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

Chen Yang; Chen Yang; Shu-Peng Lin; Pu Chen; Jie Wu; Jin-Ling Meng; Shuang Liang; Feng-Ge Zhu; Yong Wang; Zhe Feng; Xiang-Mei Chen; Guang-Yan Cai

doi:10.3389/fmed.2022.862160

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

Front Med (Lausanne). 2022 May 24:9:862160. doi: 10.3389/fmed.2022.862160. eCollection 2022.

Authors

Chen Yang^{1

2}, 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

Affiliations

¹ School of Medicine, Nankai University, Tianjin, China.
² 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, Beijing, China.
³ Department of Nephrology, Cangzhou Center Hospital, Cangzhou, China.

Abstract

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.

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