4
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      SCORE2 cardiovascular risk prediction models in an ethnic and socioeconomic diverse population in the Netherlands: an external validation study

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Summary

          Background

          Socioeconomic status and ethnicity are not explicitly incorporated as risk factors in the four SCORE2 cardiovascular disease (CVD) risk models developed for country-wide implementation across Europe (low, moderate, high and very-high model). The aim of this study was to evaluate the performance of the four SCORE2 CVD risk prediction models in an ethnic and socioeconomic diverse population in the Netherlands.

          Methods

          The SCORE2 CVD risk models were externally validated in socioeconomic and ethnic (by country of origin) subgroups, from a population-based cohort in the Netherlands, with GP, hospital and registry data. In total 155,000 individuals, between 40 and 70 years old in the study period from 2007 to 2020 and without previous CVD or diabetes were included. Variables (age, sex, smoking status, blood pressure, cholesterol) and outcome first CVD event (stroke, myocardial infarction, CVD death) were consistent with SCORE2.

          Findings

          6966 CVD events were observed, versus 5495 events predicted by the CVD low-risk model (intended for use in the Netherlands). Relative underprediction was similar in men and women (observed/predicted (OE-ratio), 1.3 and 1.2 in men and women, respectively). Underprediction was larger in low socioeconomic subgroups of the overall study population (OE-ratio 1.5 and 1.6 in men and women, respectively), and comparable in Dutch and the combined “other ethnicities” low socioeconomic subgroups. Underprediction in the Surinamese subgroup was largest (OE-ratio 1.9, in men and women), particularly in the low socioeconomic Surinamese subgroups (OE-ratio 2.5 and 2.1 in men and women). In the subgroups with underprediction in the low-risk model, the intermediate or high-risk SCORE2 models showed improved OE-ratios. Discrimination showed moderate performance in all subgroups and the four SCORE2 models, with C-statistics between 0.65 and 0.72, similar to the SCORE2 model development study.

          Interpretation

          The SCORE 2 CVD risk model for low-risk countries (as the Netherlands are) was found to underpredict CVD risk, particularly in low socioeconomic and Surinamese ethnic subgroups. Including socioeconomic status and ethnicity as predictors in CVD risk models and implementing CVD risk adjustment within countries is desirable for adequate CVD risk prediction and counselling.

          Funding

          doi 10.13039/501100005039, Leiden University Medical Centre; and doi 10.13039/501100001717, Leiden University; .

          Related collections

          Most cited references30

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019

          Cardiovascular diseases (CVDs), principally ischemic heart disease (IHD) and stroke, are the leading cause of global mortality and a major contributor to disability. This paper reviews the magnitude of total CVD burden, including 13 underlying causes of cardiovascular death and 9 related risk factors, using estimates from the Global Burden of Disease (GBD) Study 2019. GBD, an ongoing multinational collaboration to provide comparable and consistent estimates of population health over time, used all available population-level data sources on incidence, prevalence, case fatality, mortality, and health risks to produce estimates for 204 countries and territories from 1990 to 2019. Prevalent cases of total CVD nearly doubled from 271 million (95% uncertainty interval [UI]: 257 to 285 million) in 1990 to 523 million (95% UI: 497 to 550 million) in 2019, and the number of CVD deaths steadily increased from 12.1 million (95% UI:11.4 to 12.6 million) in 1990, reaching 18.6 million (95% UI: 17.1 to 19.7 million) in 2019. The global trends for disability-adjusted life years (DALYs) and years of life lost also increased significantly, and years lived with disability doubled from 17.7 million (95% UI: 12.9 to 22.5 million) to 34.4 million (95% UI:24.9 to 43.6 million) over that period. The total number of DALYs due to IHD has risen steadily since 1990, reaching 182 million (95% UI: 170 to 194 million) DALYs, 9.14 million (95% UI: 8.40 to 9.74 million) deaths in the year 2019, and 197 million (95% UI: 178 to 220 million) prevalent cases of IHD in 2019. The total number of DALYs due to stroke has risen steadily since 1990, reaching 143 million (95% UI: 133 to 153 million) DALYs, 6.55 million (95% UI: 6.00 to 7.02 million) deaths in the year 2019, and 101 million (95% UI: 93.2 to 111 million) prevalent cases of stroke in 2019. Cardiovascular diseases remain the leading cause of disease burden in the world. CVD burden continues its decades-long rise for almost all countries outside high-income countries, and alarmingly, the age-standardized rate of CVD has begun to rise in some locations where it was previously declining in high-income countries. There is an urgent need to focus on implementing existing cost-effective policies and interventions if the world is to meet the targets for Sustainable Development Goal 3 and achieve a 30% reduction in premature mortality due to noncommunicable diseases.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            2021 ESC Guidelines on cardiovascular disease prevention in clinical practice

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement

                Bookmark

                Author and article information

                Contributors
                Journal
                eClinicalMedicine
                EClinicalMedicine
                eClinicalMedicine
                Elsevier
                2589-5370
                16 February 2023
                March 2023
                16 February 2023
                : 57
                : 101862
                Affiliations
                [a ]Health Campus The Hague, Leiden University Medical Centre, The Hague, The Netherlands
                [b ]Department of Internal Medicine, HAGA Teaching Hospital, The Hague, The Netherlands
                [c ]National Institute for Public Health and the Environment, Bilthoven, The Netherlands
                [d ]National eHealth Living Lab, Leiden University Medical Centre, Leiden, The Netherlands
                [e ]Department of Internal Medicine, HMC Hospital, The Hague, The Netherlands
                [f ]Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, The Netherlands
                [g ]Department of Biomedical Data Science, Leiden University Medical Centre, Leiden, The Netherlands
                Author notes
                []Corresponding author. J.M.Kist@ 123456LUMC.nl
                Article
                S2589-5370(23)00039-1 101862
                10.1016/j.eclinm.2023.101862
                9971516
                36864978
                25e40a4b-891b-48b2-a04d-14031ff4ab08
                © 2023 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 17 November 2022
                : 19 January 2023
                : 26 January 2023
                Categories
                Articles

                cardiovascular disease,health disparities,risk factors,socioeconomic factors,ethnicity

                Comments

                Comment on this article