Predictive improvement of adding coronary calcium score and a genetic risk score to a traditional risk model for cardiovascular event prediction.

AIMS: Coronary artery calcium score (CACS) and polygenic risk score have been used as novel markers to predict cardiovascular (CV) events of asymptomatic individuals compared with traditional scores. No previous studies have directly compared the additive capacity of these two markers relative to conventional scores. The aim of the study was to evaluate the change in CV risk prediction ability when CACS, genetic risk score (GRS), or both are added to Systematic Coronary Risk Evaluation 2 (SCORE2). METHODS AND RESULTS: In a prospective, observational population-based study, 1002 asymptomatic subjects (mean age 53.1 ± 6.8 years, 73.8% male), free of clinical coronary disease and diabetes, were selected from GENEMACOR-study controls. SCORE2, CACS, and GRS were estimated to evaluate CV events' predictive and discriminative ability through Harrell's C-statistics. Net reclassification improvement (NRI) and integrated discrimination index were used to reclassify the population. Multivariable Cox proportional hazard ratio (HR) analysis assessed the variables independently associated with CV events. C-statistic demonstrated that the discriminative value for CV event occurrence was 0.608 for SCORE2, increasing to 0.749 (P = 0.001) when CACS was added, and improved to 0.802 (P = 0.0008) with GRS, showing a better discriminative capacity for CV events. Continuous NRI reclassified >70% of the population. Cox proportional analysis showed that the highest categories of SCORE2, CACS, and GRS remained in the equation with an HR of 2.9 (P = 0.003), 5.0 (P < 0.0001), and 3.2 (P = 0.003), respectively, when compared with the lowest categories. CONCLUSION: In our population, CACS added to SCORE2 had better ability than GRS in CV event risk prediction, discrimination, and reclassification. However, adding the three scores can become clinically relevant, especially in intermediate-risk persons.

Our study highlights the impact of including coronary artery calcium score (CACS) and genetic risk score (GRS) alongside Systematic Coronary Risk Evaluation 2 (SCORE2) for enhancing cardiovascular (CV) risk assessment in primary prevention. In our population, adding CACS to SCORE2 exhibited a superior discriminative capacity for CV events compared with GRS alone in terms of risk prediction, discrimination, and reclassification. Our results emphasize the potential clinical relevance of using all three scores to identify high-risk individuals who would benefit from earlier and more stringent cardiovascular risk management strategies to prevent future cardiovascular events.

KAsH: A new tool to predict in-hospital mortality in patients with myocardial infarction.

INTRODUCTION: Complex risk scores have limited applicability in the assessment of patients with myocardial infarction (MI). In this work, the authors aimed to develop a simple to use clinical score to stratify the in-hospital mortality risk of patients with MI at first medical contact. METHODS: In this single-center prospective registry assessing 1504 consecutively admitted patients with MI, the strongest predictors of in-hospital mortality were selected through multivariate logistic regression. The KAsH score was developed according to the following formula: KAsH=(Killip class×Age×Heart rate)/systolic blood pressure. Its predictive power was compared to previously validated scores using the DeLong test. The score was categorized and further compared to the Killip classification. RESULTS: The KAsH score displayed excellent predictive power for in-hospital mortality, superior to other well-validated risk scores (AUC: KAsH 0.861 vs. GRACE 0.773, p<0.001) and robust in subgroup analysis. KAsH maintained its predictive capacity after adjustment for multiple confounding factors such as diabetes, heart failure, mechanical complications and bleeding (OR 1.004, 95% CI 1.001-1.008, p=0.012) and reclassified 81.5% of patients into a better risk category compared to the Killip classification. KAsH's categorization displayed excellent mortality discrimination (KAsH 1: 1.0%, KAsH 2: 8.1%, KAsH 3: 20.4%, KAsH 4: 55.2%) and better mortality prediction than the Killip classification (AUC: KAsH 0.839 vs. Killip 0.775, p<0.0001). CONCLUSION: KAsH, an easy to use score calculated at first medical contact with patients with MI, displays better predictive power for in-hospital mortality than existing scores.