Added Value of Intraoperative Data for Predicting Postoperative Complications: The MySurgeryRisk PostOp Extension.

The Journal of Surgical Research
Shounak DattaAzra Bihorac

Abstract

Models that predict postoperative complications often ignore important intraoperative events and physiological changes. This study tested the hypothesis that accuracy, discrimination, and precision in predicting postoperative complications would improve when using both preoperative and intraoperative data input data compared with preoperative data alone. This retrospective cohort analysis included 43,943 adults undergoing 52,529 inpatient surgeries at a single institution during a 5-y period. Random forest machine learning models in the validated MySurgeryRisk platform made patient-level predictions for seven postoperative complications and mortality occurring during hospital admission using electronic health record data and patient neighborhood characteristics. For each outcome, one model trained with preoperative data alone; one model trained with both preoperative and intraoperative data. Models were compared by accuracy, discrimination (expressed as area under the receiver operating characteristic curve), precision (expressed as area under the precision-recall curve), and reclassification indices. Machine learning models incorporating both preoperative and intraoperative data had greater accuracy, discrimination, and precis...Continue Reading

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Citations

Oct 31, 2021·Langenbeck's Archives of Surgery·Jonas HennHanno Matthaei
Nov 11, 2021·JAMA Network Open·Tyler J LoftusAzra Bihorac
Jul 16, 2021·Annals of Surgery·Tyler J LoftusAzra Bihorac

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