The prognostic value of intraoperative HRV during anesthesia in patients presenting for non-cardiac surgery.

ABSTRACT: To examine the prognostic value of HRV measurements during anesthesia for postoperative clinical outcomes prediction using machine learning models. VitalDB, a comprehensive database of 6388 surgical patients admitted to Seoul National University Hospital. Cases with ECG lead II recording duration of less than one hour were excluded. Cases with more than 20% of missing HRV measurements were also excluded. A total of 5641 cases were eligible for the analyses. Six machine learning models including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Trees (GBT), Extreme Gradient Boosting (XGB), and an ensemble of the five baseline models were developed to predict postoperative clinical outcomes. The prediction models were trained using only cl

SUBMITTER: Niu J 

PROVIDER: S-EPMC10169477 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature