Department of Gastroenterology, All India Institute of Medical Sciences, New Delhi, India.
Gut. 2012 Jul;61(7):1068-75. doi: 10.1136/gutjnl-2011-301762. Epub 2012 Feb 15.
It is difficult to predict the outcome in patients with acute liver failure (ALF) using existing prognostic models. This study investigated whether early changes in the levels of dynamic variables can predict outcome better than models based on static baseline variables.
380 patients with ALF (derivation cohort n=244, validation cohort n=136) participated in a prospective observational study. The derivation cohort was used to identify predictors of mortality. The ALF early dynamic (ALFED) model was constructed based on whether the levels of predictive variables remained persistently high or increased over 3 days above the discriminatory cut-off values identified in this study. The model had four variables: arterial ammonia, serum bilirubin, international normalised ratio and hepatic encephalopathy >grade II. The model was validated in a cohort of 136 patients with ALF.
The ALFED model demonstrated excellent discrimination with an area under the receiver operator characteristic curve of 0.91 in the derivation cohort and of 0.92 in the validation cohort. The model was well calibrated in both cohorts and showed a similar increase in mortality with increasing risk scores from 0 to 6. The performance of the ALFED model was superior to the King's College Hospital criteria and the Model for End stage Liver Disease score, even when their 3-day serial values were taken into consideration. An ALFED score of ≥4 had a high positive predictive value (85%) and negative predictive value (87%) in the validation cohort.
The ALFED model accurately predicted outcome in patients with ALF, which may be useful in clinical decision-making.
使用现有的预后模型预测急性肝衰竭（ALF）患者的结局较为困难。本研究旨在探讨动态变量的早期变化是否比基于静态基线变量的模型能更好地预测结局。
380 例 ALF 患者（推导队列 n=244，验证队列 n=136）参与了前瞻性观察性研究。推导队列用于确定死亡的预测因素。基于本研究中确定的预测变量的水平是否在 3 天内持续高于或高于鉴别截断值之上持续升高，构建了急性肝衰竭早期动态（ALFED）模型。该模型有 4 个变量：动脉氨、血清胆红素、国际标准化比值和肝性脑病＞Ⅱ级。在另一组 136 例 ALF 患者中验证了该模型。
ALFED 模型在推导队列中具有出色的区分能力，其受试者工作特征曲线下面积为 0.91，在验证队列中的面积为 0.92。在两个队列中，该模型均具有良好的校准能力，且风险评分从 0 到 6 时，死亡率呈相似的增加趋势。即使考虑了 King's College Hospital 标准和终末期肝病模型评分的 3 天连续值，ALFED 模型的性能也优于上述两个评分。在验证队列中，ALFED 评分≥4 具有较高的阳性预测值（85%）和阴性预测值（87%）。
ALFED 模型可准确预测 ALF 患者的结局，可能有助于临床决策。
