Feng Lu-Huai, Lu Yang, Ren Shuang, Liang Hengkai, Wei Lu, Jiang Jianning
Department of Infectious Diseases, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Department of Comprehensive Internal Medicine, The Affiliated Tumor Hospital of Guangxi Medical University, Nanning, China.
Front Med (Lausanne). 2023 Jan 27;10:1055137. doi: 10.3389/fmed.2023.1055137. eCollection 2023.
Acute kidney injury (AKI) is one of the most common and deadly complications among cirrhotic patients at intensive care unit (ICU) admission. We aimed to develop and validate a simple and clinically useful dynamic nomogram for predicting AKI in cirrhotic patients upon ICU admission.
We analyzed the admission data of 4,375 patients with liver cirrhosis in ICU from 2008 to 2019 in the intensive care unit IV (MIMIC-IV) database. The eligible cirrhotic patients were non-randomly divided into derivation ( = 2,188) and validation ( = 2,187) cohorts at a ratio of 1:1, according to the order of admission. The least absolute shrinkage and selection operator regression model was used to identify independent predictors of AKI in the derivation cohort. A dynamic online nomogram was built using multivariate logistic regression analysis in the derivation cohort and then validated in the validation cohort. The C-index, calibration curve, and decision curve analysis were used to assess the nomogram's discrimination, calibration, and clinical usefulness, respectively.
The incidence of AKI in 4,375 patients was 71.3%. Ascites, chronic kidney disease, shock, sepsis, diuretic drugs, hepatic encephalopathy, bacterial infections, vasoactive drugs, admission age, total bilirubin, and blood urea nitrogen were identified using the multivariate logistic regression analysis as significant predictors of AKI upon ICU admission. In the derivation cohort, the model showed good discrimination (C-index, 0.786; 95% CI, 0.765-0.806) and good calibration. The model in the validation cohort yielded good discrimination (C-index, 0.774; 95% CI, 0.753-0.795) and good calibration. Decision curve analysis demonstrated that the dynamic online nomogram was clinically useful.
Our study presents a dynamic online nomogram that incorporates clinical predictors and can be conveniently used to facilitate the individualized prediction of AKI in cirrhotic patients upon ICU admission.
急性肾损伤(AKI)是肝硬化患者入住重症监护病房(ICU)时最常见且致命的并发症之一。我们旨在开发并验证一种简单且临床实用的动态列线图,用于预测肝硬化患者入住ICU时发生AKI的风险。
我们分析了重症监护病房IV(MIMIC-IV)数据库中2008年至2019年入住ICU的4375例肝硬化患者的入院数据。根据入院顺序,符合条件的肝硬化患者按1:1的比例非随机分为推导队列(n = 2188)和验证队列(n = 2187)。使用最小绝对收缩和选择算子回归模型在推导队列中识别AKI的独立预测因素。在推导队列中使用多变量逻辑回归分析构建动态在线列线图,然后在验证队列中进行验证。分别使用C指数、校准曲线和决策曲线分析来评估列线图的辨别力、校准度和临床实用性。
4375例患者中AKI的发生率为71.3%。多变量逻辑回归分析确定腹水、慢性肾脏病、休克、脓毒症、利尿剂、肝性脑病、细菌感染、血管活性药物、入院年龄、总胆红素和血尿素氮是肝硬化患者入住ICU时发生AKI的显著预测因素。在推导队列中,该模型显示出良好的辨别力(C指数,0.786;95%CI,0.765 - 0.806)和良好的校准度。验证队列中的模型也具有良好的辨别力(C指数,0.774;95%CI,0.753 - 0.795)和良好的校准度。决策曲线分析表明,该动态在线列线图具有临床实用性。
我们的研究提出了一种动态在线列线图,它纳入了临床预测因素,可方便地用于促进对肝硬化患者入住ICU时发生AKI的个体化预测。