Saved in:
| Main Authors: | , |
|---|---|
| Format: | Recurso digital |
| Language: | |
| Published: |
Zenodo
2025
|
| Online Access: | https://doi.org/10.5281/zenodo.15350098 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Table of Contents:
- <p><span lang="UZ-CYR">The surge in cardiovascular diseases (CVDs) has become a global challenge with a steadily climbing trend of cardiovascular deaths from 12.1 million in 1990 to 18.6 million in 2019 [1, 2]. Risk prediction, a primary strategy in addressing this worldwide problem, has brought significant benefits to some developed countries through the improvement of the effectiveness of life intervention and reduction of economic burden [3, 4]. Therefore, risk prediction has been expected as an efficient way to achieve World Health Organization (WHO) goals for reducing CVD-related mortality by 25% by 2025, and some classic CVD prediction models (e.g., the Framingham [5] and SCORE [6], referred to as traditional models [T-Ms] in this study) has been incorporated into clinical guidelines by the European Society of Cardiology (ESC) and the American College of Cardiology/American Heart Association (ACC/AHA) [7, 8]. </span></p>