Dataset Information

Comparison of Seven Noninvasive Models for Predicting Decompensation and Hospitalization in Patients with Cirrhosis.

ABSTRACT:

Background/aim

Patients with cirrhosis have poor outcomes once decompensation occurs; however, we lack adequate predictors of decompensation. To use a national claim database to compare the predictive accuracy of seven models for decompensation and hospitalization in patients with compensated cirrhosis.

Methods

We defined decompensation as ascites, hepatic encephalopathy, hepato-renal syndrome, and variceal bleeding. Patients without decompensation at the time of cirrhosis diagnosis were enrolled from 2001 to 2015. Patients with hepatitis B and/or C were grouped as viral cirrhosis. We compared the predictive accuracy of models with the AUC (area under the curve) and c-statistic. The cumulative incidence of decompensation and incidence risk ratios of hospitalization were calculated with the Fine-Gray competing risk and negative binomial models, respectively.

Results

A total of 3722 unique patients were enrolled with a mean follow-up time of 524 days. The mean age was 59 (standard deviation 12), and the majority were male (55%) and white (65%). Fifty-three percent of patients had non-viral cirrhosis. Sixteen and 20 percent of patients with non-viral and viral cirrhosis, respectively, developed decompensation (P = 0.589). The FIB-4 model had the highest 3-year AUC (0.73) and overall c-statistic (0.692) in patients with non-viral cirrhosis. The ALBI-FIB-4 model had the best 1-year (AUC = 0.741), 3-year (AUC = 0.754), and overall predictive accuracy (c-statistic = 0.681) in patients with viral cirrhosis. The MELD score had the best predictive power for hospitalization in both non-viral and viral patients.

Conclusions

FIB-4-based models provide more accurate prediction for decompensation, and the MELD model has the best predictive ability of hospitalization.

SUBMITTER: Hsu CY

PROVIDER: S-EPMC10239666 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Publications

Comparison of Seven Noninvasive Models for Predicting Decompensation and Hospitalization in Patients with Cirrhosis.

Hsu Chia-Yang CY Parikh Neehar D ND Huo Teh-Ia TI Tapper Elliot B EB

Digestive diseases and sciences 20210102 12

<h4>Background/aim</h4>Patients with cirrhosis have poor outcomes once decompensation occurs; however, we lack adequate predictors of decompensation. To use a national claim database to compare the predictive accuracy of seven models for decompensation and hospitalization in patients with compensated cirrhosis.<h4>Methods</h4>We defined decompensation as ascites, hepatic encephalopathy, hepato-renal syndrome, and variceal bleeding. Patients without decompensation at the time of cirrhosis diagnos ...[more]

PMID: 33387126

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Dataset Information

Comparison of Seven Noninvasive Models for Predicting Decompensation and Hospitalization in Patients with Cirrhosis.

Background/aim

Methods

Results

Conclusions

Publications

Comparison of Seven Noninvasive Models for Predicting Decompensation and Hospitalization in Patients with Cirrhosis.

Similar Datasets

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