Performance of 2019 ESC risk classification and the Steno type 1 risk engine in predicting cardiovascular events in adults with type 1 diabetes: A retrospective study. (August 2022)

• Record Type: Journal Article
• Authors: Tecce, Nicola; Masulli, Maria; Palmisano, Luisa; Gianfrancesco, Salvatore; Piccolo, Roberto; Pacella, Daniela; Bozzetto, Lutgarda; Massimino, Elena; Della Pepa, Giuseppe; Lupoli, Roberta; Vaccaro, Olga; Riccardi, Gabriele; Capaldo, Brunella
• Is Part Of: Diabetes research and clinical practice. Volume 190(2022)
• Publication Date: 2022-08
• Subjects: Type 1 diabetes -- Cardiovascular disease -- STENO Type 1 risk engine -- 2019 ESC guidelines; BMI body mass index -- SBP blood pressure -- DBP diastolic blood pressure -- T1D Type 1 diabetes -- CVD cardiovascular disease -- eGFR estimated glomerular filtration rate -- CSII continuous subcutaneous insulin infusion -- PPV positive predictive value -- NPV negative predictive value -- ST1RE Steno Type 1 Risk Engine -- ESC European society of Cardiology -- ADA American Diabetes Association; Diabetes -- Periodicals; Diabetes Mellitus -- Periodicals; 616.462
• DOI: 10.1016/j.diabres.2022.110001
• Languages: English
• ISSNs: 0168-8227

Abstract

Highlights: Adults with T1D are at high risk of premature CVD. Validated CVD risk prediction models are essential for appropriate therapeutic strategies. The 2019 ESC criteria have a good performance in predicting CV events in T1D adults at very high CV risk but a poor performance in the high/intermediate risk groups. The Steno type 1 risk engine has an overall good performance in all different CV risk groups, being able to better discriminate between high, moderate and low risk. Abstract: Aims: 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. Methods: 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. Results: 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). Conclusions: 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.