Lin Yilu, Shao Hui, Fonseca Vivian, Anderson Amanda H, Batuman Vecihi, Shi Lizheng
Department of Health Policy and Management, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States of America.
Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, United States of America.
J Diabetes Complications. 2023 Mar;37(3):108413. doi: 10.1016/j.jdiacomp.2023.108413. Epub 2023 Feb 6.
CKD progression among individuals with T2D is associated with poor health outcomes and high healthcare costs, which have not been fully studied. This study aimed to predict CKD progression among individuals with diabetes.
Using ACCORD trial data, a time-varying Cox model was developed to predict the risk of CKD progression among patients with CKD and T2D. CKD progression was defined as a 50 % decline, or 25 mL/min/1.73 m decline in eGFR from baseline, doubling of the serum creatinine, or onset of ESKD. A list of candidate variables included demographic characteristics, physical exam results, laboratory results, medical history, drug use, and healthcare utilization. A stepwise algorithm was used for variable selection. Model performance was evaluated by Brier score and C-statistics. Confidence intervals (CI) were calculated using a bootstrap method. Decomposition analysis was conducted to assess the predictor contribution. Generalizability was assessed on patient-level data of the Harmony Outcome trial and CRIC study.
A total of 6982 diabetes patients with CKD were used for model development, with a median follow-up of 4 years and 3346 events. The predictors for CKD progression included female sex, age at T2D diagnosis, smoking status, SBP, DBP, HR, HbA1c, alanine aminotransferase (ALT), eGFR, UACR, retinopathy event, hospitalization. The model demonstrated good discrimination (C-statistics 0.745 [95 % CI 0.723-0.763]) and calibration (Brier Score 0.0923 [95 % CI 0.0873-0.0965]) performance in the ACCORD data. The most contributing predictors for CKD progression were eGFR, HbA1c, and SBP. The model demonstrated acceptable discrimination and calibration performance in the two external data.
For high-risk patients with both diabetes and CKD, the tool as a dynamic risk prediction of CKD progression may help develop novel strategies to lower the risk of CKD progression.
2型糖尿病患者的慢性肾脏病进展与不良健康结局及高昂的医疗费用相关，对此尚未进行充分研究。本研究旨在预测糖尿病患者的慢性肾脏病进展情况。
利用ACCORD试验数据，建立了一个时变Cox模型，以预测慢性肾脏病和2型糖尿病患者慢性肾脏病进展的风险。慢性肾脏病进展定义为估算肾小球滤过率（eGFR）较基线水平下降50%或下降25 mL/min/1.73m²、血清肌酐翻倍或终末期肾病（ESKD）的发生。候选变量列表包括人口统计学特征、体格检查结果、实验室检查结果、病史、药物使用情况及医疗服务利用情况。采用逐步算法进行变量选择。通过Brier评分和C统计量评估模型性能。使用自助法计算置信区间（CI）。进行分解分析以评估预测变量的贡献。在Harmony Outcome试验和CRIC研究的患者水平数据上评估模型的可推广性。
共6982例患有慢性肾脏病的糖尿病患者用于模型开发，中位随访时间为4年，发生3346起事件。慢性肾脏病进展的预测因素包括女性、2型糖尿病诊断时的年龄、吸烟状况、收缩压、舒张压、心率、糖化血红蛋白（HbA1c）、丙氨酸氨基转移酶（ALT）、eGFR、尿白蛋白肌酐比值（UACR）、视网膜病变事件、住院情况。该模型在ACCORD数据中显示出良好的区分度（C统计量0.745 [95%CI 0.723 - 0.763]）和校准度（Brier评分0.0923 [95%CI 0.0873 - 0.0965]）性能。慢性肾脏病进展的最主要预测因素是eGFR、HbA1c和收缩压。该模型在两个外部数据中显示出可接受的区分度和校准度性能。
对于同时患有糖尿病和慢性肾脏病的高危患者，该工具作为慢性肾脏病进展的动态风险预测，可能有助于制定降低慢性肾脏病进展风险的新策略。
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