  To test the hypothesis that accuracy, discrimination, and precision in
predicting postoperative complications improve when using both preoperative and
intraoperative data input features versus preoperative data alone. Models that
predict postoperative complications often ignore important intraoperative
physiological changes. Incorporation of intraoperative physiological data may
improve model performance. This retrospective cohort analysis included 52,529
inpatient surgeries at a single institution during a 5 year period. Random
forest machine learning models in the validated MySurgeryRisk platform made
patient-level predictions for three postoperative complications and mortality
during hospital admission using electronic health record data and patient
neighborhood characteristics. For each outcome, one model trained with
preoperative data alone and one model trained with both preoperative and
intraoperative data. Models were compared by accuracy, discrimination
(expressed as AUROC), precision (expressed as AUPRC), and reclassification
indices (NRI). Machine learning models incorporating both preoperative and
intraoperative data had greater accuracy, discrimination, and precision than
models using preoperative data alone for predicting all three postoperative
complications (intensive care unit length of stay >48 hours, mechanical
ventilation >48 hours, and neurological complications including delirium) and
in-hospital mortality (accuracy: 88% vs. 77%, AUROC: 0.93 vs. 0.87, AUPRC: 0.21
vs. 0.15). Overall reclassification improvement was 2.9-10.0% for complications
and 11.2% for in-hospital mortality. Incorporating both preoperative and
intraoperative data significantly increased accuracy, discrimination, and
precision for machine learning models predicting postoperative complications.
