文章基本信息

标题：A prediction model for outcome in patients with HBV-ACLF based on predisposition, injury, response and organ failure
其他标题：A prediction model for outcome in patients with HBV-ACLF based on predisposition, injury, response and organ failure
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作者：Fangfang Liu ; Zhengsheng Zou ; Lijun Shen 等
期刊名称：Scientific Reports
电子版ISSN：2045-2322
出版年度：2020
卷号：10
期号：1
页码：1-12
DOI：10.1038/s41598-020-77235-3
出版社：Springer Nature
摘要：We aimed to develop a prediction model based on the PIRO concept (Predisposition, Injury, Response and Organ failure) for patients with Hepatitis B Virus (HBV) related acute-on-chronic liver failure (ACLF). 774 patients with HBV related ACLF defined in the CANONIC study were analyzed according to PIRO components. Variables associated with mortality were selected into the prediction model. Based on the regression coefficients, a score for each PIRO component was developed, and a classification and regression tree was used to stratify patients into different nodes. The prediction model was then validated using an independent cohort (n = 155). Factors significantly associated with 90-day mortality were: P: age, gender and ACLF type; I: drug, infection, surgery, and variceal bleeding; R: systemic inflammatory response syndrome (SIRS), spontaneous bacteria peritonitis (SBP), and pneumonia; and O: the CLIF consortium organ failure score (CLIF-C OFs). The areas under the receiver operating characteristics curve (95% confidence interval) for the combined PIRO model for 90-day mortality were 0.77 (0.73–0.80). Based on the scores for each of the PIRO components and the cut-offs estimated from the classification and regression tree, patients were stratified into different nodes with different estimated death probability. Based on the PIRO concept, a new prediction model was developed for patients with HBV related ACLF, allowing stratification into different clusters using the different scores obtained in each PIRO component. The proposed model will likely help to stratify patients at different risk, defining individual management plans, assessing criteria for specific therapies, and predicting outcomes.