International Journal of General Medicine (Sep 2023)
A Nomogram for Predicting the Risk of CKD Based on Cardiometabolic Risk Factors
Abstract
Peng Yu,1– 4 Ranran Kan,1,3 Xiaoyu Meng,1,3 Zhihan Wang,1,3 Yuxi Xiang,1,3 Beibei Mao,1,3 Xuefeng Yu1,3 1Department of Endocrinology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, People’s Republic of China; 2Department of Endocrinology, the Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China; 3Branch of National Clinical Research Center for Metabolic Diseases, Hubei, People’s Republic of China; 4Key Laboratory for Molecular Diagnosis of Hubei Province, Wuhan, People’s Republic of ChinaCorrespondence: Xuefeng Yu, Department of Internal Medicine, Tongji Hospital, Huazhong University of Science and Technology, No. 1095, Jiefang Road, Wuhan, 430030, People’s Republic of China, Tel +8602783663331, Email [email protected]: In China, the spectrum of causes for CKD has been changing in recent years, and the proportion of CKD caused by cardiometabolic diseases, such as diabetes and hypertension continues to increase. Thus, predicting CKD based on cardiometabolic risk factors can to a large extent help identify those at increased risk and facilitate the prevention of CKD. In this study, we aimed to develop a nomogram for predicting CKD risk based on cardiometabolic risk factors.Methods: We developed a nomogram for predicting CKD risk by using a subcohort population of the 4C study, which was located in central China. The prediction model was designed by using a logistic regression model, and a backwards procedure based on the Akaike information criterion was applied for variable selection. The performance of the model was evaluated by the concordance index (C-index), and Hosmer‒Lemeshow goodness-of-fit test. The bootstrapping method was applied for internal validation.Results: During the 3-years follow-up, 167 cases of CKD developed. By using univariate and multivariate logistic regression models, the following factors were identified as predictors in the nomogram: age, sex, HbA1c, baseline eGFR, low HDL-C levels, high TC levels and SBP. The bootstrap-corrected C-index for the model was 0.84, which indicated good discrimination ability. The Hosmer‒Lemeshow goodness-of-fit tests yielded chi-square of 13.61 (P=0.192), and the calibration curves demonstrated good consistency between the predicted and observed probabilities, which indicated satisfactory calibration ability.Conclusion: We developed a convenient and practicable nomogram for the 3‑year risk of incident CKD among a population in central China, which may help to identify high-risk individuals for CKD and contribute to the prevention of CKD.Keywords: CKD, cardiometabolic risk factors, nomogram
