PMID- 32265141 OWN - NLM STAT- MEDLINE DCOM- 20210319 LR - 20210319 IS - 1532-8406 (Electronic) IS - 0883-5403 (Linking) VI - 35 IP - 8 DP - 2020 Aug TI - Development of Machine Learning Algorithms to Predict Clinically Meaningful Improvement for the Patient-Reported Health State After Total Hip Arthroplasty. PG - 2119-2123 LID - S0883-5403(20)30267-9 [pii] LID - 10.1016/j.arth.2020.03.019 [doi] AB - BACKGROUND: Failure to achieve clinically significant outcome (CSO) improvement after total hip arthroplasty (THA) imposes a potential cost-to-risk imbalance in the context of bundle payment models. Patient perception of their health state is one component of such risk. The purpose of the current study is to develop machine learning algorithms to predict CSO for the patient-reported health state (PRHS) and build a clinical decision-making tool based on risk factors. METHODS: A retrospective review of primary THA patients between 2014 and 2017 was performed. Variables considered for prediction included demographics, medical history, preoperative PRHS, and modified Harris Hip Score. The minimal clinically important difference (MCID) for the PRHS was calculated using a distribution-based method. Five supervised machine learning algorithms were developed and assessed by discrimination, calibration, Brier score, and decision curve analysis. RESULTS: Of 616 patients, a total of 407 (69.2%) achieved the MCID for the PRHS. The random forest algorithm achieved the best performance in the independent testing set not used for algorithm development (c-statistic 0.97, calibration intercept -0.05, calibration slope 1.45, Brier score 0.054). The most important factors for achieving the MCID were preoperative PRHS, preoperative opioid use, age, and body mass index. Individual patient-level explanations were provided for the algorithm predictions and the algorithms were incorporated into an open access digital application available here: https://sorg-apps.shinyapps.io/THA_PRHS_mcid/. CONCLUSION: The current study created a clinical decision-making tool based on partially modifiable risk factors for predicting CSO after THA. The tool demonstrates excellent discriminative capacity for identifying those at greatest risk for failing to achieve CSO in their current health state and may allow for preoperative health optimization. CI - Copyright (c) 2020 Elsevier Inc. All rights reserved. FAU - Kunze, Kyle N AU - Kunze KN AD - Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL. FAU - Karhade, Aditya V AU - Karhade AV AD - Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA. FAU - Sadauskas, Alex J AU - Sadauskas AJ AD - Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL. FAU - Schwab, Joseph H AU - Schwab JH AD - Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA. FAU - Levine, Brett R AU - Levine BR AD - Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL. LA - eng PT - Journal Article DEP - 20200318 PL - United States TA - J Arthroplasty JT - The Journal of arthroplasty JID - 8703515 SB - IM MH - Algorithms MH - *Arthroplasty, Replacement, Hip MH - Humans MH - Machine Learning MH - Patient Reported Outcome Measures MH - Retrospective Studies OTO - NOTNLM OT - MCID OT - THA OT - clinical outcomes OT - clinically significant outcome OT - machine learning OT - total hip arthroplasty EDAT- 2020/04/09 06:00 MHDA- 2021/03/20 06:00 CRDT- 2020/04/09 06:00 PHST- 2020/02/04 00:00 [received] PHST- 2020/03/01 00:00 [revised] PHST- 2020/03/10 00:00 [accepted] PHST- 2020/04/09 06:00 [pubmed] PHST- 2021/03/20 06:00 [medline] PHST- 2020/04/09 06:00 [entrez] AID - S0883-5403(20)30267-9 [pii] AID - 10.1016/j.arth.2020.03.019 [doi] PST - ppublish SO - J Arthroplasty. 2020 Aug;35(8):2119-2123. doi: 10.1016/j.arth.2020.03.019. Epub 2020 Mar 18.