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. 2023 Jan;93(1-2):316-327.
doi: 10.1111/ans.18250. Epub 2023 Jan 13.

SMART choice (knee) tool: a patient-focused predictive model to predict improvement in health-related quality of life after total knee arthroplasty

Yushy Zhou  1 , Michelle Dowsey  1 , Tim Spelman  1 , Peter Choong  1 , Chris Schilling  1
Affiliations

SMART choice (knee) tool: a patient-focused predictive model to predict improvement in health-related quality of life after total knee arthroplasty

Yushy Zhou et al. ANZ J Surg. 2023 Jan.

Abstract

Background: Current predictive tools for TKA focus on clinicians rather than patients as the intended user. The purpose of this study was to develop a patient-focused model to predict health-related quality of life outcomes at 1-year post-TKA.

Methods: Patients who underwent primary TKA for osteoarthritis from a tertiary institutional registry after January 2006 were analysed. The primary outcome was improvement after TKA defined by the minimal clinically important difference in utility score at 1-year post-surgery. Potential predictors included demographic information, comorbidities, lifestyle factors, and patient-reported outcome measures. Four models were developed, including both conventional statistics and machine learning (artificial intelligence) methods: logistic regression, classification tree, extreme gradient boosted trees, and random forest models. Models were evaluated using discrimination and calibration metrics.

Results: A total of 3755 patients were included in the study. The logistic regression model performed the best with respect to both discrimination (AUC = 0.712) and calibration (intercept = -0.083, slope = 1.123, Brier score = 0.202). Less than 2% (n = 52) of the data were missing and therefore removed for complete case analysis. The final model used age (categorical), sex, baseline utility score, and baseline Veterans-RAND 12 responses as predictors.

Conclusion: The logistic regression model performed better than machine learning algorithms with respect to AUC and calibration plot. The logistic regression model was well calibrated enough to stratify patients into risk deciles based on their likelihood of improvement after surgery. Further research is required to evaluate the performance of predictive tools through pragmatic clinical trials.

Level of evidence: Level II, decision analysis.

Keywords: artificial intelligence; health-related quality of life; machine learning; predictive model; total knee arthroplasty.

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