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.

Medicine
|October 14, 2018
PubMed

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.

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