Protocol for development and validation of a prediction model for post-induction hypotension in elderly patients undergoing non-cardiac surgery: a prospective cohort study
التفاصيل البيبلوغرافية
العنوان:
Protocol for development and validation of a prediction model for post-induction hypotension in elderly patients undergoing non-cardiac surgery: a prospective cohort study
Introduction Post-induction hypotension (PIH) is a common event in elderly surgical patients and is associated with increased postoperative morbidity and mortality. This study aims to develop and validate a PIH prediction model for elderly patients undergoing elective non-cardiac surgery to identify potential PIH in advance and help to take preventive measures.Methods and analysis A total of 938 elderly surgical patients (n=657 for development and internal validation, n=281 for temporal validation) will be continuously recruited at The First Affiliated Hospital of Soochow University in Suzhou, China. The main outcome is PIH during the first 15 min after anaesthesia induction or before skin incision (whichever occurs first). We select candidate predictors based on published literature, professional knowledge and clinical expertise. For model development, we will use the least absolute shrinkage and selection operator regression analysis and multivariable logistic regression. For internal validation, we will apply the bootstrapping technique. After model development and internal validation, temporal validation will be conducted in patients recruited in another time period. We will use the discrimination, calibration and max-rescaled Brier score in the temporal validation cohort. Furthermore, the clinical utility of the prediction model will be assessed using the decision curve analysis, and the results will be presented in a nomogram and a web-based risk calculator.Ethics and dissemination Ethical approval was obtained from the Ethics Committee of the First Affiliated Hospital of Soochow University (Approval No. 2023-012). This PIH risk prediction model will be published in a peer-reviewed journal.Trial registration number ChiCTR2200066201.
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