A Prediction Model for Acute Kidney Injury in Adult Patients With Minimal Change Disease

Frontiers in Medicine

article

Chen Yang¹  Chen Yang³  Shu-Peng Lin²  Pu Chen²  Jie Wu²  Jin-Ling Meng²  Shuang Liang²  Feng-Ge Zhu²  Yong Wang²  Zhe Feng²  Xiang-Mei Chen²  Guang-Yan Cai² 

[1] School of Medicine, Nankai University;Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese PLA, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research;Department of Nephrology, Cangzhou Center Hospital

关键词: nomogram;    acute kidney injury (AKI);    minimal change disease;    prediction model;    nephrotic syndrome;   

DOI  :  10.3389/fmed.2022.862160

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Background Early prediction of acute kidney injury (AKI) can allow for timely interventions, but there are still few methods that are easy and convenient to apply in predicting AKI, specially targeted at patients with minimal change disease (MCD). Motivated by this, we aimed to develop a predicting model for AKI in patients with MCD within the KDIGO criteria. Methods Data on 401 hospitalized adult patients, whose biopsy was diagnosed as MCD from 12/31/2010 to 15/7/2021, were retrospectively collected. Among these data, patients underwent biopsy earlier formed the training set ( n = 283), while the remaining patients formed the validation set ( n = 118). Independent risk factors associated with AKI were analyzed. From this, the prediction model was developed and nomogram was plotted. Results AKI was found in 55 of 283 patients (19%) and 15 of 118 patients (13%) in the training and validation cohorts, respectively. According to the results from lasso regression and logistic regression, it was found that four factors, including mean arterial pressure, serum albumin, uric acid, and lymphocyte counts, were independent of the onset of AKI. Incorporating these factors, the nomogram achieved a reasonably good concordance index of 0.84 (95%CI 0.77–0.90) and 0.75 (95%CI 0.62–0.87) in predicting AKI in the training and validation cohorts, respectively. Decision curve analysis suggested clinical benefit of the prediction models. Conclusions Our predictive nomogram provides a feasible approach to identify high risk MCD patients who might develop AKI, which might facilitate the timely treatment.

【 授权许可】

CC BY