Steno Diabetes Center Copenhagen, Gentofte, Denmark
Steno Diabetes Center Copenhagen, Gentofte, Denmark.
Diabetes Care. 2021 Apr;44(4):901-907. doi: 10.2337/dc20-2586. Epub 2021 Jan 28.
End-stage kidney disease (ESKD) is a life-threatening complication of diabetes that can be prevented or delayed by intervention. Hence, early detection of people at increased risk is essential.
From a population-based cohort of 5,460 clinically diagnosed Danish adults with type 1 diabetes followed from 2001 to 2016, we developed a prediction model for ESKD accounting for the competing risk of death. Poisson regression analysis was used to estimate the model on the basis of information routinely collected from clinical examinations. The effect of including an extended set of predictors (lipids, alcohol intake, etc.) was further evaluated, and potential interactions identified in a survival tree analysis were tested. The final model was externally validated in 9,175 adults from Denmark and Scotland.
During a median follow-up of 10.4 years (interquartile limits 5.1; 14.7), 303 (5.5%) of the participants (mean [SD] age 42.3 [16.5] years) developed ESKD, and 764 (14.0%) died without having developed ESKD. The final ESKD prediction model included age, male sex, diabetes duration, estimated glomerular filtration rate, micro- and macroalbuminuria, systolic blood pressure, hemoglobin A, smoking, and previous cardiovascular disease. Discrimination was excellent for 5-year risk of an ESKD event, with a C-statistic of 0.888 (95% CI 0.849; 0.927) in the derivation cohort and confirmed at 0.865 (0.811; 0.919) and 0.961 (0.940; 0.981) in the external validation cohorts from Denmark and Scotland, respectively.
We have derived and validated a novel, high-performing ESKD prediction model for risk stratification in the adult type 1 diabetes population. This model may improve clinical decision making and potentially guide early intervention.
终末期肾病（ESKD）是糖尿病危及生命的并发症，可以通过干预来预防或延迟。因此，早期发现高危人群至关重要。
我们从 2001 年至 2016 年期间随访的 5460 名丹麦成年 1 型糖尿病患者的基于人群的队列中，开发了一种用于 ESKD 的预测模型，该模型考虑了死亡的竞争风险。使用泊松回归分析根据临床检查常规收集的信息对模型进行估计。进一步评估了包含扩展预测因子（脂质、酒精摄入量等）的效果，并在生存树分析中测试了潜在的相互作用。最后在丹麦和苏格兰的 9175 名成年人中对该模型进行了外部验证。
在中位随访 10.4 年（四分位距 5.1；14.7）期间，303 名参与者（平均[标准差]年龄 42.3[16.5]岁）发生了 ESKD，764 名参与者（14.0%）在未发生 ESKD 的情况下死亡。最终的 ESKD 预测模型包括年龄、性别、糖尿病病程、估算肾小球滤过率、微量白蛋白尿和大量白蛋白尿、收缩压、血红蛋白 A、吸烟和既往心血管疾病。在推导队列中，该模型对 5 年 ESKD 事件风险的判别能力非常出色，C 统计量为 0.888（95%CI 0.849；0.927），在丹麦和苏格兰的外部验证队列中分别得到验证，为 0.865（0.811；0.919）和 0.961（0.940；0.981）。
我们已经推导并验证了一种用于 1 型糖尿病成人人群风险分层的新型、高性能 ESKD 预测模型。该模型可能会改善临床决策，并可能指导早期干预。
