Division of Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, Children's Hospital Los Angeles, Los Angeles, CA.
University of Southern California, Los Angeles, CA.
Transplantation. 2024 Apr 1;108(4):930-939. doi: 10.1097/TP.0000000000004845. Epub 2024 Mar 23.
Pediatric acute liver failure (PALF) can require emergent liver transplantation (LT, >25%) or lead to death (~15%). Existing models cannot predict clinical trajectory or survival with native liver (SNL). We aimed to create a predictive model for PALF clinical outcomes based on admission variables.
A retrospective, single-center PALF cohort (April 2003 to January 2022) was identified using International Classification of Disease codes, selected using National Institutes of Health PALF Study Group (PALFSG) criteria, and grouped by clinical outcome (SNL, LT, or death). Significant admission variables were advanced for feature selection using least absolute shrinkage and selection operator regression with bootstrapping (5000×). A predictive model of SNL versus LT or death was created using logistic regression and validated using PALFSG data.
Our single-center cohort included 147 patients (58% SNL, 32% LT, 10% expired), while the PALFSG validation cohort included 492 patients (50% SNL, 35% LT, 15% expired). Admission variables associated with SNL included albumin (odds ratio [OR], 16; P < 0.01), ammonia (OR, 2.37; P < 0.01), and total bilirubin (OR, 2.25; P < 0.001). A model using these variables predicted SNL versus LT or death with high accuracy (accuracy [0.75 training, 0.70 validation], area under the curve [0.83 training, 0.78 validation]). A scaled score (CHLA-acute liver failure score) was created that predicted SNL versus LT or death with greater accuracy (C statistic 0.83) than Pediatric End-Stage Liver Disease (C statistic 0.76) and admission liver injury unit (C statistic 0.76) scores.
The CHLA-acute liver failure score predicts SNL versus LT or mortality in PALF using admission laboratories with high accuracy. This novel, externally validated model offers an objective guide for urgent referral to a pediatric LT center.
小儿急性肝衰竭（PALF）可能需要紧急进行肝移植（LT，>25%）或导致死亡（~15%）。现有的模型无法预测具有原生肝脏（SNL）的临床轨迹或存活率。我们旨在基于入院变量创建预测 PALF 临床结果的模型。
使用国际疾病分类代码，采用国家卫生研究院 PALF 研究组（PALFSG）标准，从 2003 年 4 月至 2022 年 1 月，回顾性地确定了单中心 PALF 队列，并根据临床结果（SNL、LT 或死亡）进行分组。使用最小绝对收缩和选择算子回归（带有引导的 5000×）对显著的入院变量进行高级特征选择。使用逻辑回归创建 SNL 与 LT 或死亡的预测模型，并使用 PALFSG 数据进行验证。
我们的单中心队列包括 147 名患者（58%SNL、32%LT、10%死亡），而 PALFSG 验证队列包括 492 名患者（50%SNL、35%LT、15%死亡）。与 SNL 相关的入院变量包括白蛋白（优势比[OR]，16；P<0.01）、氨（OR，2.37；P<0.01）和总胆红素（OR，2.25；P<0.001）。使用这些变量的模型可以准确预测 SNL 与 LT 或死亡（训练的准确性[0.75，验证的准确性[0.70]，曲线下面积[0.83 训练，0.78 验证]）。创建了一个 CHLA-急性肝衰竭评分，与 SNL 相比，该评分能更准确地预测 LT 或死亡（C 统计量为 0.83），而不是儿科终末期肝病（C 统计量为 0.76）和入院肝损伤单位（C 统计量为 0.76）评分。
CHLA-急性肝衰竭评分使用入院实验室以高精度预测 PALF 中的 SNL 与 LT 或死亡率。这个新颖的、经过外部验证的模型为紧急转介到儿科 LT 中心提供了一个客观的指导。
