Department of Health Policy and Management, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA.
Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, Florida, USA.
Diabetes Obes Metab. 2023 Oct;25(10):2862-2868. doi: 10.1111/dom.15177. Epub 2023 Jun 19.
Early identification of incident chronic kidney disease (CKD) in individuals with diabetes may help improve patients' clinical outcomes. This study aimed to develop a prediction equation for incident CKD among people with type 2 diabetes (T2D).
A time-varying Cox model was applied to data from the ACCORD trial to predict the risk of incident CKD. A list of candidate variables was chosen based on literature reviews and experts' consultations, including demographic characteristics, vitals, laboratory results, medical history, drug use and health care utilization. Model performance was evaluated. Decomposition analysis was conducted, and external validation was performed.
In total, 6006 patients with diabetes free of CKD were included, with a median follow-up of 3 years and 2257 events. The risk model included age at T2D diagnosed, smoking status, body mass index, high-density lipoprotein, very-low-density lipoprotein, alanine aminotransferase, estimated glomerular filtration rate, urine albumin-creatinine ratio, hypoglycaemia, retinopathy, congestive heart failure, coronary heart disease history, antihyperlipidaemic drug use, antihypertensive drug use and hospitalization. The urine albumin-creatinine ratio, estimated glomerular filtration rate and congestive heart failure were the top three factors that contributed most to the incident CKD prediction. The model showed acceptable discrimination [C-statistic: 0.772 (95% CI 0.767-0.805)] and calibration [Brier Score: 0.0504 (95% CI 0.0477-0.0531)] in the Harmony Outcomes Trial.
Incident CKD prediction among individuals with T2D was developed and validated for use in decision support of CKD prevention.
早期识别糖尿病患者中的新发慢性肾脏病（CKD）可能有助于改善患者的临床结局。本研究旨在为 2 型糖尿病（T2D）患者建立预测新发 CKD 的预测方程。
应用时变 Cox 模型对 ACCORD 试验数据进行分析，以预测新发 CKD 的风险。根据文献回顾和专家咨询，选择了一系列候选变量，包括人口统计学特征、生命体征、实验室结果、病史、药物使用和医疗保健利用。评估模型性能。进行分解分析，并进行外部验证。
共纳入 6006 例无 CKD 的糖尿病患者，中位随访时间为 3 年，发生 2257 例事件。风险模型包括 T2D 诊断时的年龄、吸烟状况、体重指数、高密度脂蛋白、极低密度脂蛋白、丙氨酸氨基转移酶、估计肾小球滤过率、尿白蛋白-肌酐比、低血糖、视网膜病变、充血性心力衰竭、冠心病史、降脂药使用、降压药使用和住院治疗。尿白蛋白-肌酐比、估计肾小球滤过率和充血性心力衰竭是导致新发 CKD 预测的前三个最重要因素。该模型在 Harmony Outcomes 试验中显示出可接受的区分度[C 统计量：0.772（95%CI 0.767-0.805）]和校准度[Brier 评分：0.0504（95%CI 0.0477-0.0531）]。
为 T2D 患者开发并验证了新发 CKD 预测模型，用于支持 CKD 预防的决策。
