A retrospective study of mortality for perioperative cardiac arrests toward a personalized treatment.

ABSTRACT: Perioperative cardiac arrest (POCA) is associated with a high mortality rate. This work aimed to study its prognostic factors for risk mitigation by means of care management and planning. A database of 380,919 surgeries was reviewed, and 150 POCAs were curated. The main outcome was mortality prior to hospital discharge. Patient demographic, medical history, and clinical characteristics (anesthesia and surgery) were the main features. Six machine learning (ML) algorithms, including LR, SVC, RF, GBM, AdaBoost, and VotingClassifier, were explored. The last algorithm was an ensemble of the first five algorithms. k-fold cross-validation and bootstrapping minimized the prediction bias and variance, respectively. Explainers (SHAP and LIME) were used to interpret the predictions. The ensemble prov

SUBMITTER: Shang H 

PROVIDER: S-EPMC9374678 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature