Diabetes, Metabolic Syndrome and Obesity (May 2021)
Diabetes Mellitus as a Risk Factor for Progression from Acute Kidney Injury to Acute Kidney Disease: A Specific Prediction Model
Abstract
Huanhuan Zhao,1– 5,* Lulu Liang,1– 5,* Shaokang Pan,1 Zhenjie Liu,1 Yan Liang,1 Yingjin Qiao,1 Dongwei Liu,1– 5 Zhangsuo Liu1– 5 1Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, People’s Republic of China; 2Research Institute of Nephrology, Zhengzhou University, Zhengzhou, 450052, People’s Republic of China; 3Research Center for Kidney Disease, Zhengzhou, 450052, Henan Province, People’s Republic of China; 4Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, 450052, People’s Republic of China; 5Core Unit of National Clinical Medical Research Center of Kidney Disease, Zhengzhou, 450052, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhangsuo Liu; Dongwei LiuDepartment of Nephrology, The First Affiliated Hospital of Zhengzhou University, No. 1, Jianshe East Road, Zhengzhou, 450052, Henan Province, People’s Republic of ChinaTel +86-0371-66295921Email [email protected]; [email protected]: Acute kidney injury is very common in hospitalized patients and carries a significant risk of mortality. Although timely intervention may improve patient prognosis, studies on the development of acute kidney disease in patients with acute kidney injury remain scarce. Thus, we constructed a prediction model to identify patients likely to develop acute kidney disease.Patients and Methods: Among 474 patients screened for eligibility, 261 were enrolled and randomly divided into training (185 patients) and independent validation cohorts (76 patients). Least absolute shrinkage and selection operator regression and multivariate logistic regression analyses were used to select features and build a nomogram incorporating the selected predictors: diabetes, anemia, oliguria, and peak creatinine. Calibration, discrimination, and the clinical usefulness of the model were assessed using calibration plots, the C-index, receiver operating characteristic curves, and decision curve analysis.Results: Diabetes was significantly associated with the presence of AKD. Peak creatinine, oliguria, and anemia also contributed to the progression of acute kidney injury. The model displayed good predictive power with a C-index of 0.834 and an AUC of 0.834 (95% confidence interval (CI): 0.773– 0.895) in the training cohort and a C-index of 0.851 and an AUC of 0.851 (95% CI: 0.753– 0.949) in the validation cohort. The calibration curves also showed that the model had a medium ability to predict acute kidney disease risk. Decision curve analysis showed that the nomogram was clinically useful when interventions were decided at the possibility threshold of 22%.Conclusion: This novel prediction nomogram may allow for convenient prediction of acute kidney disease in patients with acute kidney injury, which may help to improve outcomes.Keywords: diabetes mellitus, acute kidney injury, anemia, oliguria, nomogram
