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Does Patient History Influence Capsular Contracture? An Exploratory Analysis with Machine Learning
Journal article   Open access   Peer reviewed

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

Thomas M. Johnstone, Daniel Najafali, Jennifer K. Shah, Justin M. Camacho, Chancellor Johnstone, Rahim S. Nazerali and Gordon K. Lee
Aesthetic plastic surgery, Forthcoming
29 May 2026
DOI: https://doi.org/10.1007/s00266-026-05919-8
PMID: 42213103
url
https://doi.org/10.1007/s00266-026-05919-8View
Published, Version of Record (VoR) Open

Abstract

Life Sciences & Biomedicine Science & Technology Surgery
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 Patients were queried from the Merative (TM) MarketScan (R) 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 <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. Conclusions Administrative 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 IIIThis 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.

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