Predictors Using Machine Learning of Complete Peroneal Nerve Palsy Recovery After Multiligamentous Knee Injury: A Multicenter Retrospective Cohort Study.

Background: Peroneal nerve (PN) palsy is one of the most debilitating sequelae of multiligamentous knee injuries (MLKIs). There is limited research on recovery from complete PN palsy. Purpose/Hypothesis: The purpose of this study was to characterize PN injuries and develop a predictive model of complete PN recovery after MLKI using machine learning. It was hypothesized that elevated body mass index (BMI) would be predictive of lower likelihood of recovery. Study Design: Case-control study; Level of evidence, 3. Methods: The authors conducted a retrospective review of patients seen at 2 urban hospital systems for treatment of MLKI with associated complete PN palsy, defined as the presence of complete foot drop with or without sensory deficits on physical examination. Recovery was defined as the complete resolution of foot drop. A random forest (RF) classifier algorithm was used to identify demographic, injury, treatment, and postoperative variables that were significant predictors of recovery from complete PN palsy. Validity of the RF model was assessed using overall accuracy, F1 score, and area under the receiver operating characteristic curve (AUC). Results: Overall, 16 patients with MLKI with associated complete PN palsy were included in the cohort. Among them, 75% (12/16) had documented knee dislocation requiring reduction. Complete recovery occurred in 4 patients (25%). Nerve contusions on magnetic resonance imaging were more common among patients without PN recovery, but there were no other significant differences between recovery and nonrecovery groups. The RF model found that older age, increasing BMI, and male sex were predictive of worse likelihood of PN recovery. The model was found to have good validity, with a classification accuracy of 75%, F1 score of 0.86, and AUC of 0.64. Conclusion: The RF model in this study found that increasing age, BMI, and male sex were predictive of decreased likelihood of nerve recovery. While further study of machine learning models with larger patient data sets is required to identify the most superior model, these findings present an opportunity for orthopaedic surgeons to better identify, counsel, and treat patients with MLKIs and concomitant complete PN palsy.