Evidence-based machine learning algorithm to predict failure following cartilage procedures in the knee

Abstract

Introduction: Clinical decision-making is highly based on expert opinion. Machine learning is increasingly used to develop patient-specific risk prediction analysis to improve patient selection prior to surgery. Objectives: To develop machine learning algorithms to predict failure of surgical procedures that address cartilage defects of the knee and detect variables associated with failure. Methods: An institutional database was queried for cartilage procedures performed between 2000 and 2018. Failure was defined as revision cartilage surgery or knee arthroplasty. One hundred and one preoperative and intraoperative features were evaluated as potential predictors. Four machine learning algorithms were trained and internally validated. Results: One thousand and ninety-one patients with a minimum follow-up of 2 years were included and underwent chondroplasty (n = 560; 51%), osteochondral allograft transplantation (n = 306; 28%), microfracture (n = 150; 14%), autologous chondrocyte implantation (n = 39; 4%), or osteochondral autograft transplantation (n = 36; 3%). The Random Forest algorithm was the best-performing algorithm, with an area under the curve of 0.765 and a Brier score of 0.135. The most important features for predicting failure were symptom duration, age, body mass index, lesion grade, and total lesion area. Local Interpretable Model-agnostic Explanations analysis provided patient-specific comparisons for the risk of failure of an individual patient being assigned various types of cartilage procedures. Conclusions: Machine learning algorithms were accurate in predicting the risk of failure following cartilage procedures of the knee, with the most important features in descending order being symptom duration, age, body mass index, lesion grade, and total lesion area. Machine learning algorithms may be used to compare the risk of failure of specific patient-procedure combinations in the treatment of cartilage defects of the knee.

Keywords

• Artificial intelligence
• Cartilage defect
• Cartilage restoration
• Chondral defect
• Machine learning