Pollmanns Maike R, Kister Bastian, Abu Jhaisha Samira, Adams Jule K, Kabak Elena, Brozat Jonathan F, Schneider Carolin V, Hohlstein Philipp, Bruns Tony, Küpfer Lars, Trautwein Christian, Koch Alexander, Wirtz Theresa H
Medical Department III, RWTH Aachen University Hospital, Pauwelsstraße 30, 52074, Aachen, Germany.
Institute for Systems Medicine with Focus on Organ Interaction, RWTH Aachen University, Aachen, Germany.
Sci Rep. 2024 Dec 16;14(1):30497. doi: 10.1038/s41598-024-82178-0.
Acute-on-chronic liver failure (ACLF) defines a heterogeneous syndrome involving acute decompensation in patients with pre-existing liver disease accompanied by (multi-)organ failure. This study aimed to develop a simple, reliable machine learning (ML) model to predict mortality in ACLF patients receiving intensive care unit (ICU) treatment. Data from 206 patients admitted to the ICU at RWTH Aachen University Hospital between 2015 and 2021 were retrospectively analyzed with ICU mortality as the primary outcome. An ICU mortality prediction model was developed by logistic regression and validated by 5-fold cross validation. Performance metrics were assessed to evaluate the model's accuracy and compare to existing mortality scores. ICU mortality was 60%. The chronic-liver-failure-consortium ACLF score (CLIF-C ACLFs) was the best predictor of ICU mortality. ML generated seven models using five to thirteen features. The best-performing model included CLIF-C ACLFs, number of organ failures, Horovitz quotient (FiO/PaO), FiO and lactate. The newly developed Aachen ACLF ICU (ACICU) score demonstrated exceptional predictive accuracy for ICU mortality (AUROC 0.96), underscoring its potential for mortality and futility assessment in critically ill ACLF patients complementing existing prognostic tools. The ACICU score www.acicu-score.com is an easy-to-use tool for predicting ICU mortality in patients with ACLF offering high predictive performance.
慢加急性肝衰竭（ACLF）是一种异质性综合征，表现为已有肝病患者出现急性失代偿，并伴有（多）器官功能衰竭。本研究旨在开发一种简单、可靠的机器学习（ML）模型，以预测接受重症监护病房（ICU）治疗的ACLF患者的死亡率。对2015年至2021年间在亚琛工业大学医院ICU住院的206例患者的数据进行回顾性分析，以ICU死亡率作为主要结局。通过逻辑回归建立ICU死亡率预测模型，并通过五折交叉验证进行验证。评估性能指标以评估模型的准确性，并与现有的死亡率评分进行比较。ICU死亡率为60%。慢性肝衰竭联盟ACLF评分（CLIF-C ACLFs）是ICU死亡率的最佳预测指标。ML使用5至13个特征生成了7个模型。表现最佳的模型包括CLIF-C ACLFs、器官衰竭数量、霍洛维茨商（FiO/PaO）、FiO和乳酸。新开发的亚琛ACLF ICU（ACICU）评分对ICU死亡率显示出卓越的预测准确性（曲线下面积为0.96），突出了其在评估重症ACLF患者死亡率和治疗无效性方面的潜力，可作为现有预后工具的补充。ACICU评分网站www.acicu-score.com是一种易于使用的工具，用于预测ACLF患者的ICU死亡率，具有较高的预测性能。
