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.
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Abbreviations
- ALBI:
-
Albumin–bilirubin
- APRI:
-
Aspartate aminotransferase-to-platelet ratio index
- CI:
-
Confidence interval
- CTP:
-
Child–Turcotte–Pugh
- FIB-4:
-
Fibrosis-4
- HCC:
-
Hepatocellular carcinoma
- HR:
-
Hazard ratio
- MELD:
-
Model for end-stage liver disease
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Funding
Elliot Tapper receives funding from the National Institutes of Health through the NIDDK (1K23DK117055-01A1).
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C-YH, NDP, and EBT performed the study design, biostatistics analysis, and manuscript preparation. T-IH participated in the biostatistics analysis. All authors approved the final version of the manuscript.
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Neehar Parikh serves as a consultant for Bristol Myers-Squibb, Exact Sciences, Eli Lilly, Freenome, has served on advisory boards of Genentech, Easai, Bayer, Exelexis, Wako/Fujifilm, and has received research funding from Bayer, Target Pharmasolutions, Exact Sciences, and Glycotest. Elliot Tapper has served as a consultant to Novartis, Kaleido, Axcella, and Allergan, has served on advisory boards for Mallinckrodt, Rebiotix, and Bausch Health, and has received unrestricted research grants from Gilead and Valeant. The remaining authors indicate no potential conflicts of interest.
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Hsu, CY., Parikh, N.D., Huo, TI. et al. Comparison of Seven Noninvasive Models for Predicting Decompensation and Hospitalization in Patients with Cirrhosis. Dig Dis Sci 66, 4508–4517 (2021). https://doi.org/10.1007/s10620-020-06763-9
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DOI: https://doi.org/10.1007/s10620-020-06763-9