Institute of Liver Studies, King's College Hospital, London, UK.
Division of Health and Social Care Research, King's College London, London, UK.
Lancet Gastroenterol Hepatol. 2016 Nov;1(3):217-225. doi: 10.1016/S2468-1253(16)30007-3. Epub 2016 Jul 13.
Early, accurate prediction of survival is central to management of patients with paracetamol-induced acute liver failure to identify those needing emergency liver transplantation. Current prognostic tools are confounded by recent improvements in outcome independent of emergency liver transplantation, and constrained by static binary outcome prediction. We aimed to develop a simple prognostic tool to reflect current outcomes and generate a dynamic updated estimation of risk of death.
Patients with paracetamol-induced acute liver failure managed at intensive care units in the UK (London, Birmingham, and Edinburgh) and Denmark (Copenhagen) were studied. We developed prognostic models, excluding patients who underwent transplantation, using Cox proportional hazards in a derivation dataset, and tested in initial and recent external validation datasets. Mortality was estimated in patients who had emergency liver transplantation. Model discrimination was assessed using area under receiver operating characteristic curve (AUROC) and calibration by root mean square error (RMSE). Admission (day 1) variables of age, Glasgow coma scale, arterial pH and lactate, creatinine, international normalised ratio (INR), and cardiovascular failure were used to derive an initial predictive model, with a second (day 2) model including additional changes in INR and lactate.
We developed and validated new high-performance statistical models to support decision making in patients with paracetamol-induced acute liver failure. Applied to the derivation dataset (n=350), the AUROC for 30-day survival was 0·92 (95% CI 0·88-0·96) using the day 1 model and 0·93 (0·88-0·97) using the day 2 model. In the initial validation dataset (n=150), the AUROC for 30-day survival was 0·89 (0·84-0·95) using the day 1 model and 0·90 (0·85-0·95) using the day 2 model. Assessment of calibration using RMSE in prediction of 30-day survival gave values of 0·1642 for the day 1 model and 0·0626 for the day 2 model. In the external validation dataset (n=412), the AUROC for 30-day survival was 0·91 (0·87-0·94) using the day 1 model and 0·91 (0·88-0·95) using the day 2 model, and assessment of calibration using RMSE gave values of 0·079 for the day 1 model and 0·107 for the day 2 model. Applied to patients who underwent emergency liver transplantation (n=116), median predicted 30-day survival was 51% (95% CI 33-85).
The models developed here show very good discrimination and calibration, confirmed in independent datasets, and suggest that many patients undergoing transplantation based on existing criteria might have survived with medical management alone. The role and indications for emergency liver transplantation in paracetamol-induced acute liver failure require re-evaluation.
Foundation for Liver Research.
早期、准确地预测生存是管理对乙酰氨基酚诱导的急性肝衰竭患者的关键，以确定那些需要紧急肝移植的患者。目前的预后工具受到近期与紧急肝移植无关的预后改善的影响，并受到静态二元预后预测的限制。我们旨在开发一种简单的预后工具来反映当前的结果，并对死亡风险进行动态更新估计。
在英国（伦敦、伯明翰和爱丁堡）和丹麦（哥本哈根）的重症监护病房对接受对乙酰氨基酚诱导的急性肝衰竭的患者进行了研究。我们在推导数据集使用 Cox 比例风险开发了排除接受移植的患者的预后模型，并在初始和最近的外部验证数据集中进行了测试。在接受紧急肝移植的患者中估计死亡率。使用接收者操作特征曲线下的面积（AUROC）评估模型区分度，并使用均方根误差（RMSE）评估校准。使用入院（第 1 天）变量年龄、格拉斯哥昏迷量表、动脉 pH 值和乳酸、肌酐、国际标准化比值（INR）和心血管衰竭，来推导初始预测模型，第二个（第 2 天）模型包括 INR 和乳酸的额外变化。
我们开发并验证了新的高性能统计模型，以支持对乙酰氨基酚诱导的急性肝衰竭患者的决策。在推导数据集（n=350）中，第 1 天模型的 30 天生存率的 AUROC 为 0.92（95%CI 0.88-0.96），第 2 天模型为 0.93（0.88-0.97）。在初始验证数据集（n=150）中，第 1 天模型的 30 天生存率的 AUROC 为 0.89（0.84-0.95），第 2 天模型为 0.90（0.85-0.95）。使用 RMSE 评估校准在预测 30 天生存率方面，第 1 天模型的值为 0.1642，第 2 天模型的值为 0.0626。在外部验证数据集（n=412）中，第 1 天模型的 30 天生存率 AUROC 为 0.91（0.87-0.94），第 2 天模型为 0.91（0.88-0.95），使用 RMSE 评估校准，第 1 天模型的值为 0.079，第 2 天模型的值为 0.107。在接受紧急肝移植的患者（n=116）中，中位预测 30 天生存率为 51%（95%CI 33-85）。
这里开发的模型在独立数据集上显示出非常好的区分度和校准度，并得到了验证，这表明许多根据现有标准接受紧急肝移植的患者可能仅通过药物治疗就能存活。对乙酰氨基酚诱导的急性肝衰竭患者进行紧急肝移植的作用和适应证需要重新评估。
肝脏研究基金会。
