Development and Validation of a Prediction Model for Survival in Diabetic Patients With Acute Kidney Injury - Mo, Manqiu; Pan, Ling; Huang, Zichun; Liang, Yuzhen; Liao, Yunhua; Xia, Ning (Frontiers in Endocrinology, 2021)
Objective: To analyze the risk factors affecting all-cause mortality in diabetic patients with acute kidney injury (AKI) and to develop and validate a nomogram for predicting the 90-day survival rate of patients. Methods: Clinical data of diabetic patients with AKI who were diagnosed at the First Affiliated Hospital of Guangxi Medical University from April 30, 2011, to April 30, 2021, were collected. A total of 1042 patients were randomly divided into a development cohort and a validation cohort at a ratio of 7:3. The primary study endpoint was all-cause death within 90 days of AKI diagnosis. Clinical parameters and demographic characteristics were analyzed by Cox regression to develop a prediction model for survival in diabetic patients with AKI, and a nomogram was then constructed. The concordance index (C-index), receiver operating characteristic curve, and calibration plot were used to evaluate the prediction model. Results: The development cohort enrolled 730 patients with a median follow-up time of 87 (40, 98) days, and 86 patients (11.8%) died during follow-up. The 90-day survival rate was 88.2% (644/730), and the recovery rate for renal function in survivors was 32.9% (212/644). Multivariate analysis showed that advanced age (HR=1.064, 95% CI 1.043~1.085), lower pulse pressure (HR=0.964, 95% CI 0.951~0.977), stage 3 AKI (HR=4.803, 95% CI 1.678~13.750), lower 25-hydroxyvitamin D3 (HR=0.944, 95% CI 0.930~0.960), and multiple organ dysfunction syndrome (HR=2.056, 95% CI 1.287~3.286) were independent risk factors affecting the all-cause death of diabetic patients with AKI (all P<0.01). The C-indices of the prediction cohort and the validation cohort were 0.880 (95% CI 0.839~0.921) and 0.798 (95% CI 0.720~0.876), respectively. The calibration plot of the model showed excellent consistency between the prediction probability and actual probability. Conclusion: We developed a new prediction model that has been internally verified to have good discrimination, calibration and clinical value for predicting the 90-day survival rate of diabetic patients with AKI.