Validation of the general Framingham Risk Score (FRS), SCORE2, revised PCE and WHO CVD risk scores in an Asian population

Kasim, Sazzli; Ibrahim, Nurulain; Malek, Sorayya; Ibrahim, Khairul Shafiq; Aziz, Muhammad Firdaus; Song, Cheen; Chia, Yook Chin; Ramli, Anis Safura; Negishi, Kazuaki; Nasir, Nafiza Mat

doi:10.1016/j.lanwpc.2023.100742

Cited by 22 publications

(5 citation statements)

References 27 publications

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“…The SCORE2 model was trained and validated on cohorts mostly in European regions and populations at low or moderate risk of CVD, whereas representative data from high and very high risk regions were not used [ 2 ]. Kasim S.S. et al considered the use of SCORE2 for Asian region and indicated that SCORE2 risk model would need to be recalibrated before it could be employed in a different population [ 14 ]. We decided to validate SCORE2 for the Russian population using data from the ESSE-RF epidemiological study.…”

Section: Discussionmentioning

confidence: 99%

Validation of SCORE2 on a sample from the Russian population and adaptation for the very high cardiovascular disease risk region

Svinin,

Kutsenko,

Shalnova

et al. 2024

PLoS ONE

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SCORE2 (Systematic COronary Risk Evaluation 2) is a risk assessment scale for cardiovascular events, presented in 2021 by the European Society of Cardiology. Both for training and validation of the SCORE2 model, representative samples from the Russian population were not used. Therefore, we aimed to validate SCORE2 on a such sample. For this purpose, we used a sample from the ESSE-RF epidemiological study consisting of 7251 participants aged 40–69 years without history of CVDs. We performed the validation by comparing SCORE2 risk estimates for ESSE-RF participants with the observed incidence of cardiovascular events in the study, adjusted for event information losses. The validation demonstrated that SCORE2 risk estimates were accurate for Russian men and inaccurate for Russian women. Together with the quantitative assessment of risk, SCORE2 offers its interpretation in terms of 10-year CVD risk group: low-moderate, high, and very high. For Russian men we considered the original interpretation of the SCORE2 estimates to be questionable because almost none of the men would be categorized as having “low-to-moderate” 10-year CVD risk. This problem would be typical for all countries of the very high CVD risk region. Therefore, we proposed a new interpretation of the SCORE2 risk estimates for men from the very high risk region. According to the proposed interpretation, the fraction of men in ESSE-RF in “low-to-moderate” 10-year CVD risk increased from 2% to 18% and the fraction of men in “very high” CVD risk decreased from 63% to 20% as compared to the original interpretation. The proposed interpretation would allow a more personalized approach to CVD treatment and optimize the burden on primary healthcare in the very high risk region countries.

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Section: Discussionmentioning

confidence: 99%

Validation of SCORE2 on a sample from the Russian population and adaptation for the very high cardiovascular disease risk region

Svinin,

Kutsenko,

Shalnova

et al. 2024

PLoS ONE

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“…A study conducted by van Trier et al validated both models and confirmed that in a population cohort from the UK, SCORE2 showed reasonable accuracy and consistency in predicting risk; however, SCORE2-OP performed poorly in discriminating and underestimated the risk for both males and females, particularly in individuals aged 70-80 years [30,31]. Nevertheless, it is futile to search for a perfect concept-according to Kasim et al, many similar predictive models have shown a tendency to overestimate the 10-year cardiovascular risk by a significant amount, ranging from 3% to 1430% [32]. In routine clinical practice, it is important for cardiovascular risk prediction models to be user-friendly (especially when considering that cardiovascular diseases are the primary contributors to mortality associated with diseases in the Polish population).…”

Section: Discussionmentioning

confidence: 99%

Body Mass Index and Waist Circumference as Predictors of Above-Average Increased Cardiovascular Risk Assessed by the SCORE2 and SCORE2-OP Calculators and the Proposition of New Optimal Cut-Off Values: Cross-Sectional Single-Center Study

Suwała,

Junik

2024

JCM

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Background: Obesity has been perceived as one of the important cardiovascular risk factors, but SCORE2 calculators used in clinical practice do not include the most popular parameters assessed for body composition: body mass index (BMI) and waist circumference (WC). The objective of this research was to determine which of the aforementioned variables is a more reliable predictor of an above-average increased cardiovascular risk for gender and age (ICVR). Methods: Data from 2061 patients were analyzed; the 10-year risk of cardiovascular events was assessed by SCORE2 tables, and the correlations with BMI and WC were analyzed. Results: BMI and WC independently predicted ICVR (OR 1.10–1.27). In males, BMI was a more accurate predictor (AUC = 0.816); however, in females, it was WC (AUC = 0.739). A novel threshold for BMI (27.6 kg/m2) was suggested, which increases the risk of cardiovascular disease by 3.3–5.3 times depending on gender; the same holds true for WC (93 cm in women and 99 cm in men; 3.8–4.8-fold higher risk). Conclusions: Despite their heterogeneity, BMI and WC are effective cardiovascular risk predictors, especially BMI for males and WC for females; therefore, more research is needed to include them in future models for predicting unfavorable cardiometabolic events.

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“…On the other hand, the Framingham model improved the detection of women's risk compared with the SCORE model. In general, it was found that the Framingham and SCORE models were more effective at identifying cardiovascular risk in Asian men and women than the WHO/ISH labels [27][28][29].…”

Section: Related Workmentioning

confidence: 99%

Cardiovascular Disease Preliminary Diagnosis Application Using SQL Queries: Filling Diagnostic Gaps in Resource-Constrained Environments

Doniec,

Berepiki,

Piaseczna

et al. 2024

Applied Sciences

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Cardiovascular diseases (CVDs) are chronic diseases associated with a high risk of mortality and morbidity. Early detection of CVD is crucial to initiating timely interventions, such as appropriate counseling and medication, which can effectively manage the condition and improve patient outcomes. This study introduces an innovative ontology-based model for the diagnosis of CVD, aimed at improving decision support systems in healthcare. We developed a database model inspired by ontology principles, tailored for the efficient processing and analysis of CVD-related data. Our model’s effectiveness is demonstrated through its integration into a web application, showcasing significant improvements in diagnostic accuracy and utility in resource-limited settings. Our findings indicate a promising direction for the application of artificial intelligence (AI) in early CVD detection and management, offering a scalable solution to healthcare challenges in diverse environments.

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Validation of the general Framingham Risk Score (FRS), SCORE2, revised PCE and WHO CVD risk scores in an Asian population

Cited by 22 publications

References 27 publications

Validation of SCORE2 on a sample from the Russian population and adaptation for the very high cardiovascular disease risk region

Validation of SCORE2 on a sample from the Russian population and adaptation for the very high cardiovascular disease risk region

Body Mass Index and Waist Circumference as Predictors of Above-Average Increased Cardiovascular Risk Assessed by the SCORE2 and SCORE2-OP Calculators and the Proposition of New Optimal Cut-Off Values: Cross-Sectional Single-Center Study

Cardiovascular Disease Preliminary Diagnosis Application Using SQL Queries: Filling Diagnostic Gaps in Resource-Constrained Environments

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