Background

Multiple predictive models of mortality exist for acute-on-chronic liver failure (ACLF) patients that often create confusion during decision-making. We studied the natural history and evaluated the performance of prognostic models in ACLF patients.

Methods

Prospectively collected data of ACLF patients from APASL-ACLF Research Consortium (AARC) was analyzed for 30-day outcomes. The models evaluated at days 0, 4, and 7 of presentation for 30-day mortality were: AARC (model and score), CLIF-C (ACLF score, and OF score), NACSELD-ACLF (model and binary), SOFA, APACHE-II, MELD, MELD-Lactate, and CTP. Evaluation parameters were discrimination (c-indices), calibration [accuracy, sensitivity, specificity, and positive/negative predictive values (PPV/NPV)], Akaike/Bayesian Information Criteria (AIC/BIC), Nagelkerke-R², relative prediction errors, and odds ratios.

Results

Thirty-day survival of the cohort (n = 2864) was 64.9% and was lowest for final-AARC-grade-III (32.8%) ACLF. Performance parameters of all models were best at day 7 than at day 4 or day 0 (p < 0.05 for C-indices of all models except NACSELD-ACLF). On comparison, day-7 AARC model had the numerically highest c-index 0.872, best accuracy 84.0%, PPV 87.8%, R² 0.609 and lower prediction errors by 10–50%. Day-7 NACSELD-ACLF-binary was the simple model (minimum AIC/BIC 12/17) with the highest odds (8.859) and sensitivity (100%) but with a lower PPV (70%) for mortality. Patients with day-7 AARC score > 12 had the lowest 30-day survival (5.7%).

Conclusions

APASL-ACLF is often a progressive disease, and models assessed up to day 7 of presentation reliably predict 30-day mortality. Day-7 AARC model is a statistically robust tool for classifying risk of death and accurately predicting 30-day outcomes with relatively lower prediction errors. Day-7 AARC score > 12 may be used as a futility criterion in APASL-ACLF patients.

中文翻译：

---

急性慢性肝衰竭患者短期死亡率预后模型的比较准确性：CAP-ACLF

背景

急性慢性肝衰竭 (ACLF) 患者存在多种死亡率预测模型，这些模型经常在决策过程中造成混乱。我们研究了自然病程并评估了 ACLF 患者预后模型的表现。

方法

从 APASL-ACLF 研究联盟 (AARC) 前瞻性收集的 ACLF 患者数据分析了 30 天的结果。在第 0、4 和 7 天评估的 30 天死亡率模型包括：AARC（模型和评分）、CLIF-C（ACLF 评分和 OF 评分）、NACSELD-ACLF（模型和二进制）、SOFA、 APACHE-II、MELD、MELD-乳酸和 CTP。评价参数为鉴别（c-指数）、校准[准确度、灵敏度、特异性和阳性/阴性预测值（PPV/NPV）]、赤池/贝叶斯信息标准（AIC/BIC）、Nagelkerke- R ²、相对预测误差，和优势比。

结果

队列 ( n  = 2864) 的30 天存活率为 64.9%，并且在最终的 AARC III 级 (32.8%) ACLF 中最低。所有模型的性能参数在第 7 天比在第 4 天或第 0 天最好（ 除 NACSELD-ACLF 外，所有模型的 C 指数p < 0.05）。相比之下，第 7 天的 AARC 模型在数值上具有最高的 c-index 0.872，最佳准确度 84.0%，PPV 87.8%，R ² 0.609，预测误差降低 10-50%。第 7 天 NACSELD-ACLF 二进制是简单模型（最低 AIC/BIC 12/17），具有最高的赔率 (8.859) 和灵敏度 (100%)，但死亡率的 PPV (70%) 较低。第 7 天 AARC 评分 > 12 的患者的 30 天生存率最低 (5.7%)。

结论

APASL-ACLF 通常是一种渐进性疾病，评估至出现第 7 天的模型可以可靠地预测 30 天死亡率。Day-7 AARC 模型是一种统计上可靠的工具，用于对死亡风险进行分类并以相对较低的预测误差准确预测 30 天的结果。第 7 天 AARC 评分 > 12 可用作 APASL-ACLF 患者的无效标准。