Machine learning models may predict outcomes of MPFL reconstruction
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Key takeaways:
- Machine learning models may accurately predict outcome measures for patients undergoing MPFL reconstruction.
- Machine learning also predicted return to sports and recurrent instability.
Published results showed machine learning models may accurately predict outcomes for patients undergoing medial patellofemoral ligament reconstruction.
Hongwei Zhan, MD, from the department of sports medicine at Honghui Hospital and Xi'an Jiaotong University in Shaanxi, China, and colleagues analyzed data from 218 patients who underwent MPFL reconstruction between January 2018 and December 2022.
Zhan and colleagues constructed 42 predictive models for seven clinical outcomes to assess the ability of six machine learning models to accurately predict patients’ likelihood of achieving a minimal clinically important difference (MCID). They also assessed the ability of the model to predict factors that may affect return to sport and recurrent instability.
Zhan and colleagues found the multilayer perception model had the best performing predictions for failure to achieve MCID in terms of accuracy, specificity and sensitivity, with 87.3% accuracy for Lysholm score, 86.2% accuracy for IKDC score and 97% accuracy for Kujala score. It also exhibited optimal performance for predicting return to pivoting sports, with an accuracy of 75.4%, according to results.
Meanwhile, results showed the support vector machine algorithm model had the best performing predictions for failure to achieve MCID of the Tegner score (accuracy = 76.8%), as well as optimal performance in predicting return to pre-injury sports (accuracy = 95.2%) and recurrent instability (accuracy = 94.9%).
Zhan and colleagues noted low preoperative Tegner scores, shorter time to surgery and absence of severe trochlear dysplasia were predictors for return to pre-injury sports. Predictors for return to pivoting sports included the absence of severe trochlear dysplasia and patellar alta . They found older patient age, female patients and low preoperative Lysholm scores were predictive of recurrent instability.
“When applying predictive models validated with large, multicenter datasets to clinical practice in the future, they should not be employed to establish eligibility for MPFL reconstruction,” Zhan and colleagues wrote in the study. “Instead, their role should be to inform treatment decisions. It is imperative to avoid any misuse of these models to deny surgical treatment.”
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