Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Ren Fail. 2024 Dec;46(1):2310081. doi: 10.1080/0886022X.2024.2310081. Epub 2024 Feb 7.
Acute kidney injury (AKI) is a common serious complication in sepsis patients with a high mortality rate. This study aimed to develop and validate a predictive model for sepsis associated acute kidney injury (SA-AKI). In our study, we retrospectively constructed a development cohort comprising 733 septic patients admitted to eight Grade-A tertiary hospitals in Shanghai from January 2021 to October 2022. Additionally, we established an external validation cohort consisting of 336 septic patients admitted to our hospital from January 2017 to December 2019. Risk predictors were selected by LASSO regression, and a corresponding nomogram was constructed. We evaluated the model's discrimination, precision and clinical benefit through receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA) and clinical impact curves (CIC) in both internal and external validation. AKI incidence was 53.2% in the development cohort and 48.2% in the external validation cohort. The model included five independent indicators: chronic kidney disease stages 1 to 3, blood urea nitrogen, procalcitonin, D-dimer and creatine kinase isoenzyme. The AUC of the model in the development and validation cohorts was 0.914 (95% CI, 0.894-0.934) and 0.923 (95% CI, 0.895-0.952), respectively. The calibration plot, DCA, and CIC demonstrated the model's favorable clinical applicability. We developed and validated a robust nomogram model, which might identify patients at risk of SA-AKI and promising for clinical applications.
急性肾损伤（AKI）是脓毒症患者常见的严重并发症，死亡率高。本研究旨在开发和验证一种预测脓毒症相关急性肾损伤（SA-AKI）的模型。
在我们的研究中，我们回顾性地构建了一个开发队列，该队列包括 2021 年 1 月至 2022 年 10 月期间上海 8 家 A 级三级医院收治的 733 例脓毒症患者。此外，我们还建立了一个外部验证队列，该队列由 2017 年 1 月至 2019 年 12 月期间我院收治的 336 例脓毒症患者组成。通过 LASSO 回归选择风险预测因子，并构建相应的列线图。我们通过内部和外部验证中的受试者工作特征（ROC）曲线、校准图、决策曲线分析（DCA）和临床影响曲线（CIC）评估了该模型的区分度、精度和临床获益。
在开发队列中 AKI 的发生率为 53.2%，在外部验证队列中为 48.2%。该模型包含五个独立指标：慢性肾脏病 1 至 3 期、血尿素氮、降钙素原、D-二聚体和肌酸激酶同工酶。该模型在开发和验证队列中的 AUC 分别为 0.914（95%CI，0.894-0.934）和 0.923（95%CI，0.895-0.952）。校准图、DCA 和 CIC 表明该模型具有良好的临床适用性。
我们开发和验证了一个稳健的列线图模型，该模型可以识别出有发生 SA-AKI 风险的患者，具有良好的临床应用前景。
