A Magnetic Resonance Imaging-Based Clinical Prediction Model Accurately Identifies Patellar Instability Risk Using Common Patellofemoral Measurements.

PURPOSE

To predict parameters associated with patellar instability from magnetic resonance imaging (MRI) measurements using a machine learning model and to quantify the relative importance of radiographic risk factors that are associated with the presence of instability.

METHODS

Patients with a confirmed clinical diagnosis of patellar instability and age- and sex-matched controls without patellofemoral pathology were identified retrospectively. Multiple measurements to describe patella alta, malalignment, and trochlear dysplasia were performed on knee MRI scans. Univariate and multivariable logistic regressions were used to identify MRI measurements associated with patellar instability. Machine learning models were developed and evaluated for accuracy, discrimination, and calibration in predicting patellar instability. Shapley additive explanations (SHAP) were used to evaluate global and local variable importance.

RESULTS

A total of 256 patients were included in this study (128 with patellar instability and 128 controls, 63% female sex). Multivariable logistic regression found significant associations between diagnosis of patellar instability and lower patellotrochlear index (OR, 1.39 [95% CI, 1.15-1.69]; < .001), greater Insall-Salvati ratio (OR, 1.65 [95% CI, 1.37-2.02]; < .001), greater tibial tubercle-trochlear groove (TT-TG) distance (OR, 1.12 [95% CI, 1.06-1.19]; < .001), and lower trochlear depth (OR, 1.42 [95% CI, 1.09-1.87]; = .009). The random forest model had the highest performance among machine learning models, with an area under the receiver operating characteristic curve of 0.85. In this model, the variables with the greatest importance were Insall-Salvati ratio, TT-TG distance, and trochlear depth.

CONCLUSIONS

The final model was able to reliably predict MRI-based parameters associated with patellar instability. Insall-Salvati ratio, TT-TG distance, and trochlear depth were the most important risk factors both in the machine learning models and using conventional statistical analysis.

CLINICAL RELEVANCE

This model has the potential to improve the diagnostic accuracy of patellar instability from MRI scans. The explanations provided by the model could enable clinicians to personalize care and understand the factors driving patellar instability in individual patients.