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A model predicting short-term mortality in patients with advanced liver cirrhosis and concomitant infection
Ying Li1,2, Roongruedee Chaiteerakij2,3, Jung Hyun Kwon4
1Department of Hepatology and Gastroenterology, Tianjin Third Central Hospital Affiliated to Tianjin Medical University, Tianjin Key Laboratory of Artificial Cells, Tianjin, China.
Insights
A new model accurately predicts 90-day survival in advanced cirrhosis patients hospitalized with infection. This tool aids clinicians in optimizing treatment for better outcomes in patients with liver disease and infection.
Area of Science:
- Hepatology
- Infectious Diseases
- Clinical Prediction Modeling
Background:
- Infection significantly contributes to mortality in advanced cirrhosis patients.
- Child-Turcotte-Pugh (CTP) class C cirrhosis patients hospitalized with infection face high mortality risks.
- Optimizing treatment requires accurate predictive models for this vulnerable population.
Purpose of the Study:
- To develop and validate a predictive model for 90-day mortality in CTP class C cirrhotics hospitalized with infection.
- To compare the performance of the new model against existing scoring systems.
Main Methods:
- Retrospective data abstraction from 244 patients in China (cohort 1).
- Logistic regression identified mortality predictors; decision tree analysis constructed the predictive model.
- Validation in independent cohorts from the USA (n=91) and Korea (n=82).
Main Results:
- The 3-month mortality rate was consistently high across cohorts (54-58%).
- Key predictors identified: respiratory failure, renal failure, international normalized ratio, total bilirubin, and neutrophil percentage.
- The developed model demonstrated strong predictive performance (AUROC 0.804-0.809) and outperformed other established models.
Conclusions:
- A novel decision tree model reliably predicts 90-day survival in advanced cirrhotic patients with infection.
- The model shows excellent generalizability across Asian and US populations.
- This tool can aid clinical decision-making and improve patient outcomes.
Abstract:
Infection is a common cause of death in patients with advanced cirrhosis. We aimed to develop a predictive model in Child-Turcotte-Pugh (CTP) class C cirrhotics hospitalized with infection for optimizing treatment and improving outcomes.Clinical information was retrospectively abstracted from 244 patients at Tianjin Third Central Hospital, China (cohort 1). Factors associated with mortality were determined using logistic regression. The model for predicting 90-day mortality was then constructed by decision tree analysis. The model was further validated in 91 patients at Mayo Clinic, Rochester, MN (cohort 2) and 82 patients at Seoul St. Mary's Hospital, Korea (cohort 3). The predictive performance of the model was compared with that of the CTP, model for end-stage liver disease (MELD), MELD-Na, Chronic Liver Failure-Sequential Organ Failure Assessment, and the North American consortium for the Study of End-stage Liver Disease (NACSELD) models.The 3-month mortality was 58%, 58%, and 54% in cohort 1, 2, and 3, respectively. In cohort 1, respiratory failure, renal failure, international normalized ratio, total bilirubin, and neutrophil percentage were determinants of 3-month mortality, with odds ratios of 16.6, 3.3, 2.0, 1.1, and 1.03, respectively (P < .05). These parameters were incorporated into the decision tree model, yielding area under receiver operating characteristic (AUROC) of 0.804. The model had excellent reproducibility in the U.S. (AUROC 0.808) and Korea cohort (AUROC 0.809). The proposed model has the highest AUROC and best Youden index of 0.488 and greatest overall correctness of 75%, compared with other models evaluated.The proposed model reliably predicts survival of advanced cirrhotics with infection in both Asian and U.S.
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