Department of Clinical Medicine and Surgery, Federico II University, Naples, Italy.
Department of Public Health, Federico II University, Naples, Italy.
Diabetes Res Clin Pract. 2022 Aug;190:110001. doi: 10.1016/j.diabres.2022.110001. Epub 2022 Jul 18.
The study compares the performance of the European Society of Cardiology (ESC) risk criteria and the Steno Type 1 Risk Engine (ST1RE) in the prediction of cardiovascular (CV) events.
456 adults with type 1 diabetes (T1D) were retrospectively studied. During 8.5 ± 5.5 years of observation, twenty-four patients (5.2%) experienced a CV event. The predictive performance of the two risk models was evaluated by classical metrics and the event-free survival analysis.
The ESC criteria show excellent sensitivity (91.7%) and suboptimal specificity (64.4 %) in predicting CV events in the very high CV risk group, but a poor performance in the high/moderate risk groups. The ST1RE algorithm shows a good predictive performance in all CV risk categories. Using ESC classification, the event-free survival analysis shows a significantly higher event rate in the very high CV risk group compared to the high/moderate risk group (p < 0.0019). Using the ST1RE algorithm, a significant difference in the event-free survival curve was found between the three CV risk categories (p < 0.0001).
In T1D the ESC classification has a good performance in predicting CV events only in those at very high CV risk, whereas the ST1RE algorithm has a good performance in all risk categories.
本研究比较了欧洲心脏病学会（ESC）风险标准和 Steno Type 1 Risk Engine（ST1RE）在预测心血管（CV）事件方面的表现。
回顾性研究了 456 名 1 型糖尿病（T1D）患者。在 8.5±5.5 年的观察期间，24 名患者（5.2%）发生了 CV 事件。通过经典指标和无事件生存分析评估了两种风险模型的预测性能。
ESC 标准在预测极高 CV 风险组的 CV 事件方面具有出色的敏感性（91.7%）和不理想的特异性（64.4%），但在高/中危风险组表现不佳。ST1RE 算法在所有 CV 风险类别中均具有良好的预测性能。使用 ESC 分类，无事件生存分析显示极高 CV 风险组的事件发生率明显高于高/中危风险组（p<0.0019）。使用 ST1RE 算法，三个 CV 风险类别之间的无事件生存曲线存在显著差异（p<0.0001）。
在 T1D 中，ESC 分类在预测极高 CV 风险患者的 CV 事件方面表现良好，而 ST1RE 算法在所有风险类别中表现良好。
