Background:Prolonged mechanical ventilation in children undergoing cardiac surgery is related to the decrease of cardiac output during the postoperative intense care time, which often induced serious complications. Pressure Recording Analytical Method (PRAM) is a minimally invasive system for continuous hemodynamic monitoring, which can timely record the presence of lower cardiac function. This study is aimed to evaluate the predictive value of the several hemodynamic parameters for the duration of mechanical ventilation (DMV).Methods:This retrospective study included 60 children under 1-year-old who underwent cardiac surgery between 2017 and 2021. CI, CCE and dp/dt max derived from PRAM was documented in each patient 0, 4, 8 and 12 hours (T0, T1,T2, T3 and T4 respectively) after admission to the intensive care unit (ICU). A linear mixed model were used to deal with the repeated measurement on hemodynamic data. Correlation analysis and Receiver operating characteristic (ROC) curves were used to show the predictive value. XGBoost machine learning-based mode which produces a decision tree-heat map was used to find the key characteristics of the data in predicting the outcome. Results:There were 35(58%) children in DMV≤24 h group and 25(42%) were in DMV>24h. Prolonged DMV caused longer ICU stays and postoperative hospital stays. Linear mixed model revealed significant time and group effect in CI and dp/dt max. Prolonged DMV also have negative correlations with age, weight, CI at T2 and dp/dt max at T2. dp/dt max outweighing CI were the strongest predictors of prolonged DMV(AUC of ROC: 0.978 vs 0.811, respectively, p<0.01). The XGboost based learning machine model (Balance Accuracy=0.92, AUC of ROC=0.856) suggested that dp/dt max at T2 ≤1.049 or >1.049 in combination with CI at T0 ≤2 or > 2 can predict prolonged DMV.Conclusions:Hemodynamic monitoring in infants with PRAM after the cardiac surgery shows that the cardiac function can predict the prolonged duration of mechanical ventilation. CI measured by PRAM immediately after ICU admission and dp/dt max that 8h later are 2 key factors in predicting prolonged DMV with the application of XGboost based machine learning model.