The older vascular age group (predicted age ≥65) showed a 2.14-fold increased risk of major adverse cardiovascular events compared to the younger vascular age group (predicted age <65).
Does vascular age estimated from coronary angiography by a deep neural network predict major adverse cardiovascular events in patients with acute coronary syndrome?
A deep neural network can accurately estimate vascular age from coronary angiography images, and an older predicted vascular age is independently associated with a higher risk of major adverse cardiovascular events in patients with acute coronary syndrome.
Effect estimate: HR 2.14 (95% CI 1.07-4.29)
Absolute Event Rate: 12.3% vs 22.3%
p-value: p=0.032
Coronary angiography (CAG) is still considered the reference standard for coronary artery assessment, especially in the treatment of acute coronary syndrome (ACS). Although aging causes changes in coronary arteries, the age-related imaging features on CAG and their prognostic relevance have not been fully characterized. We hypothesized that a deep neural network (DNN) model could be trained to estimate vascular age only using CAG and that this age prediction from CAG could show significant associations with clinical outcomes of ACS. A DNN was trained to estimate vascular age using ten separate frames from each of 5,923 CAG videos from 572 patients. It was then tested on 1,437 CAG videos from 144 patients. Subsequently, 298 ACS patients who underwent percutaneous coronary intervention (PCI) were analysed to assess whether predicted age by DNN was associated with clinical outcomes. Age predicted as a continuous variable showed mean absolute error of 4 years with R squared of 0.72 ( r = 0.856). Among the ACS patients stratified by predicted age from CAG images before PCI, major adverse cardiovascular events (MACE) were more frequently observed in the older vascular age group than in the younger vascular age group (p = 0.017). Furthermore, after controlling for actual age, gender, peak creatine kinase, and history of heart failure, the older vascular age group independently suffered from more MACE (hazard ratio 2.14, 95% CI 1.07 to 4.29, p = 0.032). The vascular age estimated based on CAG imaging by DNN showed high predictive value. The age predicted from CAG images by DNN could have significant associations with clinical outcomes in patients with ACS.
Sawano et al. (Thu,) conducted a other in Acute Coronary Syndrome (n=298). Deep Neural Network for Age Prediction from Coronary Angiography vs. Younger vascular age group (predicted age <65) was evaluated on Major Adverse Cardiovascular Events (MACE) (HR 2.14, 95% CI 1.07-4.29, p=0.032). The older vascular age group (predicted age ≥65) showed a 2.14-fold increased risk of major adverse cardiovascular events compared to the younger vascular age group (predicted age <65).