Development and validation of a new algorithm for improved cardiovascular risk prediction

Area of Science:

• Cardiology
• Public Health
• Epidemiology

Background:

• Cardiovascular disease (CVD) remains a leading cause of mortality globally.
• Existing risk prediction algorithms, such as QRISK3, provide valuable tools for clinicians.
• There is a continuous need to refine CVD risk assessment by incorporating novel risk factors.

Purpose of the Study:

• To derive and externally validate a new cardiovascular disease (CVD) risk algorithm, named QR4.
• To incorporate novel risk factors into CVD risk prediction separately for men and women.
• To compare the performance of QR4 against existing CVD risk scores like QRISK3, SCORE2, and ASCVD.

Main Methods:

• Utilized health data from 9.98 million UK adults for algorithm derivation and 6.79 million for external validation.
• Employed cause-specific Cox models to develop predictive models for 10-year CVD risk.
• Externally validated the QR4 algorithm and compared its performance (C-statistic) against QRISK3, SCORE2, and ASCVD risk scores.

Main Results:

• Identified seven novel risk factors for both men and women (e.g., brain cancer, lung cancer, Down syndrome, COPD) and two additional for women (pre-eclampsia, postnatal depression).
• QR4 demonstrated a higher C-statistic than QRISK3 in both women (0.835 vs. 0.831) and men (0.814 vs. 0.812) upon external validation.
• QR4 exhibited superior accuracy compared to ASCVD and SCORE2 risk scores in both sexes.

Conclusions:

• The QR4 algorithm offers enhanced cardiovascular disease risk prediction in the UK population.
• Incorporation of novel risk factors significantly improves the accuracy of CVD risk assessment.
• QR4 identifies new at-risk groups and provides a more precise tool for clinical decision-making compared to international scoring systems.