Does Patient History Influence Capsular Contracture? An Exploratory Analysis with Machine Learning

BACKGROUND: Capsular contracture (CC) is a frequent and distressing complication of breast augmentation and reconstruction. Although numerous patient-, surgical-, and implant-related risk factors have been proposed, reliable population-level predictors remain inconsistent across studies. This study evaluates whether administrative medical history, as encoded by ICD and CPT codes, contains sufficient predictive signal to identify patients at risk for CC using machine learning. METHODS: MarketScan® Research Databases from 2003 to 2017 with CPT codes for implant-based breast reconstruction and augmentation. ICD codes were then used to identify all events and conditions of a patient's history. Hyperparameter-tuned random forest models were combined with multivariable logistic regression models to validate risk factors for the development of CC. RESULTS: A total of 112,489 patients were included, and the rate of capsular contracture was 9.55%. In total, 4,825 common conditions and procedures were included as features in the model. The random forest's error rate was 9.54%. Prior CC was the most important variable in the model. When removed, model accuracy decreased by 0.0011% (p