A Multifactorial Risk Score System for the Prediction of Diabetic Kidney Disease in Patients with Type 2 Diabetes Mellitus

Abstract

Dongna Hui,1,2 Fang Zhang,3 Yuanyue Lu,4 Huiqiang Hao,3 Shuangshuang Tian,3 Xiuzhao Fan,3 Yanqin Liu,3 Xiaoshuang Zhou,2 Rongshan Li1,2 1Institute of Biomedical Sciences, Shanxi University, Taiyuan, People’s Republic of China; 2Department of Nephrology, Shanxi Provincial People’s Hospital, Taiyuan, People’s Republic of China; 3Kidney Disease Data Center, Shanxi Provincial People’s Hospital, Taiyuan, People’s Republic of China; 4Department of Nephrology, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan, People’s Republic of ChinaCorrespondence: Xiaoshuang Zhou, Department of Nephrology, Shanxi Provincial People’s Hospital, No. 29 Shuangta Street, Yingze District, Taiyuan, Shanxi, 030012, People’s Republic of China, Tel [phone], Email [email protected] Rongshan Li, Institute of Biomedical Sciences, Shanxi University, No. 92 Wucheng Road, Xiaodian District, Taiyuan, Shanxi, 030006, People’s Republic of China, Tel [phone], Email [email protected]: In-depth investigations of risk factors for the identification of diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) are rare. We aimed to investigate the risk factors for developing DKD from multiple types of clinical data and conduct a comprehensive risk assessment for individuals with diabetes.Methods: We carried out a case-control study, enrolling 958 patients to identify the risk factors for developing DKD in T2DM patients from a database established from inpatient electronic medical records. Multivariable logistic regression was applied to develop a prediction model and the performance of the model was evaluated using the area under the curve (AUC) and calibration curve. A multifactorial risk score system was established according to the Framingham Study risk score.Results: DKD accounted for 34.03% of eligible patients in total. Twelve risk factors were selected in the final prediction model, including age, duration of diabetes, duration of hypertension, fasting blood glucose, fasting C-peptide, insulin use, systolic blood pressure, low-density lipoprotein, γ-glutamyl transpeptidase, platelet, uric acid, and thyroid stimulating hormone; and one protective factor, serum albumin. The prediction model showed an AUC of 0.862 (95% Confidence Interval (CI) 0.834– 0.890) with an accuracy of 81.5% in the derivation dataset and an AUC of 0.876 (95% CI 0.825– 0.928) in the validation dataset. The calibration curves were excellent and the estimated probability of DKD was more than 80% when the cumulative score for risk factors reached 17 points.Conclusion: Newly recognized risk factors were applied to assess the development of DKD in T2DM patients and the established risk score system was a reliable and feasible tool for assisting clinicians to identify patients at high risk of DKD.Keywords: diabetic kidney disease, multifactorial, prediction model, risk factors, type 2 diabetes

Keywords

• diabetic kidney disease
• multifactorial
• prediction model
• risk factors
• type 2 diabetes