A prediction model for 30-day deaths of cirrhotic patients in intensive care unit hospitalization

The aim of this study was to establish a prediction model for 30-day deaths of cirrhotic patients in intensive care unit. A case-control study involving 1840 patients was conducted in the Medical Information Mart of the Intensive Care Database III version 1.4. The logistic regression with L1 regularization was used to screen out the variables. The 30-day in-hospital death was used as the dependent variable and the selected variables were used as the independent variable to build a random forest model. The performance of the model was validated by the internal validation. The variables screened by logistic regression analysis were the age, heart rate, respiratory rate, systolic blood pressure, diastolic blood pressure, Oxygen saturation, white blood cells, platelets, red cell distribution width, glucose, blood urea nitrogen, bicarbonate, total bilirubin, hematocrit, alanine transaminase, aspartate transaminase, bilirubin, Simplified Acute Physiology Score II and Sequential Organ Failure Assessment. The areas under the curve of the random forest model based on these variables was 0.908, and the performance of this model were internally validated with an areas under the curve of 0.801. The random forest model displayed that Simplified Acute Physiology Score, Sequential Organ Failure Assessment, blood urea nitrogen, total bilirubin and bilirubin were more important predictors for the 30-day death of cirrhotic patients in intensive care unit. A prediction model for death of cirrhotic patients was developed based on a random forest analysis, providing a tool to evaluate the patients with a high risk of 30-day in-hospital deaths to help clinician make preventive intervention to decrease the mortality.