Risk factors for the development of sepsis in patients with cirrhosis in intensive care units

Abstract

Sepsis is a serious complication of liver cirrhosis. This study aimed to develop a risk prediction model for sepsis among patients with liver cirrhosis. A total of 3130 patients with liver cirrhosis were enrolled from the Medical Information Mart for Intensive Care IV database, and randomly assigned into training and validation cohorts in a 7:3 ratio. The least absolute shrinkage and selection operator (LASSO) regression was used to filter variables and select predictor variables. Multivariate logistic regression was used to establish the prediction model. Based on LASSO and multivariate logistic regression, gender, base excess, bicarbonate, white blood cells, potassium, fibrinogen, systolic blood pressure, mechanical ventilation, and vasopressor use were identified as independent risk variables, and then a nomogram was constructed and validated. The consistency index (C‐index), receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA) were used to measure the predictive performance of the nomogram. As a result of the nomogram, good discrimination was achieved, with C‐indexes of 0.814 and 0.828 for the training and validation cohorts, respectively, and an area under the curve of 0.849 in the training cohort and 0.821 in the validation cohort. The calibration curves demonstrated good agreement between the predictions and observations. The DCA curves showed the nomogram had significant clinical value. We developed and validated a risk‐prediction model for sepsis in patients with liver cirrhosis. This model can assist clinicians in the early detection and prevention of sepsis in patients with liver cirrhosis.

Translating real‐world evidence/real‐world data

“…As currently multiple equations are used for the estimation of GFR and/or CrCl across patients populations and age ranges, this RWD study may be a first step in the implementation of one or two equations across the full age spectrum. Finally, Kou et al 10 developed and validated a risk prediction model for sepsis among patients with liver cirrhosis using data from the Medical Information Mart for Intensive Care IV database. Multivariate logistic regression was used to establish the prediction model and was then validated.…”

“…Finally, Kou et al 10 . developed and validated a risk prediction model for sepsis among patients with liver cirrhosis using data from the Medical Information Mart for Intensive Care IV database.…”

“…Finally, Kou et al. 10 developed and validated a risk prediction model for sepsis among patients with liver cirrhosis using data from the Medical Information Mart for Intensive Care IV database. Multivariate logistic regression was used to establish the prediction model and was then validated.…”