Development and validation of a risk prediction model for chronic kidney disease among individuals with type 2 diabetes

ASIA unversity > 醫學暨健康學院 > 健康產業管理學系 > 期刊論文 > Item 310904400/115268

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题名:	Development and validation of a risk prediction model for chronic kidney disease among individuals with type 2 diabetes
作者:	林正介;Jingchee, May;Niu, May Jingchee;Li, Chia-Ing;Li, Chia-Ing;Chiu-Shong, L;Liu, Chiu-Shong;Chih-Hsueh, L;Lin, Chih-Hsueh;Yan, Shing-Yu;Yang, Shing-Yu;李采娟;Li, Tsai-Chung
贡献者:	醫學暨健康學院健康產業管理學系
日期:	2022-03-01
上传时间:	2023-03-29 01:17:37 (UTC+0)
出版者:	亞洲大學
摘要:	Many studies had established the chronic kidney disease (CKD) prediction models, but most of them were conducted on the general population and not on patients with type 2 diabetes, especially in Asian populations. This study aimed to develop a risk prediction model for CKD in patients with type 2 diabetes from the Diabetes Care Management Program (DCMP) in Taiwan. This research was a retrospective cohort study. We used the DCMP database to set up a cohort of 4,601 patients with type 2 diabetes without CKD aged 40-92 years enrolled in the DCMP program of a Taichung medical center in 2002-2016. All patients were followed up until incidences of CKD, death, and loss to follow-up or 2016. The dataset for participants of national DCMP in 2002-2004 was used as external validation. The incident CKD cases were defined as having one of the following three conditions: ACR data greater than or equal to 300 (mg/g); both eGFR data less than 60 (ml/min/1.73 m2) and ACR data greater than or equal to 30 (mg/g); and eGFR data less than 45 (ml/min/1.73 m2). The study subjects were randomly allocated to derivation and validation sets at a 2:1 ratio. Cox proportional hazards regression model was used to identify the risk factors of CKD in the derivation set. Time-varying area under receiver operating characteristics curve (AUC) was used to evaluate the performance of the risk model. After an average of 3.8 years of follow-up period, 3,067 study subjects were included in the derivation set, and 786 (25.63%) were newly diagnosed CKD cases. A total of 1,534 participants were designated to the validation set, and 378 (24.64%) were newly diagnosed CKD cases. The final CKD risk factors consisted of age, duration of diabetes, insulin use, estimated glomerular filtration rate, albumin-to-creatinine ratio, high-density lipoprotein cholesterol, triglyceride, diabetes retinopathy, variation in HbA1c, variation in FPG, and hypertension drug use. The AUC values of 1-, 3-, and 5-year CKD risks were 0.74, 0.76, and 0.77 in the validation set, respectively, and were 0.76, 0.77, and 0.76 in the sample for external validation, respectively. The value of Harrell's c-statistics was 0.76 (0.74, 0.78). The proposed model is the first CKD risk prediction model for type 2 diabetes patients in Taiwan. The 1-, 3-, and 5-year CKD risk prediction models showed good prediction accuracy. The model can be used as a guide for clinicians to develop medical plans for future CKD preventive intervention in Chinese patients with type 2 diabetes.
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