Advertisement

The Impact of Machine Learning on Total Joint Arthroplasty Patient Outcomes: A Systemic Review

Published:October 29, 2022DOI:https://doi.org/10.1016/j.arth.2022.10.039

      Abstract

      Background

      Supervised machine learning techniques have been increasingly applied to predict patient outcomes after hip and knee arthroplasty procedures. The purpose of this study was to systematically review the applications of supervised machine learning techniques to predict patient outcomes after primary total hip and knee arthroplasty.

      Methods

      A comprehensive literature search using the electronic databases MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and Cochrane Database of Systematic Reviews was conducted in July of 2021. The inclusion criteria were studies that utilized supervised machine learning techniques to predict patient outcomes after primary total hip or knee arthroplasty.

      Results

      Search criteria yielded n = 30 relevant studies. Topics of study included patient complications (n = 6), readmissions (n = 1), revision (n = 2), patient-reported outcome measures (n = 4), patient satisfaction (n = 4), inpatient status and length of stay (LOS) (n = 9), opioid usage (n = 3), and patient function (n = 1). Studies involved TKA (n = 12), THA (n = 11), or a combination (n = 7). Less than 35% of predictive outcomes had an area under the receiver operating characteristic curve (AUC) in the excellent or outstanding range. Additionally, only 9 of the studies found improvement over logistic regression, and only 9 studies were externally validated.

      Conclusion

      Supervised machine learning algorithms are powerful tools that have been increasingly applied to predict patient outcomes after total hip and knee arthroplasty. However, these algorithms should be evaluated in the context of prognostic accuracy, comparison to traditional statistical techniques for outcome prediction, and application to populations outside the training set. While machine learning algorithms have been received with considerable interest, they should be critically assessed and validated prior to clinical adoption.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to The Journal of Arthroplasty
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Deo R.C.
        Machine learning in medicine.
        Circulation. 2015; 132: 1920-1930https://doi.org/10.1161/CIRCULATIONAHA.115.001593
        • Baştanlar Y.
        • Özuysal M.
        Introduction to machine learning.
        Methods Mol Biol. 2014; 1107: 105-128https://doi.org/10.1007/978-1-62703-748-8_7
        • Goecks J.
        • Jalili V.
        • Heiser L.M.
        • Gray J.W.
        How machine learning will transform biomedicine.
        Cell. 2020; 181: 92-101https://doi.org/10.1016/j.cell.2020.03.022
        • Choy G.
        • Khalilzadeh O.
        • Michalski M.
        • Do S.
        • Samir A.E.
        • Pianykh O.S.
        • et al.
        Current applications and future impact of machine learning in radiology.
        Radiology. 2018; 288: 318-328https://doi.org/10.1148/radiol.2018171820
        • Bayliss L.
        • Jones L.D.
        The role of artificial intelligence and machine learning in predicting orthopaedic outcomes.
        Bone Joint J. 2019; 101-B: 1476-1478https://doi.org/10.1302/0301-620X.101B12.BJJ-2019-0850.R1
        • Jiang F.
        • Jiang Y.
        • Zhi H.
        • Dong Y.
        • Li H.
        • Ma S.
        • et al.
        Artificial intelligence in healthcare: past, present and future.
        Stroke Vasc Neurol. 2017; 2: 230-243https://doi.org/10.1136/svn-2017-000101
        • Levy J.J.
        • Levy J.J.
        • Levy J.J.
        • O’Malley A.J.
        • O’Malley A.J.
        Don’t dismiss logistic regression: the case for sensible extraction of interactions in the era of machine learning.
        BMC Med Res Methodol. 2020; 20: 171https://doi.org/10.1186/s12874-020-01046-3
        • Daly J.
        • Willis K.
        • Small R.
        • Green J.
        • Welch N.
        • Kealy M.
        • et al.
        A hierarchy of evidence for assessing qualitative health research.
        J Clin Epidemiol. 2007; 60: 43-49https://doi.org/10.1016/j.jclinepi.2006.03.014
        • Harris A.H.S.
        • Kuo A.C.
        • Weng Y.
        • Trickey A.W.
        • Bowe T.
        • Giori N.J.
        Can machine learning methods produce accurate and easy-to-use prediction models of 30-day complications and mortality after knee or hip arthroplasty?.
        Clin Orthop Relat Res. 2019; 477: 452-460https://doi.org/10.1097/CORR.0000000000000601
        • Ko S.
        • Jo C.
        • Chang C.B.
        • Lee Y.S.
        • Moon Y.-W.
        • Youm J.w.
        • et al.
        A web-based machine-learning algorithm predicting postoperative acute kidney injury after total knee arthroplasty.
        Knee Surg Sports Traumatol Arthrosc. 2020; 30: 545-554https://doi.org/10.1007/s00167-020-06258-0
        • Shah A.A.
        • Devana S.K.
        • Lee C.
        • Kianian R.
        • van der Schaar M.
        • SooHoo N.F.
        Development of a novel, potentially universal machine learning algorithm for prediction of complications after total hip arthroplasty.
        J Arthroplasty. 2021; 36: 1655-1662.e1https://doi.org/10.1016/j.arth.2020.12.040
        • Van de Meulebroucke C.
        • Beckers J.
        • Corten K.
        What can we expect following anterior total hip arthroplasty on a regular operating table? A validation study of an artificial intelligence algorithm to monitor adverse events in a high-volume, nonacademic setting.
        J Arthroplasty. 2019; 34: 2260-2266https://doi.org/10.1016/j.arth.2019.07.039
        • Magneli M.
        • Unbeck M.
        • Rogmark C.
        • Skoldenberg O.
        • Gordon M.
        Measuring adverse events following hip arthroplasty surgery using administrative data without relying on ICD-codes.
        PLoS One. 2020; 15: e0242008https://doi.org/10.1371/journal.pone.0242008
        • Jo C.
        • Ko S.
        • Shin W.C.
        • Han H.S.
        • Lee M.C.
        • Ko T.
        • et al.
        Transfusion after total knee arthroplasty can be predicted using the machine learning algorithm.
        Knee Surg Sports Traumatol Arthrosc. 2020; 28: 1757-1764https://doi.org/10.1007/s00167-019-05602-3
        • Mohammadi R.
        • Jain S.
        • Namin A.T.
        • Scholem Heller M.
        • Palacholla R.
        • Kamarthi S.
        • et al.
        Predicting unplanned readmissions following a hip or knee arthroplasty: retrospective observational study.
        JMIR Med Inform. 2020; 8: e19761https://doi.org/10.2196/19761
        • Aram P.
        • Trela-Larsen L.
        • Sayers A.
        • Hills A.F.
        • Blom A.W.
        • McCloskey E.V.
        • et al.
        Estimating an individual’s probability of revision surgery after knee replacement: a comparison of modeling approaches using a national data set.
        Am J Epidemiol. 2018; 187: 2252-2262https://doi.org/10.1093/aje/kwy121
        • El-Galaly A.
        • Grazal C.
        • Kappel A.
        • Nielsen P.T.
        • Jensen S.L.
        • Forsberg J.A.
        Can machine-learning algorithms predict early revision TKA in the Danish knee arthroplasty registry?.
        Clin Orthop Relat Res. 2020; 478: 2088-2101https://doi.org/10.1097/CORR.0000000000001343
        • Fontana M.A.
        • Lyman S.
        • Sarker G.K.
        • Padgett D.E.
        • MacLean C.H.
        Can machine learning algorithms predict which patients will achieve minimally clinically important differences from total joint arthroplasty?.
        Clin Orthop Relat Res. 2019; 477: 1267-1279https://doi.org/10.1097/CORR.0000000000000687
        • Harris A.H.S.
        • Kuo A.C.
        • Bowe T.R.
        • Manfredi L.
        • Lalani N.F.
        • Giori N.J.
        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: 112-117.e6https://doi.org/10.1016/j.arth.2020.07.026
        • Huber M.
        • Kurz C.
        • Leidl R.
        Predicting patient-reported outcomes following hip and knee replacement surgery using supervised machine learning.
        BMC Med Inform Decis Mak. 2019; 19: 3https://doi.org/10.1186/s12911-018-0731-6
        • Kunze K.N.
        • Karhade A.V.
        • Sadauskas A.J.
        • Schwab J.H.
        • Levine B.R.
        Development of machine learning algorithms to predict clinically meaningful improvement for the patient-reported health state after total hip arthroplasty.
        J Arthroplasty. 2020; 35: 2119-2123https://doi.org/10.1016/j.arth.2020.03.019
        • Farooq H.
        • Deckard E.R.
        • Ziemba-Davis M.
        • Madsen A.
        • Meneghini R.M.
        Predictors of patient satisfaction following primary total knee arthroplasty: results from a traditional statistical model and a machine learning algorithm.
        J Arthroplasty. 2020; 35: 3123-3130https://doi.org/10.1016/j.arth.2020.05.077
        • Kunze K.N.
        • Polce E.M.
        • Sadauskas A.J.
        • Levine B.R.
        Development of machine learning algorithms to predict patient dissatisfaction after primary total knee arthroplasty.
        J Arthroplasty. 2020; 35: 3117-3122https://doi.org/10.1016/j.arth.2020.05.061
        • Schwartz M.H.
        • Ward R.E.
        • Macwilliam C.
        • Verner J.J.
        Using neural networks to identify patients unlikely to achieve a reduction in bodily pain after total hip replacement surgery.
        Med Care. 1997; 35: 1020-1030https://doi.org/10.1097/00005650-199710000-00004
        • Sniderman J.
        • Stark R.B.
        • Schwartz C.E.
        • Imam H.
        • Finkelstein J.A.
        • Nousiainen M.T.
        Patient factors that matter in predicting hip arthroplasty outcomes: a machine-learning approach.
        J Arthroplasty. 2021; 36: 2024-2032https://doi.org/10.1016/j.arth.2020.12.038
        • Kugelman D.N.
        • Teo G.
        • Huang S.
        • Doran M.G.
        • Singh V.
        • Long W.J.
        A novel machine learning predictive tool assessing outpatient or inpatient designation for Medicare patients undergoing total hip arthroplasty.
        Arthroplast Today. 2021; 8: 194-199https://doi.org/10.1016/j.artd.2021.03.001
        • Lu Y.
        • Khazi Z.M.
        • Agarwalla A.
        • Forsythe B.
        • Taunton M.J.
        Development of a machine learning algorithm to predict nonroutine discharge following unicompartmental knee arthroplasty.
        J Arthroplasty. 2021; 36: 1568-1576https://doi.org/10.1016/j.arth.2020.12.003
        • Ramkumar P.N.
        • Navarro S.M.
        • Haeberle H.S.
        • Karnuta J.M.
        • Mont M.A.
        • Iannotti J.P.
        • et al.
        Development and validation of a machine learning algorithm after primary total hip arthroplasty: applications to length of stay and payment models.
        J Arthroplasty. 2019; 34: 632-637https://doi.org/10.1016/j.arth.2018.12.030
        • Ramkumar P.N.
        • Karnuta J.M.
        • Navarro S.M.
        • Haeberle H.S.
        • Scuderi G.R.
        • Mont M.A.
        • et al.
        Deep learning preoperatively predicts value metrics for primary total knee arthroplasty: development and validation of an artificial neural network model.
        J Arthroplasty. 2019; 34: 2220-2227.e1https://doi.org/10.1016/j.arth.2019.05.034
        • Ramkumar P.N.
        • Karnuta J.M.
        • Navarro S.M.
        • Haeberle H.S.
        • Iorio R.
        • Mont M.A.
        • et al.
        Preoperative prediction of value metrics and a patient-specific payment model for primary total hip arthroplasty: development and validation of a deep learning model.
        J Arthroplasty. 2019; 34: 2228-2234.e1https://doi.org/10.1016/j.arth.2019.04.055
        • Navarro S.M.
        • Wang E.Y.
        • Haeberle H.S.
        • Mont M.A.
        • Krebs V.E.
        • Patterson B.M.
        • et al.
        Machine learning and primary total knee arthroplasty: patient forecasting for a patient-specific payment model.
        J Arthroplasty. 2018; 33: 3617-3623https://doi.org/10.1016/j.arth.2018.08.028
        • Li H.
        • Jiao J.
        • Zhang S.
        • Tang H.
        • Qu X.
        • Yue B.
        Construction and comparison of predictive models for length of stay after total knee arthroplasty: regression model and machine learning analysis based on 1,826 cases in a single Singapore center.
        J Knee Surg. 2020; 35: 007-014https://doi.org/10.1055/s-0040-1710573
        • Greenstein A.S.
        • Teitel J.
        • Mitten D.J.
        • Ricciardi B.F.
        • Myers T.G.
        An electronic medical record–based discharge disposition tool gets bundle busted: decaying relevance of clinical data accuracy in machine learning.
        Arthroplast Today. 2020; 6: 850-855https://doi.org/10.1016/j.artd.2020.08.007
        • Gabriel R.A.
        • Sharma B.S.
        • Doan C.N.
        • Jiang X.
        • Schmidt U.H.
        • Vaida F.
        A predictive model for determining patients not requiring prolonged hospital length of stay after elective primary total hip arthroplasty.
        Anesth Analg. 2019; 129: 43-50https://doi.org/10.1213/ANE.0000000000003798
        • Harrison-Brown M.
        • Scholes C.
        • Sandhu K.S.
        • Ebrahimi M.
        • Bell C.
        • Kirwan G.
        Applying models of care for total hip and knee arthroplasty: external validation of predictive models to identify extended stay prior to lower-limb arthroplasty (preprint).
        medRxiv. 2020; https://doi.org/10.1101/2020.08.24.20180653
        • Karhade A.V.
        • Schwab J.H.
        • Bedair H.S.
        Development of machine learning algorithms for prediction of sustained postoperative opioid prescriptions after total hip arthroplasty.
        J Arthroplasty. 2019; 34: 2272-2277https://doi.org/10.1016/j.arth.2019.06.013
        • Katakam A.
        • Karhade A.V.
        • Schwab J.H.
        • Chen A.F.
        • Bedair H.S.
        Development and validation of machine learning algorithms for postoperative opioid prescriptions after TKA.
        J Orthop. 2020; 22: 95-99https://doi.org/10.1016/j.jor.2020.03.052
        • Lee S.
        • Wei S.
        • White V.
        • Bain P.A.
        • Baker C.
        • Li J.
        Classification of opioid usage through semi-supervised learning for total joint replacement patients.
        IEEE J Biomed Heal Inform. 2021; 25: 189-200https://doi.org/10.1109/JBHI.2020.2992973
        • Pua Y.H.
        • Kang H.
        • Thumboo J.
        • Clark R.A.
        • Chew E.S.
        • Poon C.L.
        • et al.
        Machine learning methods are comparable to logistic regression techniques in predicting severe walking limitation following total knee arthroplasty.
        Knee Surg Sports Traumatol Arthrosc. 2020; 28: 3207-3216https://doi.org/10.1007/s00167-019-05822-7
        • Hosmer D.W.
        • Lemeshow S.
        • Sturdivant R.X.
        Applied Logistic Regression.
        3rd ed. John Wiley & Sons, NJ2013
        • Fernández-Delgado M.
        • Cernadas E.
        • Barro S.
        • Amorim D.
        Do we need hundreds of classifiers to solve real world classification problems?.
        J Mach Learn Res. 2014; 15: 3133-3181
        • Christodoulou E.
        • Ma J.
        • Collins G.S.
        • Steyerberg E.W.
        • Verbakel J.Y.
        • Van Calster B.
        A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models.
        J Clin Epidemiol. 2019; 110: 12-22https://doi.org/10.1016/j.jclinepi.2019.02.004
        • Mahmoudi E.
        • Kamdar N.
        • Kim N.
        • Gonzales G.
        • Singh K.
        • Waljee A.K.
        Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review.
        BMJ. 2020; 369: m958https://doi.org/10.1136/bmj.m958
        • Robberechts P.
        • Davis J.
        How data availability affects the ability to learn good xG models.
        Commun Comp Inf Sci. 2020; 1324: 17-27https://doi.org/10.1007/978-3-030-64912-8_2
        • Feldman S.S.
        • Davlyatov G.
        • Hall A.G.
        Toward understanding the value of missing social determinants of health data in care transition planning.
        Appl Clin Inform. 2020; 11: 556-563https://doi.org/10.1055/s-0040-1715650
        • Dietvorst B.J.
        • Simmons J.P.
        • Massey C.
        Algorithm aversion: people erroneously avoid algorithms after seeing them err.
        J Exp Psychol Gen. 2015; 144: 114-126https://doi.org/10.1037/xge0000033
        • Polce E.M.
        • Kunze K.N.
        • Dooley M.S.
        • Piuzzi N.S.
        • Boettner F.
        • Sculco P.K.
        Efficacy and applications of artificial intelligence and machine learning analyses in total joint arthroplasty: a call for improved reporting.
        J Bone Joint Surg Am. 2022; 104: 821-832
        • Groot O.Q.
        • Ogink P.T.
        • Lans A.
        • Twining P.K.
        • Kapoor N.D.
        • DiGiovanni W.
        • et al.
        Machine learning prediction models in orthopedic surgery: a systematic review in transparent reporting.
        J Orthop Res. 2022; 40: 475-483https://doi.org/10.1002/jor.25036
        • Singh K.
        • Beam A.L.
        • Nallamothu B.K.
        Machine learning in clinical journals: moving from inscrutable to informative.
        Circ Cardiovasc Qual Outcomes. 2020; 13: e007491https://doi.org/10.1161/CIRCOUTCOMES.120.007491
        • Aristidou A.
        • Jena R.
        • Topol E.J.
        Bridging the chasm between AI and clinical implementation.
        Lancet. 2022; 399: 620https://doi.org/10.1016/S0140-6736(22)00235-5