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- Title
A simple covert hepatic encephalopathy screening model based on blood biochemical parameters in patients with cirrhosis.
- Authors
Miwa, Takao; Hanai, Tatsunori; Nishimura, Kayoko; Maeda, Toshihide; Tajirika, Satoko; Imai, Kenji; Suetsugu, Atsushi; Takai, Koji; Yamamoto, Mayumi; Shimizu, Masahito
- Abstract
Aim: Covert hepatic encephalopathy (CHE) adversely affects clinical outcomes in patients with liver cirrhosis, although its diagnosis is difficult. This study aimed to establish a simple CHE screening model based on blood-related biochemical parameters. Methods: This retrospective study enrolled 439 patients who were assessed for CHE using a neuropsychiatric test between January 2011 and June 2019. A simple CHE (sCHE) score was calculated with hypoalbuminemia (≤ 3.5 g/dL) and hyperammonemia (≥ 80 μg/dL) as 1 point each. The association between sCHE score and CHE or overt hepatic encephalopathy (OHE) was assessed using logistic regression and Fine-Gray competing risk regression models. Results: Of 381 eligible patients, 79 (21%) were diagnosed with CHE. The distribution of sCHE scores was 48% with 0 point, 33% with 1 point, and 19% with 2 points. Patients with sCHE score ≥ 1 point had a higher prevalence of CHE than those with sCHE score of 0 (27% vs. 14%, P = 0.002). A cut-off value of 1 point showed high discriminative ability for identifying CHE, with a sensitivity of 0.67, specificity of 0.56, positive predictive value of 0.27, and negative predictive value of 0.86. During the median follow-up period of 2.2 years, 58 (15%) patients developed OHE. Multivariate analysis showed that sCHE score ≥ 1 (sub-distribution hazard ratio [SHR], 2.69; 95% confidence interval [CI], 1.41–5.15) and CHE (SHR, 2.17; 95% CI, 1.26–3.73) independently predicted OHE. Conclusions: The sCHE score is a useful screening model for identifying patients with CHE and for predicting OHE occurrence.
- Subjects
HEPATIC encephalopathy; MEDICAL screening; CIRRHOSIS of the liver; MULTIVARIATE analysis; REGRESSION analysis; LOGISTIC regression analysis
- Publication
PLoS ONE, 2022, Vol 17, Issue 11, p1
- ISSN
1932-6203
- Publication type
Article
- DOI
10.1371/journal.pone.0277829