Tang Xiao-Wei, Ren Wen-Sen, Huang Shu, Zou Kang, Xu Huan, Shi Xiao-Min, Zhang Wei, Shi Lei, Lü Mu-Han
Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou 646099, Sichuan Province, China.
Nuclear Medicine and Molecular Imaging Key Laboratory, The Affiliated Hospital of Southwest Medical University, Luzhou 646099, Sichuan Province, China.
World J Hepatol. 2024 Apr 27;16(4):625-639. doi: 10.4254/wjh.v16.i4.625.
Liver cirrhosis patients admitted to intensive care unit (ICU) have a high mortality rate.
To establish and validate a nomogram for predicting in-hospital mortality of ICU patients with liver cirrhosis.
We extracted demographic, etiological, vital sign, laboratory test, comorbidity, complication, treatment, and severity score data of liver cirrhosis patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and electronic ICU (eICU) collaborative research database (eICU-CRD). Predictor selection and model building were based on the MIMIC-IV dataset. The variables selected through least absolute shrinkage and selection operator analysis were further screened through multivariate regression analysis to obtain final predictors. The final predictors were included in the multivariate logistic regression model, which was used to construct a nomogram. Finally, we conducted external validation using the eICU-CRD. The area under the receiver operating characteristic curve (AUC), decision curve, and calibration curve were used to assess the efficacy of the models.
Risk factors, including the mean respiratory rate, mean systolic blood pressure, mean heart rate, white blood cells, international normalized ratio, total bilirubin, age, invasive ventilation, vasopressor use, maximum stage of acute kidney injury, and sequential organ failure assessment score, were included in the multivariate logistic regression. The model achieved AUCs of 0.864 and 0.808 in the MIMIC-IV and eICU-CRD databases, respectively. The calibration curve also confirmed the predictive ability of the model, while the decision curve confirmed its clinical value.
The nomogram has high accuracy in predicting in-hospital mortality. Improving the included predictors may help improve the prognosis of patients.
入住重症监护病房（ICU）的肝硬化患者死亡率很高。
建立并验证用于预测肝硬化ICU患者院内死亡率的列线图。
我们从重症监护医学信息集市IV（MIMIC-IV）和电子ICU（eICU）协作研究数据库（eICU-CRD）中提取了肝硬化患者的人口统计学、病因、生命体征、实验室检查、合并症、并发症、治疗及严重程度评分数据。预测指标的选择和模型构建基于MIMIC-IV数据集。通过最小绝对收缩和选择算子分析选择的变量，再经多因素回归分析进一步筛选以获得最终预测指标。将最终预测指标纳入多因素逻辑回归模型，用于构建列线图。最后，我们使用eICU-CRD进行外部验证。采用受试者操作特征曲线下面积（AUC）、决策曲线和校准曲线评估模型的效能。
多因素逻辑回归纳入的危险因素包括平均呼吸频率、平均收缩压、平均心率、白细胞、国际标准化比值、总胆红素、年龄、有创通气、血管活性药物使用、急性肾损伤最大分期及序贯器官衰竭评估评分。该模型在MIMIC-IV和eICU-CRD数据库中的AUC分别为0.864和0.808。校准曲线也证实了模型的预测能力，而决策曲线证实了其临床价值。
该列线图在预测院内死亡率方面具有较高准确性。改善纳入的预测指标可能有助于改善患者预后。
