Machine Learning: the Future of Total Knee Replacement

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Total knee replacement (TKR) is one of the most common surgeries worldwide, with > 1 million performed last year. Many patients have seen tremendous benefit from TKR; however, studies have shown that up to 20% of patients are not satisfied with the results of this procedure.1,2 This equates to about 200,000 patients worldwide every year who are dissatisfied. This is a huge concern to patients, surgeons, implant manufacturers, hospitals, and health care payers.

Many attempts to improve satisfaction in TKR have been tried, including computer navigation, minimally invasive surgery, rotating platform prostheses, gender-specific implants, different materials, changes in pain management, and revised postoperative rehabilitation.3-7 However, these efforts show no significant improvement in satisfaction.

The most common method of TKR today involves using a long rod placed through a drill hole in the femur. Standardized cuts on the femur and tibia are made through metal cutting blocks. Only metal mechanical instruments are used to perform the surgery, and all patients are aligned the same. However, anatomic studies have shown that patient anatomy in 3 dimensions (3D) varies widely from patient to patient.8 Our current technique seems far removed from modern engineering, where we now see extensive use of artificial intelligence (AI) to improve outcomes.

Machine learning (ML) is considered a subset of AI that involves the use of various computer algorithms. ML allows the computer to learn and continually improve analysis of data. Large sets of inputs and outputs are used to train the machine to make autonomous recommendations or decisions.9,10

Seven years ago, our team at the Phoenix Veteran Affairs Medical Center in Arizona published a randomized controlled trial evaluating a new, individualized alignment technique for TKR.11 This method used 3D-printed guides made from an MRI of an individual patient’s knee. Instead of aligning all knee replacements the same, each patient was aligned according to their unique anatomy. Compared with the conventional alignment technique, the newer technique showed significant improvement in all outcome scores and range of motion at 2 years postsurgery. There has been a great deal of interest in individualizing TKR, and many articles and techniques have followed.12

Our surgical technique has evolved since publishing our trial. Currently, knee X-rays are digitally templated for each patient. Understanding the patient’s preoperative alignment can then assist in planning a TKR in 3D. A plastic 3D-printed guide is manufactured in Belgium, shipped to the US, sterilized, and used in surgery. These guides fit accurately on the patient’s anatomy and allow precise angles and depth of resection for each surgical bone cut. Our research has shown that these guides are accurate to within 0.5° and 0.5 mm for the bone cuts performed in surgery. After surgery, we track patient-reported outcomes (PROs), which can then be used in ML or logistic regression analysis to determine alignment factors that contribute to the best outcome.13

Soon, use of a robot will take the place of the templating and preplanning, allowing the 3D plan to be immediately produced in surgery by the software installed in the robot.14-16 Each patient’s preoperative alignment can then be immediately compared with the postoperative result, and smartphone technology can allow a patient to input their PRO after the surgery is healed.17

Collecting all this information in a large database can allow ML analyses of the outcomes and individual alignment.14-17 As the factors contributing to the best clinical results are determined, the computer can be programmed to learn how to make the best recommendations for alignment of each patient, which can be incorporated into the robotic platform for each surgery. Also pre- and postoperative factors can be added to the ML platform so we can identify the best preoperative patient parameters, anticoagulation program postoperative rehabilitation program, etc, to help drive higher PROs and satisfaction.

Multiple surgical robots for TKR are now on the market. Orthopedic literature includes ML algorithms to improve outcomes after total hip arthroplasty.18 The EHR can be used to develop models to predict poor outcomes after TKR. Integrating these models into clinical decision support could improve patient selection, education, and satisfaction.19 AI for adult spinal surgery using predictive analytics can help surgeons better inform patients about outcomes after corrective surgery.20,21

With worldwide TKRs expected to exceed 3 million over the next decade, ML using large databases, robotic surgery, and PROs could be key to improving our TKR outcomes.22 This form of AI may reduce the large number of patients currently not satisfied with their knee replacement.

References

1. Baker PN, van der Meulen JH, Lewsey J, Gregg PJ; National Joint Registry for England and Wales. The role of pain and function in determining patient satisfaction after total knee replacement. Data from the National Joint Registry for England and Wales. J Bone Joint Surg Br. 2007;89(7):893-900. doi:10.1302/0301-620X.89B7.19091

2. Noble PC, Conditt MA, Cook KF, Mathis KB. The John Insall Award: patient expectations affect satisfaction with total knee arthroplasty. Clin Orthop Relat Res. 2006;452:35-43. doi:10.1097/01.blo.0000238825.63648.1e

3. Matziolis G, Krocker D, Weiss U, Tohtz S, Perka C. A prospective, randomized study of computer-assisted and conventional total knee arthroplasty. Three-dimensional evaluation of implant alignment and rotation. J Bone Joint Surg Am. 2007;89(2):236-243. doi:10.2106/JBJS.F.00386

4. Stulberg SD, Yaffe MA, Koo SS. Computer-assisted surgery versus manual total knee arthroplasty: a case-controlled study. J Bone Joint Surg Am. 2006;88(suppl 4):47-54. doi:10.2106/JBJS.F.00698

5. Kalisvaart MM, Pagnano MW, Trousdale RT, Stuart MJ, Hanssen AD. Randomized clinical trial of rotating-platform and fixed-bearing total knee arthroplasty: no clinically detectable differences at five years. J Bone Joint Surg Am. 2012;94(6):481-489. doi:10.2106/JBJS.K.00315

6. Wülker N, Lambermont JP, Sacchetti L, Lazaró JG, Nardi J. A prospective randomized study of minimally invasive total knee arthroplasty compared with conventional surgery. J Bone Joint Surg Am. 2010;92(7):1584-1590. doi:10.2106/JBJS.H.01070

7. Thomsen MG, Husted H, Bencke J, Curtis D, Holm G, Troelsen A. Do we need a gender-specific total knee replacement? A randomised controlled trial comparing a high-flex and a gender-specific posterior design. J Bone Joint Surg Br. 2012;94(6):787-792. doi:10.1302/0301-620X.94B6.28781

8. Eckhoff D, Hogan C, DiMatteo L, Robinson M, Bach J. Difference between the epicondylar and cylindrical axis of the knee. Clin Orthop Relat Res. 2007;461:238-244. doi:10.1097/BLO.0b013e318112416b

9. Martin RK, Ley C, Pareek A, Groll A, Tischer T, Seil R. Artificial intelligence and machine learning: an introduction for orthopaedic surgeons [published online ahead of print, 2021 Sep 15]. Knee Surg Sports Traumatol Arthrosc. 2021;10.1007/s00167-021-06741-2. doi:10.1007/s00167-021-06741-2

10. Helm JM, Swiergosz AM, Haeberle HS, et al. Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions. Curr Rev Musculoskelet Med. 2020;13(1):69-76. doi:10.1007/s12178-020-09600-8

11. Dossett HG, Estrada NA, Swartz GJ, LeFevre GW, Kwasman BG. A randomised controlled trial of kinematically and mechanically aligned total knee replacements: two-year clinical results. Bone Joint J. 2014;96-B(7):907-913. doi:10.1302/0301-620X.96B7.32812

12. Rivière C, Iranpour F, Auvinet E, et al. Alignment options for total knee arthroplasty: a systematic review. Orthop Traumatol Surg Res. 2017;103(7):1047-1056. doi:10.1016/j.otsr.2017.07.010

13. Dossett HG. High reliability in total knee replacement surgery: is it possible? Orthop Proc. 2018;95-B(suppl 34):292-293.

14. Schock J, Truhn D, Abrar DB, et al. Automated analysis of alignment in long-leg radiographs by using a fully automated support system based on artificial intelligence. Radiol: Artif Intell. Dec 23, 2020;3(2). doi:10.1148/ryai.2020200198

15. Cabitza F, Locoro A, Banfi G. Machine learning in orthopedics: a literature review. Front Bioeng Biotechnol. 2018;6:75. Published 2018 Jun 27. doi:10.3389/fbioe.2018.00075

16. von Schacky CE, Wilhelm NJ, Schäfer VS, et al. Multitask deep learning for segmentation and classification of primary bone tumors on radiographs. Radiology. 2021;301(2):398-406. doi:10.1148/radiol.2021204531

17. Myers TG, Ramkumar PN, Ricciardi BF, Urish KL, Kipper J, Ketonis C. Artificial intelligence and orthopaedics: an introduction for clinicians. J Bone Joint Surg Am. 2020;102(9):830-840. doi:10.2106/JBJS.19.01128

18. Kunze KN, Karhade AV, Sadauskas AJ, Schwab JH, Levine BR. Development of machine learning algorithms to predict clinically meaningful improvement for the patient-reported health state after total hip arthroplasty. J Arthroplasty. 2020;35(8):2119-2123. doi:10.1016/j.arth.2020.03.019

19. Harris AHS, Kuo AC, Bowe TR, Manfredi L, Lalani NF, Giori NJ. Can machine learning methods produce accurate and easy-to-use preoperative prediction models of one-year improvements in pain and functioning after knee arthroplasty? J Arthroplasty. 2021;36(1):112-117.e6. doi:10.1016/j.arth.2020.07.026

20. Rasouli JJ, Shao J, Neifert S, et al. Artificial intelligence and robotics in spine surgery. Global Spine J. 2021;11(4):556-564. doi:10.1177/2192568220915718

21. Joshi RS, Haddad AF, Lau D, Ames CP. Artificial intelligence for adult spinal deformity. Neurospine. 2019;16(4):686-694. doi:10.14245/ns.1938414.207

22. Kurtz S, Ong K, Lau E, Mowat F, Halpern M. Projections of primary and revision hip and knee arthroplasty in the United States from 2005 to 2030. J Bone Joint Surg Am. 2007;89(4):780-785. doi:10.2106/JBJS.F.00222

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Total knee replacement (TKR) is one of the most common surgeries worldwide, with > 1 million performed last year. Many patients have seen tremendous benefit from TKR; however, studies have shown that up to 20% of patients are not satisfied with the results of this procedure.1,2 This equates to about 200,000 patients worldwide every year who are dissatisfied. This is a huge concern to patients, surgeons, implant manufacturers, hospitals, and health care payers.

Many attempts to improve satisfaction in TKR have been tried, including computer navigation, minimally invasive surgery, rotating platform prostheses, gender-specific implants, different materials, changes in pain management, and revised postoperative rehabilitation.3-7 However, these efforts show no significant improvement in satisfaction.

The most common method of TKR today involves using a long rod placed through a drill hole in the femur. Standardized cuts on the femur and tibia are made through metal cutting blocks. Only metal mechanical instruments are used to perform the surgery, and all patients are aligned the same. However, anatomic studies have shown that patient anatomy in 3 dimensions (3D) varies widely from patient to patient.8 Our current technique seems far removed from modern engineering, where we now see extensive use of artificial intelligence (AI) to improve outcomes.

Machine learning (ML) is considered a subset of AI that involves the use of various computer algorithms. ML allows the computer to learn and continually improve analysis of data. Large sets of inputs and outputs are used to train the machine to make autonomous recommendations or decisions.9,10

Seven years ago, our team at the Phoenix Veteran Affairs Medical Center in Arizona published a randomized controlled trial evaluating a new, individualized alignment technique for TKR.11 This method used 3D-printed guides made from an MRI of an individual patient’s knee. Instead of aligning all knee replacements the same, each patient was aligned according to their unique anatomy. Compared with the conventional alignment technique, the newer technique showed significant improvement in all outcome scores and range of motion at 2 years postsurgery. There has been a great deal of interest in individualizing TKR, and many articles and techniques have followed.12

Our surgical technique has evolved since publishing our trial. Currently, knee X-rays are digitally templated for each patient. Understanding the patient’s preoperative alignment can then assist in planning a TKR in 3D. A plastic 3D-printed guide is manufactured in Belgium, shipped to the US, sterilized, and used in surgery. These guides fit accurately on the patient’s anatomy and allow precise angles and depth of resection for each surgical bone cut. Our research has shown that these guides are accurate to within 0.5° and 0.5 mm for the bone cuts performed in surgery. After surgery, we track patient-reported outcomes (PROs), which can then be used in ML or logistic regression analysis to determine alignment factors that contribute to the best outcome.13

Soon, use of a robot will take the place of the templating and preplanning, allowing the 3D plan to be immediately produced in surgery by the software installed in the robot.14-16 Each patient’s preoperative alignment can then be immediately compared with the postoperative result, and smartphone technology can allow a patient to input their PRO after the surgery is healed.17

Collecting all this information in a large database can allow ML analyses of the outcomes and individual alignment.14-17 As the factors contributing to the best clinical results are determined, the computer can be programmed to learn how to make the best recommendations for alignment of each patient, which can be incorporated into the robotic platform for each surgery. Also pre- and postoperative factors can be added to the ML platform so we can identify the best preoperative patient parameters, anticoagulation program postoperative rehabilitation program, etc, to help drive higher PROs and satisfaction.

Multiple surgical robots for TKR are now on the market. Orthopedic literature includes ML algorithms to improve outcomes after total hip arthroplasty.18 The EHR can be used to develop models to predict poor outcomes after TKR. Integrating these models into clinical decision support could improve patient selection, education, and satisfaction.19 AI for adult spinal surgery using predictive analytics can help surgeons better inform patients about outcomes after corrective surgery.20,21

With worldwide TKRs expected to exceed 3 million over the next decade, ML using large databases, robotic surgery, and PROs could be key to improving our TKR outcomes.22 This form of AI may reduce the large number of patients currently not satisfied with their knee replacement.

Total knee replacement (TKR) is one of the most common surgeries worldwide, with > 1 million performed last year. Many patients have seen tremendous benefit from TKR; however, studies have shown that up to 20% of patients are not satisfied with the results of this procedure.1,2 This equates to about 200,000 patients worldwide every year who are dissatisfied. This is a huge concern to patients, surgeons, implant manufacturers, hospitals, and health care payers.

Many attempts to improve satisfaction in TKR have been tried, including computer navigation, minimally invasive surgery, rotating platform prostheses, gender-specific implants, different materials, changes in pain management, and revised postoperative rehabilitation.3-7 However, these efforts show no significant improvement in satisfaction.

The most common method of TKR today involves using a long rod placed through a drill hole in the femur. Standardized cuts on the femur and tibia are made through metal cutting blocks. Only metal mechanical instruments are used to perform the surgery, and all patients are aligned the same. However, anatomic studies have shown that patient anatomy in 3 dimensions (3D) varies widely from patient to patient.8 Our current technique seems far removed from modern engineering, where we now see extensive use of artificial intelligence (AI) to improve outcomes.

Machine learning (ML) is considered a subset of AI that involves the use of various computer algorithms. ML allows the computer to learn and continually improve analysis of data. Large sets of inputs and outputs are used to train the machine to make autonomous recommendations or decisions.9,10

Seven years ago, our team at the Phoenix Veteran Affairs Medical Center in Arizona published a randomized controlled trial evaluating a new, individualized alignment technique for TKR.11 This method used 3D-printed guides made from an MRI of an individual patient’s knee. Instead of aligning all knee replacements the same, each patient was aligned according to their unique anatomy. Compared with the conventional alignment technique, the newer technique showed significant improvement in all outcome scores and range of motion at 2 years postsurgery. There has been a great deal of interest in individualizing TKR, and many articles and techniques have followed.12

Our surgical technique has evolved since publishing our trial. Currently, knee X-rays are digitally templated for each patient. Understanding the patient’s preoperative alignment can then assist in planning a TKR in 3D. A plastic 3D-printed guide is manufactured in Belgium, shipped to the US, sterilized, and used in surgery. These guides fit accurately on the patient’s anatomy and allow precise angles and depth of resection for each surgical bone cut. Our research has shown that these guides are accurate to within 0.5° and 0.5 mm for the bone cuts performed in surgery. After surgery, we track patient-reported outcomes (PROs), which can then be used in ML or logistic regression analysis to determine alignment factors that contribute to the best outcome.13

Soon, use of a robot will take the place of the templating and preplanning, allowing the 3D plan to be immediately produced in surgery by the software installed in the robot.14-16 Each patient’s preoperative alignment can then be immediately compared with the postoperative result, and smartphone technology can allow a patient to input their PRO after the surgery is healed.17

Collecting all this information in a large database can allow ML analyses of the outcomes and individual alignment.14-17 As the factors contributing to the best clinical results are determined, the computer can be programmed to learn how to make the best recommendations for alignment of each patient, which can be incorporated into the robotic platform for each surgery. Also pre- and postoperative factors can be added to the ML platform so we can identify the best preoperative patient parameters, anticoagulation program postoperative rehabilitation program, etc, to help drive higher PROs and satisfaction.

Multiple surgical robots for TKR are now on the market. Orthopedic literature includes ML algorithms to improve outcomes after total hip arthroplasty.18 The EHR can be used to develop models to predict poor outcomes after TKR. Integrating these models into clinical decision support could improve patient selection, education, and satisfaction.19 AI for adult spinal surgery using predictive analytics can help surgeons better inform patients about outcomes after corrective surgery.20,21

With worldwide TKRs expected to exceed 3 million over the next decade, ML using large databases, robotic surgery, and PROs could be key to improving our TKR outcomes.22 This form of AI may reduce the large number of patients currently not satisfied with their knee replacement.

References

1. Baker PN, van der Meulen JH, Lewsey J, Gregg PJ; National Joint Registry for England and Wales. The role of pain and function in determining patient satisfaction after total knee replacement. Data from the National Joint Registry for England and Wales. J Bone Joint Surg Br. 2007;89(7):893-900. doi:10.1302/0301-620X.89B7.19091

2. Noble PC, Conditt MA, Cook KF, Mathis KB. The John Insall Award: patient expectations affect satisfaction with total knee arthroplasty. Clin Orthop Relat Res. 2006;452:35-43. doi:10.1097/01.blo.0000238825.63648.1e

3. Matziolis G, Krocker D, Weiss U, Tohtz S, Perka C. A prospective, randomized study of computer-assisted and conventional total knee arthroplasty. Three-dimensional evaluation of implant alignment and rotation. J Bone Joint Surg Am. 2007;89(2):236-243. doi:10.2106/JBJS.F.00386

4. Stulberg SD, Yaffe MA, Koo SS. Computer-assisted surgery versus manual total knee arthroplasty: a case-controlled study. J Bone Joint Surg Am. 2006;88(suppl 4):47-54. doi:10.2106/JBJS.F.00698

5. Kalisvaart MM, Pagnano MW, Trousdale RT, Stuart MJ, Hanssen AD. Randomized clinical trial of rotating-platform and fixed-bearing total knee arthroplasty: no clinically detectable differences at five years. J Bone Joint Surg Am. 2012;94(6):481-489. doi:10.2106/JBJS.K.00315

6. Wülker N, Lambermont JP, Sacchetti L, Lazaró JG, Nardi J. A prospective randomized study of minimally invasive total knee arthroplasty compared with conventional surgery. J Bone Joint Surg Am. 2010;92(7):1584-1590. doi:10.2106/JBJS.H.01070

7. Thomsen MG, Husted H, Bencke J, Curtis D, Holm G, Troelsen A. Do we need a gender-specific total knee replacement? A randomised controlled trial comparing a high-flex and a gender-specific posterior design. J Bone Joint Surg Br. 2012;94(6):787-792. doi:10.1302/0301-620X.94B6.28781

8. Eckhoff D, Hogan C, DiMatteo L, Robinson M, Bach J. Difference between the epicondylar and cylindrical axis of the knee. Clin Orthop Relat Res. 2007;461:238-244. doi:10.1097/BLO.0b013e318112416b

9. Martin RK, Ley C, Pareek A, Groll A, Tischer T, Seil R. Artificial intelligence and machine learning: an introduction for orthopaedic surgeons [published online ahead of print, 2021 Sep 15]. Knee Surg Sports Traumatol Arthrosc. 2021;10.1007/s00167-021-06741-2. doi:10.1007/s00167-021-06741-2

10. Helm JM, Swiergosz AM, Haeberle HS, et al. Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions. Curr Rev Musculoskelet Med. 2020;13(1):69-76. doi:10.1007/s12178-020-09600-8

11. Dossett HG, Estrada NA, Swartz GJ, LeFevre GW, Kwasman BG. A randomised controlled trial of kinematically and mechanically aligned total knee replacements: two-year clinical results. Bone Joint J. 2014;96-B(7):907-913. doi:10.1302/0301-620X.96B7.32812

12. Rivière C, Iranpour F, Auvinet E, et al. Alignment options for total knee arthroplasty: a systematic review. Orthop Traumatol Surg Res. 2017;103(7):1047-1056. doi:10.1016/j.otsr.2017.07.010

13. Dossett HG. High reliability in total knee replacement surgery: is it possible? Orthop Proc. 2018;95-B(suppl 34):292-293.

14. Schock J, Truhn D, Abrar DB, et al. Automated analysis of alignment in long-leg radiographs by using a fully automated support system based on artificial intelligence. Radiol: Artif Intell. Dec 23, 2020;3(2). doi:10.1148/ryai.2020200198

15. Cabitza F, Locoro A, Banfi G. Machine learning in orthopedics: a literature review. Front Bioeng Biotechnol. 2018;6:75. Published 2018 Jun 27. doi:10.3389/fbioe.2018.00075

16. von Schacky CE, Wilhelm NJ, Schäfer VS, et al. Multitask deep learning for segmentation and classification of primary bone tumors on radiographs. Radiology. 2021;301(2):398-406. doi:10.1148/radiol.2021204531

17. Myers TG, Ramkumar PN, Ricciardi BF, Urish KL, Kipper J, Ketonis C. Artificial intelligence and orthopaedics: an introduction for clinicians. J Bone Joint Surg Am. 2020;102(9):830-840. doi:10.2106/JBJS.19.01128

18. Kunze KN, Karhade AV, Sadauskas AJ, Schwab JH, Levine BR. Development of machine learning algorithms to predict clinically meaningful improvement for the patient-reported health state after total hip arthroplasty. J Arthroplasty. 2020;35(8):2119-2123. doi:10.1016/j.arth.2020.03.019

19. Harris AHS, Kuo AC, Bowe TR, Manfredi L, Lalani NF, Giori NJ. Can machine learning methods produce accurate and easy-to-use preoperative prediction models of one-year improvements in pain and functioning after knee arthroplasty? J Arthroplasty. 2021;36(1):112-117.e6. doi:10.1016/j.arth.2020.07.026

20. Rasouli JJ, Shao J, Neifert S, et al. Artificial intelligence and robotics in spine surgery. Global Spine J. 2021;11(4):556-564. doi:10.1177/2192568220915718

21. Joshi RS, Haddad AF, Lau D, Ames CP. Artificial intelligence for adult spinal deformity. Neurospine. 2019;16(4):686-694. doi:10.14245/ns.1938414.207

22. Kurtz S, Ong K, Lau E, Mowat F, Halpern M. Projections of primary and revision hip and knee arthroplasty in the United States from 2005 to 2030. J Bone Joint Surg Am. 2007;89(4):780-785. doi:10.2106/JBJS.F.00222

References

1. Baker PN, van der Meulen JH, Lewsey J, Gregg PJ; National Joint Registry for England and Wales. The role of pain and function in determining patient satisfaction after total knee replacement. Data from the National Joint Registry for England and Wales. J Bone Joint Surg Br. 2007;89(7):893-900. doi:10.1302/0301-620X.89B7.19091

2. Noble PC, Conditt MA, Cook KF, Mathis KB. The John Insall Award: patient expectations affect satisfaction with total knee arthroplasty. Clin Orthop Relat Res. 2006;452:35-43. doi:10.1097/01.blo.0000238825.63648.1e

3. Matziolis G, Krocker D, Weiss U, Tohtz S, Perka C. A prospective, randomized study of computer-assisted and conventional total knee arthroplasty. Three-dimensional evaluation of implant alignment and rotation. J Bone Joint Surg Am. 2007;89(2):236-243. doi:10.2106/JBJS.F.00386

4. Stulberg SD, Yaffe MA, Koo SS. Computer-assisted surgery versus manual total knee arthroplasty: a case-controlled study. J Bone Joint Surg Am. 2006;88(suppl 4):47-54. doi:10.2106/JBJS.F.00698

5. Kalisvaart MM, Pagnano MW, Trousdale RT, Stuart MJ, Hanssen AD. Randomized clinical trial of rotating-platform and fixed-bearing total knee arthroplasty: no clinically detectable differences at five years. J Bone Joint Surg Am. 2012;94(6):481-489. doi:10.2106/JBJS.K.00315

6. Wülker N, Lambermont JP, Sacchetti L, Lazaró JG, Nardi J. A prospective randomized study of minimally invasive total knee arthroplasty compared with conventional surgery. J Bone Joint Surg Am. 2010;92(7):1584-1590. doi:10.2106/JBJS.H.01070

7. Thomsen MG, Husted H, Bencke J, Curtis D, Holm G, Troelsen A. Do we need a gender-specific total knee replacement? A randomised controlled trial comparing a high-flex and a gender-specific posterior design. J Bone Joint Surg Br. 2012;94(6):787-792. doi:10.1302/0301-620X.94B6.28781

8. Eckhoff D, Hogan C, DiMatteo L, Robinson M, Bach J. Difference between the epicondylar and cylindrical axis of the knee. Clin Orthop Relat Res. 2007;461:238-244. doi:10.1097/BLO.0b013e318112416b

9. Martin RK, Ley C, Pareek A, Groll A, Tischer T, Seil R. Artificial intelligence and machine learning: an introduction for orthopaedic surgeons [published online ahead of print, 2021 Sep 15]. Knee Surg Sports Traumatol Arthrosc. 2021;10.1007/s00167-021-06741-2. doi:10.1007/s00167-021-06741-2

10. Helm JM, Swiergosz AM, Haeberle HS, et al. Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions. Curr Rev Musculoskelet Med. 2020;13(1):69-76. doi:10.1007/s12178-020-09600-8

11. Dossett HG, Estrada NA, Swartz GJ, LeFevre GW, Kwasman BG. A randomised controlled trial of kinematically and mechanically aligned total knee replacements: two-year clinical results. Bone Joint J. 2014;96-B(7):907-913. doi:10.1302/0301-620X.96B7.32812

12. Rivière C, Iranpour F, Auvinet E, et al. Alignment options for total knee arthroplasty: a systematic review. Orthop Traumatol Surg Res. 2017;103(7):1047-1056. doi:10.1016/j.otsr.2017.07.010

13. Dossett HG. High reliability in total knee replacement surgery: is it possible? Orthop Proc. 2018;95-B(suppl 34):292-293.

14. Schock J, Truhn D, Abrar DB, et al. Automated analysis of alignment in long-leg radiographs by using a fully automated support system based on artificial intelligence. Radiol: Artif Intell. Dec 23, 2020;3(2). doi:10.1148/ryai.2020200198

15. Cabitza F, Locoro A, Banfi G. Machine learning in orthopedics: a literature review. Front Bioeng Biotechnol. 2018;6:75. Published 2018 Jun 27. doi:10.3389/fbioe.2018.00075

16. von Schacky CE, Wilhelm NJ, Schäfer VS, et al. Multitask deep learning for segmentation and classification of primary bone tumors on radiographs. Radiology. 2021;301(2):398-406. doi:10.1148/radiol.2021204531

17. Myers TG, Ramkumar PN, Ricciardi BF, Urish KL, Kipper J, Ketonis C. Artificial intelligence and orthopaedics: an introduction for clinicians. J Bone Joint Surg Am. 2020;102(9):830-840. doi:10.2106/JBJS.19.01128

18. Kunze KN, Karhade AV, Sadauskas AJ, Schwab JH, Levine BR. Development of machine learning algorithms to predict clinically meaningful improvement for the patient-reported health state after total hip arthroplasty. J Arthroplasty. 2020;35(8):2119-2123. doi:10.1016/j.arth.2020.03.019

19. Harris AHS, Kuo AC, Bowe TR, Manfredi L, Lalani NF, Giori NJ. Can machine learning methods produce accurate and easy-to-use preoperative prediction models of one-year improvements in pain and functioning after knee arthroplasty? J Arthroplasty. 2021;36(1):112-117.e6. doi:10.1016/j.arth.2020.07.026

20. Rasouli JJ, Shao J, Neifert S, et al. Artificial intelligence and robotics in spine surgery. Global Spine J. 2021;11(4):556-564. doi:10.1177/2192568220915718

21. Joshi RS, Haddad AF, Lau D, Ames CP. Artificial intelligence for adult spinal deformity. Neurospine. 2019;16(4):686-694. doi:10.14245/ns.1938414.207

22. Kurtz S, Ong K, Lau E, Mowat F, Halpern M. Projections of primary and revision hip and knee arthroplasty in the United States from 2005 to 2030. J Bone Joint Surg Am. 2007;89(4):780-785. doi:10.2106/JBJS.F.00222

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Improving Care and Reducing Length of Stay in Patients Undergoing Total Knee Replacement

Article Type
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Wed, 01/31/2018 - 15:01
A team approach to orthopedic surgery process improvement helped reduce length of stay without increasing 30-day readmission rates.

Many improvements in health care today involve care coordination across the entire health care system. Active management of an orthopedic surgery service from a system perspective allows for improvements that can favorably impact readmissions and length of stay (LOS) for patients.1 The following is an example of a systemwide process improvement in total knee replacement (TKR) surgery that dramatically decreased 30-day readmissions and shortened the LOS during a 12-month period.\

Background

The VA is the largest integrated health care system in the U.S. VA hospitals use the VA Surgical Quality Improvement Program (VASQIP) to monitor surgical services. Initially known as the National Surgery Quality Improvement Program (NSQIP), the program began in 1994 to help provide reliable, valid information on patient presurgical factors, processes of care during surgery, and 30-day morbidity and mortality rates in VA hospitals.2 Since its inception, NSQIP has spread to the private sector and is now widely used throughout the U.S.

Using on-site data acquisition by specially trained and dedicated registered nurses, information on each surgical case is input into a quality program. Quarterly reports are distributed to each hospital, and a comparison of mortality, LOS, 30-day readmissions to the hospital, and other data are analyzed and presented by quarter and rolling 12-month time frames. Use of VASQIP data allows improvement of the structures and processes of care throughout the VA, providing safer surgery for veterans.

At the Phoenix VA Health Care System (PVAHCS) in Arizona, the third quarter 2014 report showed the rolling 12-month average LOS for orthopedic TKR patients was 3.5 days and corresponding 30-day readmissions were 7.9%. Using a systems improvement approach, the authors set a goal of reducing these metrics by 10%.

The orthopedic service engaged members of the hospitalist, anesthesia, physical therapy (PT), nursing, social work, primary care, and pharmacy services, as well as hospital administration. Twelve months later, the LOS for TKR patients declined 20% to 2.8 days. Corresponding 30-day readmissions declined for the patients with knee replacement to 3.4%—a 57% reduction in 1 year. Mortality for these 177 cases was zero.

To accomplish these improvements, the authors divided the surgical procedure into preoperative, perioperative, and postoperative time frames and looked at process improvement during each of these periods. The following is a summary of the various processes that the authors feel contributed to the reduced LOS and 30-day readmission rate. Although some of these interventions were in place before the study period, all the processes were standardized for TKRs through surgeon consensus, and each of the surgeons adopted all the processes during the study period.

Preoperative Processes

In the VA primary care-based model orthopedic surgery is accessed through a consult process in the electronic health record. The orthopedic surgery service reviews each new consult and makes recommendations for optimization at the time the consult was received. This process was used to work closely with primary care providers to preoperatively prepare patients. The orthopedic surgery service advocates smoking cessation, substance abuse treatment, weight loss with an ideal body mass index of ≤ 35, and diabetes mellitus (DM) management with a ≤ 7 hemoglobin A1c value.3-7

This management did not result in fewer patients receiving TKR. In fact, the volume of TKR patients increased by 8% over the study period. Although part of this increase could have been due to increased scheduling efficiency, the orthopedic surgery service worked closely with primary care, nutrition, and medicine services to optimize these patients so they could be placed on the schedule for surgery.

Preoperative Education

Physical therapy and the orthopedic preprocedure clinic provided preoperative education to patients, covering preoperative chlorhexidine body washes, home safety, use of a walker, anticipated LOS, use of ambulatory sequential compressive devices, use of a knee cooling device, as well as PT protocols during hospitalization.8 This helped increase postoperative patient adherence and helped patients anticipate an appropriate LOS. Health care providers worked with patients to understand their home environment and plan for caregivers to assist them in the immediate postoperative period.

Intraoperative Processes

Reducing Blood Loss

The orthopedic surgery service reviewed literature related to the efficacy and safety of tranexamic acid. Based on the literature, the orthopedic surgery service arrived at a consensus agreement to implement a topical tranexamic acid dose of 3 g/100 cc saline for each TKR. Presentation of the pertinent literature to the pharmacy service allowed placement of this medication on the formulary for intraoperative use in the TKR cases.

Specific processes were implemented that involved the orthopedic service ordering tranexamic acid in advance for each patient, pharmacy mixing the solution and having it ready in a timely manner, and the operating room sending a messenger to the pharmacy to pick up a sterile container of the tranexamic acid/saline solution. Postoperative blood loss and transfusions decreased. Less anemia contributed to better performance and less fatigue in PT, which helped move patients down a pathway for quicker discharge.9,10

 

 

DVT Mechanical Prophylaxis

The orthopedic surgery service was concerned about adherence with stationary sequential compressive devices for mechanical thromboembolic prophylaxis. Patients had to remove them for PT, ambulation in the halls, and visiting the restroom, and then nurses had to replace them. A literature review examined a mobile compressive device that could be maintained during ambulation, and a demonstration for the orthopedic surgery service was arranged. The orthopedic service decided to change to the newer device, and the mobile compression device was presented to the PVAHCS Therapeutics Committee. Subsequently the new device was implemented after the appropriate in-service of the various clinic, PT, ward, surgery, preoperative, and postoperative personnel.11 The device was initiated in the holding area prior to surgery, continued throughout the hospitalization, and taken home by the patient for 2 weeks of use following surgery. Patients were instructed to return the device to clinic at their 2-week follow-up appointment.

Infection Control

A dilute betadine lavage was instituted for each surgical case, using the pulsatile lavage followed by a lactated Ringer solution rinse prior to TKR implantation. Additionally, the wound was lavaged prior to closure with this dilute betadine solution.12

 

Pain Control

Immediately before surgery, patients received oral morphine sulfate and celecoxib. A local 2% lidocaine with epinephrine injection was used at the surgical incision and joint after the skin prep and immediately prior to the skin incision. Patients received a mixture of ropivicaine .5%/20 mL, morphine sulfate 10 mg, and toradol 30 mg at the capsular region prior to implantation of the total knee prosthesis. At the end of the procedure, an additional 20 mL of 2% lidocaine was injected into the joint once the capsule was closed. This improved postoperative pain, decreased postoperative opioid dosing, and allowed for earlier ambulation with PT.13

PostOperative Processes

Deep Vein Thrombosis (DVT) Chemoprophylaxis

Once the chest physician guidelines-approved stand-alone mobile compressive devices was implemented, orthopedic surgery service revisited the chemoprophylaxis for routine low-risk patients. Use of subcutaneously daily injections of 2.5 mg fondiparinux was switched to 81 mg enteric-coated aspirin administered orally twice daily. The authors believe this further reduced the postoperative bleeding and transfusion risks. There was not an increase in DVT or pulmonary embolism complications.14,15

Physical Therapy

Partnering with PT, a 2-day LOS protocol was established. Patients were introduced to this protocol in a preoperative PT teaching class, and it was reinforced during the hospital stay. Patients who had earlier cases in the day were seen by PT the day of surgery when staffing and scheduling permitted. Early ambulation contributed significantly to earlier discharge for patients.16 Early ambulation also has been shown to decrease thromboembolic complications in orthopedic total joint patients.

Pain and Nausea Management

Parenteral narcotics were avoided, and oral narcotics were implemented with a graduated dosing based on a 10-point pain scale. For most patients, this was adequate and avoided the nausea frequently seen with the injectable narcotics.

Use of a postoperative cooling device that circulated cool water through a pad over the patient’s knee was instituted to assist with pain control. The patient received instruction on this device at the preoperative education sessions and was given the device to continue at home postdischarge.

Hospitalist Comanagement

Comanagement of orthopedic patients with hospitalists has become a standard practice nationally. The orthopedic surgery service works closely with the hospitalist team who see each total joint patient on postoperative admission to the ward. The orthopedic team handles all aspects of PT, wound management, pain control, and DVT prophylaxis. The hospitalist focuses on the remainder of comorbid conditions such as DM, chronic obstructive pulmonary disease, and underlying cardiac conditions.

The American Society of Anesthesiologists (ASA) average score was 2.8 for these procedures. Despite comprehensive preoperative screening, older patients with more comorbidities (higher ASA score) are more prone to emerging complications.17 Integration of the hospitalist team into the care of every orthopedic total joint patient facilitates prompt recognition and mitigation of these complications as they occur, directly reducing overall severity and LOS and allowing safe recovery from the surgical procedure.18,19

Conclusion

At the start of this system improvement, the previous 12-month data showed 164 knee replacements with a 4.9-day VA national LOS and 3.5- day PVAHCS LOS. At the end of the 12-month system improvement, the VA national LOS for TKR was 4.8 days, and at PVAHCS it was 2.8 days.

The 30-day readmission rate was 8.4% nationally and 7.9% at PVAHCS. After the system improvements, the national 30-day readmission rate was 7.1%, while the PVAHCS rate dropped to less than half the national rate: 3.4%.

It is important to note, that the improvements in the aforementioned multiple processes could not have been possible without a dedicated effort from the multiple stakeholders involved. Hospitalists, primary care, PT, pharmacy, operating room staff, anesthesia, preprocedure staff, floor nurses, the Commodities and Therapeutics Committee, and administration all partnered with the orthopedic surgery service to produce the improvements in LOS and corresponding reduction in 30-day readmissions.

These data suggest that there does not need to be an inherent tradeoff between LOS and 30-day readmissions. Rather, both measures can be managed independently to produce improvements across the service. A team approach to process improvement can allow for increased efficiency while providing safer care for patients.

References

1.  Dundon JM, Bosco J, Slover J, Yu S, Sayeed Y, Iorio R. Improvement in total joint replacement quality metrics, year one versus year three of the bundled payments for care improvement initiative. J Bone Joint Surg Am. 2016;98(23):1949-1953. 

2.  Itani KM. Fifteen years of the National Surgical Quality Improvement Program in review. Am J Surg. 2009;198(suppl 5):S9-S18.  

3.  Tayton ER, Frampton C, Hooper GJ, Young SW. The impact of patient and surgical factors on the rate of infection after primary total knee arthroplasty: an analysis of 64,566 joints from the New Zealand Joint Registry. Bone Joint J. 2016;98-B(3):334-340.   

4.  Heller S, Rezapoor M, Parvizi J. Minimising the risk of infection: a peri-operative checklist. Bone Joint J. 2016;98-B(1)(suppl A):18-22.  

5.  Thornqvist C, Gislason GH, Køber L, Jensen PF, Torp-Pedersen C, Andersson C. Body mass index and risk of perioperative cardiovascular adverse events and mortality in 34,744 Danish patients undergoing hip or knee replacement. Acta Orthop. 2014;85(5):456-462.  

6.  Stryker LS, Abdel MP, Morrey ME, Morrow MM, Kor DJ, Morrey BF. Elevated postoperative blood glucose and preoperative hemoglobin A1C are associated with increased wound complications following total joint arthroplasty. J Bone Joint Surg Am. 2013;95(9):808-814.  

7.  Akhavan S, Nguyen LC, Chan V, Saleh J, Bozic KJ. Impact of smoking cessation counseling prior to total joint arthroplasty. Orthopedics. 2017;40(2):e323-e328.  

8. Kim DH, Spencer M, Davidson SM, et al. Institutional prescreening for detection and eradication of methicillin-resistant Staphylococcus aureus in patients undergoing elective orthopaedic surgery. J Bone Joint Surg Am. 2010;92(9):1820-1826.   

9.  Goyal N, Chen DB, Harris IA, Rowden NJ, Kirsh G, MacDessi SJ. Intravenous vs intra-articular tranexamic acid in total knee arthroplasty: a randomized, double-blind trial. J Arthroplasty. 2017;32(1):28-32.  

10. Phan DL, Ani F, Schwarzkopf R. Cost analysis of tranexamic acid in anemic total joint arthroplasty patients. J Arthroplasty. 2016;31(3):579-582.   

11. Colwell CW Jr, Froimson MI, Mont MA, et al. Thrombosis prevention after total hip arthroplasty a prospective, randomized trial comparing a mobile compression device with low-molecular-weight heparin. J Bone Joint Surg Am. 2010;92(3):527-535.  

12.  Chundamala J, Wright JG. The efficacy and risks of using povidone-iodine irrigation to prevent surgical site infection: an evidence-based review. Can J Surg. 2007;50(6):473-481.  

13.  Fang R, Liu Z, Alijiang A, et al. Efficacy of intra-articular local anesthetics in total knee arthroplasty. Orthopedics. 2015;38(7):e573-e581.   

14.  Odeh K, Doran J, Yu S, Bolz N, Bosco J, Iorio R. Risk-stratified venous thromboembolism prophylaxis after total joint arthroplasty: aspirin and sequential pneumatic compression devices vs aggressive chemoprophylaxis. J Arthroplasty. 2016;31(suppl 9):78-82.  

15.  Parvizi J, Huang R, Restrepo C, et al. Low-dose aspirin is effective chemoprophylaxis against clinically important venous thromboembolism following total joint arthroplasty: a preliminary analysis. J Bone Joint Surg Am. 2017;99(2):91-98.  

16.  Robertson NB, Warganich T, Ghazarossian J, Khatod M. Implementation of an accelerated rehabilitation protocol for total joint arthroplasty in the managed care setting: the experience of one institution. Adv Orthop Surg. 2015;(2015):387197.  

17.  Hooper GJ, Rothwell AG, Hooper NM, Frampton C. The relationship between the American Society of Anesthesiologists physical rating and outcome following total hip and knee arthroplasty: an analysis of the New Zealand Joint Registry. J Bone Joint Surg Am. 2012;94(12):1065-1070.   

18.  Parry MC, Smith AJ, Blom AW. Early death following primary total knee arthroplasty. J Bone Joint Surg Am. 2011;93(10):948-953.  

19.  Parvizi J, Mui A, Purtill JJ, Sharkey PF, Hozack WJ, Rothman RH. Total joint arthroplasty: when do fatal or near-fatal complications occur? J Bone Joint Surg Am. 2007;89(1):27-32.

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Dr. Dossett is the chief of orthopedic surgery, and Dr. Chesser is a hospitalist, both at Phoenix VA Healthcare System in Arizona. Dr. Dossett is a clinical assistant professor of orthopedic surgery and Dr. Chesser is a clinical assistant professor of internal medicine, both at the University of Arizona in Phoenix.

Author disclosures
The authors report no actual or potential conflicts of interest with regard to this article.

Disclaimer
The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the U.S. Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review the complete prescribing information for specific drugs or drug combinations—including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.

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Dr. Dossett is the chief of orthopedic surgery, and Dr. Chesser is a hospitalist, both at Phoenix VA Healthcare System in Arizona. Dr. Dossett is a clinical assistant professor of orthopedic surgery and Dr. Chesser is a clinical assistant professor of internal medicine, both at the University of Arizona in Phoenix.

Author disclosures
The authors report no actual or potential conflicts of interest with regard to this article.

Disclaimer
The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the U.S. Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review the complete prescribing information for specific drugs or drug combinations—including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.

Author and Disclosure Information

Dr. Dossett is the chief of orthopedic surgery, and Dr. Chesser is a hospitalist, both at Phoenix VA Healthcare System in Arizona. Dr. Dossett is a clinical assistant professor of orthopedic surgery and Dr. Chesser is a clinical assistant professor of internal medicine, both at the University of Arizona in Phoenix.

Author disclosures
The authors report no actual or potential conflicts of interest with regard to this article.

Disclaimer
The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the U.S. Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review the complete prescribing information for specific drugs or drug combinations—including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.

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Related Articles
A team approach to orthopedic surgery process improvement helped reduce length of stay without increasing 30-day readmission rates.
A team approach to orthopedic surgery process improvement helped reduce length of stay without increasing 30-day readmission rates.

Many improvements in health care today involve care coordination across the entire health care system. Active management of an orthopedic surgery service from a system perspective allows for improvements that can favorably impact readmissions and length of stay (LOS) for patients.1 The following is an example of a systemwide process improvement in total knee replacement (TKR) surgery that dramatically decreased 30-day readmissions and shortened the LOS during a 12-month period.\

Background

The VA is the largest integrated health care system in the U.S. VA hospitals use the VA Surgical Quality Improvement Program (VASQIP) to monitor surgical services. Initially known as the National Surgery Quality Improvement Program (NSQIP), the program began in 1994 to help provide reliable, valid information on patient presurgical factors, processes of care during surgery, and 30-day morbidity and mortality rates in VA hospitals.2 Since its inception, NSQIP has spread to the private sector and is now widely used throughout the U.S.

Using on-site data acquisition by specially trained and dedicated registered nurses, information on each surgical case is input into a quality program. Quarterly reports are distributed to each hospital, and a comparison of mortality, LOS, 30-day readmissions to the hospital, and other data are analyzed and presented by quarter and rolling 12-month time frames. Use of VASQIP data allows improvement of the structures and processes of care throughout the VA, providing safer surgery for veterans.

At the Phoenix VA Health Care System (PVAHCS) in Arizona, the third quarter 2014 report showed the rolling 12-month average LOS for orthopedic TKR patients was 3.5 days and corresponding 30-day readmissions were 7.9%. Using a systems improvement approach, the authors set a goal of reducing these metrics by 10%.

The orthopedic service engaged members of the hospitalist, anesthesia, physical therapy (PT), nursing, social work, primary care, and pharmacy services, as well as hospital administration. Twelve months later, the LOS for TKR patients declined 20% to 2.8 days. Corresponding 30-day readmissions declined for the patients with knee replacement to 3.4%—a 57% reduction in 1 year. Mortality for these 177 cases was zero.

To accomplish these improvements, the authors divided the surgical procedure into preoperative, perioperative, and postoperative time frames and looked at process improvement during each of these periods. The following is a summary of the various processes that the authors feel contributed to the reduced LOS and 30-day readmission rate. Although some of these interventions were in place before the study period, all the processes were standardized for TKRs through surgeon consensus, and each of the surgeons adopted all the processes during the study period.

Preoperative Processes

In the VA primary care-based model orthopedic surgery is accessed through a consult process in the electronic health record. The orthopedic surgery service reviews each new consult and makes recommendations for optimization at the time the consult was received. This process was used to work closely with primary care providers to preoperatively prepare patients. The orthopedic surgery service advocates smoking cessation, substance abuse treatment, weight loss with an ideal body mass index of ≤ 35, and diabetes mellitus (DM) management with a ≤ 7 hemoglobin A1c value.3-7

This management did not result in fewer patients receiving TKR. In fact, the volume of TKR patients increased by 8% over the study period. Although part of this increase could have been due to increased scheduling efficiency, the orthopedic surgery service worked closely with primary care, nutrition, and medicine services to optimize these patients so they could be placed on the schedule for surgery.

Preoperative Education

Physical therapy and the orthopedic preprocedure clinic provided preoperative education to patients, covering preoperative chlorhexidine body washes, home safety, use of a walker, anticipated LOS, use of ambulatory sequential compressive devices, use of a knee cooling device, as well as PT protocols during hospitalization.8 This helped increase postoperative patient adherence and helped patients anticipate an appropriate LOS. Health care providers worked with patients to understand their home environment and plan for caregivers to assist them in the immediate postoperative period.

Intraoperative Processes

Reducing Blood Loss

The orthopedic surgery service reviewed literature related to the efficacy and safety of tranexamic acid. Based on the literature, the orthopedic surgery service arrived at a consensus agreement to implement a topical tranexamic acid dose of 3 g/100 cc saline for each TKR. Presentation of the pertinent literature to the pharmacy service allowed placement of this medication on the formulary for intraoperative use in the TKR cases.

Specific processes were implemented that involved the orthopedic service ordering tranexamic acid in advance for each patient, pharmacy mixing the solution and having it ready in a timely manner, and the operating room sending a messenger to the pharmacy to pick up a sterile container of the tranexamic acid/saline solution. Postoperative blood loss and transfusions decreased. Less anemia contributed to better performance and less fatigue in PT, which helped move patients down a pathway for quicker discharge.9,10

 

 

DVT Mechanical Prophylaxis

The orthopedic surgery service was concerned about adherence with stationary sequential compressive devices for mechanical thromboembolic prophylaxis. Patients had to remove them for PT, ambulation in the halls, and visiting the restroom, and then nurses had to replace them. A literature review examined a mobile compressive device that could be maintained during ambulation, and a demonstration for the orthopedic surgery service was arranged. The orthopedic service decided to change to the newer device, and the mobile compression device was presented to the PVAHCS Therapeutics Committee. Subsequently the new device was implemented after the appropriate in-service of the various clinic, PT, ward, surgery, preoperative, and postoperative personnel.11 The device was initiated in the holding area prior to surgery, continued throughout the hospitalization, and taken home by the patient for 2 weeks of use following surgery. Patients were instructed to return the device to clinic at their 2-week follow-up appointment.

Infection Control

A dilute betadine lavage was instituted for each surgical case, using the pulsatile lavage followed by a lactated Ringer solution rinse prior to TKR implantation. Additionally, the wound was lavaged prior to closure with this dilute betadine solution.12

 

Pain Control

Immediately before surgery, patients received oral morphine sulfate and celecoxib. A local 2% lidocaine with epinephrine injection was used at the surgical incision and joint after the skin prep and immediately prior to the skin incision. Patients received a mixture of ropivicaine .5%/20 mL, morphine sulfate 10 mg, and toradol 30 mg at the capsular region prior to implantation of the total knee prosthesis. At the end of the procedure, an additional 20 mL of 2% lidocaine was injected into the joint once the capsule was closed. This improved postoperative pain, decreased postoperative opioid dosing, and allowed for earlier ambulation with PT.13

PostOperative Processes

Deep Vein Thrombosis (DVT) Chemoprophylaxis

Once the chest physician guidelines-approved stand-alone mobile compressive devices was implemented, orthopedic surgery service revisited the chemoprophylaxis for routine low-risk patients. Use of subcutaneously daily injections of 2.5 mg fondiparinux was switched to 81 mg enteric-coated aspirin administered orally twice daily. The authors believe this further reduced the postoperative bleeding and transfusion risks. There was not an increase in DVT or pulmonary embolism complications.14,15

Physical Therapy

Partnering with PT, a 2-day LOS protocol was established. Patients were introduced to this protocol in a preoperative PT teaching class, and it was reinforced during the hospital stay. Patients who had earlier cases in the day were seen by PT the day of surgery when staffing and scheduling permitted. Early ambulation contributed significantly to earlier discharge for patients.16 Early ambulation also has been shown to decrease thromboembolic complications in orthopedic total joint patients.

Pain and Nausea Management

Parenteral narcotics were avoided, and oral narcotics were implemented with a graduated dosing based on a 10-point pain scale. For most patients, this was adequate and avoided the nausea frequently seen with the injectable narcotics.

Use of a postoperative cooling device that circulated cool water through a pad over the patient’s knee was instituted to assist with pain control. The patient received instruction on this device at the preoperative education sessions and was given the device to continue at home postdischarge.

Hospitalist Comanagement

Comanagement of orthopedic patients with hospitalists has become a standard practice nationally. The orthopedic surgery service works closely with the hospitalist team who see each total joint patient on postoperative admission to the ward. The orthopedic team handles all aspects of PT, wound management, pain control, and DVT prophylaxis. The hospitalist focuses on the remainder of comorbid conditions such as DM, chronic obstructive pulmonary disease, and underlying cardiac conditions.

The American Society of Anesthesiologists (ASA) average score was 2.8 for these procedures. Despite comprehensive preoperative screening, older patients with more comorbidities (higher ASA score) are more prone to emerging complications.17 Integration of the hospitalist team into the care of every orthopedic total joint patient facilitates prompt recognition and mitigation of these complications as they occur, directly reducing overall severity and LOS and allowing safe recovery from the surgical procedure.18,19

Conclusion

At the start of this system improvement, the previous 12-month data showed 164 knee replacements with a 4.9-day VA national LOS and 3.5- day PVAHCS LOS. At the end of the 12-month system improvement, the VA national LOS for TKR was 4.8 days, and at PVAHCS it was 2.8 days.

The 30-day readmission rate was 8.4% nationally and 7.9% at PVAHCS. After the system improvements, the national 30-day readmission rate was 7.1%, while the PVAHCS rate dropped to less than half the national rate: 3.4%.

It is important to note, that the improvements in the aforementioned multiple processes could not have been possible without a dedicated effort from the multiple stakeholders involved. Hospitalists, primary care, PT, pharmacy, operating room staff, anesthesia, preprocedure staff, floor nurses, the Commodities and Therapeutics Committee, and administration all partnered with the orthopedic surgery service to produce the improvements in LOS and corresponding reduction in 30-day readmissions.

These data suggest that there does not need to be an inherent tradeoff between LOS and 30-day readmissions. Rather, both measures can be managed independently to produce improvements across the service. A team approach to process improvement can allow for increased efficiency while providing safer care for patients.

Many improvements in health care today involve care coordination across the entire health care system. Active management of an orthopedic surgery service from a system perspective allows for improvements that can favorably impact readmissions and length of stay (LOS) for patients.1 The following is an example of a systemwide process improvement in total knee replacement (TKR) surgery that dramatically decreased 30-day readmissions and shortened the LOS during a 12-month period.\

Background

The VA is the largest integrated health care system in the U.S. VA hospitals use the VA Surgical Quality Improvement Program (VASQIP) to monitor surgical services. Initially known as the National Surgery Quality Improvement Program (NSQIP), the program began in 1994 to help provide reliable, valid information on patient presurgical factors, processes of care during surgery, and 30-day morbidity and mortality rates in VA hospitals.2 Since its inception, NSQIP has spread to the private sector and is now widely used throughout the U.S.

Using on-site data acquisition by specially trained and dedicated registered nurses, information on each surgical case is input into a quality program. Quarterly reports are distributed to each hospital, and a comparison of mortality, LOS, 30-day readmissions to the hospital, and other data are analyzed and presented by quarter and rolling 12-month time frames. Use of VASQIP data allows improvement of the structures and processes of care throughout the VA, providing safer surgery for veterans.

At the Phoenix VA Health Care System (PVAHCS) in Arizona, the third quarter 2014 report showed the rolling 12-month average LOS for orthopedic TKR patients was 3.5 days and corresponding 30-day readmissions were 7.9%. Using a systems improvement approach, the authors set a goal of reducing these metrics by 10%.

The orthopedic service engaged members of the hospitalist, anesthesia, physical therapy (PT), nursing, social work, primary care, and pharmacy services, as well as hospital administration. Twelve months later, the LOS for TKR patients declined 20% to 2.8 days. Corresponding 30-day readmissions declined for the patients with knee replacement to 3.4%—a 57% reduction in 1 year. Mortality for these 177 cases was zero.

To accomplish these improvements, the authors divided the surgical procedure into preoperative, perioperative, and postoperative time frames and looked at process improvement during each of these periods. The following is a summary of the various processes that the authors feel contributed to the reduced LOS and 30-day readmission rate. Although some of these interventions were in place before the study period, all the processes were standardized for TKRs through surgeon consensus, and each of the surgeons adopted all the processes during the study period.

Preoperative Processes

In the VA primary care-based model orthopedic surgery is accessed through a consult process in the electronic health record. The orthopedic surgery service reviews each new consult and makes recommendations for optimization at the time the consult was received. This process was used to work closely with primary care providers to preoperatively prepare patients. The orthopedic surgery service advocates smoking cessation, substance abuse treatment, weight loss with an ideal body mass index of ≤ 35, and diabetes mellitus (DM) management with a ≤ 7 hemoglobin A1c value.3-7

This management did not result in fewer patients receiving TKR. In fact, the volume of TKR patients increased by 8% over the study period. Although part of this increase could have been due to increased scheduling efficiency, the orthopedic surgery service worked closely with primary care, nutrition, and medicine services to optimize these patients so they could be placed on the schedule for surgery.

Preoperative Education

Physical therapy and the orthopedic preprocedure clinic provided preoperative education to patients, covering preoperative chlorhexidine body washes, home safety, use of a walker, anticipated LOS, use of ambulatory sequential compressive devices, use of a knee cooling device, as well as PT protocols during hospitalization.8 This helped increase postoperative patient adherence and helped patients anticipate an appropriate LOS. Health care providers worked with patients to understand their home environment and plan for caregivers to assist them in the immediate postoperative period.

Intraoperative Processes

Reducing Blood Loss

The orthopedic surgery service reviewed literature related to the efficacy and safety of tranexamic acid. Based on the literature, the orthopedic surgery service arrived at a consensus agreement to implement a topical tranexamic acid dose of 3 g/100 cc saline for each TKR. Presentation of the pertinent literature to the pharmacy service allowed placement of this medication on the formulary for intraoperative use in the TKR cases.

Specific processes were implemented that involved the orthopedic service ordering tranexamic acid in advance for each patient, pharmacy mixing the solution and having it ready in a timely manner, and the operating room sending a messenger to the pharmacy to pick up a sterile container of the tranexamic acid/saline solution. Postoperative blood loss and transfusions decreased. Less anemia contributed to better performance and less fatigue in PT, which helped move patients down a pathway for quicker discharge.9,10

 

 

DVT Mechanical Prophylaxis

The orthopedic surgery service was concerned about adherence with stationary sequential compressive devices for mechanical thromboembolic prophylaxis. Patients had to remove them for PT, ambulation in the halls, and visiting the restroom, and then nurses had to replace them. A literature review examined a mobile compressive device that could be maintained during ambulation, and a demonstration for the orthopedic surgery service was arranged. The orthopedic service decided to change to the newer device, and the mobile compression device was presented to the PVAHCS Therapeutics Committee. Subsequently the new device was implemented after the appropriate in-service of the various clinic, PT, ward, surgery, preoperative, and postoperative personnel.11 The device was initiated in the holding area prior to surgery, continued throughout the hospitalization, and taken home by the patient for 2 weeks of use following surgery. Patients were instructed to return the device to clinic at their 2-week follow-up appointment.

Infection Control

A dilute betadine lavage was instituted for each surgical case, using the pulsatile lavage followed by a lactated Ringer solution rinse prior to TKR implantation. Additionally, the wound was lavaged prior to closure with this dilute betadine solution.12

 

Pain Control

Immediately before surgery, patients received oral morphine sulfate and celecoxib. A local 2% lidocaine with epinephrine injection was used at the surgical incision and joint after the skin prep and immediately prior to the skin incision. Patients received a mixture of ropivicaine .5%/20 mL, morphine sulfate 10 mg, and toradol 30 mg at the capsular region prior to implantation of the total knee prosthesis. At the end of the procedure, an additional 20 mL of 2% lidocaine was injected into the joint once the capsule was closed. This improved postoperative pain, decreased postoperative opioid dosing, and allowed for earlier ambulation with PT.13

PostOperative Processes

Deep Vein Thrombosis (DVT) Chemoprophylaxis

Once the chest physician guidelines-approved stand-alone mobile compressive devices was implemented, orthopedic surgery service revisited the chemoprophylaxis for routine low-risk patients. Use of subcutaneously daily injections of 2.5 mg fondiparinux was switched to 81 mg enteric-coated aspirin administered orally twice daily. The authors believe this further reduced the postoperative bleeding and transfusion risks. There was not an increase in DVT or pulmonary embolism complications.14,15

Physical Therapy

Partnering with PT, a 2-day LOS protocol was established. Patients were introduced to this protocol in a preoperative PT teaching class, and it was reinforced during the hospital stay. Patients who had earlier cases in the day were seen by PT the day of surgery when staffing and scheduling permitted. Early ambulation contributed significantly to earlier discharge for patients.16 Early ambulation also has been shown to decrease thromboembolic complications in orthopedic total joint patients.

Pain and Nausea Management

Parenteral narcotics were avoided, and oral narcotics were implemented with a graduated dosing based on a 10-point pain scale. For most patients, this was adequate and avoided the nausea frequently seen with the injectable narcotics.

Use of a postoperative cooling device that circulated cool water through a pad over the patient’s knee was instituted to assist with pain control. The patient received instruction on this device at the preoperative education sessions and was given the device to continue at home postdischarge.

Hospitalist Comanagement

Comanagement of orthopedic patients with hospitalists has become a standard practice nationally. The orthopedic surgery service works closely with the hospitalist team who see each total joint patient on postoperative admission to the ward. The orthopedic team handles all aspects of PT, wound management, pain control, and DVT prophylaxis. The hospitalist focuses on the remainder of comorbid conditions such as DM, chronic obstructive pulmonary disease, and underlying cardiac conditions.

The American Society of Anesthesiologists (ASA) average score was 2.8 for these procedures. Despite comprehensive preoperative screening, older patients with more comorbidities (higher ASA score) are more prone to emerging complications.17 Integration of the hospitalist team into the care of every orthopedic total joint patient facilitates prompt recognition and mitigation of these complications as they occur, directly reducing overall severity and LOS and allowing safe recovery from the surgical procedure.18,19

Conclusion

At the start of this system improvement, the previous 12-month data showed 164 knee replacements with a 4.9-day VA national LOS and 3.5- day PVAHCS LOS. At the end of the 12-month system improvement, the VA national LOS for TKR was 4.8 days, and at PVAHCS it was 2.8 days.

The 30-day readmission rate was 8.4% nationally and 7.9% at PVAHCS. After the system improvements, the national 30-day readmission rate was 7.1%, while the PVAHCS rate dropped to less than half the national rate: 3.4%.

It is important to note, that the improvements in the aforementioned multiple processes could not have been possible without a dedicated effort from the multiple stakeholders involved. Hospitalists, primary care, PT, pharmacy, operating room staff, anesthesia, preprocedure staff, floor nurses, the Commodities and Therapeutics Committee, and administration all partnered with the orthopedic surgery service to produce the improvements in LOS and corresponding reduction in 30-day readmissions.

These data suggest that there does not need to be an inherent tradeoff between LOS and 30-day readmissions. Rather, both measures can be managed independently to produce improvements across the service. A team approach to process improvement can allow for increased efficiency while providing safer care for patients.

References

1.  Dundon JM, Bosco J, Slover J, Yu S, Sayeed Y, Iorio R. Improvement in total joint replacement quality metrics, year one versus year three of the bundled payments for care improvement initiative. J Bone Joint Surg Am. 2016;98(23):1949-1953. 

2.  Itani KM. Fifteen years of the National Surgical Quality Improvement Program in review. Am J Surg. 2009;198(suppl 5):S9-S18.  

3.  Tayton ER, Frampton C, Hooper GJ, Young SW. The impact of patient and surgical factors on the rate of infection after primary total knee arthroplasty: an analysis of 64,566 joints from the New Zealand Joint Registry. Bone Joint J. 2016;98-B(3):334-340.   

4.  Heller S, Rezapoor M, Parvizi J. Minimising the risk of infection: a peri-operative checklist. Bone Joint J. 2016;98-B(1)(suppl A):18-22.  

5.  Thornqvist C, Gislason GH, Køber L, Jensen PF, Torp-Pedersen C, Andersson C. Body mass index and risk of perioperative cardiovascular adverse events and mortality in 34,744 Danish patients undergoing hip or knee replacement. Acta Orthop. 2014;85(5):456-462.  

6.  Stryker LS, Abdel MP, Morrey ME, Morrow MM, Kor DJ, Morrey BF. Elevated postoperative blood glucose and preoperative hemoglobin A1C are associated with increased wound complications following total joint arthroplasty. J Bone Joint Surg Am. 2013;95(9):808-814.  

7.  Akhavan S, Nguyen LC, Chan V, Saleh J, Bozic KJ. Impact of smoking cessation counseling prior to total joint arthroplasty. Orthopedics. 2017;40(2):e323-e328.  

8. Kim DH, Spencer M, Davidson SM, et al. Institutional prescreening for detection and eradication of methicillin-resistant Staphylococcus aureus in patients undergoing elective orthopaedic surgery. J Bone Joint Surg Am. 2010;92(9):1820-1826.   

9.  Goyal N, Chen DB, Harris IA, Rowden NJ, Kirsh G, MacDessi SJ. Intravenous vs intra-articular tranexamic acid in total knee arthroplasty: a randomized, double-blind trial. J Arthroplasty. 2017;32(1):28-32.  

10. Phan DL, Ani F, Schwarzkopf R. Cost analysis of tranexamic acid in anemic total joint arthroplasty patients. J Arthroplasty. 2016;31(3):579-582.   

11. Colwell CW Jr, Froimson MI, Mont MA, et al. Thrombosis prevention after total hip arthroplasty a prospective, randomized trial comparing a mobile compression device with low-molecular-weight heparin. J Bone Joint Surg Am. 2010;92(3):527-535.  

12.  Chundamala J, Wright JG. The efficacy and risks of using povidone-iodine irrigation to prevent surgical site infection: an evidence-based review. Can J Surg. 2007;50(6):473-481.  

13.  Fang R, Liu Z, Alijiang A, et al. Efficacy of intra-articular local anesthetics in total knee arthroplasty. Orthopedics. 2015;38(7):e573-e581.   

14.  Odeh K, Doran J, Yu S, Bolz N, Bosco J, Iorio R. Risk-stratified venous thromboembolism prophylaxis after total joint arthroplasty: aspirin and sequential pneumatic compression devices vs aggressive chemoprophylaxis. J Arthroplasty. 2016;31(suppl 9):78-82.  

15.  Parvizi J, Huang R, Restrepo C, et al. Low-dose aspirin is effective chemoprophylaxis against clinically important venous thromboembolism following total joint arthroplasty: a preliminary analysis. J Bone Joint Surg Am. 2017;99(2):91-98.  

16.  Robertson NB, Warganich T, Ghazarossian J, Khatod M. Implementation of an accelerated rehabilitation protocol for total joint arthroplasty in the managed care setting: the experience of one institution. Adv Orthop Surg. 2015;(2015):387197.  

17.  Hooper GJ, Rothwell AG, Hooper NM, Frampton C. The relationship between the American Society of Anesthesiologists physical rating and outcome following total hip and knee arthroplasty: an analysis of the New Zealand Joint Registry. J Bone Joint Surg Am. 2012;94(12):1065-1070.   

18.  Parry MC, Smith AJ, Blom AW. Early death following primary total knee arthroplasty. J Bone Joint Surg Am. 2011;93(10):948-953.  

19.  Parvizi J, Mui A, Purtill JJ, Sharkey PF, Hozack WJ, Rothman RH. Total joint arthroplasty: when do fatal or near-fatal complications occur? J Bone Joint Surg Am. 2007;89(1):27-32.

References

1.  Dundon JM, Bosco J, Slover J, Yu S, Sayeed Y, Iorio R. Improvement in total joint replacement quality metrics, year one versus year three of the bundled payments for care improvement initiative. J Bone Joint Surg Am. 2016;98(23):1949-1953. 

2.  Itani KM. Fifteen years of the National Surgical Quality Improvement Program in review. Am J Surg. 2009;198(suppl 5):S9-S18.  

3.  Tayton ER, Frampton C, Hooper GJ, Young SW. The impact of patient and surgical factors on the rate of infection after primary total knee arthroplasty: an analysis of 64,566 joints from the New Zealand Joint Registry. Bone Joint J. 2016;98-B(3):334-340.   

4.  Heller S, Rezapoor M, Parvizi J. Minimising the risk of infection: a peri-operative checklist. Bone Joint J. 2016;98-B(1)(suppl A):18-22.  

5.  Thornqvist C, Gislason GH, Køber L, Jensen PF, Torp-Pedersen C, Andersson C. Body mass index and risk of perioperative cardiovascular adverse events and mortality in 34,744 Danish patients undergoing hip or knee replacement. Acta Orthop. 2014;85(5):456-462.  

6.  Stryker LS, Abdel MP, Morrey ME, Morrow MM, Kor DJ, Morrey BF. Elevated postoperative blood glucose and preoperative hemoglobin A1C are associated with increased wound complications following total joint arthroplasty. J Bone Joint Surg Am. 2013;95(9):808-814.  

7.  Akhavan S, Nguyen LC, Chan V, Saleh J, Bozic KJ. Impact of smoking cessation counseling prior to total joint arthroplasty. Orthopedics. 2017;40(2):e323-e328.  

8. Kim DH, Spencer M, Davidson SM, et al. Institutional prescreening for detection and eradication of methicillin-resistant Staphylococcus aureus in patients undergoing elective orthopaedic surgery. J Bone Joint Surg Am. 2010;92(9):1820-1826.   

9.  Goyal N, Chen DB, Harris IA, Rowden NJ, Kirsh G, MacDessi SJ. Intravenous vs intra-articular tranexamic acid in total knee arthroplasty: a randomized, double-blind trial. J Arthroplasty. 2017;32(1):28-32.  

10. Phan DL, Ani F, Schwarzkopf R. Cost analysis of tranexamic acid in anemic total joint arthroplasty patients. J Arthroplasty. 2016;31(3):579-582.   

11. Colwell CW Jr, Froimson MI, Mont MA, et al. Thrombosis prevention after total hip arthroplasty a prospective, randomized trial comparing a mobile compression device with low-molecular-weight heparin. J Bone Joint Surg Am. 2010;92(3):527-535.  

12.  Chundamala J, Wright JG. The efficacy and risks of using povidone-iodine irrigation to prevent surgical site infection: an evidence-based review. Can J Surg. 2007;50(6):473-481.  

13.  Fang R, Liu Z, Alijiang A, et al. Efficacy of intra-articular local anesthetics in total knee arthroplasty. Orthopedics. 2015;38(7):e573-e581.   

14.  Odeh K, Doran J, Yu S, Bolz N, Bosco J, Iorio R. Risk-stratified venous thromboembolism prophylaxis after total joint arthroplasty: aspirin and sequential pneumatic compression devices vs aggressive chemoprophylaxis. J Arthroplasty. 2016;31(suppl 9):78-82.  

15.  Parvizi J, Huang R, Restrepo C, et al. Low-dose aspirin is effective chemoprophylaxis against clinically important venous thromboembolism following total joint arthroplasty: a preliminary analysis. J Bone Joint Surg Am. 2017;99(2):91-98.  

16.  Robertson NB, Warganich T, Ghazarossian J, Khatod M. Implementation of an accelerated rehabilitation protocol for total joint arthroplasty in the managed care setting: the experience of one institution. Adv Orthop Surg. 2015;(2015):387197.  

17.  Hooper GJ, Rothwell AG, Hooper NM, Frampton C. The relationship between the American Society of Anesthesiologists physical rating and outcome following total hip and knee arthroplasty: an analysis of the New Zealand Joint Registry. J Bone Joint Surg Am. 2012;94(12):1065-1070.   

18.  Parry MC, Smith AJ, Blom AW. Early death following primary total knee arthroplasty. J Bone Joint Surg Am. 2011;93(10):948-953.  

19.  Parvizi J, Mui A, Purtill JJ, Sharkey PF, Hozack WJ, Rothman RH. Total joint arthroplasty: when do fatal or near-fatal complications occur? J Bone Joint Surg Am. 2007;89(1):27-32.

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