Prediction model based on machine learning for short- and long-term adverse events in left atrial appendage closure

Background: At present, the prediction of adverse events (AE) had practical significance in clinic and the accuracy of AE prediction model after left atrial appendage closure (LAAC) needed to be improved. To identify a good prediction model based on machine learning for short- and long-term AE after LAAC. Methods: In this study, 869 patients were included from the Department of Cardiovascular Medicine of Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital during 2017 and 2021. Univariate and multivariate analyses were conducted for short-term AE after LAAC to determine possible risk factors related with AE. We compared 8 machine learning algorithms for prediction short-term AE, and XGBoost was found to have the best performance. In addition, Cox-regression was used for long-term AE to find out the risk factors and establish a prediction model. Results: score [hazard ratio (HR) =1.43], hypertension (HR =2.18), age more than 75 (HR =0.49), diabetes (HR =0.57), BLED score (HR=0.28), stroke (HR =19.8), hepatopathy (HR =3.97), nephropathy (HR =2.93), INR instability (HR =4.18), drinking (HR =2.67), and drugs (HR =2.36) were significant risk factors for long-term AE. The XGBoost had a good receiver operating characteristic (ROC) curve and area under the curve (AUC) was 0.85. The accuracy of the XGBoost model stayed at nearly 0.95. Conclusions: score and BLED score were the most obvious risk factors. Several other risk factors also played roles in AE of LAAC. The incidence of long-term AE is under 15% and LAAC is effective and safe. The XGBoost model had good prediction accuracy and ROC curve.