Development and validation of short-term renal prognosis prediction model in diabetic patients with acute kidney injury

Abstract

Abstract Objective Diabetes is a major cause of the progression of acute kidney injury (AKI). Few prediction models have been developed to predict the renal prognosis in diabetic patients with AKI so far. The aim of this study was to develop and validate a predictive model to identify high-risk individuals with non-recovery of renal function at 90 days in diabetic patients with AKI. Methods Demographic data and related laboratory indicators of diabetic patients with AKI in the First Affiliated Hospital of Guangxi Medical University from January 31, 2012 to January 31, 2022 were retrospectively analysed, and patients were followed up to 90 days after AKI diagnosis. Based on the results of Logistic regression, a model predicting the risk of non-recovery of renal function at 90 days in diabetic patients with AKI was developed and internal validated. Consistency index (C-index), calibration curve, and decision curve analysis were used to evaluate the differentiation, accuracy, and clinical utility of the prediction model, respectively. Results A total of 916 diabetic patients with AKI were enrolled, with a male to female ratio of 2.14:1. The rate of non-recovery of renal function at 90 days was 66.8% (612/916). There were 641 in development cohort and 275 in validation cohort (ration of 7:3). In the development cohort, a prediction model was developed based on the results of Logistic regression analysis. The variables included in the model were: diabetes duration (OR = 1.022, 95% CI  1.012–1.032), hypertension (OR = 1.574, 95% CI 1.043–2.377), chronic kidney disease (OR = 2.241, 95% CI 1.399–3.591), platelet (OR = 0.997, 95% CI 0.995–1.000), 25-hydroxyvitamin D3 (OR = 0.966, 95% CI  0.956–0.976), postprandial blood glucose (OR = 1.104, 95% CI 1.032–1.181), discharged serum creatinine (OR = 1.003, 95% CI 1.001–1.005). The C-indices of the prediction model were 0.807 (95% CI 0.738–0.875) and 0.803 (95% CI 0.713–0.893) in the development and validation cohorts, respectively. The calibration curves were all close to the straight line with slope 1. The decision curve analysis showed that in a wide range of threshold probabilities. Conclusion A prediction model was developed to help predict short-term renal prognosis of diabetic patients with AKI, which has been verified to have good differentiation, calibration degree and clinical practicability.