A nomogram incorporating functional and tubular damage biomarkers to predict the risk of acute kidney injury for septic patients

BMC Nephrology

Yujun Deng¹  Yiyu Deng¹  Silin Liang²  Yifan Wang²  Xin Ouyang²  Fen Yao²  Chunbo Chen³  Jianchao Ma⁴  Haiyan Lao⁵ 

[1] Department of Critical Care Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 510080, Guangzhou, Guangdong Province, PR China;Department of Intensive Care Unit of Cardiovascular Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, 510080, Guangzhou, PR China;Department of Intensive Care Unit of Cardiovascular Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, 510080, Guangzhou, PR China;The Second School of Clinical Medicine, Southern Medical University, 510280, Guangzhou, Guangdong, PR China;Department of Nephrology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 510080, Guangzhou, Guangdong, PR China;Department of Pharmacy, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, 510080, Guangzhou, Guangdong, PR China;

关键词: Acute kidney injury;    Sepsis;    Serum cystatin C;    Nomogram;    N-acetyl-β-D-glucosaminidase;    Intensive care unit;   

DOI  :  10.1186/s12882-021-02388-w

来源: Springer

【 摘 要 】

BackgroundCombining tubular damage and functional biomarkers may improve prediction precision of acute kidney injury (AKI). Serum cystatin C (sCysC) represents functional damage of kidney, while urinary N-acetyl-β-D-glucosaminidase (uNAG) is considered as a tubular damage biomarker. So far, there is no nomogram containing this combination to predict AKI in septic cohort. We aimed to compare the performance of AKI prediction models with or without incorporating these two biomarkers and develop an effective nomogram for septic patients in intensive care unit (ICU).MethodsThis was a prospective study conducted in the mixed medical-surgical ICU of a tertiary care hospital. Adults with sepsis were enrolled. The patients were divided into development and validation cohorts in chronological order of ICU admission. A logistic regression model for AKI prediction was first constructed in the development cohort. The contribution of the biomarkers (sCysC, uNAG) to this model for AKI prediction was assessed with the area under the receiver operator characteristic curve (AUC), continuous net reclassification index (cNRI), and incremental discrimination improvement (IDI). Then nomogram was established based on the model with the best performance. This nomogram was validated in the validation cohort in terms of discrimination and calibration. The decision curve analysis (DCA) was performed to evaluate the nomogram’s clinical utility.ResultsOf 358 enrolled patients, 232 were in the development cohort (69 AKI), while 126 in the validation cohort (52 AKI). The first clinical model included the APACHE II score, serum creatinine, and vasopressor used at ICU admission. Adding sCysC and uNAG to this model improved the AUC to 0.831. Furthermore, incorporating them significantly improved risk reclassification over the predictive model alone, with cNRI (0.575) and IDI (0.085). A nomogram was then established based on the new model including sCysC and uNAG. Application of this nomogram in the validation cohort yielded fair discrimination with an AUC of 0.784 and good calibration. The DCA revealed good clinical utility of this nomogram.ConclusionsA nomogram that incorporates functional marker (sCysC) and tubular damage marker (uNAG), together with routine clinical factors may be a useful prognostic tool for individualized prediction of AKI in septic patients.

【 授权许可】

CC BY