Chiness Journal of Clinical Medicine

Establishment of a predictive model for acute kidney injury in emergency inpatients

Received:September 03, 2020 Revised:April 02, 2021 Click here to download the full text

Citation of this paper:SU Yi-qi,SHEN Dao-qi,WANG Yi-mei,XU Xia-lian,TENG Jie,DING Xiao-qiang.Establishment of a predictive model for acute kidney injury in emergency inpatients[J].Chinese Journal of Clinical Medicine,2021,28(4):562-567

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Author Name	Affiliation	E-mail
SU Yi-qi	Department of Nephrology, Xiamen Branch, Zhongshan Hospital, Fudan University, Xiamen 361006, Fujian, China
SHEN Dao-qi	Department of Nephrology, Zhongshan Hospital, Fudan University Shanghai Medical Center of Kidney Disease Shanghai Institute of Kidney Disease and Dialysis Shanghai Laboratory of Kidney Disease and Dialysis, Shanghai 200032, China
WANG Yi-mei	Department of Nephrology, Zhongshan Hospital, Fudan University Shanghai Medical Center of Kidney Disease Shanghai Institute of Kidney Disease and Dialysis Shanghai Laboratory of Kidney Disease and Dialysis, Shanghai 200032, China
XU Xia-lian	Department of Nephrology, Zhongshan Hospital, Fudan University Shanghai Medical Center of Kidney Disease Shanghai Institute of Kidney Disease and Dialysis Shanghai Laboratory of Kidney Disease and Dialysis, Shanghai 200032, China	xu-xialian@zs-hospital.sh.cn
TENG Jie	Department of Nephrology, Xiamen Branch, Zhongshan Hospital, Fudan University, Xiamen 361006, Fujian, China Department of Nephrology, Zhongshan Hospital, Fudan University Shanghai Medical Center of Kidney Disease Shanghai Institute of Kidney Disease and Dialysis Shanghai Laboratory of Kidney Disease and Dialysis, Shanghai 200032, China
DING Xiao-qiang	Department of Nephrology, Zhongshan Hospital, Fudan University Shanghai Medical Center of Kidney Disease Shanghai Institute of Kidney Disease and Dialysis Shanghai Laboratory of Kidney Disease and Dialysis, Shanghai 200032, China

Abstract:Objective: To establish and verify the prediction model of acute kidney injury (AKI) in emergency inpatients. Methods: The clinical data of 310 inpatients admitted to the Emergency Department of Zhongshan Hospital, Fudan University from October 2014 to September 2015 were collected. Univariate logistic regression was used to calculate the OR value and P value of each index for AKI occurrence. Independent risk factors were selected by stepwise multiple factor logistic regression method and included in the AKI prediction model. 206 patients were included in the training group, and 104 patients were included in the validation group. The discrimination (ROC curve), calibration degree (calibration curve), and clinical applicability (DCA curve) of the model in the two groups were calculated. Multivariate logistic regression was used to draw a nomogram to predict the risk of AKI in emergency inpatients. Results: The incidence rate of AKI was 36.1% (112/310). There were significant differences in albumin, urea, creatinine, serum magnesium, and other 7 indexes between the AKI and non-AKI groups (P<0.05). Multivariate logistic regression showed that albumin, creatinine, serum magnesium, and blood glucose were independent risk factors for AKI. The prediction model of AKI was logitic (PAKI)=-0.113albumin+0.021creatinine+3.837serum magnesium + 0.108 blood glucose -2.878, and AKI is considered if the result is higher than 0.398. The AUC of the model in the training group and validation group were 0.790(95%CI 0.722-0.859,P<0.001) and 0.752(95%CI 0.646-0.858,P<0.001), respectively. Calibration curve U-test showed that the calibration degree of the training group and validation group were good (P>0.05). DCA curve showed that the model had good clinical applicability. Conclusions: The model including serum magnesium has a good predictive value in the incidence of AKI in emergency inpatients and has a good guiding role in clinical practice.

keywords:emergency acute kidney injury prediction model serum magnesium risk factor clinical applicability

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