ArticleCanadian journal of anaesthesia = Journal canadien d'anesthesie2022
Postoperative mortality risk prediction that incorporates intraoperative vital signs: development and internal validation in a historical cohort.
Article in Canadian journal of anaesthesia = Journal canadien d'anesthesie, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04014010 (Machine Learning Modeling of Intraoperative Hemodynamic Predictors of 30-day Mortality and Major In-hospital Morbidity After Noncardiac Surgery), which is not on this map. Cited by 5 papers.
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The trial behind it
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Machine Learning Modeling of Intraoperative Hemodynamic Predictors of 30-day Mortality and Major In-hospital Morbidity After Noncardiac Surgery: a Retrospective Population Cohort Study
Who cites it
5 citing papers in PubMed.
- Using a Wearable-Based Animated Patient Avatar to Improve Patients' Perception of Vital Signs: Multicenter Computer-Based Study.JMIR formative research · 2026Trial
- Domain-Aware Interpretable Machine Learning Model for Predicting Postoperative Hospital Length of Stay from Perioperative Data: A Retrospective Observational Cohort Study.Bioengineering (Basel, Switzerland) · 2026Article
- The effect of resampling techniques on the performances of machine learning clinical risk prediction models in the setting of severe class imbalance: development and internal validation in a retrospective cohort.Discover artificial intelligence · 2024Article
- Article
- Prehabilitation in preparation for surgery.BMJ medicineReview
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeAccurate 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.
methodsFollowing 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.
resultsThere 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.
conclusionWe 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: www. CLINICALTRIALS: gov (NCT04014010); registered on 10 July 2019.
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