Which Osteochondritis Dissecans Lesions Will Heal Nonoperatively? An Application of Machine Learning to the ROCK Prospective Cohort

Background: There are limited evidence-based guidelines to predict which osteochondritis dissecans (OCD) lesions will heal with nonoperative treatment.

Purpose: To train a set of classification algorithms to predict nonoperative OCD healing while identifying new clinically meaningful predictors.

Study design: Case-control study; Level of evidence, 3.

Methods: Patients with OCD of the knee with open physes undergoing nonoperative management were prospectively queried from the Research on OCD of the Knee (ROCK) cohort (https://kneeocd.org) in April 2022. Patients were included if they met the study criteria for nonoperative treatment success or failure. Nonoperative treatment success was defined as complete healing on magnetic resonance imaging (MRI) and total return to sports participation. Failure was defined as the crossover from nonoperative management to surgery at any point at or beyond the 3-month follow-up. If a patient did not meet one of these criteria, they were not included. Normalized lesion size, lesion location, patient characteristics, and symptoms were used as clinically relevant predictors.

Results: A total of 64 patients were included, of whom 24 (37.5%) patients successfully healed with nonoperative management. Multivariate logistic regression revealed that a 1% increase in normalized lesion width was associated with an increase in the likelihood of nonoperative failure (odds ratio [OR], 1.41 [95% CI, 1.17-1.81]; P < .01). By contrast, lesions in the posterior sagittal zone (OR, 0.08 [95% CI, 0.009-0.43]; P < .01) or the medial-most coronal zone (for lesions of the medial femoral) and lateral-most coronal zone (for lesions of the lateral femoral condyle) on MRI (OR, 0.05 [95% CI, 0.004-0.44]; P < .01) were associated with a decrease in the likelihood of nonoperative treatment failure. Support vector machines had a cross-validated area under the receiver operating characteristic curve of 0.89 and a classification accuracy of 83.3%.

Conclusion: Lesion location in the posterior aspect of the condyle on sagittal MRI and lesion location in the medial-most or lateral-most locations on coronal MRI were identified as statistically significant predictors of increased nonoperative treatment success on multivariate analysis. Machine learning models can predict which OCD lesions will heal with nonoperative management with superior accuracy compared with previously published models.