Journal of the American College of Surgeons
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The American College of Surgeons NSQIP risk calculator (RC) uses regression to make predictions for fourteen 30-day surgical outcomes. While this approach provides accurate (discrimination and calibration) risk estimates, they might be improved by machine learning (ML). To investigate this possibility, accuracy for regression-based risk estimates were compared to estimates from an extreme gradient boosting (XGB)-ML algorithm. ⋯ XGB-ML provided more accurate risk estimates than regression in terms of discrimination and calibration. Differences in calibration between regression and XGB-ML were of substantial magnitude and support transitioning the RC to XGB-ML.
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Multimorbidity in surgery is common and associated with worse postoperative outcomes. However, conventional multimorbidity definitions (≥2 comorbidities) label the vast majority of older patients as multimorbid, limiting clinical usefulness. We sought to develop and validate better surgical specialty-specific multimorbidity definitions based on distinct comorbidity combinations. ⋯ Our new multimorbidity definitions identified far more specific, higher-risk pools of patients than conventional definitions, potentially aiding clinical decision-making.
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Multicenter Study Observational Study
Dispelling Dogma: American Association for Surgery of Trauma Prospective, Multicenter Trial of Index vs Delayed Fasciotomy after Extremity Trauma.
Surgical dogma states that "if you think about doing a fasciotomy, you do it," yet the benefit of this approach remains unclear. We hypothesized that early fasciotomy during index operative procedures for extremity vascular trauma would be associated with improved patient outcomes. ⋯ Routine, index operation fasciotomy failed to demonstrate an outcome benefit in this prospective, multicenter analysis. Our data suggest that a careful observation and fasciotomy-when-needed approach may limit unnecessary surgery and its resulting morbidity in extremity vascular trauma patients.