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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Patients were queried from the Merative\n                      <jats:sup>TM<\/jats:sup>\n                      MarketScan\u00ae 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\u2019s history. Hyperparameter-tuned random forest models were combined with multivariable logistic regression models to validate risk factors for the development of CC.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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\u2019s error rate was 9.54%. Prior CC was the most important variable in the model. When removed, model accuracy decreased by 0.0011% (\n                      <jats:italic>p<\/jats:italic>\n                      &lt;0.001). Removing prior irradiation as a feature decreased model accuracy by 0.00014% (\n                      <jats:italic>p<\/jats:italic>\n                      &lt; 0.001). In line with the previous literature, prior CC (OR = 2.47,\n                      <jats:italic>p<\/jats:italic>\n                      &lt;0.001) and irradiation (OR = 1.16,\n                      <jats:italic>p<\/jats:italic>\n                      &lt;0.001) were significantly associated with CC development in a multivariable logistic regression model.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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\u2019s ability to reliably predict which patients are at increased risk for CC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Level of Evidence III<\/jats:title>\n                    <jats:p>\n                      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\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/www.springer.com\/00266\" ext-link-type=\"uri\">www.springer.com\/00266<\/jats:ext-link>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00266-026-05919-8","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T14:48:53Z","timestamp":1780066133000},"page":"5102-5108","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Does Patient History Influence Capsular Contracture? 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Data for this project were accessed using the Stanford Center for Population Health Sciences Data Core. The PHS Data Core is supported by a National Institutes of Health National Center for Advancing Translational Science Clinical and Translational Science Award (UL1TR003142) and from Internal Stanford funding. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.","order":2,"name":"Ethics","label":"Human and Animal Rights","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"For this type of study, informed consent is not required.","order":3,"name":"Ethics","label":"Informed Consent","group":{"name":"EthicsHeading","label":"Declarations"}}]}}