Predicting Surgical Outcomes in Patients With Recurrent Patellar Dislocations.

Background: A lateral dislocation of the patella is a common injury in adolescents and young adults that is largely caused by underlying anatomic risk factors. Surgically managed patients have a significantly lower risk of recurrent dislocations. However, determining the optimal surgical treatment remains a challenge, with patients sometimes undergoing multiple surgical procedures before achieving successful stabilization.

Purpose: To computationally evaluate patients who have undergone multiple surgical procedures to treat recurrent lateral patellar dislocations and predict their clinical outcomes.

Study design: Controlled laboratory study.

Methods: Our cohort consisted of 16 patients with trochlear dysplasia and recurrent lateral patellar dislocations. We used magnetic resonance imaging to create 3-dimensional patient-specific finite element models of the knee joint and evaluated patellofemoral stability before and after surgery. We applied these models to computationally predict the clinical outcome of each surgical procedure. We simulated a knee extension activity coupled with external tibial torsion to assess patellofemoral stability. We also included a healthy control group of 12 participants in the computational evaluation. Finally, we developed and trained a logistic regression model based on anatomic risk factors and applied this model to classify whether patients had a likelihood of a dislocation to efficiently differentiate between surgical outcomes.

Results: Of 12 control, 12 preoperative, and 9 postoperative scans, the finite element model correctly predicted 29 of 33 surgical outcomes (87.9% accuracy). Postoperative simulations predicted patellofemoral stability metrics similar to those of the control group. Specifically, patients after trochleoplasty were associated with increased constraint force on the patellar lateral facet and lower involvement of the medial patellofemoral ligament. The logistic regression model demonstrated 81.8% accuracy in classification.

Conclusion: Preliminary results are promising, but an improvement of the model and a larger clinical dataset are necessary to increase accuracy and comprehensively validate model performance.

Clinical relevance: The aim of this study was to provide surgeons with a useful computational tool that can predict the likelihood of a patellar dislocation and differentiate, before a clinical intervention, between successful versus unsuccessful surgery to determine the optimal treatment pathway for individual patients.