Predictive models of mortality and hospital readmission of patients with decompensated liver cirrhosis.

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Predictive models of mortality and hospital readmission of patients with decompensated liver cirrhosis.

Dig Liver Dis. 2019 May 18;:

Authors: Gaspar R, Rodrigues S, Silva M, Costa-Moreira P, Morais R, Andrade P, Cardoso H, Albuquerque A, Liberal R, Macedo G

INTRODUCTION: Complications of cirrhosis are one the major causes of hospital admission associated with high morbimortality rates and social and economic charges. The aims of this study were to evaluate hospital readmission and mortality rates and predictive factors for hospital readmission and mortality.
METHODS: Patients with decompensated cirrhosis admitted to our institution between 2008-2014 were retrospectively analyzed.
RESULTS: Included 427 admissions from 177 patients with cirrhosis with mean age of 59.0 ± 12.3 years. The major cause was alcoholic-related liver disease and the median duration of admission was 9.0 days (IQR 6.0-14.0). During the follow-up period,there were 250 readmissions from 95 patients, with a median of 58 (IQR27-134) days for readmission, representing 58.5% of the total number of admissions.The 180-day mortality rate was 35.0%. In the multivariate analysis, ascites, smoking and MELD Na were associated with 180-day mortality. Creatinine, albumin, esophageal variceal bleeding, previous variceal banding, lactulose, rifaximin and proton pump inhibitors use were independently associated with need of readmission. Based on regression analysis, two models were calculated to predict 180-day mortality (AUROC 0.74 (0.682-0.794)) and need for readmission(AUROC 0.821 (0.781-0.861)), p < 0.001.
CONCLUSION: The readmission rate and mortality of cirrhotic patients are still very high and it is a priority to determine preventable risk factors to improve patient outcome. Two models were created to predict 180-day mortality(AUROC 0.74) and need for readmission(AUROC 0.821), that could guide the management of the patients at the time of admission.

PMID: 31113738 [PubMed - as supplied by publisher]

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