• Heather L Yeo, Jonathan S Abelson, Jialin Mao, Frank Lewis, Fabrizio Michelassi, Richard Bell, Art Sedrakyan, and Julie A Sosa.
• *NewYork-Presbyterian Hospital/Weill Cornell Medicine, Department of Surgery, New York, NY †Weill Cornell Medicine, Department of Healthcare Policy and Research, New York, NY ‡American Board of Surgery, Inc., Philadelphia, PA §Department of Surgery, Duke Cancer Institute and Duke Clinical Research Institute, Duke University Medical Center, Durham, NC.
• Ann. Surg. 2017 Sep 1; 266 (3): 499-507.

ObjectiveWe present 8-year follow-up data from the intern class of 2007 to 2008 using a novel, nonparametric predictive model to identify those residents who are at greatest risk of not completing their training.BackgroundNearly 1 in every 4 categorical general surgery residents does not complete training. There has been no study at a national level to identify individual resident and programmatic factors that can be used to accurately anticipate which residents are most at risk of attrition out.MethodsA cross-sectional survey of categorical general surgery interns was conducted between June and August 2007. Intern data including demographics, attendance at US or Canadian medical school, proximity of family members, and presence of family members in medicine were de-identified and linked with American Board of Surgery data to determine residency completion and program characteristics. A Classification and Regression Tree analysis was performed to identify groups at greatest risk for non-completion.ResultsOf 1048 interns, 870 completed the initial survey (response rate 83%), 836 of which had linkage data (96%). Also, 672 residents had evidence of completion of residency (noncompletion rate 20%). On Classification and Regression Tree analysis, sex was the independent factor most strongly associated with attrition. The lowest noncompletion rate for men was among interns at small community programs who were White, non-Hispanic, and married (6%). The lowest noncompletion rate for women was among interns training at smaller academic programs (11%).ConclusionsThis is the first longitudinal cohort study to identify factors at the start of training that put residents at risk for not completing training. Data from this study offer a method to identify interns at higher risk for attrition at the start of training, and next steps would be to create and test interventions in a directed fashion.

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