Prediction of the Length of Intensive Care After Cardiac Surgery Under Cardiopulmonary Bypass: Logistic Regression Analysis Based on Troponin I And EuroSCORE II

Abstract

Aim: This study is to establish a model for patients undergoing cardiac surgery under cardiopulmonary bypass (CPB) to predict the length of intensive care. Methods: This is a single center retrospective study. A total of 265 patients admitted to the ICU after CPB from 2016 to 2017 were enrolled in the study. Preoperative indicators, intraoperative parameters, and postoperative data were collected. Each patient was scored for EuroSCORE II before surgery. According to the length of intensive care, all patients were divided into two groups: short stay (< 72 h) and long stay (≥ 72 h). A binary logistic regression analysis was performed to establish a regression model to evaluate the predictive performance of the indicators and the EuroSCORE II scoring system on the length of the intensive care. Results: Both troponin I and EuroSCORE II could predict the length of intensive care of patients undergoing cardiac surgery under CPB. After combing the two factors, the prediction efficiency was higher. Comparing the prediction results with the actual data, it showed that the method had high overall accuracy. Conclusions: The predictive model based on cTnI and EuroSCORE II can accurately predict the length of intensive care of patients undergoing cardiac surgery under CPB. This predictive model may help to improve ICU resource management.