4.3 Article

Postoperative mortality risk prediction that incorporates intraoperative vital signs: development and internal validation in a historical cohort

Journal

Publisher

SPRINGER
DOI: 10.1007/s12630-022-02287-0

Keywords

informatics; mortality; perioperative medicine; risk prediction; vital signs

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Funding

  1. Department of Anesthesia, Pain Management & Perioperative Medicine at Dalhousie University
  2. Nova Scotia Health Authority Research Fund

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This study aimed to develop and validate a model incorporating intraoperative vital signs to improve risk stratification after surgery. The results showed that the addition of intraoperative vital signs had minimal impact on discrimination and calibration.
Purpose Accurate risk reassessment after surgery is crucial for postoperative planning for monitoring and disposition. Existing postoperative mortality risk prediction models using preoperative features do not incorporate intraoperative hemodynamic derangements that may alter risk stratification. Intraoperative vital signs may provide an objective and readily available prognostic resource. Our primary objective was to derive and internally validate a logistic regression (LR) model by adding intraoperative features to established preoperative predictors to predict 30-day postoperative mortality. Methods Following Research Ethics Board approval, we analyzed a historical cohort that included patients aged >= 45 undergoing noncardiac surgery with an overnight stay at two tertiary hospitals (2013 to 2017). Features included intraoperative vital signs (blood pressure, heart rate, end-tidal carbon dioxide partial pressure, oxygen saturation, and temperature) by threshold and duration of exposure, as well as patient, surgical, and anesthetic factors. The cohort was divided temporally 75:25 into derivation and validation sets. We constructed a multivariable LR model with 30-day all-cause mortality as the outcome and evaluated performance metrics. Results There were 30,619 patients in the cohort (mean [standard deviation] age, 66 [11] yr; 50.2% female; 2.0% mortality). In the validation set, the primary LR model showed a c-statistic of 0.893 (99% confidence interval [CI], 0.853 to 0.927), a Nagelkerke R-squared of 0.269, a scaled Brier score of 0.082, and an area under precision-recall curve of 0.158 (baseline 0.017 for an uninformative model). The addition of intraoperative vital signs to preoperative factors minimally improved discrimination and calibration. Conclusion We derived and internally validated a model that incorporated vital signs to improve risk stratification after surgery. Preoperative factors were strongly predictive of mortality risk, and intraoperative predictors only minimally improved discrimination. External and prospective validations are needed. Study registration (NCT04014010); registered on 10 July 2019.

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