropriate therapy for bacterial infections have encompassed the realization that health care‐associated infections (HAIs) represent a distinct syndrome.6, 7 Traditionally, infections were considered either community‐acquired or nosocomial in origin. However, with the spread of health care delivery beyond the hospital, multiple studies indicate that patients may now present to the emergency department with infections caused by pathogens such as Methicillin‐resistant Staphylococcus aureus (MRSA) and P. aeruginosaorganisms that were previously thought limited to hospital‐acquired processes.69 Furthermore, hospitalists often encounter subjects presenting to the hospital with suspected BSIs who have an active and ongoing interaction with the healthcare system.

The importance of candida as a health care‐associated pathogen in BSI remains unclear. We hypothesized that health care‐associated candidemia (HCAC) represented a distinct clinical entity. In order to confirm our theory, we conducted a retrospective analysis of all cases of candidal BSI at our institution over a 3‐year period.

Methods

We reviewed the records of all patients diagnosed with candidemia at our hospital between January 1, 2004 and December 31, 2006. Our institutional review board approved this study. We included adult patients diagnosed with candidemia. The diagnosis of candidemia was based on the isolation of yeast from the blood in at least one blood culture. We employ the BACTEC 9240 blood Culture System (Becton Dickinson Microbiology Systems, Sparks, MD). We excluded subjects who were admitted to the hospital within one month of a known diagnosis of candidemia.

We defined a nosocomial candidal BSI as the diagnosis of candidemia based on cultures drawn after the patient had been hospitalized for >48 hours. We considered HCAC to be present based on previously employed criteria for identifying HAI.69 Specifically, for patients with candidemia based on blood cultures obtained within 48 hours of hospitalization, a patient had to meet at least 1 of the following criteria: (1) receipt of intravenous therapy outside the hospital, (2) end stage renal disease necessitating hemodialysis (ESRD requiring HD), (3) hospitalization within previous 30 days, (4) residence in a nursing home or long term care facility, or (5) underwent an invasive procedure as an outpatient within 30 days of presentation. Community‐acquired candidemia was restricted to patients whose index culture was drawn within 48 hours of admission but who failed to meet the definition for HCAC.

The prevalence of the various forms of candidemia served as our primary endpoint. In addition, we compared patients with respect to demographic factors, comorbidities, and severity of illness. Severity of illness was calculated based on the Acute Physiology and Chronic Health Evaluation (APACHE) II score. We further noted rates of immune suppression in the cohort and defined this as treatment with corticosteroids (10 mg of prednisone or equivalent daily for more than 30 consecutive days), other immunosuppressants (eg, methotrexate), or chemotherapy. Those with acquired immune deficiency syndrome (AIDS) or another immunodeficiency syndrome were defined as immunosuppressed as well. We examined the distribution of yeast species across the 3 forms of candidemia. Finally, we assessed the prevalence of fluconazole resistance. Fluconazole susceptibilities were determined based on Etest (AB BIODISK, Solna, Sweden). An isolate was considered resistant to fluconazole if the minimum inhibitory concentration was >64 g/mL.

We compared categorical variables with the Fisher's exact test. Continuous variables were analyzed with either the Student's t‐test or a Mann‐Whitney test, as appropriate. All tests were 2 tailed and a P value of 0.05 was assumed to represent statistical significance. Analyses were performed with Stata 9.1 (Stata Corp., College Station, TX).

Results

The final cohort included 223 subjects. The mean age of the patients was 59.6 15.7 years and 49% were male. Nearly one quarter (n = 55) fulfilled our criteria for HCAC. The remainder met the definition for nosocomial candidemia. We observed no cases of community‐acquired candidemia. Most (n = 33) patients with HCAC had exposure to more than 1 health care‐related source and many were initially admitted to the medicine/hospitalist service as opposed to the intensive care unit (ICU). The most common criteria leading to categorization as HCAC was recent hospitalization (n = 30, 54.5% of all HCAC). The median time from recent hospitalization to admission was 17 days (Range: 5‐28 days). Other common reasons for classification as HCAC included ESRD requiring HD (30.9%), residence in a nursing home (25.5%), and undergoing an invasive outpatient procedure (16.4%). More than 75% of subjects with HCAC (n = 42) had central venous catheters in place at presentation. Between 2004 and 2006, the proportion of all candidemia due to HCAC increased from 20.9% to 26.9%, but this difference was not statistically significant.

Patients with HCAC were similar to those with nosocomial candidemia (Table 1). There was no difference in either severity of illness or the frequency of neutropenia. The prevalence of most comorbidities did not differ between those with nosocomial candidemia and persons with HCAC. However, immunosuppression was more prevalent among patients with HCAC (prevalence ratio, 1.67; 95% CI, 1.13‐3.08; P = 0.004). In part this finding is expected given that our definition of HCAC includes exposure to agents which may lead to immunosuppression, such as chemotherapy. Of patients with HCAC, the majority (n = 38, 69.1%) were initially admitted to the general medicine service and not to the ICU.

A multitude of various yeast species were recovered (Figure 1). Overall, nonalbicans candida were responsible for nearly 60% of all infections. Nonalbicans yeast were as likely to be recovered in HCAC as in nosocomial yeast infection. Among both types of Candidemia, C. krusei was a rare culprit accounting for fewer than 2% of infections. C. glabrata, however, occurred more often in HCAC. Specifically, C. glabrata represented 1 in 5 cases of HCAC as opposed to approximately 10% of all nosocomial yeast BSIs (P = 0.05). In part reflecting this, fluconazole resistance was noted more often in HCAC (18.2% of patients vs. 7.7% among nosocomial candidemia, P = 0.036). There was no difference in the eventual diagnosis of deep‐seeded yeast infections (ie, endocarditis, endopthlamitis, or osteomyelitis) between those with HCAC and persons with nosocomial candidemia (3 cases in each group).

Figure 1

Discussion

This analysis demonstrates that HCAC accounts for approximately a quarter of all candidemia. Our findings underscore that candidemia can present to the emergency department as an HAI and may potentially be initially cared for by a hospitalist. In addition, patients with HCAC and nosocomial candidemia share many attributes. Furthermore, nonalbicans yeast are as prevalent in HCAC as in nosocomial candidal infection. Nonetheless, there appear to be important differences in these syndromes. Immunosuppression appears to be more common in HCAC as does infection due to C. glabrata.

Others have explored the concept of HCAC. Kung et al.10 described community‐onset candidemia at a single center over a 10‐year period. They described 56 patients and noted that the majority had been recently hospitalized or had ongoing interaction with the healthcare system. Sofair et al.11 followed subjects presenting to emergency departments with candidemia. Overall, more than one‐third met criteria for community‐onset infection. In this analysis, though, Sofair et al.11 did not distinguish between community‐acquired processes and HCAC. From a population perspective, Chen et al.12 explored candidemia in Australia. Among over 1000 patients, the noted that 11.6% represented HCAC and, as we note, that select nonalbicans yeast occurred more often in HCAC than in nosocomial candidemia. Our project builds on and adds to these earlier efforts. First, we confirm the general observation that candidemia is no longer solely a nosocomial pathogen. Second, unlike several of these earlier reports we examined a larger cohort of candidemia. Third, beyond the observations of Chen et al.,12 we note that currently, the proportion of Candidal BSI classified as HACA relative to nosocomial candidemia seems larger than reported in the past. Finally, a unique aspect of our report is that we employed express criteria to define HAI.

Our findings have several implications. First, hospitalists and emergency department physicians, along with others, must remain vigilant when approaching patients presenting to the hospital with signs and symptoms of BSI and multiple risk factors for candidal BSI. The fact that the patient has not been hospitalized should not preclude consideration of and treatment for candidemia. The current evidence does not support broad, empiric use of antifungal agents, as this would lead to excessive costs and potentially expose many patients to unnecessary antifungal coverage. On the other hand, given the association between delayed antifungal therapy and the risk for death in candidemia, failure to consider this infection in at‐risk subjects may have adverse consequences. Second, our observations emphasize the need for clinical risk stratification schemes and rapid diagnostic modalities. Such tools are urgently needed if physicians hope to target antifungal therapies more appropriately. Third, if the clinician opts to initiate therapy for possible HCAC, reliance on fluconazole alone may prove inadequate. As the generalizability of our conclusions is necessarily limited, we recommend that infection control practitioners review local epidemiologic data regarding HCAC so that physicians can have the best available guidance.

Our study has several important limitations. Its retrospective nature exposes it to several forms of bias. The single center design limits the generalizability of our findings. Prospective, multicenter studies are needed to validate our results. Additionally, no universally accepted criteria exist to define HAI syndromes. Nonetheless, the criteria we employed have been used by others. We also lacked data on exposure to recent broad spectrum antimicrobials. Selection pressure via exposure to such agents is a risk factor for candidemia and without this data we cannot gauge the impact of this on our findings. Finally, we cannot control for the possibility that some patients were miscategorized. This could have arisen because of: (1) either limitations inherent in the definition of HCAC or (2) because the clinician delayed the decision to obtain blood cultures. Some patients classified as nosocomial may actually have had HCAC or community‐acquired diseasebut for some reason blood cultures were not drawn at time of admission but were deferred until later. Although a difficult issue to address in any study of the epidemiology of infection, the significance of this misclassification bias must be considered a significant concern.

In summary, Candidemia can be the cause of BSI presenting to the hospital. Moreover, HCAC represents a significant proportion of all Candidemia. Although patients with HCAC and nosocomial candidemia share select characteristics, there appear to be some differences in the microbiology of these syndromes.

In the United States, candida now accounts for between 8% and 12% of all catheter‐associated blood stream infections (BSIs).1 Additionally, crude mortality rates in candidemia exceed 40%, and a recent systematic review demonstrated that the attributable mortality of candidemia ranges from 5% to 71%.2 Candidal BSIs also affect resource utilization. These infections independently increase length of stay and result in substantial excess costs.3 Most cases of candidemia arise in noncritically ill patients and thus may be managed by hospitalists.

Historically, the majority of candidal BSIs were caused by C. albicans. Presently, C. albicans accounts for only half of all yeast BSIs, and approximately 20% of these infections are caused by organisms such as C. glabrata and C. krusei.4 These 2 organisms have either variable or no susceptibility to agents, such as fluconazole, empirically employed against yeast. Parallel with the evolution in microbiology of candidemia has been recognition that inappropriate treatment of these infections independently increases mortality.5 These factors underscore the need for the clinician to treat suspected candidal BSIs aggressively in order to avoid the risks associated with inappropriate treatment.

Efforts to enhance rates of initial appropriate therapy for bacterial infections have encompassed the realization that health care‐associated infections (HAIs) represent a distinct syndrome.6, 7 Traditionally, infections were considered either community‐acquired or nosocomial in origin. However, with the spread of health care delivery beyond the hospital, multiple studies indicate that patients may now present to the emergency department with infections caused by pathogens such as Methicillin‐resistant Staphylococcus aureus (MRSA) and P. aeruginosaorganisms that were previously thought limited to hospital‐acquired processes.69 Furthermore, hospitalists often encounter subjects presenting to the hospital with suspected BSIs who have an active and ongoing interaction with the healthcare system.

The importance of candida as a health care‐associated pathogen in BSI remains unclear. We hypothesized that health care‐associated candidemia (HCAC) represented a distinct clinical entity. In order to confirm our theory, we conducted a retrospective analysis of all cases of candidal BSI at our institution over a 3‐year period.

Methods

We reviewed the records of all patients diagnosed with candidemia at our hospital between January 1, 2004 and December 31, 2006. Our institutional review board approved this study. We included adult patients diagnosed with candidemia. The diagnosis of candidemia was based on the isolation of yeast from the blood in at least one blood culture. We employ the BACTEC 9240 blood Culture System (Becton Dickinson Microbiology Systems, Sparks, MD). We excluded subjects who were admitted to the hospital within one month of a known diagnosis of candidemia.

We defined a nosocomial candidal BSI as the diagnosis of candidemia based on cultures drawn after the patient had been hospitalized for >48 hours. We considered HCAC to be present based on previously employed criteria for identifying HAI.69 Specifically, for patients with candidemia based on blood cultures obtained within 48 hours of hospitalization, a patient had to meet at least 1 of the following criteria: (1) receipt of intravenous therapy outside the hospital, (2) end stage renal disease necessitating hemodialysis (ESRD requiring HD), (3) hospitalization within previous 30 days, (4) residence in a nursing home or long term care facility, or (5) underwent an invasive procedure as an outpatient within 30 days of presentation. Community‐acquired candidemia was restricted to patients whose index culture was drawn within 48 hours of admission but who failed to meet the definition for HCAC.

The prevalence of the various forms of candidemia served as our primary endpoint. In addition, we compared patients with respect to demographic factors, comorbidities, and severity of illness. Severity of illness was calculated based on the Acute Physiology and Chronic Health Evaluation (APACHE) II score. We further noted rates of immune suppression in the cohort and defined this as treatment with corticosteroids (10 mg of prednisone or equivalent daily for more than 30 consecutive days), other immunosuppressants (eg, methotrexate), or chemotherapy. Those with acquired immune deficiency syndrome (AIDS) or another immunodeficiency syndrome were defined as immunosuppressed as well. We examined the distribution of yeast species across the 3 forms of candidemia. Finally, we assessed the prevalence of fluconazole resistance. Fluconazole susceptibilities were determined based on Etest (AB BIODISK, Solna, Sweden). An isolate was considered resistant to fluconazole if the minimum inhibitory concentration was >64 g/mL.

We compared categorical variables with the Fisher's exact test. Continuous variables were analyzed with either the Student's t‐test or a Mann‐Whitney test, as appropriate. All tests were 2 tailed and a P value of 0.05 was assumed to represent statistical significance. Analyses were performed with Stata 9.1 (Stata Corp., College Station, TX).

Results

The final cohort included 223 subjects. The mean age of the patients was 59.6 15.7 years and 49% were male. Nearly one quarter (n = 55) fulfilled our criteria for HCAC. The remainder met the definition for nosocomial candidemia. We observed no cases of community‐acquired candidemia. Most (n = 33) patients with HCAC had exposure to more than 1 health care‐related source and many were initially admitted to the medicine/hospitalist service as opposed to the intensive care unit (ICU). The most common criteria leading to categorization as HCAC was recent hospitalization (n = 30, 54.5% of all HCAC). The median time from recent hospitalization to admission was 17 days (Range: 5‐28 days). Other common reasons for classification as HCAC included ESRD requiring HD (30.9%), residence in a nursing home (25.5%), and undergoing an invasive outpatient procedure (16.4%). More than 75% of subjects with HCAC (n = 42) had central venous catheters in place at presentation. Between 2004 and 2006, the proportion of all candidemia due to HCAC increased from 20.9% to 26.9%, but this difference was not statistically significant.

Patients with HCAC were similar to those with nosocomial candidemia (Table 1). There was no difference in either severity of illness or the frequency of neutropenia. The prevalence of most comorbidities did not differ between those with nosocomial candidemia and persons with HCAC. However, immunosuppression was more prevalent among patients with HCAC (prevalence ratio, 1.67; 95% CI, 1.13‐3.08; P = 0.004). In part this finding is expected given that our definition of HCAC includes exposure to agents which may lead to immunosuppression, such as chemotherapy. Of patients with HCAC, the majority (n = 38, 69.1%) were initially admitted to the general medicine service and not to the ICU.

A multitude of various yeast species were recovered (Figure 1). Overall, nonalbicans candida were responsible for nearly 60% of all infections. Nonalbicans yeast were as likely to be recovered in HCAC as in nosocomial yeast infection. Among both types of Candidemia, C. krusei was a rare culprit accounting for fewer than 2% of infections. C. glabrata, however, occurred more often in HCAC. Specifically, C. glabrata represented 1 in 5 cases of HCAC as opposed to approximately 10% of all nosocomial yeast BSIs (P = 0.05). In part reflecting this, fluconazole resistance was noted more often in HCAC (18.2% of patients vs. 7.7% among nosocomial candidemia, P = 0.036). There was no difference in the eventual diagnosis of deep‐seeded yeast infections (ie, endocarditis, endopthlamitis, or osteomyelitis) between those with HCAC and persons with nosocomial candidemia (3 cases in each group).

Figure 1

Discussion

This analysis demonstrates that HCAC accounts for approximately a quarter of all candidemia. Our findings underscore that candidemia can present to the emergency department as an HAI and may potentially be initially cared for by a hospitalist. In addition, patients with HCAC and nosocomial candidemia share many attributes. Furthermore, nonalbicans yeast are as prevalent in HCAC as in nosocomial candidal infection. Nonetheless, there appear to be important differences in these syndromes. Immunosuppression appears to be more common in HCAC as does infection due to C. glabrata.

Others have explored the concept of HCAC. Kung et al.10 described community‐onset candidemia at a single center over a 10‐year period. They described 56 patients and noted that the majority had been recently hospitalized or had ongoing interaction with the healthcare system. Sofair et al.11 followed subjects presenting to emergency departments with candidemia. Overall, more than one‐third met criteria for community‐onset infection. In this analysis, though, Sofair et al.11 did not distinguish between community‐acquired processes and HCAC. From a population perspective, Chen et al.12 explored candidemia in Australia. Among over 1000 patients, the noted that 11.6% represented HCAC and, as we note, that select nonalbicans yeast occurred more often in HCAC than in nosocomial candidemia. Our project builds on and adds to these earlier efforts. First, we confirm the general observation that candidemia is no longer solely a nosocomial pathogen. Second, unlike several of these earlier reports we examined a larger cohort of candidemia. Third, beyond the observations of Chen et al.,12 we note that currently, the proportion of Candidal BSI classified as HACA relative to nosocomial candidemia seems larger than reported in the past. Finally, a unique aspect of our report is that we employed express criteria to define HAI.

Our findings have several implications. First, hospitalists and emergency department physicians, along with others, must remain vigilant when approaching patients presenting to the hospital with signs and symptoms of BSI and multiple risk factors for candidal BSI. The fact that the patient has not been hospitalized should not preclude consideration of and treatment for candidemia. The current evidence does not support broad, empiric use of antifungal agents, as this would lead to excessive costs and potentially expose many patients to unnecessary antifungal coverage. On the other hand, given the association between delayed antifungal therapy and the risk for death in candidemia, failure to consider this infection in at‐risk subjects may have adverse consequences. Second, our observations emphasize the need for clinical risk stratification schemes and rapid diagnostic modalities. Such tools are urgently needed if physicians hope to target antifungal therapies more appropriately. Third, if the clinician opts to initiate therapy for possible HCAC, reliance on fluconazole alone may prove inadequate. As the generalizability of our conclusions is necessarily limited, we recommend that infection control practitioners review local epidemiologic data regarding HCAC so that physicians can have the best available guidance.

Our study has several important limitations. Its retrospective nature exposes it to several forms of bias. The single center design limits the generalizability of our findings. Prospective, multicenter studies are needed to validate our results. Additionally, no universally accepted criteria exist to define HAI syndromes. Nonetheless, the criteria we employed have been used by others. We also lacked data on exposure to recent broad spectrum antimicrobials. Selection pressure via exposure to such agents is a risk factor for candidemia and without this data we cannot gauge the impact of this on our findings. Finally, we cannot control for the possibility that some patients were miscategorized. This could have arisen because of: (1) either limitations inherent in the definition of HCAC or (2) because the clinician delayed the decision to obtain blood cultures. Some patients classified as nosocomial may actually have had HCAC or community‐acquired diseasebut for some reason blood cultures were not drawn at time of admission but were deferred until later. Although a difficult issue to address in any study of the epidemiology of infection, the significance of this misclassification bias must be considered a significant concern.

In summary, Candidemia can be the cause of BSI presenting to the hospital. Moreover, HCAC represents a significant proportion of all Candidemia. Although patients with HCAC and nosocomial candidemia share select characteristics, there appear to be some differences in the microbiology of these syndromes.

An 80‐year‐old man with end‐stage renal disease requiring maintenance hemodialysis was admitted to the emergency department (ED) with complaints of fever, generalized fatigue, and lethargy. Presenting electrocardiogram (ECG) revealed normal sinus rhythm at 82 beats per minute (bpm), prolonged PR interval, complete left bundle branch block (LBBB) with wide QRS interval and tall T waves (Figure 1). A baseline ECG done 3 months ago also showed LBBB (Figure 2). In view of the underlying LBBB, changes in the presenting ECG were ignored.

Figure 1

Figure 2

Hemodialysis was planned for the patient. A few hours later, repeat ECG revealed a sine wave pattern suggestive of severe hyperkalemia (Figure 3). Laboratory results were available then and his serum potassium was found to be 6.8 mmol/L. He was started on insulin, dextrose, and calcium gluconate, but he developed cardiorespiratory arrest and died.

Figure 3

Retrospectively, looking at the presenting ECG (Figure 1), it was found that the PR interval was longer, the QRS was broader, and the T waves were taller and more peaked than the baseline ECG (Figure 2).

Discussion

Hyperkalemia is a true medical emergency with potential lethal consequences that must be treated accordingly.1, 2 It can be difficult to diagnose due to the paucity of distinctive signs and symptoms. Any ECG change due to hyperkalemia becomes an indication for stabilizing the myocardium with calcium infusion.

Often, the sequence of repolarization due to myocardial infarction is altered on ECG in patients with baseline LBBB, making it difficult to diagnose accurately. Although it is thought that changes due to electrolyte imbalances will also be masked by the presence of LBBB, there is no evidence supporting this in the literature. Hence, it is wrongly believed that LBBB masks changes due to hyperkalemia. It is important that in patients with suspected electrolyte imbalance, baseline ECG showing LBBB is compared to the presenting ECG. In our patient, the presenting ECG (Figure 1) might not look too impressive, but in comparison to the baseline ECG (Figure 2), the PR interval is longer, QRS is wider, and T waves are more peaked and taller. If the admitting physician had closely compared the presenting ECG (Figure 1) to the baseline ECG (Figure 2), the suspicion of hyperkalemia would have been high.

An 80‐year‐old man with end‐stage renal disease requiring maintenance hemodialysis was admitted to the emergency department (ED) with complaints of fever, generalized fatigue, and lethargy. Presenting electrocardiogram (ECG) revealed normal sinus rhythm at 82 beats per minute (bpm), prolonged PR interval, complete left bundle branch block (LBBB) with wide QRS interval and tall T waves (Figure 1). A baseline ECG done 3 months ago also showed LBBB (Figure 2). In view of the underlying LBBB, changes in the presenting ECG were ignored.

Figure 1

Figure 2

Hemodialysis was planned for the patient. A few hours later, repeat ECG revealed a sine wave pattern suggestive of severe hyperkalemia (Figure 3). Laboratory results were available then and his serum potassium was found to be 6.8 mmol/L. He was started on insulin, dextrose, and calcium gluconate, but he developed cardiorespiratory arrest and died.

Figure 3

Retrospectively, looking at the presenting ECG (Figure 1), it was found that the PR interval was longer, the QRS was broader, and the T waves were taller and more peaked than the baseline ECG (Figure 2).

Discussion

Hyperkalemia is a true medical emergency with potential lethal consequences that must be treated accordingly.1, 2 It can be difficult to diagnose due to the paucity of distinctive signs and symptoms. Any ECG change due to hyperkalemia becomes an indication for stabilizing the myocardium with calcium infusion.

Often, the sequence of repolarization due to myocardial infarction is altered on ECG in patients with baseline LBBB, making it difficult to diagnose accurately. Although it is thought that changes due to electrolyte imbalances will also be masked by the presence of LBBB, there is no evidence supporting this in the literature. Hence, it is wrongly believed that LBBB masks changes due to hyperkalemia. It is important that in patients with suspected electrolyte imbalance, baseline ECG showing LBBB is compared to the presenting ECG. In our patient, the presenting ECG (Figure 1) might not look too impressive, but in comparison to the baseline ECG (Figure 2), the PR interval is longer, QRS is wider, and T waves are more peaked and taller. If the admitting physician had closely compared the presenting ECG (Figure 1) to the baseline ECG (Figure 2), the suspicion of hyperkalemia would have been high.

An 80‐year‐old man with end‐stage renal disease requiring maintenance hemodialysis was admitted to the emergency department (ED) with complaints of fever, generalized fatigue, and lethargy. Presenting electrocardiogram (ECG) revealed normal sinus rhythm at 82 beats per minute (bpm), prolonged PR interval, complete left bundle branch block (LBBB) with wide QRS interval and tall T waves (Figure 1). A baseline ECG done 3 months ago also showed LBBB (Figure 2). In view of the underlying LBBB, changes in the presenting ECG were ignored.

Figure 1

Figure 2

Hemodialysis was planned for the patient. A few hours later, repeat ECG revealed a sine wave pattern suggestive of severe hyperkalemia (Figure 3). Laboratory results were available then and his serum potassium was found to be 6.8 mmol/L. He was started on insulin, dextrose, and calcium gluconate, but he developed cardiorespiratory arrest and died.

Figure 3

Retrospectively, looking at the presenting ECG (Figure 1), it was found that the PR interval was longer, the QRS was broader, and the T waves were taller and more peaked than the baseline ECG (Figure 2).

Discussion

Hyperkalemia is a true medical emergency with potential lethal consequences that must be treated accordingly.1, 2 It can be difficult to diagnose due to the paucity of distinctive signs and symptoms. Any ECG change due to hyperkalemia becomes an indication for stabilizing the myocardium with calcium infusion.

Often, the sequence of repolarization due to myocardial infarction is altered on ECG in patients with baseline LBBB, making it difficult to diagnose accurately. Although it is thought that changes due to electrolyte imbalances will also be masked by the presence of LBBB, there is no evidence supporting this in the literature. Hence, it is wrongly believed that LBBB masks changes due to hyperkalemia. It is important that in patients with suspected electrolyte imbalance, baseline ECG showing LBBB is compared to the presenting ECG. In our patient, the presenting ECG (Figure 1) might not look too impressive, but in comparison to the baseline ECG (Figure 2), the PR interval is longer, QRS is wider, and T waves are more peaked and taller. If the admitting physician had closely compared the presenting ECG (Figure 1) to the baseline ECG (Figure 2), the suspicion of hyperkalemia would have been high.

Inpatient medication errors represent an important patient safety issue. The magnitude of the problem is staggering, with 1 review finding almost 1 in every 5 medication doses in error, with 7% having potential for adverse drug events.1 While mistakes made at the ordering stage are frequently intercepted by pharmacist or nursing review, administration errors are particularly difficult to prevent.2 The patient, as the last link in the medication administration chain, represents the final individual capable of preventing an incorrect medication administration. It is perhaps surprising then that patients generally lack a formal role in detecting and preventing adverse medication administration events.3

There have been some ambitious attempts to improve patient education regarding hospital medications and involve selected patients in the medication administration process. Such initiatives may result in increased patient participation and satisfaction.47 There is also potential that increased patient knowledge of their hospital medications could promote the goal of medication safety, as the actively involved patient may be able to catch medication errors in the hospital.

Knowledge of prescribed medications is a prerequisite to patient involvement in prevention of inpatient medication errors and yet there is little research on patient knowledge of their hospital medications. Furthermore, as the experience of hospitalization may be disorienting and disempowering for patients, it remains to be seen if patient attitudes toward participation in inpatient medication safety are favorable. To that end, we conducted a pilot study in which we assessed current patient awareness of their in‐hospital medications and surveyed attitudes toward increased patient knowledge of hospital medications.

PATIENTS AND METHODS

We conducted a cross‐sectional study of 50 cognitively intact adult internal medicine inpatients at the University of Colorado Hospital, a tertiary‐care academic teaching hospital. This study was part of a larger project designed to examine potential for patient involvement in the medication reconciliation process. A professional research assistant approached eligible patients within 24 hours of admission. To be eligible, patients had to self‐identify as knowing their outpatient medications, speak English, and have been admitted from the community. Nursing home residents and patients with a past medical history of dementia were excluded. Enrollment was tracked during the first half of the study to estimate effect of inclusion/exclusion criteria. Thirty‐eight percent of hospital admissions to medicine services were excluded based on the specified criteria. Thirty‐four percent of eligible patients were approached and 50% of approached patients agreed to participate in the study. Patient knowledge of their outpatient medication regimen was compared to admitting physician medication reconciliation to assess accuracy of patient self‐report of outpatient medication knowledge.

After consenting to participate, study patients completed a structured list of their outpatient medications and a survey of attitudes about being shown their in‐hospital medications, hospital medication errors, and patient involvement in hospital safety. They then completed a list of the medications they believed to be prescribed to them in the hospital.

The primary outcomes were the proportions of as needed (PRN), scheduled, and total hospital medications omitted by the patient, compared to the inpatient medication administration record (MAR) (patient errors of omission). Secondary outcomes included the number of in‐hospital medications listed by the patient that did not appear on the inpatient MAR (patient errors of commission), as well as patient attitudes measured on a 5‐point Likert scale (1 indicated strongly disagree and 5 indicated strongly agree.) Descriptive data included age, race, gender, and number of inpatient medications prescribed. Separate analysis of variance (ANOVA) models provided mean estimates of the primary outcomes and tested differences according to each of the patient characteristics: age in years (65 or 65), self‐reported knowledge of hospital medications, and self‐reported desire to be involved in medication safety. Similar ANOVA models adjusted for number of medications were also examined to determine whether the relationship between the primary outcomes according to patient characteristics were altered by the number of medications. The protocol was approved by the Colorado Multiple Institutional Review Board.

RESULTS

Participants averaged 54 years of age (standard deviation [SD] = 17, range = 21‐89). Forty‐six percent (23/50) were male, and 74% (37/50) were non‐Hispanic white. Using a structured, patient‐completed, outpatient medication list, patients in the study were on an average of 5.3 outpatient prescription medications (range = 0‐17), 2.2 over‐the‐counter medications (range = 0‐8), and 0.2 herbal medications (range = 0‐7). The admitting physician's medication reconciliation list demonstrated similar number of outpatient prescription medications (average = 5.7) to the patient‐generated list. Fifty‐four percent of patient‐completed home medication lists included all of the prescription medications on the physician's medication reconciliation at admission. According to the inpatient MAR, study patients were prescribed an average of 11.3 scheduled and PRN hospital medications (range = 2‐26) at time of study enrollment.

Patient Knowledge of Their Hospital Medication List

Ninety‐six percent (48/50) of study patients omitted 1 or more of their hospital medications. On average, patients omitted 6.8 medications (range = 0‐22) (Table 1). Among scheduled medications, patients most commonly omitted antibiotics (17%), cardiovascular medications (16%), and antithrombotics (15%) (Figure 1). Among PRN medications, patients most commonly omitted analgesics (33%) and gastrointestinal medications (29%) (Figure 2).

Table 1.Patient Knowledge of Their Hospital Medications List

	Total Medications	Scheduled Medications	PRN Medications
NOTE: n = 50 patients. Abbreviations: CI, confidence interval; PRN, as needed.
Percent of patients with at least 1 hospital medication they could not name (95% CI)	96% (90‐100%)	94% (87‐100%)	80% (69‐92%)
Average number of hospital medications omitted by patient (range)	6.8 (0‐22)	5.2 (0‐15)	1.6 (0‐7)
Percentage of hospital medications omitted by patient (95% CI)	60% (52‐67%)	60% (52‐67%)	68% (57‐78%)

Figure 1

Figure 2

Patients less than 65 years omitted 60% of their PRN medications whereas patients greater than 65 years omitted 88% (P = 0.01). This difference remained even after adjustment for number of medications. There were no significant differences, based on age, in ability to name scheduled or total medications. Forty‐four percent of patients (22/50) believed they were receiving a medication in the hospital that was not actually prescribed.

DISCUSSION

Overall, patients in the study were able to name fewer than one‐half of their hospital medications. Our study suggests that adult medicine inpatients believe learning about their hospital medications would increase their satisfaction and has potential to promote medication safety. At the same time, patients did not know many of their hospital medications and this would limit their ability to fully participate in the medication safety process. Study patients frequently committed both errors of omission (ie, they did not know which medications were prescribed), and errors of commission (ie, they believed they were prescribed medications that were not prescribed). Younger patients were aware of more of their PRN medications than older patients, potentially reflecting greater patient care involvement in younger generations. However, study patients, regardless of age, were able to name fewer than one‐half of their PRN hospital medications. The most common scheduled hospital medications that patients were unable to name come from medication classes which can be associated with significant adverse events, including antibiotics, cardiovascular medications, and antithrombotics.

We posit that without systematically educating patients about their hospital medications, significant deficits in patient knowledge are inevitable. Some might argue that patients should not be asked to know their hospital medications or identify medication errors while sick and vulnerable. Certainly with multiple medication changes, formulary substitutions, and frequent modifications based on changes in clinical status, inpatient medication education could be time consuming and potentially introduce patient confusion or anxiety. Incorrect patient feedback could have potential to introduce new errors. An educational program might use graded participation based on patient interest and ability. Models for this exist in the literature, even extending to patient medication self‐administration.57 In our sample of inpatients, the majority desired a more active role in learning about their hospital medications and believed that their involvement might prevent hospital medication errors from occurring.

Medication literacy, education, and active patient involvement in medication monitoring as a means to improve patient outcomes has received significant attention in the outpatient setting, with lessons applicable to the hospital.8, 9 More broadly, the Joint Commission has established a Hospital National Patient Safety Goal to encourage patients' active involvement in their own care as a patient safety strategy.10 Examples set forth by the Joint Commission include involving patients in infection control measures, marking of procedural sites, and reporting of safety concerns relating to treatment.

While this study identifies patient knowledge deficit as a barrier to utilizing patients as part of the hospital medication safety process, it does not test whether reducing this knowledge deficit would actually reduce medication error. Our study population was limited to cognitively intact adult medicine patients at a single institution, limiting the generalizability of our conclusions. Our enrollment process may have resulted in a study population with less serious illness, greater knowledge of their hospital medications, and greater interest in participating in medication safety potentially overestimating patient knowledge of hospital medications. Finally, our small sample size limits the power to find differences in study comparisons.

Our findings are striking in that we found significant deficits in patient understanding of their hospital medications even among patients who believed they knew, or desired to know, what is being prescribed to them in the hospital. Without a system to incorporate the patient into hospital medication management, these patients will be disenfranchised from participating in inpatient medication safety. These results are a call to reexamine how we educate and involve patients regarding hospital medications. Mechanisms to allow patients to provide feedback to the medical team on their hospital medications might identify errors or improve patient satisfaction with their care. However, the systems and cultural changes needed to provide education on inpatient medications are considerable. Future research is needed to determine if increasing patient knowledge regarding their hospital medications would reduce medication errors in the inpatient setting and how this could be effectively implemented.

Inpatient medication errors represent an important patient safety issue. The magnitude of the problem is staggering, with 1 review finding almost 1 in every 5 medication doses in error, with 7% having potential for adverse drug events.1 While mistakes made at the ordering stage are frequently intercepted by pharmacist or nursing review, administration errors are particularly difficult to prevent.2 The patient, as the last link in the medication administration chain, represents the final individual capable of preventing an incorrect medication administration. It is perhaps surprising then that patients generally lack a formal role in detecting and preventing adverse medication administration events.3

There have been some ambitious attempts to improve patient education regarding hospital medications and involve selected patients in the medication administration process. Such initiatives may result in increased patient participation and satisfaction.47 There is also potential that increased patient knowledge of their hospital medications could promote the goal of medication safety, as the actively involved patient may be able to catch medication errors in the hospital.

Knowledge of prescribed medications is a prerequisite to patient involvement in prevention of inpatient medication errors and yet there is little research on patient knowledge of their hospital medications. Furthermore, as the experience of hospitalization may be disorienting and disempowering for patients, it remains to be seen if patient attitudes toward participation in inpatient medication safety are favorable. To that end, we conducted a pilot study in which we assessed current patient awareness of their in‐hospital medications and surveyed attitudes toward increased patient knowledge of hospital medications.

PATIENTS AND METHODS

We conducted a cross‐sectional study of 50 cognitively intact adult internal medicine inpatients at the University of Colorado Hospital, a tertiary‐care academic teaching hospital. This study was part of a larger project designed to examine potential for patient involvement in the medication reconciliation process. A professional research assistant approached eligible patients within 24 hours of admission. To be eligible, patients had to self‐identify as knowing their outpatient medications, speak English, and have been admitted from the community. Nursing home residents and patients with a past medical history of dementia were excluded. Enrollment was tracked during the first half of the study to estimate effect of inclusion/exclusion criteria. Thirty‐eight percent of hospital admissions to medicine services were excluded based on the specified criteria. Thirty‐four percent of eligible patients were approached and 50% of approached patients agreed to participate in the study. Patient knowledge of their outpatient medication regimen was compared to admitting physician medication reconciliation to assess accuracy of patient self‐report of outpatient medication knowledge.

After consenting to participate, study patients completed a structured list of their outpatient medications and a survey of attitudes about being shown their in‐hospital medications, hospital medication errors, and patient involvement in hospital safety. They then completed a list of the medications they believed to be prescribed to them in the hospital.

The primary outcomes were the proportions of as needed (PRN), scheduled, and total hospital medications omitted by the patient, compared to the inpatient medication administration record (MAR) (patient errors of omission). Secondary outcomes included the number of in‐hospital medications listed by the patient that did not appear on the inpatient MAR (patient errors of commission), as well as patient attitudes measured on a 5‐point Likert scale (1 indicated strongly disagree and 5 indicated strongly agree.) Descriptive data included age, race, gender, and number of inpatient medications prescribed. Separate analysis of variance (ANOVA) models provided mean estimates of the primary outcomes and tested differences according to each of the patient characteristics: age in years (65 or 65), self‐reported knowledge of hospital medications, and self‐reported desire to be involved in medication safety. Similar ANOVA models adjusted for number of medications were also examined to determine whether the relationship between the primary outcomes according to patient characteristics were altered by the number of medications. The protocol was approved by the Colorado Multiple Institutional Review Board.

RESULTS

Participants averaged 54 years of age (standard deviation [SD] = 17, range = 21‐89). Forty‐six percent (23/50) were male, and 74% (37/50) were non‐Hispanic white. Using a structured, patient‐completed, outpatient medication list, patients in the study were on an average of 5.3 outpatient prescription medications (range = 0‐17), 2.2 over‐the‐counter medications (range = 0‐8), and 0.2 herbal medications (range = 0‐7). The admitting physician's medication reconciliation list demonstrated similar number of outpatient prescription medications (average = 5.7) to the patient‐generated list. Fifty‐four percent of patient‐completed home medication lists included all of the prescription medications on the physician's medication reconciliation at admission. According to the inpatient MAR, study patients were prescribed an average of 11.3 scheduled and PRN hospital medications (range = 2‐26) at time of study enrollment.

Patient Knowledge of Their Hospital Medication List

Ninety‐six percent (48/50) of study patients omitted 1 or more of their hospital medications. On average, patients omitted 6.8 medications (range = 0‐22) (Table 1). Among scheduled medications, patients most commonly omitted antibiotics (17%), cardiovascular medications (16%), and antithrombotics (15%) (Figure 1). Among PRN medications, patients most commonly omitted analgesics (33%) and gastrointestinal medications (29%) (Figure 2).

Table 1.Patient Knowledge of Their Hospital Medications List

	Total Medications	Scheduled Medications	PRN Medications
NOTE: n = 50 patients. Abbreviations: CI, confidence interval; PRN, as needed.
Percent of patients with at least 1 hospital medication they could not name (95% CI)	96% (90‐100%)	94% (87‐100%)	80% (69‐92%)
Average number of hospital medications omitted by patient (range)	6.8 (0‐22)	5.2 (0‐15)	1.6 (0‐7)
Percentage of hospital medications omitted by patient (95% CI)	60% (52‐67%)	60% (52‐67%)	68% (57‐78%)

Figure 1

Figure 2

Patients less than 65 years omitted 60% of their PRN medications whereas patients greater than 65 years omitted 88% (P = 0.01). This difference remained even after adjustment for number of medications. There were no significant differences, based on age, in ability to name scheduled or total medications. Forty‐four percent of patients (22/50) believed they were receiving a medication in the hospital that was not actually prescribed.

DISCUSSION

Overall, patients in the study were able to name fewer than one‐half of their hospital medications. Our study suggests that adult medicine inpatients believe learning about their hospital medications would increase their satisfaction and has potential to promote medication safety. At the same time, patients did not know many of their hospital medications and this would limit their ability to fully participate in the medication safety process. Study patients frequently committed both errors of omission (ie, they did not know which medications were prescribed), and errors of commission (ie, they believed they were prescribed medications that were not prescribed). Younger patients were aware of more of their PRN medications than older patients, potentially reflecting greater patient care involvement in younger generations. However, study patients, regardless of age, were able to name fewer than one‐half of their PRN hospital medications. The most common scheduled hospital medications that patients were unable to name come from medication classes which can be associated with significant adverse events, including antibiotics, cardiovascular medications, and antithrombotics.

We posit that without systematically educating patients about their hospital medications, significant deficits in patient knowledge are inevitable. Some might argue that patients should not be asked to know their hospital medications or identify medication errors while sick and vulnerable. Certainly with multiple medication changes, formulary substitutions, and frequent modifications based on changes in clinical status, inpatient medication education could be time consuming and potentially introduce patient confusion or anxiety. Incorrect patient feedback could have potential to introduce new errors. An educational program might use graded participation based on patient interest and ability. Models for this exist in the literature, even extending to patient medication self‐administration.57 In our sample of inpatients, the majority desired a more active role in learning about their hospital medications and believed that their involvement might prevent hospital medication errors from occurring.

Medication literacy, education, and active patient involvement in medication monitoring as a means to improve patient outcomes has received significant attention in the outpatient setting, with lessons applicable to the hospital.8, 9 More broadly, the Joint Commission has established a Hospital National Patient Safety Goal to encourage patients' active involvement in their own care as a patient safety strategy.10 Examples set forth by the Joint Commission include involving patients in infection control measures, marking of procedural sites, and reporting of safety concerns relating to treatment.

While this study identifies patient knowledge deficit as a barrier to utilizing patients as part of the hospital medication safety process, it does not test whether reducing this knowledge deficit would actually reduce medication error. Our study population was limited to cognitively intact adult medicine patients at a single institution, limiting the generalizability of our conclusions. Our enrollment process may have resulted in a study population with less serious illness, greater knowledge of their hospital medications, and greater interest in participating in medication safety potentially overestimating patient knowledge of hospital medications. Finally, our small sample size limits the power to find differences in study comparisons.

Our findings are striking in that we found significant deficits in patient understanding of their hospital medications even among patients who believed they knew, or desired to know, what is being prescribed to them in the hospital. Without a system to incorporate the patient into hospital medication management, these patients will be disenfranchised from participating in inpatient medication safety. These results are a call to reexamine how we educate and involve patients regarding hospital medications. Mechanisms to allow patients to provide feedback to the medical team on their hospital medications might identify errors or improve patient satisfaction with their care. However, the systems and cultural changes needed to provide education on inpatient medications are considerable. Future research is needed to determine if increasing patient knowledge regarding their hospital medications would reduce medication errors in the inpatient setting and how this could be effectively implemented.

Inpatient medication errors represent an important patient safety issue. The magnitude of the problem is staggering, with 1 review finding almost 1 in every 5 medication doses in error, with 7% having potential for adverse drug events.1 While mistakes made at the ordering stage are frequently intercepted by pharmacist or nursing review, administration errors are particularly difficult to prevent.2 The patient, as the last link in the medication administration chain, represents the final individual capable of preventing an incorrect medication administration. It is perhaps surprising then that patients generally lack a formal role in detecting and preventing adverse medication administration events.3

There have been some ambitious attempts to improve patient education regarding hospital medications and involve selected patients in the medication administration process. Such initiatives may result in increased patient participation and satisfaction.47 There is also potential that increased patient knowledge of their hospital medications could promote the goal of medication safety, as the actively involved patient may be able to catch medication errors in the hospital.

Knowledge of prescribed medications is a prerequisite to patient involvement in prevention of inpatient medication errors and yet there is little research on patient knowledge of their hospital medications. Furthermore, as the experience of hospitalization may be disorienting and disempowering for patients, it remains to be seen if patient attitudes toward participation in inpatient medication safety are favorable. To that end, we conducted a pilot study in which we assessed current patient awareness of their in‐hospital medications and surveyed attitudes toward increased patient knowledge of hospital medications.

PATIENTS AND METHODS

We conducted a cross‐sectional study of 50 cognitively intact adult internal medicine inpatients at the University of Colorado Hospital, a tertiary‐care academic teaching hospital. This study was part of a larger project designed to examine potential for patient involvement in the medication reconciliation process. A professional research assistant approached eligible patients within 24 hours of admission. To be eligible, patients had to self‐identify as knowing their outpatient medications, speak English, and have been admitted from the community. Nursing home residents and patients with a past medical history of dementia were excluded. Enrollment was tracked during the first half of the study to estimate effect of inclusion/exclusion criteria. Thirty‐eight percent of hospital admissions to medicine services were excluded based on the specified criteria. Thirty‐four percent of eligible patients were approached and 50% of approached patients agreed to participate in the study. Patient knowledge of their outpatient medication regimen was compared to admitting physician medication reconciliation to assess accuracy of patient self‐report of outpatient medication knowledge.

After consenting to participate, study patients completed a structured list of their outpatient medications and a survey of attitudes about being shown their in‐hospital medications, hospital medication errors, and patient involvement in hospital safety. They then completed a list of the medications they believed to be prescribed to them in the hospital.

The primary outcomes were the proportions of as needed (PRN), scheduled, and total hospital medications omitted by the patient, compared to the inpatient medication administration record (MAR) (patient errors of omission). Secondary outcomes included the number of in‐hospital medications listed by the patient that did not appear on the inpatient MAR (patient errors of commission), as well as patient attitudes measured on a 5‐point Likert scale (1 indicated strongly disagree and 5 indicated strongly agree.) Descriptive data included age, race, gender, and number of inpatient medications prescribed. Separate analysis of variance (ANOVA) models provided mean estimates of the primary outcomes and tested differences according to each of the patient characteristics: age in years (65 or 65), self‐reported knowledge of hospital medications, and self‐reported desire to be involved in medication safety. Similar ANOVA models adjusted for number of medications were also examined to determine whether the relationship between the primary outcomes according to patient characteristics were altered by the number of medications. The protocol was approved by the Colorado Multiple Institutional Review Board.

RESULTS

Participants averaged 54 years of age (standard deviation [SD] = 17, range = 21‐89). Forty‐six percent (23/50) were male, and 74% (37/50) were non‐Hispanic white. Using a structured, patient‐completed, outpatient medication list, patients in the study were on an average of 5.3 outpatient prescription medications (range = 0‐17), 2.2 over‐the‐counter medications (range = 0‐8), and 0.2 herbal medications (range = 0‐7). The admitting physician's medication reconciliation list demonstrated similar number of outpatient prescription medications (average = 5.7) to the patient‐generated list. Fifty‐four percent of patient‐completed home medication lists included all of the prescription medications on the physician's medication reconciliation at admission. According to the inpatient MAR, study patients were prescribed an average of 11.3 scheduled and PRN hospital medications (range = 2‐26) at time of study enrollment.

Patient Knowledge of Their Hospital Medication List

Ninety‐six percent (48/50) of study patients omitted 1 or more of their hospital medications. On average, patients omitted 6.8 medications (range = 0‐22) (Table 1). Among scheduled medications, patients most commonly omitted antibiotics (17%), cardiovascular medications (16%), and antithrombotics (15%) (Figure 1). Among PRN medications, patients most commonly omitted analgesics (33%) and gastrointestinal medications (29%) (Figure 2).

Table 1.Patient Knowledge of Their Hospital Medications List

	Total Medications	Scheduled Medications	PRN Medications
NOTE: n = 50 patients. Abbreviations: CI, confidence interval; PRN, as needed.
Percent of patients with at least 1 hospital medication they could not name (95% CI)	96% (90‐100%)	94% (87‐100%)	80% (69‐92%)
Average number of hospital medications omitted by patient (range)	6.8 (0‐22)	5.2 (0‐15)	1.6 (0‐7)
Percentage of hospital medications omitted by patient (95% CI)	60% (52‐67%)	60% (52‐67%)	68% (57‐78%)

Figure 1

Figure 2

Patients less than 65 years omitted 60% of their PRN medications whereas patients greater than 65 years omitted 88% (P = 0.01). This difference remained even after adjustment for number of medications. There were no significant differences, based on age, in ability to name scheduled or total medications. Forty‐four percent of patients (22/50) believed they were receiving a medication in the hospital that was not actually prescribed.

DISCUSSION

Overall, patients in the study were able to name fewer than one‐half of their hospital medications. Our study suggests that adult medicine inpatients believe learning about their hospital medications would increase their satisfaction and has potential to promote medication safety. At the same time, patients did not know many of their hospital medications and this would limit their ability to fully participate in the medication safety process. Study patients frequently committed both errors of omission (ie, they did not know which medications were prescribed), and errors of commission (ie, they believed they were prescribed medications that were not prescribed). Younger patients were aware of more of their PRN medications than older patients, potentially reflecting greater patient care involvement in younger generations. However, study patients, regardless of age, were able to name fewer than one‐half of their PRN hospital medications. The most common scheduled hospital medications that patients were unable to name come from medication classes which can be associated with significant adverse events, including antibiotics, cardiovascular medications, and antithrombotics.

We posit that without systematically educating patients about their hospital medications, significant deficits in patient knowledge are inevitable. Some might argue that patients should not be asked to know their hospital medications or identify medication errors while sick and vulnerable. Certainly with multiple medication changes, formulary substitutions, and frequent modifications based on changes in clinical status, inpatient medication education could be time consuming and potentially introduce patient confusion or anxiety. Incorrect patient feedback could have potential to introduce new errors. An educational program might use graded participation based on patient interest and ability. Models for this exist in the literature, even extending to patient medication self‐administration.57 In our sample of inpatients, the majority desired a more active role in learning about their hospital medications and believed that their involvement might prevent hospital medication errors from occurring.

Medication literacy, education, and active patient involvement in medication monitoring as a means to improve patient outcomes has received significant attention in the outpatient setting, with lessons applicable to the hospital.8, 9 More broadly, the Joint Commission has established a Hospital National Patient Safety Goal to encourage patients' active involvement in their own care as a patient safety strategy.10 Examples set forth by the Joint Commission include involving patients in infection control measures, marking of procedural sites, and reporting of safety concerns relating to treatment.

While this study identifies patient knowledge deficit as a barrier to utilizing patients as part of the hospital medication safety process, it does not test whether reducing this knowledge deficit would actually reduce medication error. Our study population was limited to cognitively intact adult medicine patients at a single institution, limiting the generalizability of our conclusions. Our enrollment process may have resulted in a study population with less serious illness, greater knowledge of their hospital medications, and greater interest in participating in medication safety potentially overestimating patient knowledge of hospital medications. Finally, our small sample size limits the power to find differences in study comparisons.

Our findings are striking in that we found significant deficits in patient understanding of their hospital medications even among patients who believed they knew, or desired to know, what is being prescribed to them in the hospital. Without a system to incorporate the patient into hospital medication management, these patients will be disenfranchised from participating in inpatient medication safety. These results are a call to reexamine how we educate and involve patients regarding hospital medications. Mechanisms to allow patients to provide feedback to the medical team on their hospital medications might identify errors or improve patient satisfaction with their care. However, the systems and cultural changes needed to provide education on inpatient medications are considerable. Future research is needed to determine if increasing patient knowledge regarding their hospital medications would reduce medication errors in the inpatient setting and how this could be effectively implemented.

Adequate sleep is important for health, yet the hospital environment commonly disrupts sleep.13 Sleep improves after several days in the hospital.3, 4 Sleep deprivation increases cortisol levels5 and sleep loss of greater than 4 hours may be hyperalgesic.6 Even a few days' suppression of slow‐wave sleep worsens glucose tolerance.7 Sleep disruption may cause irritability and aggressiveness,8 impaired memory consolidation, and delirium.2

Noise may disrupt sleep. The World Health Organization recommends a maximum of 30 to 40 dBA in patients' rooms at night.9, 10 Normal conversation occurs at 60 dBA. Medical equipment alarms are about 80 dBA.

Sedative use is common in the hospital.3 Sedatives typically shorten sleep latency and suppress rapid eye movement (REM) sleep. However, some sedatives cause delirium, falls, amnesia, and confusion, particularly in the elderly.1113

Most research on sleep in hospitalized patients has been done in the critical care setting, often in sedated ventilated patients, where sleep disruption is well‐described.1416 Only a few small studies have assessed the sleep of hospitalized patients outside critical care.17, 18

A single blinded interventional trial assessed sedative use, but was a nonrandomized study.19, 20 As‐needed sedative use was measured among hospitalized elderly patients as a secondary endpoint. The intervention, known as the Hospital Elder Life Program (HELP), included a protocol with noise reduction, massage, music, and warm drinks, as well as rescheduling of medications and procedures; it resulted in a 24% reduction in as‐needed sedative use. Another trial decreased noise and reduced overnight X‐rays on a surgical unit, then measured staff and patient attitudes.21 Two interventional studies in nursing homes reduced noise and light, and/or increased daytime activity and found no effect on most objective measures of sleep.22, 23 One descriptive study found most sleep disturbances in medical‐surgical patients came from noise and sleeping in an unfamiliar bed.4

We hypothesized that an intervention designed to improve patient sleep through changes in staff behavior would decrease sedative use among unselected patients in a medical‐surgical unit. We measured sedative use as our primary endpoint as a marker for effective sleep, and because decreased sedative use is desirable. We also hypothesized that the intervention would lead to improved sleep experiences, as measured by a questionnaire and Verran Snyder‐Halpern (VSH) sleep scores as secondary endpoints.24

Materials And Methods

Study Protocol

A single investigator surveyed patients in the morning about the prior night's sleep experience. The surveys consisted of the VSH sleep scale, as well as an 8‐item questionnaire developed from informal pilot interviews with about 18 patients conducted by 1 of the investigators (M.B.) (Supporting Information Figure 1). The VSH scale is a visual analog scale using a 100‐cm line,24 which we modified with a 100‐mm line to make it easier to collect data. The questionnaire and VSH scores of patients with cognitive impairment were not included in the final analysis. Cognitive impairment was determined by diagnoses present in chart review. Surveys and consent forms were available in 4 languages and trained interpreters were used as needed. Nurses, providers, and patients were blinded to the measurement of as‐needed sedative use, and staff were unaware of which patients were study subjects.

Figure 1

Results

During the preintervention phase, 334 patients were screened, 294 were eligible, and 54.7% of eligible subjects were enrolled (n = 161). During the intervention phase, 211 patients were screened, 188 were eligible, and 56.3% of eligible patients were enrolled (n = 106). The mean patient age was 60.6 years. The preintervention and intervention groups did not differ significantly in enrollment rate, age, gender, cognitive impairment, surgical status, or hearing deficiencies (Table 1). Over 93% of patients were nonsurgical.

Sedative Use

Preintervention, 31.7% of patients received nighttime as‐needed sedatives, versus 16.0% of the intervention group, a 49.4% reduction (P = 0.0041; 95% confidence interval [CI]: 0.056‐0.26) (Figure 2). In patients aged 65 years or older, 38.2% received nighttime as‐needed sedatives preintervention, and 14.6% did postintervention, a 61.2% reduction (P = 0.0054; 95% CI: 0.084‐0.39).

Figure 2

Questionnaire Results

Preintervention, hospital staff was by far the biggest factor keeping patients awake, with 42.4% of patients reporting it (Figure 3). This dropped to only 25.7% with the intervention, a 39.3% decrease (P = 0.009; 95% CI: 0.0452‐0.2765). Preintervention, 19.2% of patients selected voices as the noise most likely to bother them at night, and this dropped to 9.9% with the intervention, a 48% decrease (P = 0.045; 95% CI: 0.0074‐0.1787). No other significant differences were found.

Figure 3

Discussion

Our trial found that hospital staff was the factor most responsible for patient sleep disruption, and that behavioral interventions on hospital staff can reduce use of as‐needed sedatives. The only previously reported intervention to reduce sedative use, the HELP strategy, involved a complex intervention requiring extra staff, with adherence ranging from 10% to 75%.19, 20, 25 In contrast, our protocol can be easily replicated at minimal cost.

Our results are consistent with those of Freedman et al.,26 who found that noise was not the primary factor responsible for sleep disruption in ICU patients, and that staff activities were at least as important a factor. The study is also consistent with the nursing home studies in which decreases in noise and light did not improve sleep.22, 23 It refutes the study that showed that most sleep disturbance in medical‐surgical patients comes from noise and sleeping in an unfamiliar bed.4 Our results call into question the use of the VSH scale in hospitalized patients, which was designed for use in healthy subjects.

Limitations of this study were as follows: moderate size, lack of refined measures of disease severity, and, as in previous studies,19, 2123 the lack of randomized concurrent controls. Evaluation of secondary endpoints was limited by lack of validation of the questionnaire with objective observations, and inability to use the modified VSH scale. Self‐reports of sleep may correlate imperfectly with objective measures, such as polysomnography.27

A larger concurrent trial randomizing similar units at multiple hospitals would be ideal. Future research is needed to determine whether improving sleep in the hospital improves other outcomes, such as recovery times, delirium, falls, or cost.

The need to reduce as‐needed sedatives is an important safety issue and similar interventions in other hospitals may be helpful. Simple changes in staff routines and provider prescribing habits can yield significant reductions in sedative use.

Adequate sleep is important for health, yet the hospital environment commonly disrupts sleep.13 Sleep improves after several days in the hospital.3, 4 Sleep deprivation increases cortisol levels5 and sleep loss of greater than 4 hours may be hyperalgesic.6 Even a few days' suppression of slow‐wave sleep worsens glucose tolerance.7 Sleep disruption may cause irritability and aggressiveness,8 impaired memory consolidation, and delirium.2

Noise may disrupt sleep. The World Health Organization recommends a maximum of 30 to 40 dBA in patients' rooms at night.9, 10 Normal conversation occurs at 60 dBA. Medical equipment alarms are about 80 dBA.

Sedative use is common in the hospital.3 Sedatives typically shorten sleep latency and suppress rapid eye movement (REM) sleep. However, some sedatives cause delirium, falls, amnesia, and confusion, particularly in the elderly.1113

Most research on sleep in hospitalized patients has been done in the critical care setting, often in sedated ventilated patients, where sleep disruption is well‐described.1416 Only a few small studies have assessed the sleep of hospitalized patients outside critical care.17, 18

A single blinded interventional trial assessed sedative use, but was a nonrandomized study.19, 20 As‐needed sedative use was measured among hospitalized elderly patients as a secondary endpoint. The intervention, known as the Hospital Elder Life Program (HELP), included a protocol with noise reduction, massage, music, and warm drinks, as well as rescheduling of medications and procedures; it resulted in a 24% reduction in as‐needed sedative use. Another trial decreased noise and reduced overnight X‐rays on a surgical unit, then measured staff and patient attitudes.21 Two interventional studies in nursing homes reduced noise and light, and/or increased daytime activity and found no effect on most objective measures of sleep.22, 23 One descriptive study found most sleep disturbances in medical‐surgical patients came from noise and sleeping in an unfamiliar bed.4

We hypothesized that an intervention designed to improve patient sleep through changes in staff behavior would decrease sedative use among unselected patients in a medical‐surgical unit. We measured sedative use as our primary endpoint as a marker for effective sleep, and because decreased sedative use is desirable. We also hypothesized that the intervention would lead to improved sleep experiences, as measured by a questionnaire and Verran Snyder‐Halpern (VSH) sleep scores as secondary endpoints.24

Materials And Methods

Study Protocol

A single investigator surveyed patients in the morning about the prior night's sleep experience. The surveys consisted of the VSH sleep scale, as well as an 8‐item questionnaire developed from informal pilot interviews with about 18 patients conducted by 1 of the investigators (M.B.) (Supporting Information Figure 1). The VSH scale is a visual analog scale using a 100‐cm line,24 which we modified with a 100‐mm line to make it easier to collect data. The questionnaire and VSH scores of patients with cognitive impairment were not included in the final analysis. Cognitive impairment was determined by diagnoses present in chart review. Surveys and consent forms were available in 4 languages and trained interpreters were used as needed. Nurses, providers, and patients were blinded to the measurement of as‐needed sedative use, and staff were unaware of which patients were study subjects.

Figure 1

Results

During the preintervention phase, 334 patients were screened, 294 were eligible, and 54.7% of eligible subjects were enrolled (n = 161). During the intervention phase, 211 patients were screened, 188 were eligible, and 56.3% of eligible patients were enrolled (n = 106). The mean patient age was 60.6 years. The preintervention and intervention groups did not differ significantly in enrollment rate, age, gender, cognitive impairment, surgical status, or hearing deficiencies (Table 1). Over 93% of patients were nonsurgical.

Sedative Use

Preintervention, 31.7% of patients received nighttime as‐needed sedatives, versus 16.0% of the intervention group, a 49.4% reduction (P = 0.0041; 95% confidence interval [CI]: 0.056‐0.26) (Figure 2). In patients aged 65 years or older, 38.2% received nighttime as‐needed sedatives preintervention, and 14.6% did postintervention, a 61.2% reduction (P = 0.0054; 95% CI: 0.084‐0.39).

Figure 2

Questionnaire Results

Preintervention, hospital staff was by far the biggest factor keeping patients awake, with 42.4% of patients reporting it (Figure 3). This dropped to only 25.7% with the intervention, a 39.3% decrease (P = 0.009; 95% CI: 0.0452‐0.2765). Preintervention, 19.2% of patients selected voices as the noise most likely to bother them at night, and this dropped to 9.9% with the intervention, a 48% decrease (P = 0.045; 95% CI: 0.0074‐0.1787). No other significant differences were found.

Figure 3

Discussion

Our trial found that hospital staff was the factor most responsible for patient sleep disruption, and that behavioral interventions on hospital staff can reduce use of as‐needed sedatives. The only previously reported intervention to reduce sedative use, the HELP strategy, involved a complex intervention requiring extra staff, with adherence ranging from 10% to 75%.19, 20, 25 In contrast, our protocol can be easily replicated at minimal cost.

Our results are consistent with those of Freedman et al.,26 who found that noise was not the primary factor responsible for sleep disruption in ICU patients, and that staff activities were at least as important a factor. The study is also consistent with the nursing home studies in which decreases in noise and light did not improve sleep.22, 23 It refutes the study that showed that most sleep disturbance in medical‐surgical patients comes from noise and sleeping in an unfamiliar bed.4 Our results call into question the use of the VSH scale in hospitalized patients, which was designed for use in healthy subjects.

Limitations of this study were as follows: moderate size, lack of refined measures of disease severity, and, as in previous studies,19, 2123 the lack of randomized concurrent controls. Evaluation of secondary endpoints was limited by lack of validation of the questionnaire with objective observations, and inability to use the modified VSH scale. Self‐reports of sleep may correlate imperfectly with objective measures, such as polysomnography.27

A larger concurrent trial randomizing similar units at multiple hospitals would be ideal. Future research is needed to determine whether improving sleep in the hospital improves other outcomes, such as recovery times, delirium, falls, or cost.

The need to reduce as‐needed sedatives is an important safety issue and similar interventions in other hospitals may be helpful. Simple changes in staff routines and provider prescribing habits can yield significant reductions in sedative use.

Adequate sleep is important for health, yet the hospital environment commonly disrupts sleep.13 Sleep improves after several days in the hospital.3, 4 Sleep deprivation increases cortisol levels5 and sleep loss of greater than 4 hours may be hyperalgesic.6 Even a few days' suppression of slow‐wave sleep worsens glucose tolerance.7 Sleep disruption may cause irritability and aggressiveness,8 impaired memory consolidation, and delirium.2

Noise may disrupt sleep. The World Health Organization recommends a maximum of 30 to 40 dBA in patients' rooms at night.9, 10 Normal conversation occurs at 60 dBA. Medical equipment alarms are about 80 dBA.

Sedative use is common in the hospital.3 Sedatives typically shorten sleep latency and suppress rapid eye movement (REM) sleep. However, some sedatives cause delirium, falls, amnesia, and confusion, particularly in the elderly.1113

Most research on sleep in hospitalized patients has been done in the critical care setting, often in sedated ventilated patients, where sleep disruption is well‐described.1416 Only a few small studies have assessed the sleep of hospitalized patients outside critical care.17, 18

A single blinded interventional trial assessed sedative use, but was a nonrandomized study.19, 20 As‐needed sedative use was measured among hospitalized elderly patients as a secondary endpoint. The intervention, known as the Hospital Elder Life Program (HELP), included a protocol with noise reduction, massage, music, and warm drinks, as well as rescheduling of medications and procedures; it resulted in a 24% reduction in as‐needed sedative use. Another trial decreased noise and reduced overnight X‐rays on a surgical unit, then measured staff and patient attitudes.21 Two interventional studies in nursing homes reduced noise and light, and/or increased daytime activity and found no effect on most objective measures of sleep.22, 23 One descriptive study found most sleep disturbances in medical‐surgical patients came from noise and sleeping in an unfamiliar bed.4

We hypothesized that an intervention designed to improve patient sleep through changes in staff behavior would decrease sedative use among unselected patients in a medical‐surgical unit. We measured sedative use as our primary endpoint as a marker for effective sleep, and because decreased sedative use is desirable. We also hypothesized that the intervention would lead to improved sleep experiences, as measured by a questionnaire and Verran Snyder‐Halpern (VSH) sleep scores as secondary endpoints.24

Materials And Methods

Study Protocol

A single investigator surveyed patients in the morning about the prior night's sleep experience. The surveys consisted of the VSH sleep scale, as well as an 8‐item questionnaire developed from informal pilot interviews with about 18 patients conducted by 1 of the investigators (M.B.) (Supporting Information Figure 1). The VSH scale is a visual analog scale using a 100‐cm line,24 which we modified with a 100‐mm line to make it easier to collect data. The questionnaire and VSH scores of patients with cognitive impairment were not included in the final analysis. Cognitive impairment was determined by diagnoses present in chart review. Surveys and consent forms were available in 4 languages and trained interpreters were used as needed. Nurses, providers, and patients were blinded to the measurement of as‐needed sedative use, and staff were unaware of which patients were study subjects.

Figure 1

Results

During the preintervention phase, 334 patients were screened, 294 were eligible, and 54.7% of eligible subjects were enrolled (n = 161). During the intervention phase, 211 patients were screened, 188 were eligible, and 56.3% of eligible patients were enrolled (n = 106). The mean patient age was 60.6 years. The preintervention and intervention groups did not differ significantly in enrollment rate, age, gender, cognitive impairment, surgical status, or hearing deficiencies (Table 1). Over 93% of patients were nonsurgical.

Sedative Use

Preintervention, 31.7% of patients received nighttime as‐needed sedatives, versus 16.0% of the intervention group, a 49.4% reduction (P = 0.0041; 95% confidence interval [CI]: 0.056‐0.26) (Figure 2). In patients aged 65 years or older, 38.2% received nighttime as‐needed sedatives preintervention, and 14.6% did postintervention, a 61.2% reduction (P = 0.0054; 95% CI: 0.084‐0.39).

Figure 2

Questionnaire Results

Preintervention, hospital staff was by far the biggest factor keeping patients awake, with 42.4% of patients reporting it (Figure 3). This dropped to only 25.7% with the intervention, a 39.3% decrease (P = 0.009; 95% CI: 0.0452‐0.2765). Preintervention, 19.2% of patients selected voices as the noise most likely to bother them at night, and this dropped to 9.9% with the intervention, a 48% decrease (P = 0.045; 95% CI: 0.0074‐0.1787). No other significant differences were found.

Figure 3

Discussion

Our trial found that hospital staff was the factor most responsible for patient sleep disruption, and that behavioral interventions on hospital staff can reduce use of as‐needed sedatives. The only previously reported intervention to reduce sedative use, the HELP strategy, involved a complex intervention requiring extra staff, with adherence ranging from 10% to 75%.19, 20, 25 In contrast, our protocol can be easily replicated at minimal cost.

Our results are consistent with those of Freedman et al.,26 who found that noise was not the primary factor responsible for sleep disruption in ICU patients, and that staff activities were at least as important a factor. The study is also consistent with the nursing home studies in which decreases in noise and light did not improve sleep.22, 23 It refutes the study that showed that most sleep disturbance in medical‐surgical patients comes from noise and sleeping in an unfamiliar bed.4 Our results call into question the use of the VSH scale in hospitalized patients, which was designed for use in healthy subjects.

Limitations of this study were as follows: moderate size, lack of refined measures of disease severity, and, as in previous studies,19, 2123 the lack of randomized concurrent controls. Evaluation of secondary endpoints was limited by lack of validation of the questionnaire with objective observations, and inability to use the modified VSH scale. Self‐reports of sleep may correlate imperfectly with objective measures, such as polysomnography.27

A larger concurrent trial randomizing similar units at multiple hospitals would be ideal. Future research is needed to determine whether improving sleep in the hospital improves other outcomes, such as recovery times, delirium, falls, or cost.

The need to reduce as‐needed sedatives is an important safety issue and similar interventions in other hospitals may be helpful. Simple changes in staff routines and provider prescribing habits can yield significant reductions in sedative use.

The benefits of glycemic control include decreased patient morbidity, mortality, length of stay, and reduced hospital costs. In 2004, the American College of Endocrinology (ACE) issued glycemic guidelines for non‐critical‐care units (fasting glucose 110 mg/dL, nonfasting glucose 180 mg/dL).1 A comprehensive review of inpatient glycemic management called for development and evaluation of inpatient programs and tools.2 The 2006 ACE/American Diabetes Association (ADA) Statement on Inpatient Diabetes and Glycemic Control identified key components of an inpatient glycemic control program as: (1) solid administrative support; (2) a multidisciplinary committee; (3) assessment of current processes, care, and barriers; (4) development and implementation of order sets, protocols, policies, and educational efforts; and (5) metrics for evaluation.3

In 2003, Harborview Medical Center (HMC) formed a multidisciplinary committee to institute a Glycemic Control Program. The early goals were to decrease the use of sliding‐scale insulin, increase the appropriate use of basal and prandial insulin, and to avoid hypoglycemia. Here we report our program design and trends in physician insulin ordering from 2003 through 2006.

Patients and Methods

Program Description

Since 2003, the multidisciplinary committeephysicians, nurses, pharmacy representatives, and dietary and administrative representativeshas directed the development of the Glycemic Control Program with support from hospital administration and the Department of Quality Improvement. Funding for this program has been provided by the hospital based on the prominence of glycemic control among quality and safety measures, a projected decrease in costs, and the high incidence of diabetes in our patient population. Figure 1 outlines the program's key interventions.

Figure 1

First, a Subcutaneous Insulin Order Form was released for elective use in May 2004 (Figure 2). This form incorporated the 3 components of quality insulin ordering (basal, scheduled prandial, and prandial correction dosing) and provides prompts and education. A Diabetes Nurse Specialist trained nursing staff on the use of the form.

Figure 2

Second, we developed an automated daily data report identifying patients with out‐of‐range glucose levels defined as having any single glucose readings 60 mg/dL or any 2 readings 180 mg/dL within the prior 24 hours. In February 2006, this daily report became available to the clinicians on the committee.

Third, the Glycemic Control Program recruited a full‐time clinical Advanced Registered Nurse Practitioner (ARNP) and part‐time supervising physician to provide directed intervention and education for patients and medical personnel. Since August 2006, the ARNP has reviewed the out‐of‐range report daily, performs assessments, refines insulin orders, and educates clinicians. The assessments include chart review (of history and glycemic control), discussion with primary physician and nurse (and often the dietician), and interview of the patient and/or family. This leads to development and implementation of a glycemic control plan. Clinician education is performed both as direct education of the primary physician at the time of intervention and as didactic sessions.

Results

Physician Insulin Ordering

Ordering of both short‐acting and basal insulin increased (Figure 3). The ratio of short‐acting to basal orders decreased from 3.36 (1668/496) in 2003 to 1.97 (2226/1128) in 2006.

Figure 3

Chart review of the 100 randomly selected dysglycemic patients revealed increased ordering of prandial correction dosing from 8% of patients in 2003 to 32% in 2006. Yet, only 1 patient in 2003 and only 2 in 2006 had scheduled prandial. Ordering of sliding scale insulin fell from 16% in 2003 to 4% in 2006.

Glycemic Control Outcomes

The percentage of dysglycemic patients with hyperglycemia ranged from 19 to 24 without significant decline over the 4 years (Figure 4A). The percentage of hypoglycemic dysglycemic patients was increasing from 2003 to 2004, but in the years following the interventions (2005 through 2006) this declined significantly (P = 0.003; Figure 4B). On average, the observed LOS was higher for dysglycemic vs. euglycemic patients (mean SD days: 9.4 12.2 and 5.8 8.5, respectively). The mean observed to expected mortality ratio was 0.45 0.08 and 0.44 0.17 for the dysglycemic and euglycemic patients, respectively. Over the 4 years no statistically significant change in observed LOS or adjusted mortality was found (data not shown).

Figure 4

Conclusions

HMC, a safety net hospital with the highest UHC expected mortality of 131 hospitals nationwide, has demonstrated early successes in building its Glycemic Control Program, including: (1) decreased prescription of sliding scale; (2) a marked increase in prescription of basal insulin; and (3) significantly decreasing hypoglycemic events subsequent to the interventions. The decreased sliding scale and increased overall ordering of insulin could reflect increased awareness brought internationally through the literature and locally through our program. Two distinctive aspects of HMC's Glycemic Control Program, when compared to others,68 include: (1) the daily use of real‐time data to identify and target patients with out‐of‐range glucose levels; and (2) the coverage of all non‐critical‐care floors with a single clinician.

In 2003 and 2004, the increasing hypoglycemia we observed paralleled the international focus on aggressively treating hyperglycemia in the acute care setting. We observed a significant decrease in hypoglycemia in 2005 and 2006 that could be attributed to the education provided by the Glycemic Control Program and 2 features on the subcutaneous insulin order set: the prominent hypoglycemia protocol and the order hold prandial insulin if the patient cannot eat. These are similar features identified in a report on preventing hospital hypoglycemia.9 Additionally, hypoglycemia may have decreased secondary to the emphasis on not using short‐acting insulin at bedtime.

Despite increased and improved insulin ordering, we did not observe a significant change in the percent of dysglycemic patients with 2 glucose levels 180 mg/dL. In our program patients are identified for intervention after their glucose levels are out‐of‐range. To better evaluate the impact of our interventions on the glycemic control of each patient, we plan to analyze the glucose levels in the days following identification of patients. Alternatively, we could provide intervention to all patients with dysglycemia rather than waiting for glucoses to be out‐of‐range. Though this approach would require greater resources than the single clinician we currently employ.

Our early experience highlights areas for future evaluation and intervention. First, the lack of scheduled prandial insulin and that less than one‐third of dysglycemic patients have basal insulin ordered underscore a continued need to target quality insulin ordering to include all componentsbasal, scheduled prandial, and prandial correction. Second, while the daily report is a good rudimentary identification tool for at‐risk patients, it offers limited information as to the impact of our clinical intervention. Thus, refined evaluative metrics need be developed to prospectively assess the course of glycemic control for patients.

We acknowledge the limitations of this study. First, our most involved interventionthe addition of the clinical intervention teamcame only 6 months before the end of the study period. Second, this is an observational retrospective analysis and cannot distinguish confounders, such as physician preferences and decisions, that not easily quantified or controlled for. Third, our definition of dysglycemic incorporated 41% of non‐critical‐care patients, possibly reflecting too broad a definition.

In summary, we have described an inpatient Glycemic Control Program that relies on real‐time data to identify patients in need of intervention. Early in our program we observed improved insulin ordering quality and decreased rates of hypoglycemia. Future steps include evaluating the impact of our clinical intervention team and further refining glycemic control metrics to prospectively identify patients at risk for hyper‐ and hypoglycemia.

The benefits of glycemic control include decreased patient morbidity, mortality, length of stay, and reduced hospital costs. In 2004, the American College of Endocrinology (ACE) issued glycemic guidelines for non‐critical‐care units (fasting glucose 110 mg/dL, nonfasting glucose 180 mg/dL).1 A comprehensive review of inpatient glycemic management called for development and evaluation of inpatient programs and tools.2 The 2006 ACE/American Diabetes Association (ADA) Statement on Inpatient Diabetes and Glycemic Control identified key components of an inpatient glycemic control program as: (1) solid administrative support; (2) a multidisciplinary committee; (3) assessment of current processes, care, and barriers; (4) development and implementation of order sets, protocols, policies, and educational efforts; and (5) metrics for evaluation.3

In 2003, Harborview Medical Center (HMC) formed a multidisciplinary committee to institute a Glycemic Control Program. The early goals were to decrease the use of sliding‐scale insulin, increase the appropriate use of basal and prandial insulin, and to avoid hypoglycemia. Here we report our program design and trends in physician insulin ordering from 2003 through 2006.

Patients and Methods

Program Description

Since 2003, the multidisciplinary committeephysicians, nurses, pharmacy representatives, and dietary and administrative representativeshas directed the development of the Glycemic Control Program with support from hospital administration and the Department of Quality Improvement. Funding for this program has been provided by the hospital based on the prominence of glycemic control among quality and safety measures, a projected decrease in costs, and the high incidence of diabetes in our patient population. Figure 1 outlines the program's key interventions.

Figure 1

First, a Subcutaneous Insulin Order Form was released for elective use in May 2004 (Figure 2). This form incorporated the 3 components of quality insulin ordering (basal, scheduled prandial, and prandial correction dosing) and provides prompts and education. A Diabetes Nurse Specialist trained nursing staff on the use of the form.

Figure 2

Second, we developed an automated daily data report identifying patients with out‐of‐range glucose levels defined as having any single glucose readings 60 mg/dL or any 2 readings 180 mg/dL within the prior 24 hours. In February 2006, this daily report became available to the clinicians on the committee.

Third, the Glycemic Control Program recruited a full‐time clinical Advanced Registered Nurse Practitioner (ARNP) and part‐time supervising physician to provide directed intervention and education for patients and medical personnel. Since August 2006, the ARNP has reviewed the out‐of‐range report daily, performs assessments, refines insulin orders, and educates clinicians. The assessments include chart review (of history and glycemic control), discussion with primary physician and nurse (and often the dietician), and interview of the patient and/or family. This leads to development and implementation of a glycemic control plan. Clinician education is performed both as direct education of the primary physician at the time of intervention and as didactic sessions.

Results

Physician Insulin Ordering

Ordering of both short‐acting and basal insulin increased (Figure 3). The ratio of short‐acting to basal orders decreased from 3.36 (1668/496) in 2003 to 1.97 (2226/1128) in 2006.

Figure 3

Chart review of the 100 randomly selected dysglycemic patients revealed increased ordering of prandial correction dosing from 8% of patients in 2003 to 32% in 2006. Yet, only 1 patient in 2003 and only 2 in 2006 had scheduled prandial. Ordering of sliding scale insulin fell from 16% in 2003 to 4% in 2006.

Glycemic Control Outcomes

The percentage of dysglycemic patients with hyperglycemia ranged from 19 to 24 without significant decline over the 4 years (Figure 4A). The percentage of hypoglycemic dysglycemic patients was increasing from 2003 to 2004, but in the years following the interventions (2005 through 2006) this declined significantly (P = 0.003; Figure 4B). On average, the observed LOS was higher for dysglycemic vs. euglycemic patients (mean SD days: 9.4 12.2 and 5.8 8.5, respectively). The mean observed to expected mortality ratio was 0.45 0.08 and 0.44 0.17 for the dysglycemic and euglycemic patients, respectively. Over the 4 years no statistically significant change in observed LOS or adjusted mortality was found (data not shown).

Figure 4

Conclusions

HMC, a safety net hospital with the highest UHC expected mortality of 131 hospitals nationwide, has demonstrated early successes in building its Glycemic Control Program, including: (1) decreased prescription of sliding scale; (2) a marked increase in prescription of basal insulin; and (3) significantly decreasing hypoglycemic events subsequent to the interventions. The decreased sliding scale and increased overall ordering of insulin could reflect increased awareness brought internationally through the literature and locally through our program. Two distinctive aspects of HMC's Glycemic Control Program, when compared to others,68 include: (1) the daily use of real‐time data to identify and target patients with out‐of‐range glucose levels; and (2) the coverage of all non‐critical‐care floors with a single clinician.

In 2003 and 2004, the increasing hypoglycemia we observed paralleled the international focus on aggressively treating hyperglycemia in the acute care setting. We observed a significant decrease in hypoglycemia in 2005 and 2006 that could be attributed to the education provided by the Glycemic Control Program and 2 features on the subcutaneous insulin order set: the prominent hypoglycemia protocol and the order hold prandial insulin if the patient cannot eat. These are similar features identified in a report on preventing hospital hypoglycemia.9 Additionally, hypoglycemia may have decreased secondary to the emphasis on not using short‐acting insulin at bedtime.

Despite increased and improved insulin ordering, we did not observe a significant change in the percent of dysglycemic patients with 2 glucose levels 180 mg/dL. In our program patients are identified for intervention after their glucose levels are out‐of‐range. To better evaluate the impact of our interventions on the glycemic control of each patient, we plan to analyze the glucose levels in the days following identification of patients. Alternatively, we could provide intervention to all patients with dysglycemia rather than waiting for glucoses to be out‐of‐range. Though this approach would require greater resources than the single clinician we currently employ.

Our early experience highlights areas for future evaluation and intervention. First, the lack of scheduled prandial insulin and that less than one‐third of dysglycemic patients have basal insulin ordered underscore a continued need to target quality insulin ordering to include all componentsbasal, scheduled prandial, and prandial correction. Second, while the daily report is a good rudimentary identification tool for at‐risk patients, it offers limited information as to the impact of our clinical intervention. Thus, refined evaluative metrics need be developed to prospectively assess the course of glycemic control for patients.

We acknowledge the limitations of this study. First, our most involved interventionthe addition of the clinical intervention teamcame only 6 months before the end of the study period. Second, this is an observational retrospective analysis and cannot distinguish confounders, such as physician preferences and decisions, that not easily quantified or controlled for. Third, our definition of dysglycemic incorporated 41% of non‐critical‐care patients, possibly reflecting too broad a definition.

In summary, we have described an inpatient Glycemic Control Program that relies on real‐time data to identify patients in need of intervention. Early in our program we observed improved insulin ordering quality and decreased rates of hypoglycemia. Future steps include evaluating the impact of our clinical intervention team and further refining glycemic control metrics to prospectively identify patients at risk for hyper‐ and hypoglycemia.

The benefits of glycemic control include decreased patient morbidity, mortality, length of stay, and reduced hospital costs. In 2004, the American College of Endocrinology (ACE) issued glycemic guidelines for non‐critical‐care units (fasting glucose 110 mg/dL, nonfasting glucose 180 mg/dL).1 A comprehensive review of inpatient glycemic management called for development and evaluation of inpatient programs and tools.2 The 2006 ACE/American Diabetes Association (ADA) Statement on Inpatient Diabetes and Glycemic Control identified key components of an inpatient glycemic control program as: (1) solid administrative support; (2) a multidisciplinary committee; (3) assessment of current processes, care, and barriers; (4) development and implementation of order sets, protocols, policies, and educational efforts; and (5) metrics for evaluation.3

In 2003, Harborview Medical Center (HMC) formed a multidisciplinary committee to institute a Glycemic Control Program. The early goals were to decrease the use of sliding‐scale insulin, increase the appropriate use of basal and prandial insulin, and to avoid hypoglycemia. Here we report our program design and trends in physician insulin ordering from 2003 through 2006.

Patients and Methods

Program Description

Since 2003, the multidisciplinary committeephysicians, nurses, pharmacy representatives, and dietary and administrative representativeshas directed the development of the Glycemic Control Program with support from hospital administration and the Department of Quality Improvement. Funding for this program has been provided by the hospital based on the prominence of glycemic control among quality and safety measures, a projected decrease in costs, and the high incidence of diabetes in our patient population. Figure 1 outlines the program's key interventions.

Figure 1

First, a Subcutaneous Insulin Order Form was released for elective use in May 2004 (Figure 2). This form incorporated the 3 components of quality insulin ordering (basal, scheduled prandial, and prandial correction dosing) and provides prompts and education. A Diabetes Nurse Specialist trained nursing staff on the use of the form.

Figure 2

Second, we developed an automated daily data report identifying patients with out‐of‐range glucose levels defined as having any single glucose readings 60 mg/dL or any 2 readings 180 mg/dL within the prior 24 hours. In February 2006, this daily report became available to the clinicians on the committee.

Third, the Glycemic Control Program recruited a full‐time clinical Advanced Registered Nurse Practitioner (ARNP) and part‐time supervising physician to provide directed intervention and education for patients and medical personnel. Since August 2006, the ARNP has reviewed the out‐of‐range report daily, performs assessments, refines insulin orders, and educates clinicians. The assessments include chart review (of history and glycemic control), discussion with primary physician and nurse (and often the dietician), and interview of the patient and/or family. This leads to development and implementation of a glycemic control plan. Clinician education is performed both as direct education of the primary physician at the time of intervention and as didactic sessions.

Results

Physician Insulin Ordering

Ordering of both short‐acting and basal insulin increased (Figure 3). The ratio of short‐acting to basal orders decreased from 3.36 (1668/496) in 2003 to 1.97 (2226/1128) in 2006.

Figure 3

Chart review of the 100 randomly selected dysglycemic patients revealed increased ordering of prandial correction dosing from 8% of patients in 2003 to 32% in 2006. Yet, only 1 patient in 2003 and only 2 in 2006 had scheduled prandial. Ordering of sliding scale insulin fell from 16% in 2003 to 4% in 2006.

Glycemic Control Outcomes

The percentage of dysglycemic patients with hyperglycemia ranged from 19 to 24 without significant decline over the 4 years (Figure 4A). The percentage of hypoglycemic dysglycemic patients was increasing from 2003 to 2004, but in the years following the interventions (2005 through 2006) this declined significantly (P = 0.003; Figure 4B). On average, the observed LOS was higher for dysglycemic vs. euglycemic patients (mean SD days: 9.4 12.2 and 5.8 8.5, respectively). The mean observed to expected mortality ratio was 0.45 0.08 and 0.44 0.17 for the dysglycemic and euglycemic patients, respectively. Over the 4 years no statistically significant change in observed LOS or adjusted mortality was found (data not shown).

Figure 4

Conclusions

HMC, a safety net hospital with the highest UHC expected mortality of 131 hospitals nationwide, has demonstrated early successes in building its Glycemic Control Program, including: (1) decreased prescription of sliding scale; (2) a marked increase in prescription of basal insulin; and (3) significantly decreasing hypoglycemic events subsequent to the interventions. The decreased sliding scale and increased overall ordering of insulin could reflect increased awareness brought internationally through the literature and locally through our program. Two distinctive aspects of HMC's Glycemic Control Program, when compared to others,68 include: (1) the daily use of real‐time data to identify and target patients with out‐of‐range glucose levels; and (2) the coverage of all non‐critical‐care floors with a single clinician.

In 2003 and 2004, the increasing hypoglycemia we observed paralleled the international focus on aggressively treating hyperglycemia in the acute care setting. We observed a significant decrease in hypoglycemia in 2005 and 2006 that could be attributed to the education provided by the Glycemic Control Program and 2 features on the subcutaneous insulin order set: the prominent hypoglycemia protocol and the order hold prandial insulin if the patient cannot eat. These are similar features identified in a report on preventing hospital hypoglycemia.9 Additionally, hypoglycemia may have decreased secondary to the emphasis on not using short‐acting insulin at bedtime.

Despite increased and improved insulin ordering, we did not observe a significant change in the percent of dysglycemic patients with 2 glucose levels 180 mg/dL. In our program patients are identified for intervention after their glucose levels are out‐of‐range. To better evaluate the impact of our interventions on the glycemic control of each patient, we plan to analyze the glucose levels in the days following identification of patients. Alternatively, we could provide intervention to all patients with dysglycemia rather than waiting for glucoses to be out‐of‐range. Though this approach would require greater resources than the single clinician we currently employ.

Our early experience highlights areas for future evaluation and intervention. First, the lack of scheduled prandial insulin and that less than one‐third of dysglycemic patients have basal insulin ordered underscore a continued need to target quality insulin ordering to include all componentsbasal, scheduled prandial, and prandial correction. Second, while the daily report is a good rudimentary identification tool for at‐risk patients, it offers limited information as to the impact of our clinical intervention. Thus, refined evaluative metrics need be developed to prospectively assess the course of glycemic control for patients.

We acknowledge the limitations of this study. First, our most involved interventionthe addition of the clinical intervention teamcame only 6 months before the end of the study period. Second, this is an observational retrospective analysis and cannot distinguish confounders, such as physician preferences and decisions, that not easily quantified or controlled for. Third, our definition of dysglycemic incorporated 41% of non‐critical‐care patients, possibly reflecting too broad a definition.

In summary, we have described an inpatient Glycemic Control Program that relies on real‐time data to identify patients in need of intervention. Early in our program we observed improved insulin ordering quality and decreased rates of hypoglycemia. Future steps include evaluating the impact of our clinical intervention team and further refining glycemic control metrics to prospectively identify patients at risk for hyper‐ and hypoglycemia.

In 2007, the Joint Commission for Accreditation of Healthcare Organizations (JCAHO) recommended deployment of rapid response teams (RRTs) in U.S. hospitals to hasten identification and treatment of physiologically unstable hospitalized patients.1 Clinical studies that have focused on whether RRTs improve restorative care outcomes, frequency of cardiac arrest, and critical care utilization have yielded mixed results.2‐11 One study suggested that RRTs might provide an opportunity to enhance palliative care of hospitalized patients.11 In this study, RRT personnel felt that prior do‐not‐resuscitate orders would have been appropriate in nearly a quarter of cases. However, no previous study has examined whether the RRT might be deployed to identify acutely decompensating patients who either do not want or would not benefit from a trial of aggressive restorative treatments. We hypothesized that actuation of an RRT in our hospital would expedite identification of patients not likely to benefit from restorative care and would promote more timely commencement of end‐of‐life comfort care, thereby improving their quality of death (QOD).12‐16

Materials and Methods

Results

A total of 394 patients died on the hospital wards and were not admitted with palliative, end‐of‐life medical therapies. The combined (pre‐RRT and post‐RRT epochs) cohort had a mean age of 77.2 13.2 years. A total of 48% were male, 79% White, 12% Black, and 8% Hispanic. A total of 128 patients (33%) were admitted to the hospital from a skilled nursing facility and 135 (35%) had written advance directives.

A total of 197 patients met the inclusion criteria during the pre‐RRT (October 1, 2005 to May 31, 2006) and 197 during the post‐RRT epochs (October 1, 2006 to May 31, 2007). There were no differences in age, sex, advance directives, ethnicity, or religion between the groups (Table 1). Primary admission diagnoses were significantly different; pre‐RRT patients were 9% more likely to die with malignancy compared to post‐RRT patients and less likely to come from nursing homes (38% vs. 27%; P = 0.02).

Discussion

This pilot study hypothesizes that our RRT impacted patients' QOD. Deployment of the RRT in our hospital was associated with improvement in both symptom and psychospiritual domains of care. Theoretically, RRTs should improve quality‐of‐care via early identification/reversal of physiologic decompensation. By either reversing acute diatheses with an expeditious trial of therapy or failing to reverse with early actuation of palliative therapies, the duration and magnitude of human suffering should be reduced. Attenuation of both duration and magnitude of suffering is the ultimate goal of both restorative and palliative care and is as important an outcome as mortality or length of stay. Previous studies of RRTs have focused on efficacy in reversing the decompensation: preventing cardiopulmonary arrest, avoiding the need for invasive, expensive, labor‐intensive interventions. Our RRT, like others, had no demonstrable impact on restorative outcomes. However, deployment of the RRT was highly associated with improved QOD of our patients. The impact was significant across WHO‐specified domains: pain scores decreased by 19%; (documentation of) patients' distress decreased by 50%; and chaplains' visits were more often documented in the 24 hours prior to death. These relationships held across common disease diagnoses, so the association is unlikely to be spurious.

Outcomes were similarly improved in patients who did not receive RRT care in the post‐RRT epoch. Our hospital did not have a palliative care service in either time period. No new educational efforts among physicians or nurses accounted for this observation. While it is possible that temporal effects accounted for our observation, an equally plausible explanation is that staff observed RRT interventions and applied them to dying patients not seen by the RRT. Our hospital educated caregivers regarding the RRT triggers, and simply making hospital personnel more vigilant for signs of suffering and/or observing the RRT approach may have contributed to enhanced end‐of‐life care for non‐RRT patients.

There are a number of limitations in this study. First, the sample size was relatively small compared to other published studies,2‐11 promoting the possibility that either epoch was not representative of pre‐RRT and post‐RRT parent populations. Another weakness is that QOD was measured using surrogate endpoints. The dead cannot be interviewed to definitively examine QOD; indices of cardiopulmonary distress and psychosocial measures (eg, religious preparations, family involvement) are endpoints suggested by palliative care investigators12, 13 and the World Health Organization.14 While some validated tools17 and consensus measures18 exist for critically ill patients, they do not readily apply to RRT patients. Retrospective records reviews raise the possibility of bias in extracting objective and subjective data. While we attempted to control for this by creating uniform a priori rules for data acquisition (ie, at what intervals and in which parts of the record they could be extracted), we cannot discount the possibility that bias affected the observed results. Finally, improvements in end‐of‐life care could have resulted from temporal trends. This retrospective study cannot prove a causeeffect relationship; a prospective randomized trial would be required to answer the question definitively. Based on the available data suggesting some benefit in restorative outcomes2‐8 and pressure from federal regulators to deploy RRTs regardless,1 a retrospective cohort design may provide the only realistic means of addressing this question.

In conclusion, this is the first (pilot) study to examine end‐of‐life outcomes associated with deployment of an RRT. While the limitations of these observations preclude firm conclusions, the plausibility of the hypothesis, coupled with our observations, suggests that this is a fertile area for future research. While RRTs may enhance restorative outcomes, to the extent that they hasten identification of candidates for palliative end‐of‐life‐care, before administration of invasive modalities that some patients do not want, these teams may simultaneously serve patients and reduce hospital resource utilization.

Addendum

Prior to publication, a contemporaneous study was published that concluded: These findings suggest that rapid response teams may not be decreasing code rates as much as catalyzing a compassionate dialogue of end‐of‐life care among terminally ill patients. This ability to improve end‐of‐life care may be an important benefit of rapid response teams, particularly given the difficulties in prior trials to increase rates of DNR status among seriously ill inpatients and potential decreases in resource use. Chan PS, Khalid A, Longmore LS, Berg RA, Midhail Kosiborod M, Spertus JA. Hospital‐wide code rates and mortality before and after implementation of a rapid response team. JAMA 2008;300: 25062513.

In 2007, the Joint Commission for Accreditation of Healthcare Organizations (JCAHO) recommended deployment of rapid response teams (RRTs) in U.S. hospitals to hasten identification and treatment of physiologically unstable hospitalized patients.1 Clinical studies that have focused on whether RRTs improve restorative care outcomes, frequency of cardiac arrest, and critical care utilization have yielded mixed results.2‐11 One study suggested that RRTs might provide an opportunity to enhance palliative care of hospitalized patients.11 In this study, RRT personnel felt that prior do‐not‐resuscitate orders would have been appropriate in nearly a quarter of cases. However, no previous study has examined whether the RRT might be deployed to identify acutely decompensating patients who either do not want or would not benefit from a trial of aggressive restorative treatments. We hypothesized that actuation of an RRT in our hospital would expedite identification of patients not likely to benefit from restorative care and would promote more timely commencement of end‐of‐life comfort care, thereby improving their quality of death (QOD).12‐16

Materials and Methods

Results

A total of 394 patients died on the hospital wards and were not admitted with palliative, end‐of‐life medical therapies. The combined (pre‐RRT and post‐RRT epochs) cohort had a mean age of 77.2 13.2 years. A total of 48% were male, 79% White, 12% Black, and 8% Hispanic. A total of 128 patients (33%) were admitted to the hospital from a skilled nursing facility and 135 (35%) had written advance directives.

A total of 197 patients met the inclusion criteria during the pre‐RRT (October 1, 2005 to May 31, 2006) and 197 during the post‐RRT epochs (October 1, 2006 to May 31, 2007). There were no differences in age, sex, advance directives, ethnicity, or religion between the groups (Table 1). Primary admission diagnoses were significantly different; pre‐RRT patients were 9% more likely to die with malignancy compared to post‐RRT patients and less likely to come from nursing homes (38% vs. 27%; P = 0.02).

Discussion

This pilot study hypothesizes that our RRT impacted patients' QOD. Deployment of the RRT in our hospital was associated with improvement in both symptom and psychospiritual domains of care. Theoretically, RRTs should improve quality‐of‐care via early identification/reversal of physiologic decompensation. By either reversing acute diatheses with an expeditious trial of therapy or failing to reverse with early actuation of palliative therapies, the duration and magnitude of human suffering should be reduced. Attenuation of both duration and magnitude of suffering is the ultimate goal of both restorative and palliative care and is as important an outcome as mortality or length of stay. Previous studies of RRTs have focused on efficacy in reversing the decompensation: preventing cardiopulmonary arrest, avoiding the need for invasive, expensive, labor‐intensive interventions. Our RRT, like others, had no demonstrable impact on restorative outcomes. However, deployment of the RRT was highly associated with improved QOD of our patients. The impact was significant across WHO‐specified domains: pain scores decreased by 19%; (documentation of) patients' distress decreased by 50%; and chaplains' visits were more often documented in the 24 hours prior to death. These relationships held across common disease diagnoses, so the association is unlikely to be spurious.

Outcomes were similarly improved in patients who did not receive RRT care in the post‐RRT epoch. Our hospital did not have a palliative care service in either time period. No new educational efforts among physicians or nurses accounted for this observation. While it is possible that temporal effects accounted for our observation, an equally plausible explanation is that staff observed RRT interventions and applied them to dying patients not seen by the RRT. Our hospital educated caregivers regarding the RRT triggers, and simply making hospital personnel more vigilant for signs of suffering and/or observing the RRT approach may have contributed to enhanced end‐of‐life care for non‐RRT patients.

There are a number of limitations in this study. First, the sample size was relatively small compared to other published studies,2‐11 promoting the possibility that either epoch was not representative of pre‐RRT and post‐RRT parent populations. Another weakness is that QOD was measured using surrogate endpoints. The dead cannot be interviewed to definitively examine QOD; indices of cardiopulmonary distress and psychosocial measures (eg, religious preparations, family involvement) are endpoints suggested by palliative care investigators12, 13 and the World Health Organization.14 While some validated tools17 and consensus measures18 exist for critically ill patients, they do not readily apply to RRT patients. Retrospective records reviews raise the possibility of bias in extracting objective and subjective data. While we attempted to control for this by creating uniform a priori rules for data acquisition (ie, at what intervals and in which parts of the record they could be extracted), we cannot discount the possibility that bias affected the observed results. Finally, improvements in end‐of‐life care could have resulted from temporal trends. This retrospective study cannot prove a causeeffect relationship; a prospective randomized trial would be required to answer the question definitively. Based on the available data suggesting some benefit in restorative outcomes2‐8 and pressure from federal regulators to deploy RRTs regardless,1 a retrospective cohort design may provide the only realistic means of addressing this question.

In conclusion, this is the first (pilot) study to examine end‐of‐life outcomes associated with deployment of an RRT. While the limitations of these observations preclude firm conclusions, the plausibility of the hypothesis, coupled with our observations, suggests that this is a fertile area for future research. While RRTs may enhance restorative outcomes, to the extent that they hasten identification of candidates for palliative end‐of‐life‐care, before administration of invasive modalities that some patients do not want, these teams may simultaneously serve patients and reduce hospital resource utilization.

Addendum

Prior to publication, a contemporaneous study was published that concluded: These findings suggest that rapid response teams may not be decreasing code rates as much as catalyzing a compassionate dialogue of end‐of‐life care among terminally ill patients. This ability to improve end‐of‐life care may be an important benefit of rapid response teams, particularly given the difficulties in prior trials to increase rates of DNR status among seriously ill inpatients and potential decreases in resource use. Chan PS, Khalid A, Longmore LS, Berg RA, Midhail Kosiborod M, Spertus JA. Hospital‐wide code rates and mortality before and after implementation of a rapid response team. JAMA 2008;300: 25062513.

In 2007, the Joint Commission for Accreditation of Healthcare Organizations (JCAHO) recommended deployment of rapid response teams (RRTs) in U.S. hospitals to hasten identification and treatment of physiologically unstable hospitalized patients.1 Clinical studies that have focused on whether RRTs improve restorative care outcomes, frequency of cardiac arrest, and critical care utilization have yielded mixed results.2‐11 One study suggested that RRTs might provide an opportunity to enhance palliative care of hospitalized patients.11 In this study, RRT personnel felt that prior do‐not‐resuscitate orders would have been appropriate in nearly a quarter of cases. However, no previous study has examined whether the RRT might be deployed to identify acutely decompensating patients who either do not want or would not benefit from a trial of aggressive restorative treatments. We hypothesized that actuation of an RRT in our hospital would expedite identification of patients not likely to benefit from restorative care and would promote more timely commencement of end‐of‐life comfort care, thereby improving their quality of death (QOD).12‐16

Materials and Methods

Results

A total of 394 patients died on the hospital wards and were not admitted with palliative, end‐of‐life medical therapies. The combined (pre‐RRT and post‐RRT epochs) cohort had a mean age of 77.2 13.2 years. A total of 48% were male, 79% White, 12% Black, and 8% Hispanic. A total of 128 patients (33%) were admitted to the hospital from a skilled nursing facility and 135 (35%) had written advance directives.

A total of 197 patients met the inclusion criteria during the pre‐RRT (October 1, 2005 to May 31, 2006) and 197 during the post‐RRT epochs (October 1, 2006 to May 31, 2007). There were no differences in age, sex, advance directives, ethnicity, or religion between the groups (Table 1). Primary admission diagnoses were significantly different; pre‐RRT patients were 9% more likely to die with malignancy compared to post‐RRT patients and less likely to come from nursing homes (38% vs. 27%; P = 0.02).

Discussion

This pilot study hypothesizes that our RRT impacted patients' QOD. Deployment of the RRT in our hospital was associated with improvement in both symptom and psychospiritual domains of care. Theoretically, RRTs should improve quality‐of‐care via early identification/reversal of physiologic decompensation. By either reversing acute diatheses with an expeditious trial of therapy or failing to reverse with early actuation of palliative therapies, the duration and magnitude of human suffering should be reduced. Attenuation of both duration and magnitude of suffering is the ultimate goal of both restorative and palliative care and is as important an outcome as mortality or length of stay. Previous studies of RRTs have focused on efficacy in reversing the decompensation: preventing cardiopulmonary arrest, avoiding the need for invasive, expensive, labor‐intensive interventions. Our RRT, like others, had no demonstrable impact on restorative outcomes. However, deployment of the RRT was highly associated with improved QOD of our patients. The impact was significant across WHO‐specified domains: pain scores decreased by 19%; (documentation of) patients' distress decreased by 50%; and chaplains' visits were more often documented in the 24 hours prior to death. These relationships held across common disease diagnoses, so the association is unlikely to be spurious.

Outcomes were similarly improved in patients who did not receive RRT care in the post‐RRT epoch. Our hospital did not have a palliative care service in either time period. No new educational efforts among physicians or nurses accounted for this observation. While it is possible that temporal effects accounted for our observation, an equally plausible explanation is that staff observed RRT interventions and applied them to dying patients not seen by the RRT. Our hospital educated caregivers regarding the RRT triggers, and simply making hospital personnel more vigilant for signs of suffering and/or observing the RRT approach may have contributed to enhanced end‐of‐life care for non‐RRT patients.

There are a number of limitations in this study. First, the sample size was relatively small compared to other published studies,2‐11 promoting the possibility that either epoch was not representative of pre‐RRT and post‐RRT parent populations. Another weakness is that QOD was measured using surrogate endpoints. The dead cannot be interviewed to definitively examine QOD; indices of cardiopulmonary distress and psychosocial measures (eg, religious preparations, family involvement) are endpoints suggested by palliative care investigators12, 13 and the World Health Organization.14 While some validated tools17 and consensus measures18 exist for critically ill patients, they do not readily apply to RRT patients. Retrospective records reviews raise the possibility of bias in extracting objective and subjective data. While we attempted to control for this by creating uniform a priori rules for data acquisition (ie, at what intervals and in which parts of the record they could be extracted), we cannot discount the possibility that bias affected the observed results. Finally, improvements in end‐of‐life care could have resulted from temporal trends. This retrospective study cannot prove a causeeffect relationship; a prospective randomized trial would be required to answer the question definitively. Based on the available data suggesting some benefit in restorative outcomes2‐8 and pressure from federal regulators to deploy RRTs regardless,1 a retrospective cohort design may provide the only realistic means of addressing this question.

In conclusion, this is the first (pilot) study to examine end‐of‐life outcomes associated with deployment of an RRT. While the limitations of these observations preclude firm conclusions, the plausibility of the hypothesis, coupled with our observations, suggests that this is a fertile area for future research. While RRTs may enhance restorative outcomes, to the extent that they hasten identification of candidates for palliative end‐of‐life‐care, before administration of invasive modalities that some patients do not want, these teams may simultaneously serve patients and reduce hospital resource utilization.

Addendum

Prior to publication, a contemporaneous study was published that concluded: These findings suggest that rapid response teams may not be decreasing code rates as much as catalyzing a compassionate dialogue of end‐of‐life care among terminally ill patients. This ability to improve end‐of‐life care may be an important benefit of rapid response teams, particularly given the difficulties in prior trials to increase rates of DNR status among seriously ill inpatients and potential decreases in resource use. Chan PS, Khalid A, Longmore LS, Berg RA, Midhail Kosiborod M, Spertus JA. Hospital‐wide code rates and mortality before and after implementation of a rapid response team. JAMA 2008;300: 25062513.

Peripherally inserted central catheters (PICCs) are being used with greater frequency than ever before for intravenous access in hospitals, and PICCs may offer advantages in safety over traditional central venous catheters (CVCs). Despite these potential advantages, a large number of CVCs are still being placed. In a recent 1‐day survey of 6 large urban teaching hospitals, 29% of all patients had a CVC in place (59.3% of intensive care unit [ICU] patients and 23.7% of non‐ICU patients).1 Most catheters were inserted in the subclavian (55%) or jugular (22%) veins, with femoral (6%) and peripheral (15%) sites less commonly used. Even in the non‐ICU setting, only 20% of all central catheters were PICCs.

PICCs may offer advantages over centrally‐inserted intravenous catheters, such as the reduced risks of pneumothorax,2 arterial puncture, uncontrolled bleeding of large central veins, central lineassociated bloodstream infections (CLAB),3, 4 and lower cost.5 In addition, central venous pressure monitoring can now be performed with the larger‐bore PICCs.6

The low risk of mechanical complications for PICC insertion has been well documented.7, 8 In contrast, femoral or retroperitoneal hematoma occurs in up to 1.3% of cases following femoral catheter insertion,9 and pneumothorax occurs in 1.5% to 2.3% of subclavian catheter insertions.10 However, there are only limited data to suggest that the risk of PICC‐related bacteremia is lower than that of centrally‐placed catheters.11, 12

The benefit of PICCs over centrally‐placed catheters in terms of venous thromboembolism (VTE) is also not as easy to show, and in fact the rate may be greater in PICCs. The reported incidence of PICC‐related VTE has been between 0.3% and 56.0%, and the wide variation in rates is likely related to the method of diagnosis.1315 It is likely that most patients with PICC‐related VTE are asymptomatic, and that its incidence is underestimated.16

In many hospitals PICCs are placed by a certified nurse, or by an interventional radiologist if the nurse is unsuccessful.17 There are few reports of PICCs being placed by nonradiology physicians. In one report of 894 patients referred to a critical care specialist for PICC insertion, venous access was achieved 100% of the time, there were no referrals to interventional radiology, and there were no incidents of pneumothorax or bleeding.8 In a university‐affiliated community hospital, we carried out a retrospective review of our experience with training hospital physicians to place PICCs.

Methods

In July 2006 our community hospital, which is affiliated with the University of Pittsburgh Medical Center, instituted a hospitalist program. Prior to the hospitalist program, 1 house physician was available to place PICCs in the antecubital vein without the aid of ultrasound, and there was no PICC‐certified nurse in the hospital. An interventional radiologist was available to place PICCs that could not be placed by the house physician. After July 2006 under the hospitalist service, 3 of the 5 physicians were trained to place PICCs in the deep veins of the arm with the use of ultrasound guidance.

Training included 1 day with the PICC training nurse at the tertiary hospital, followed by supervised placements in the community hospital until proficiency was obtained. Proficiency was relative and cumulative. Approximately 3 supervised procedures were necessary before the physician was able to place PICCs by him or herself. All PICCs were placed using 5 barrier precautions, with chlorhexidine cleansing, and with a time‐out prior to the procedure.

Retrospective hospital data for central catheter placement were examined for the 18 months prior to and following the start of the hospitalist program. These data were collected routinely by the hospital infection control nurse for purposes of quality improvement and patient safety. The data included central catheters placed by all physicians in the hospital; however, the vast majority of these were placed by the hospitalists. The catheters were placed throughout the hospital, both on the medical floors, cardiac step‐down unit, and the ICU. Information regarding the number of central catheters placed and the specific type of catheter (subclavian, jugular, femoral, or PICC) was available from July 2005 through December 2007. Also available from January 2005 were the numbers of femoral and nonfemoral catheter days (number of catheters multiplied by number of days in place) and the central catheterassociated bacteremia rates (number per 1000 catheter days) for femoral and nonfemoral catheters. The Centers for Disease Control and Prevention (CDC) definition of central lineassociated bacteremia was used, which is any documented bloodstream infection within 48 hours of the presence of a CVC in the absence of an alternate source of infection. Data for other complications such as pneumothorax and major bleeding were not consistently recorded.

Results

Figure 1 shows the number of internal jugular, subclavian, femoral, PICC, and total catheter placements from July 2005 through December 2007. The data are grouped into 3‐month increments for visual convenience. Comparing the periods before and after the inception of the hospitalist PICC service (Figure 1, dotted vertical line), the rate of PICC placements rose 4‐fold and the rate of total catheter placements approximately doubled. The rates of femoral and subclavian catheter placements decreased by approximately 50% and the rate of internal jugular catheter placement was roughly unchanged.

Figure 1

Figure 2 shows the numbers of femoral and nonfemoral catheter days by month for 2005 through 2007. The nonfemoral catheter days began to rise prior to the start of the hospitalist program and continued to rise afterward, showing an approximately 3‐fold increase by the end of the study period. The number of femoral catheters days was highly variable, but seemed to decrease by approximately 50%.

Figure 2

Figure 3 shows the rates of femoral and nonfemoral catheter‐associated bacteremia by month for 2005 through 2007. The absolute number of infections in both periods was low and is shown at the top of each bar in the figure.

Figure 3

To our knowledge, there were no episodes of pneumothorax or major bleeding with PICC placement. There were 3 inadvertent arterial punctures, each of which was easily controlled with local pressure. There was 1 incident of a coiled guidewire that could not be removed at the bedside and had to be removed in interventional radiology with no significant consequence to the patient.

Discussion

The complications associated with central catheter insertion continue to place the hospitalized patient at risk. PICCs may offer significant advantages over other types of central catheters in terms of decreased rates of mechanical and infectious complications. Despite this, hospital physicians have not traditionally been trained to place PICCs. We have shown in our small, university‐affiliated community hospital that training hospital physicians to place PICCs was associated with a decrease in the placement of centrally‐inserted venous catheters and a reduced rate of femoral catheter days. At the same time, the rate of central catheterrelated bacteremia remained low.

There are many limitations to our study. Since the analysis was retrospective and uncontrolled, it is not possible to attribute the decrease in femoral catheter days and the low infection rates solely to the use of PICCs. There may have been other factors, either related or unrelated to the transition to a hospitalist service, that influenced the results, such as improved hand hygiene, attention to the use of 5 barrier precautions, and the use of chlorhexidine cleansing. Also, since the study was descriptive and outcome measures were either not available or the numbers small, we cannot prove that there was benefit to the patients or that the changes in rates were statistically significant.

Training hospital physicians to place PICCs in our study was associated with a 2‐fold increase in the overall rate of catheter placements. The reason for this increase in the total number of catheter placements is not clear, but it is likely related to the ease of PICC placement and the increasing number of patients with difficult intravenous access. It is unclear if an equivalent number of traditional central catheters would have been placed were the hospitalists not trained in PICC placement. However, this increase in total number of catheters did not appear to result in an increase in catheter‐related bacteremia or in mechanical complications.

We observed no apparent decrease in the insertion rate of internal jugular catheters in our study, despite a decrease in the rates of subclavian and femoral catheter placements. Although the current CDC guideline recommends using the subclavian vein as the preferred site, the UK National Institute for Clinical Excellence (NICE) is now recommending the use of real‐time ultrasound with each placement,18 and we find that this is best done in the internal jugular vein. Also, the rate of placement of femoral catheters remained higher than that of subclavian cathetersmost likely because the femoral vein remained the site of choice for emergently‐placed cathetersas PICC, more so than subclavian, became the preferred site for elective catheters.

Training physicians to place PICCs was not a simple task. In our experience, the availability of trainers at the tertiary care hospital was limited and the distractions of other duties of the hospitalist complicated the learning process. Two of our 5 physicians could not schedule time with the training nurse and were not able to acquire the skill. However, after training, the 3 hospitalists found that there was such a demand for PICCs that with time it was easy to maintain and even refine this skill. Since we only had 3 of 5 hospitalists trained in PICC placement, we could not have a PICC‐trained hospitalist on site 24 hours a day and the remaining 2 physicians had to rely on centrally‐placed catheters for access or have 1 of the trained physicians come to the hospital from home.

In summary, PICCs may be a safe and easy alternative to centrally‐placed catheters for the hospital physician attempting to secure central intravenous access and may lead to a decrease in the need for more risky CVC insertions. More definitive, controlled investigation, with patient outcome data, will be required before this can be advocated as a universal recommendation.

Peripherally inserted central catheters (PICCs) are being used with greater frequency than ever before for intravenous access in hospitals, and PICCs may offer advantages in safety over traditional central venous catheters (CVCs). Despite these potential advantages, a large number of CVCs are still being placed. In a recent 1‐day survey of 6 large urban teaching hospitals, 29% of all patients had a CVC in place (59.3% of intensive care unit [ICU] patients and 23.7% of non‐ICU patients).1 Most catheters were inserted in the subclavian (55%) or jugular (22%) veins, with femoral (6%) and peripheral (15%) sites less commonly used. Even in the non‐ICU setting, only 20% of all central catheters were PICCs.

PICCs may offer advantages over centrally‐inserted intravenous catheters, such as the reduced risks of pneumothorax,2 arterial puncture, uncontrolled bleeding of large central veins, central lineassociated bloodstream infections (CLAB),3, 4 and lower cost.5 In addition, central venous pressure monitoring can now be performed with the larger‐bore PICCs.6

The low risk of mechanical complications for PICC insertion has been well documented.7, 8 In contrast, femoral or retroperitoneal hematoma occurs in up to 1.3% of cases following femoral catheter insertion,9 and pneumothorax occurs in 1.5% to 2.3% of subclavian catheter insertions.10 However, there are only limited data to suggest that the risk of PICC‐related bacteremia is lower than that of centrally‐placed catheters.11, 12

The benefit of PICCs over centrally‐placed catheters in terms of venous thromboembolism (VTE) is also not as easy to show, and in fact the rate may be greater in PICCs. The reported incidence of PICC‐related VTE has been between 0.3% and 56.0%, and the wide variation in rates is likely related to the method of diagnosis.1315 It is likely that most patients with PICC‐related VTE are asymptomatic, and that its incidence is underestimated.16

In many hospitals PICCs are placed by a certified nurse, or by an interventional radiologist if the nurse is unsuccessful.17 There are few reports of PICCs being placed by nonradiology physicians. In one report of 894 patients referred to a critical care specialist for PICC insertion, venous access was achieved 100% of the time, there were no referrals to interventional radiology, and there were no incidents of pneumothorax or bleeding.8 In a university‐affiliated community hospital, we carried out a retrospective review of our experience with training hospital physicians to place PICCs.

Methods

In July 2006 our community hospital, which is affiliated with the University of Pittsburgh Medical Center, instituted a hospitalist program. Prior to the hospitalist program, 1 house physician was available to place PICCs in the antecubital vein without the aid of ultrasound, and there was no PICC‐certified nurse in the hospital. An interventional radiologist was available to place PICCs that could not be placed by the house physician. After July 2006 under the hospitalist service, 3 of the 5 physicians were trained to place PICCs in the deep veins of the arm with the use of ultrasound guidance.

Training included 1 day with the PICC training nurse at the tertiary hospital, followed by supervised placements in the community hospital until proficiency was obtained. Proficiency was relative and cumulative. Approximately 3 supervised procedures were necessary before the physician was able to place PICCs by him or herself. All PICCs were placed using 5 barrier precautions, with chlorhexidine cleansing, and with a time‐out prior to the procedure.

Retrospective hospital data for central catheter placement were examined for the 18 months prior to and following the start of the hospitalist program. These data were collected routinely by the hospital infection control nurse for purposes of quality improvement and patient safety. The data included central catheters placed by all physicians in the hospital; however, the vast majority of these were placed by the hospitalists. The catheters were placed throughout the hospital, both on the medical floors, cardiac step‐down unit, and the ICU. Information regarding the number of central catheters placed and the specific type of catheter (subclavian, jugular, femoral, or PICC) was available from July 2005 through December 2007. Also available from January 2005 were the numbers of femoral and nonfemoral catheter days (number of catheters multiplied by number of days in place) and the central catheterassociated bacteremia rates (number per 1000 catheter days) for femoral and nonfemoral catheters. The Centers for Disease Control and Prevention (CDC) definition of central lineassociated bacteremia was used, which is any documented bloodstream infection within 48 hours of the presence of a CVC in the absence of an alternate source of infection. Data for other complications such as pneumothorax and major bleeding were not consistently recorded.

Results

Figure 1 shows the number of internal jugular, subclavian, femoral, PICC, and total catheter placements from July 2005 through December 2007. The data are grouped into 3‐month increments for visual convenience. Comparing the periods before and after the inception of the hospitalist PICC service (Figure 1, dotted vertical line), the rate of PICC placements rose 4‐fold and the rate of total catheter placements approximately doubled. The rates of femoral and subclavian catheter placements decreased by approximately 50% and the rate of internal jugular catheter placement was roughly unchanged.

Figure 1

Figure 2 shows the numbers of femoral and nonfemoral catheter days by month for 2005 through 2007. The nonfemoral catheter days began to rise prior to the start of the hospitalist program and continued to rise afterward, showing an approximately 3‐fold increase by the end of the study period. The number of femoral catheters days was highly variable, but seemed to decrease by approximately 50%.

Figure 2

Figure 3 shows the rates of femoral and nonfemoral catheter‐associated bacteremia by month for 2005 through 2007. The absolute number of infections in both periods was low and is shown at the top of each bar in the figure.

Figure 3

To our knowledge, there were no episodes of pneumothorax or major bleeding with PICC placement. There were 3 inadvertent arterial punctures, each of which was easily controlled with local pressure. There was 1 incident of a coiled guidewire that could not be removed at the bedside and had to be removed in interventional radiology with no significant consequence to the patient.

Discussion

The complications associated with central catheter insertion continue to place the hospitalized patient at risk. PICCs may offer significant advantages over other types of central catheters in terms of decreased rates of mechanical and infectious complications. Despite this, hospital physicians have not traditionally been trained to place PICCs. We have shown in our small, university‐affiliated community hospital that training hospital physicians to place PICCs was associated with a decrease in the placement of centrally‐inserted venous catheters and a reduced rate of femoral catheter days. At the same time, the rate of central catheterrelated bacteremia remained low.

There are many limitations to our study. Since the analysis was retrospective and uncontrolled, it is not possible to attribute the decrease in femoral catheter days and the low infection rates solely to the use of PICCs. There may have been other factors, either related or unrelated to the transition to a hospitalist service, that influenced the results, such as improved hand hygiene, attention to the use of 5 barrier precautions, and the use of chlorhexidine cleansing. Also, since the study was descriptive and outcome measures were either not available or the numbers small, we cannot prove that there was benefit to the patients or that the changes in rates were statistically significant.

Training hospital physicians to place PICCs in our study was associated with a 2‐fold increase in the overall rate of catheter placements. The reason for this increase in the total number of catheter placements is not clear, but it is likely related to the ease of PICC placement and the increasing number of patients with difficult intravenous access. It is unclear if an equivalent number of traditional central catheters would have been placed were the hospitalists not trained in PICC placement. However, this increase in total number of catheters did not appear to result in an increase in catheter‐related bacteremia or in mechanical complications.

We observed no apparent decrease in the insertion rate of internal jugular catheters in our study, despite a decrease in the rates of subclavian and femoral catheter placements. Although the current CDC guideline recommends using the subclavian vein as the preferred site, the UK National Institute for Clinical Excellence (NICE) is now recommending the use of real‐time ultrasound with each placement,18 and we find that this is best done in the internal jugular vein. Also, the rate of placement of femoral catheters remained higher than that of subclavian cathetersmost likely because the femoral vein remained the site of choice for emergently‐placed cathetersas PICC, more so than subclavian, became the preferred site for elective catheters.

Training physicians to place PICCs was not a simple task. In our experience, the availability of trainers at the tertiary care hospital was limited and the distractions of other duties of the hospitalist complicated the learning process. Two of our 5 physicians could not schedule time with the training nurse and were not able to acquire the skill. However, after training, the 3 hospitalists found that there was such a demand for PICCs that with time it was easy to maintain and even refine this skill. Since we only had 3 of 5 hospitalists trained in PICC placement, we could not have a PICC‐trained hospitalist on site 24 hours a day and the remaining 2 physicians had to rely on centrally‐placed catheters for access or have 1 of the trained physicians come to the hospital from home.

In summary, PICCs may be a safe and easy alternative to centrally‐placed catheters for the hospital physician attempting to secure central intravenous access and may lead to a decrease in the need for more risky CVC insertions. More definitive, controlled investigation, with patient outcome data, will be required before this can be advocated as a universal recommendation.

Peripherally inserted central catheters (PICCs) are being used with greater frequency than ever before for intravenous access in hospitals, and PICCs may offer advantages in safety over traditional central venous catheters (CVCs). Despite these potential advantages, a large number of CVCs are still being placed. In a recent 1‐day survey of 6 large urban teaching hospitals, 29% of all patients had a CVC in place (59.3% of intensive care unit [ICU] patients and 23.7% of non‐ICU patients).1 Most catheters were inserted in the subclavian (55%) or jugular (22%) veins, with femoral (6%) and peripheral (15%) sites less commonly used. Even in the non‐ICU setting, only 20% of all central catheters were PICCs.

PICCs may offer advantages over centrally‐inserted intravenous catheters, such as the reduced risks of pneumothorax,2 arterial puncture, uncontrolled bleeding of large central veins, central lineassociated bloodstream infections (CLAB),3, 4 and lower cost.5 In addition, central venous pressure monitoring can now be performed with the larger‐bore PICCs.6

The low risk of mechanical complications for PICC insertion has been well documented.7, 8 In contrast, femoral or retroperitoneal hematoma occurs in up to 1.3% of cases following femoral catheter insertion,9 and pneumothorax occurs in 1.5% to 2.3% of subclavian catheter insertions.10 However, there are only limited data to suggest that the risk of PICC‐related bacteremia is lower than that of centrally‐placed catheters.11, 12

The benefit of PICCs over centrally‐placed catheters in terms of venous thromboembolism (VTE) is also not as easy to show, and in fact the rate may be greater in PICCs. The reported incidence of PICC‐related VTE has been between 0.3% and 56.0%, and the wide variation in rates is likely related to the method of diagnosis.1315 It is likely that most patients with PICC‐related VTE are asymptomatic, and that its incidence is underestimated.16

In many hospitals PICCs are placed by a certified nurse, or by an interventional radiologist if the nurse is unsuccessful.17 There are few reports of PICCs being placed by nonradiology physicians. In one report of 894 patients referred to a critical care specialist for PICC insertion, venous access was achieved 100% of the time, there were no referrals to interventional radiology, and there were no incidents of pneumothorax or bleeding.8 In a university‐affiliated community hospital, we carried out a retrospective review of our experience with training hospital physicians to place PICCs.

Methods

In July 2006 our community hospital, which is affiliated with the University of Pittsburgh Medical Center, instituted a hospitalist program. Prior to the hospitalist program, 1 house physician was available to place PICCs in the antecubital vein without the aid of ultrasound, and there was no PICC‐certified nurse in the hospital. An interventional radiologist was available to place PICCs that could not be placed by the house physician. After July 2006 under the hospitalist service, 3 of the 5 physicians were trained to place PICCs in the deep veins of the arm with the use of ultrasound guidance.

Training included 1 day with the PICC training nurse at the tertiary hospital, followed by supervised placements in the community hospital until proficiency was obtained. Proficiency was relative and cumulative. Approximately 3 supervised procedures were necessary before the physician was able to place PICCs by him or herself. All PICCs were placed using 5 barrier precautions, with chlorhexidine cleansing, and with a time‐out prior to the procedure.

Retrospective hospital data for central catheter placement were examined for the 18 months prior to and following the start of the hospitalist program. These data were collected routinely by the hospital infection control nurse for purposes of quality improvement and patient safety. The data included central catheters placed by all physicians in the hospital; however, the vast majority of these were placed by the hospitalists. The catheters were placed throughout the hospital, both on the medical floors, cardiac step‐down unit, and the ICU. Information regarding the number of central catheters placed and the specific type of catheter (subclavian, jugular, femoral, or PICC) was available from July 2005 through December 2007. Also available from January 2005 were the numbers of femoral and nonfemoral catheter days (number of catheters multiplied by number of days in place) and the central catheterassociated bacteremia rates (number per 1000 catheter days) for femoral and nonfemoral catheters. The Centers for Disease Control and Prevention (CDC) definition of central lineassociated bacteremia was used, which is any documented bloodstream infection within 48 hours of the presence of a CVC in the absence of an alternate source of infection. Data for other complications such as pneumothorax and major bleeding were not consistently recorded.

Results

Figure 1 shows the number of internal jugular, subclavian, femoral, PICC, and total catheter placements from July 2005 through December 2007. The data are grouped into 3‐month increments for visual convenience. Comparing the periods before and after the inception of the hospitalist PICC service (Figure 1, dotted vertical line), the rate of PICC placements rose 4‐fold and the rate of total catheter placements approximately doubled. The rates of femoral and subclavian catheter placements decreased by approximately 50% and the rate of internal jugular catheter placement was roughly unchanged.

Figure 1

Figure 2 shows the numbers of femoral and nonfemoral catheter days by month for 2005 through 2007. The nonfemoral catheter days began to rise prior to the start of the hospitalist program and continued to rise afterward, showing an approximately 3‐fold increase by the end of the study period. The number of femoral catheters days was highly variable, but seemed to decrease by approximately 50%.

Figure 2

Figure 3 shows the rates of femoral and nonfemoral catheter‐associated bacteremia by month for 2005 through 2007. The absolute number of infections in both periods was low and is shown at the top of each bar in the figure.

Figure 3

To our knowledge, there were no episodes of pneumothorax or major bleeding with PICC placement. There were 3 inadvertent arterial punctures, each of which was easily controlled with local pressure. There was 1 incident of a coiled guidewire that could not be removed at the bedside and had to be removed in interventional radiology with no significant consequence to the patient.

Discussion

The complications associated with central catheter insertion continue to place the hospitalized patient at risk. PICCs may offer significant advantages over other types of central catheters in terms of decreased rates of mechanical and infectious complications. Despite this, hospital physicians have not traditionally been trained to place PICCs. We have shown in our small, university‐affiliated community hospital that training hospital physicians to place PICCs was associated with a decrease in the placement of centrally‐inserted venous catheters and a reduced rate of femoral catheter days. At the same time, the rate of central catheterrelated bacteremia remained low.

There are many limitations to our study. Since the analysis was retrospective and uncontrolled, it is not possible to attribute the decrease in femoral catheter days and the low infection rates solely to the use of PICCs. There may have been other factors, either related or unrelated to the transition to a hospitalist service, that influenced the results, such as improved hand hygiene, attention to the use of 5 barrier precautions, and the use of chlorhexidine cleansing. Also, since the study was descriptive and outcome measures were either not available or the numbers small, we cannot prove that there was benefit to the patients or that the changes in rates were statistically significant.

Training hospital physicians to place PICCs in our study was associated with a 2‐fold increase in the overall rate of catheter placements. The reason for this increase in the total number of catheter placements is not clear, but it is likely related to the ease of PICC placement and the increasing number of patients with difficult intravenous access. It is unclear if an equivalent number of traditional central catheters would have been placed were the hospitalists not trained in PICC placement. However, this increase in total number of catheters did not appear to result in an increase in catheter‐related bacteremia or in mechanical complications.

We observed no apparent decrease in the insertion rate of internal jugular catheters in our study, despite a decrease in the rates of subclavian and femoral catheter placements. Although the current CDC guideline recommends using the subclavian vein as the preferred site, the UK National Institute for Clinical Excellence (NICE) is now recommending the use of real‐time ultrasound with each placement,18 and we find that this is best done in the internal jugular vein. Also, the rate of placement of femoral catheters remained higher than that of subclavian cathetersmost likely because the femoral vein remained the site of choice for emergently‐placed cathetersas PICC, more so than subclavian, became the preferred site for elective catheters.

Training physicians to place PICCs was not a simple task. In our experience, the availability of trainers at the tertiary care hospital was limited and the distractions of other duties of the hospitalist complicated the learning process. Two of our 5 physicians could not schedule time with the training nurse and were not able to acquire the skill. However, after training, the 3 hospitalists found that there was such a demand for PICCs that with time it was easy to maintain and even refine this skill. Since we only had 3 of 5 hospitalists trained in PICC placement, we could not have a PICC‐trained hospitalist on site 24 hours a day and the remaining 2 physicians had to rely on centrally‐placed catheters for access or have 1 of the trained physicians come to the hospital from home.

In summary, PICCs may be a safe and easy alternative to centrally‐placed catheters for the hospital physician attempting to secure central intravenous access and may lead to a decrease in the need for more risky CVC insertions. More definitive, controlled investigation, with patient outcome data, will be required before this can be advocated as a universal recommendation.