預測肝硬化患者代償和住院的七個非侵入性模型的比較

StephenW

Comparison of Seven Noninvasive Models for Predicting Decompensation and Hospitalization in Patients with Cirrhosis
Chia-Yang Hsu  1 , Neehar D Parikh  2 , Teh-Ia Huo  3   4 , Elliot B Tapper  2
Affiliations

    PMID: 33387126 DOI: 10.1007/s10620-020-06763-9

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.

Keywords: Alcohol; Ascites; Decompensation; Hepatic encephalopathy; Nonalcoholic fatty liver disease (NAFLD).

StephenW

預測肝硬化患者代償和住院的七個非侵入性模型的比較
許家陽1，Neehar D Parikh 2，Teh-Ia Huo 3 4，Elliot B Tapper 2
隸屬關係

    PMID：33387126 DOI：10.1007 / s10620-020-06763-9

抽象

背景/目的：一旦發生代償失調，肝硬化患者的預後不良。但是，我們缺乏適當的失代償預測指標。使用國家索賠數據庫比較補償性肝硬化患者失代償和住院治療的七個模型的預測準確性。

方法：我們將失代償定義為腹水，肝性腦病，肝腎綜合徵和靜脈曲張破裂出血。在2001年至2015年期間，對肝硬化診斷時無失代償的患者進行了登記。乙型和/或丙型肝炎患者被分類為病毒性肝硬化。我們將模型的預測準確性與AUC（曲線下面積）和c統計量進行了比較。分別使用Fine-Gray競爭風險模型和負二項式模型來計算失代償的累積發生率和住院的發生風險比。

結果：共納入3722名獨特患者，平均隨訪時間為524天。平均年齡為59歲（標準差為12），大多數為男性（55％）和白人（65％）。 53％的患者患有非病毒性肝硬化。非病毒性和病毒性肝硬化患者分別有16％和20％發生代償失調（P = 0.589）。對於非病毒性肝硬化患者，FIB-4模型的3年AUC最高（0.73），總體c統計量（0.692）也最高。在病毒性肝硬化患者中，ALBI-FIB-4模型的最佳1年（AUC = 0.741），3年（AUC = 0.754）和總體預測準確性（c-statistic = 0.681）。在非病毒和病毒患者中，MELD評分對住院的預測能力最佳。

結論：基於FIB-4的模型可提供更準確的失代償預測，而MELD模型具有最佳的住院預測能力。

關鍵字：酒精；腹水;失代償；肝性腦病;非酒精性脂肪肝疾病（NAFLD）