Abstract
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.
Highlights
Spontaneous bacterial peritonitis (SBP) is a life-threatening complication in patients with cirrhosis
We identified 6 important variables closely related to the occurrence of SBP
A total of 1399 inpatients who met the enrollment criteria were included in the model construction, of which 538 (38.5%) patients were identified as having cirrhosis ascites complicated with SBP
Summary
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. 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. We applied CatBoost algorithm to establish a practical and explainable prediction model for risk of SBP in cirrhotic patients with ascites. Spontaneous bacterial peritonitis (SBP) is the most common life-threatening infection in patients with ascites due to liver cirrhosis. Machine learning has been applied in many fields of medicine such as outcome prediction, diagnosis, medical image interpretation, and t reatment[9]. Interpretability of models are crucial in medical environments where results have to be explained to medical providers to be widely accepted[10,11]
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