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

backgroundCapsular 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.

methodsPatients were queried from the Merative

resultsA 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 <0.001). Removing prior irradiation as a feature decreased model accuracy by 0.00014% (p < 0.001). In line with the previous literature, prior CC (OR = 2.47, p <0.001) and irradiation (OR = 1.16, p <0.001) were significantly associated with CC development in a multivariable logistic regression model.

conclusionsAdministrative medical history alone demonstrated limited predictive utility for CC development. Although established associations such as prior CC and irradiation were confirmed, their incremental contribution to predictive performance was negligible. These associations do not meaningfully enhance a clinician's ability to reliably predict which patients are at increased risk for CC. LEVEL OF EVIDENCE III: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .