Development and external validation of a nomogram for the early prediction of acute kidney injury in septic patients: a multicenter retrospective clinical study
Qin-Yue Su,
Wen-Jie Chen,
Yan-Jun Zheng,
Wen Shi,
Fang-Chen Gong,
Shun-Wei Huang,
Zhi-tao Yang,
Hong-Ping Qu,
En-Qiang Mao,
Rui-Lan Wang,
Du-Ming Zhu,
Gang Zhao,
Wei Chen,
Sheng Wang,
Qian Wang,
Chang-Qing Zhu,
Gao Yuan,
Er-Zhen Chen,
Ying Chen
Affiliations
Qin-Yue Su
Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Wen-Jie Chen
Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Yan-Jun Zheng
Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Wen Shi
Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Fang-Chen Gong
Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Shun-Wei Huang
Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Zhi-tao Yang
Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Hong-Ping Qu
Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
En-Qiang Mao
Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Rui-Lan Wang
Department of Emergency Medicine, Shanghai First People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Du-Ming Zhu
Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
Gang Zhao
Department of Emergency Medicine, Shanghai Sixth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Wei Chen
Department of Critical Care Medicine, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
Sheng Wang
Department of Critical Care Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
Qian Wang
Department of Emergency Medicine, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
Chang-Qing Zhu
Department of Emergency Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Gao Yuan
Department of Critical Care Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Er-Zhen Chen
Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Ying Chen
Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Methods 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.Results 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.Conclusion We developed and validated a robust nomogram model, which might identify patients at risk of SA-AKI and promising for clinical applications.
