Zheng Luyan, Lu Yining, Wu Jie, Zheng Min
State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, China.
State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, China.
Dig Liver Dis. 2023 Apr;55(4):498-504. doi: 10.1016/j.dld.2023.01.148. Epub 2023 Jan 22.
The prognosis of patients with alcohol-associated cirrhosis (ALC) admitted to the intensive care unit (ICU) is poor. We developed and validated a nomogram (NIALC) for ICU patients with ALC.
Predictors of mortality were defined by a machine learning method in a cohort of 394 ICU patients with ALC from the Medical Information Mart for Intensive Care database. Then the nomogram (NIALC) was constructed and evaluated using the AUC. The MELD, MELD-sodium, Child-Pugh, and CLIF-SOFA scores were then compared with NIALC. Two datasets of 394 and 501 ICU patients with ALC were utilized for model validation.
In-hospital mortality was 41% and 21% in the training and external validation sets. Predictors included were blood urea nitrogen, total bilirubin, prothrombin time, serum creatinine, lactate, partial thromboplastin time, phosphate, mean arterial pressure, lymphocytes, fibrinogen, and albumin. The AUCs for the NIALC were 0.767 and 0.760 in the two validation cohorts, which were better than those of the MELD, MELD-sodium, Child-Pugh, and CLIF-SOFA.
We developed a nomogram for ICU patients with ALC, which demonstrated better discriminative ability than previous prognostic scores. This nomogram could be conveniently used to facilitate the individualized prediction of death in ICU patients with ALC.
入住重症监护病房（ICU）的酒精性肝硬化（ALC）患者预后较差。我们为ICU中的ALC患者开发并验证了一种列线图（NIALC）。
通过机器学习方法在重症监护医学信息数据库的394例ICU的ALC患者队列中确定死亡率的预测因素。然后构建列线图（NIALC）并使用AUC进行评估。随后将终末期肝病模型（MELD）、MELD-钠评分、Child-Pugh评分和CLIF-序贯器官衰竭评估（CLIF-SOFA）评分与NIALC进行比较。利用两个分别包含394例和501例ICU的ALC患者的数据集进行模型验证。
训练集和外部验证集的住院死亡率分别为41%和21%。纳入的预测因素包括血尿素氮、总胆红素、凝血酶原时间、血清肌酐、乳酸、活化部分凝血活酶时间、磷酸盐、平均动脉压、淋巴细胞、纤维蛋白原和白蛋白。在两个验证队列中，NIALC的AUC分别为0.767和0.760，优于MELD、MELD-钠评分、Child-Pugh评分和CLIF-SOFA。
我们为ICU中的ALC患者开发了一种列线图，其判别能力优于先前的预后评分。该列线图可方便地用于促进对ICU中ALC患者死亡的个体化预测。
