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Introduction Polygenic risk scores (PRS) predict the risk of developing atherosclerotic cardiovascular disease (ASCVD). However, their utility in combination with existing clinical risk scores remains uncertain. Purpose We first validated four different PRS in a Swiss population-based cohort. Second, using the PRS with the best predictive capacity, we assessed its benefit when combined with two clinical risk scores: the Systematic COronary Risk Evaluation 2 (SCORE2) and the Pooled Cohort Equation (PCE). Methods We used data from a prospective cohort involving 6733 European participants at baseline (2003–2006). The predictive accuracy of the PRS was assessed with discrimination and calibration metrics. For the second aim, subjects with prevalent ASCVD or statin therapy at baseline were excluded. We tested associations between risk prediction models (PRS alone and combined clinical and PRS) and incident ASCVD, using Cox proportional hazard regressions. Net reclassification index (NRI) detected any improvement of ASCVD risk categorisation following the addition of the PRS to clinical risk scores in overall sample and in subgroups (e.g., sex, age, clinical intermediate-risk category) Results For the first aim, 4215 subjects (53% women; mean age 53.7±10.7), with 357 prevalent ASCVD, were analysed. The PRS developed by Inouye et al. [1], comprising >6 million variants, presented the best predictive capacity (area under the receiver operating characteristic of 0.77) and was used in the following analyses. For the second aim, 3390 subjects (mean follow-up of 12.0±3.3 years), with 188 incident ASCVD, were analysed. Individuals in the top 20% of the PRS distribution had the same magnitude of association with ASCVD as current smokers or diabetic subjects (see Figure 1). Combining the PRS with SCORE2 led to a reclassification of 17.1% (95% CI, 4.7–29.5) of subjects in the intermediate-risk category (see Figure 2). Likewise, adding the PRS to PCE translated into an NRI of 19.2% (95% CI, 4.8–22.4) in the intermediate-risk category (not shown). Conclusion Using a Swiss population-based cohort, PRS presented good predictive capacities for ASCVD. Combining a PRS with clinical risk scores improved reclassification of risk for ASCVD, especially for subjects in the intermediate-risk category. Introducing PRS in clinical practice may refine cardiovascular prevention for subgroups of patients in whom prevention strategies are uncertain. Funding Acknowledgement Type of funding sources: None.

Introduction The European Society of Cardiology (ESC) released in 2021 a new cardiovascular risk prediction model, SCORE2. We aimed to: 1) compare the proportion of individuals included in each category of risk according to 2016 and 2021 ESC and 2019 American Heart Association/American College of Cardiology (AHA/ACC) guidelines on cardiovascular prevention; and 2) assess the discriminative and calibration performances of ESC SCORE1, SCORE2, and AHA/ACC Pooled Cohort Equations (PCE) to predict atherosclerotic cardiovascular disease (ASCVD). Methods We used data from the first follow-up of the CoLaus|PsyCoLaus study, a Swiss population-based cohort, including individuals (40–80 years) recruited between 2009–2012 and having a 10-year follow-up. We included participants without lipid-lowering treatment and free from ASCVD at baseline. We computed SCORE1, SCORE2 (SCORE2-OP in people >70 years) and PCE in individuals eligible for score computation according to each guidelines separately. We assessed the performance of the scores based on discrimination and calibration metrics using first incident ASCVD as outcome. Results Among 4,107 included participants (women, 55.7%), 128 (3.1%) experienced an incident ASCVD during a mean follow-up time of 8.1 (±1.9) years. Statins would be recommended or considered in 40.3% (95% of confident interval [CI], 38.3–42.3), 27.3% (25.4–29.1) and 35.5% (33.6–37.5) of women, and in 62.2% (60.0–64.5), 59.3% (57.0–61.5) and 65.4% (63.2–67.6) of men according to ESC 2016, ESC 2021 and AHA/ACC 2019 guidelines, respectively. Scores were computed in 3,456 (women, 58.2%), 3,318 (women, 57.1%) and 3,384 (women, 56.7%) participants in primary prevention according to ESC 2016, ESC 2021 and AHA/ACC 2019 guidelines, respectively. 50% of women and 17.4% of men developing an incident ASCVD were not eligible for lipid-lowering treatment at baseline according to SCORE2 (compared to 27.5% of women and 14.5% of men using SCORE1, and 42.1% of women and 14.9% of men using PCE). SCORE2 and PCE presented comparable discriminative capacities with area under the receiver operating characteristic (AUROC) of 0.776 (95% CI, 0.730–0.822) and 0.775 (0.729–0.821), respectively. SCORE1 presented a lower AUROC (0.717 [95% CI, 0.665–0.769], p-value=0.0001). All scores underestimated risk in subjects in intermediate deciles of risk and overestimated risk in people in high deciles of risk. SCORE2 was better calibrated in high-risk individuals compared to SCORE1 and PCE. Conclusions Based on ESC 2021 guidelines, if fully implemented, less than a third of women would be eligible for a lipid-lowering therapy, which is lower than according to ESC 2016 and AHA/ACC 2019 recommendations. Among women developing an ASCVD in this sample, half of them were not eligible for a lipid-lowering therapy based on ESC 2021 guidelines. Both SCORE2 and PCE presented good predictive capacities and could be interchangeably used in comparable populations. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): The CoLaus|PsyCoLaus study was and is supported by research grants from GlaxoSmithKline, the Faculty of Biology and Medicine of Lausanne, and the Swiss National Science Foundation (grants 3200B0–105993, 3200B0-118308, 33CSCO-122661, 33CS30-139468, 33CS30-148401 and 33CS30_177535/1).

UNSTRUCTURED The metaverse has been trending recently in various sectors, from real estate to arts and healthcare. It promises to revolutionize the way medical care is being delivered by transforming medical visits and consultations. As virtual patient-physician consultations via avatars inside the metaverse remains a hypothetical concept, we attempt to illustrate the design of an optimal cardiovascular medical consultation and how it can integrate remote exam and parameters acquisition (blood pressure, pulse oxymetry, heart frequency, ECG) in order to achieve an optimal virtual consultation. This type of consultation provides various benefits but equally presents challenges that need to be overcome.