Predicting de-ventilator extubation in post-cardiac surgery patients using machine learning

Background : The aim of this study was to develop machine learning models to use machine learning algorithms to predict the factors associated with cardiac surgery that influence patient extubation from a ventilator after cardiac surgery. Method : Clinical data of cardiac surgery patients in Nanjing First Hospital between June 2019 and December 2022 were retrospectively extracted from electronic medical records. Five traditional machine learning algorithms, including logistic regression (LR), decision tree (DT), random forest (RF), extreme gradient boosting (XGB), and light gradient boosting machine (LGB), were used to construct predictive models for risk prediction of long-distance mechanical ventilation in post-cardiovascular surgery patients. The discriminative power of these models was assessed by the area under the receiver operating characteristic curve (AUC). Shapley Additive explanation(SHAP) was used to interpret the predictive models. Results : Data from 4487 patients were employed to train and validate a model of offline extubation risk in post-cardiac surgery patients. Among the full models, the RF model (AUC: 0.86; Sensitivity: 0.781, Specficity: 0.756) and the XGB model (AUC: 0.850; Sensitivity: 0.818; Specificity: 0.768) showed well predictive power for off-ventilator predicting. Eleven variables were finally selected by Boruta and LASSO features selection procedure, including age, hypertension, optime, preoperation EF, preoperation LVPW, HR, reoperation, body-weight, sex, AMBP, ST-II. Among the eleven variables, age, hypertension, operation time, preoperation EF, preoperation LVPW significantly contributed to the prediction model. Conclusion : In this study, we successfully developed several machine learning models to predict factors affecting off-ventilator extubation after cardiac surgery, which may be useful to help clinicians assess the success of off-ventilator extubation in cardiac surgery patients after surgery.