Comprehensive Summary
This study aims to determine whether machine learning could help identify patients who, after undergoing total knee arthroplasty (TKA), are likely to see improvement based on how patients self-report their outcomes. Using a dataset of 1,064 individuals treated between 2016 and 2022, the researchers analyzed 81 variables available before surgery. These included standard demographic details, comorbid conditions, medication usage, X-ray findings, and several patient-reported measures like the KOOS JR and PROMIS-10. The primary aim was to predict who wouldn’t reach two key benchmarks of postoperative recovery: the minimal clinically important difference (MCID) and the substantial clinical benefit (SCB), which both reflect levels of improvement that are actually noticeable and meaningful to patients. Among the various models tested, penalized logistic regression and random forest came out on top, consistently delivering area under the curve (AUC) scores around or above 0.70 for all outcomes. It was determined that patients with poorer knee function before surgery, younger age, lower overall physical and mental health, no antidepressant use, and less severe radiographic changes (lower Kellgren–Lawrence grades) were more likely to fall short of those improvement thresholds. Interestingly, individuals with worse initial knee scores but better overall health and higher KL grades tended to benefit more from surgery and were more likely to see substantial gains a year later.
Outcomes and Implications
This research highlights how machine learning can play a valuable role in improving preoperative planning for total knee arthroplasty (TKA), particularly by helping to flag patients who may not experience significant benefits from the procedure. By leveraging predictive tools like these, clinicians could take a more tailored approach by adjusting treatment strategies, setting more realistic expectations, and potentially improving physical and mental health factors ahead of surgery. When thoughtfully integrated into clinical practice, these models have the potential to support more collaborative decision-making between patients and providers, while also advancing the goals of value-based care. Even though the model’s predictive accuracy was moderate and drawn from data at a single institution, the findings lay important groundwork. They point toward a future where outcome prediction in joint replacement could become more data-informed, ultimately contributing to better patient selection and quality improvement efforts across orthopedic care.