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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

Authors :
Nicola Tecce
Maria Masulli
Luisa Palmisano
Salvatore Gianfrancesco
Roberto Piccolo
Daniela Pacella
Lutgarda Bozzetto
Elena Massimino
Giuseppe Della Pepa
Roberta Lupoli
Olga Vaccaro
Gabriele Riccardi
Brunella Capaldo
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
Publication Year :
2022

Abstract

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.

Details

Language :
English
Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....0971c2f10c2eb0c36f1402ad2a4d203f