Explainable machine learning model for predicting spontaneous bacterial peritonitis in cirrhotic patients with ascites

• Published in: Scientific reports Vol. 11; no. 1; pp. 21639 - 11
• Main Authors: Hu, Yingying, Chen, Ruijia, Gao, Haibing, Lin, Haitao, Wang, Jinye, Wang, Xiaowei, Liu, Jingfeng, Zeng, Yongyi
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 04.11.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/114/2397; 692/4020/4021/1607/1604; Adult; Algorithms; Ascites; Ascites - complications; Bacterial Infections - epidemiology; C-reactive protein; C-Reactive Protein - metabolism; Cholinesterase; Cirrhosis; Female; Fibrosis - complications; Forecasting - methods; Humanities and Social Sciences; Humans; Learning algorithms; Liver cirrhosis; Liver Cirrhosis - complications; Liver Cirrhosis - microbiology; Lymphocytes; Machine Learning; Male; Middle Aged; Models, Theoretical; multidisciplinary; Peritonitis; Peritonitis - microbiology; Prediction models; Predictive Value of Tests; Prothrombin; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-00218-5

Spontaneous bacterial peritonitis (SBP) is a life-threatening complication in patients with cirrhosis. We aimed to develop an explainable machine learning model to achieve the early prediction and outcome interpretation of SBP. We used CatBoost algorithm to construct MODEL-1 with 46 variables. After dimensionality reduction, we constructed MODEL-2. We calculated and compared the sensitivity and negative predictive value (NPV) of MODEL-1 and MODEL-2. Finally, we used the SHAP (SHapley Additive exPlanations) method to provide insights into the model’s outcome or prediction. MODEL-2 (AUROC: 0.822; 95% confidence interval [CI] 0.783–0.856), liked MODEL-1 (AUROC: 0.822; 95% CI 0.784–0.856), could well predict the risk of SBP in cirrhotic ascites patients. The 6 most influential predictive variables were total protein, C-reactive protein, prothrombin activity, cholinesterase, lymphocyte ratio and apolipoprotein A1. For binary classifier, the sensitivity and NPV of MODEL-1 were 0.894 and 0.885, respectively, while for MODEL-2 they were 0.927 and 0.904, respectively. We applied CatBoost algorithm to establish a practical and explainable prediction model for risk of SBP in cirrhotic patients with ascites. We also identified 6 important variables closely related to the occurrence of SBP.