Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy.
Department of Cardiovascular and Metabolic Diseases, IRCCS Gruppo Multimedica, MI, Italy.
Diabetes Res Clin Pract. 2022 Oct;192:110092. doi: 10.1016/j.diabres.2022.110092. Epub 2022 Sep 24.
To develop and validate a model for predicting 5-year eGFR-loss in type 2 diabetes mellitus (T2DM) patients with preserved renal function at baseline.
A cohort of 504.532 T2DM outpatients participating to the Medical Associations of Diabetologists (AMD) Annals Initiative was splitted into the Learning and Validation cohorts, in which the predictive model was respectively developed and validated. A multivariate Cox proportional hazard regression model including all baseline characteristics was performed to identify predictors of eGFR-loss. A weight derived from regression coefficients was assigned to each variable and the overall sum of weights determined the 0 to 8-risk score.
A set of demographic, clinical and laboratory parameters entered the final model. The eGFR-loss score showed a good performance in the Validation cohort. Increasing score values progressively identified a higher risk of GFR loss: a score ≥ 8 was associated with a HR of 13.48 (12.96-14.01) in the Learning and a HR of 13.45 (12.93-13.99) in the Validation cohort. The 5 years-probability of developing the study outcome was 55.9% higher in subjects with a score ≥ 8.
In the large AMD Annals Initiative cohort, we developed and validated an eGFR-loss prediction model to identify T2DM patients at risk of developing clinically meaningful renal complications within a 5-years time frame.
为了在基线肾功能正常的 2 型糖尿病(T2DM)患者中建立并验证一个预测 5 年 eGFR 丢失的模型。
一项包含 504532 名 2 型糖尿病门诊患者的队列研究参与了医学糖尿病协会(AMD)年度计划,将其分为学习和验证队列,分别在其中建立和验证预测模型。采用包含所有基线特征的多变量 Cox 比例风险回归模型来识别 eGFR 丢失的预测因素。根据回归系数为每个变量分配一个权重,权重的总和确定 0 到 8 分的风险评分。
一组人口统计学、临床和实验室参数进入了最终模型。eGFR 丢失评分在验证队列中表现出良好的性能。评分值的增加逐渐确定了更高的 GFR 丢失风险:评分≥8 与学习队列中的 HR 为 13.48(12.96-14.01)和验证队列中的 HR 为 13.45(12.93-13.99)相关。在评分≥8 的患者中,5 年内发生研究结局的概率增加了 55.9%。
在 AMD 年度计划的大型队列中,我们建立并验证了一个 eGFR 丢失预测模型,以识别在 5 年内可能发生临床相关肾脏并发症的 T2DM 患者。