A Simple And Quick Screening Method For Intrapulmonary Vascular Dilation In Cirrhotic Patients Based On Machine Learning

Background and Aims: Screening for hepatopulmonary syndrome in cirrhotic patients is limited due to the need to perform contrast enhanced echocardiography (CEE) and arterial blood gas (ABG) analysis. We aimed to develop a simple and quick method to screen for the presence of in-trapulmonary vascular dilation (IPVD) using noninvasive and easily available variables with machine learning (ML) algorithms. Methods: Cirrhotic patients were enrolled from our hospital. All eligible patients underwent CEE, ABG analysis and physical examination. We developed a two-step model based on three ML algorithms, namely, adap-tive boosting (termed AdaBoost), gradient boosting deci-sion tree (termed GBDT) and eXtreme gradient boosting (termed Xgboost). Noninvasive variables were input in the first step (the NI model), and for the second step (the NIBG model), a combination of noninvasive variables and ABG re-sults were used. Model performance was determined by the area under the curve of receiver operating characteristics (AUCROCs), precision, recall, F1-score and accuracy. Re-sults: A total of 193 cirrhotic patients were ultimately ana-lyzed. The AUCROCs of the NI and NIBG models were 0.850