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      Cardiovascular Disease Prognostic Models in Latin America and the Caribbean : A Systematic Review

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          Abstract

          Background

          Cardiovascular prognostic models guide treatment allocation and support clinical decisions. Whether there are valid models for Latin American and Caribbean (LAC) populations is unknown.

          Objective

          This study sought to identify and critically appraise cardiovascular prognostic models developed, tested, or recalibrated in LAC populations.

          Methods

          The systematic review followed the CHARMS (CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) framework (PROSPERO [International Prospective Register of Systemic Reviews]: CRD42018096553). Reports were included if they followed a prospective design and presented a multivariable prognostic model; reports were excluded if they studied symptomatic individuals or patients. The following search engines were used: EMBASE, MEDLINE, Scopus, SciELO, and LILACS. Risk of bias assessment was conducted with PROBAST (Prediction model Risk Of Bias ASsessment Tool). No quantitative summary was conducted due to large heterogeneity.

          Results

          From 2,506 search results, 8 studies (N = 130,482 participants) were included for qualitative synthesis. We could not identify any cardiovascular prognostic model developed for LAC populations; reviewed reports evaluated available models or conducted a recalibration analysis. Only 1 study included a Caribbean population (Puerto Rico); 3 studies were retrieved from Chile; 2 from Argentina, Brazil, Colombia, and Uruguay; and 1 from Mexico. Four studies included population-based samples, and the other 4 included people affiliated to a health facility (e.g., prevention clinics). Most studied participants were older than 50 years, and there were more women in 5 reports. The Framingham model was assessed 6 times, and the American College of Cardiology/American Heart Association pooled equation was assessed twice. Across the prognostic models assessed, calibration varied widely from one population to another, showing great overestimation particularly in some subgroups (e.g., highest risk). Discrimination (e.g., C-statistic) was acceptable for most models; for Framingham it ranged from 0.66 to 0.76. The American College of Cardiology/American Heart Association pooled equation showed the best discrimination (0.78). That there were few outcome events was the most important methodological limitation of the identified studies.

          Conclusions

          No cardiovascular prognostic models have been developed in LAC, hampering key evidence to inform public health and clinical practice. Validation studies need to improve methodological issues.

          Highlights

          • There has never been a cardiovascular prognostic model developed in LAC.

          • Few studies have tested available models, but they have methodological limitations.

          • Discrimination estimates were acceptable across studies.

          • Calibration estimates showed important overestimation across studies.

          • Many countries in Latin America do not have tools for cardiovascular prevention.

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          Most cited references26

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          PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration

          Prediction models in health care use predictors to estimate for an individual the probability that a condition or disease is already present (diagnostic model) or will occur in the future (prognostic model). Publications on prediction models have become more common in recent years, and competing prediction models frequently exist for the same outcome or target population. Health care providers, guideline developers, and policymakers are often unsure which model to use or recommend, and in which persons or settings. Hence, systematic reviews of these studies are increasingly demanded, required, and performed. A key part of a systematic review of prediction models is examination of risk of bias and applicability to the intended population and setting. To help reviewers with this process, the authors developed PROBAST (Prediction model Risk Of Bias ASsessment Tool) for studies developing, validating, or updating (for example, extending) prediction models, both diagnostic and prognostic. PROBAST was developed through a consensus process involving a group of experts in the field. It includes 20 signaling questions across 4 domains (participants, predictors, outcome, and analysis). This explanation and elaboration document describes the rationale for including each domain and signaling question and guides researchers, reviewers, readers, and guideline developers in how to use them to assess risk of bias and applicability concerns. All concepts are illustrated with published examples across different topics. The latest version of the PROBAST checklist, accompanying documents, and filled-in examples can be downloaded from www.probast.org.
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            Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation.

            The Framingham Heart Study produced sex-specific coronary heart disease (CHD) prediction functions for assessing risk of developing incident CHD in a white middle-class population. Concern exists regarding whether these functions can be generalized to other populations. To test the validity and transportability of the Framingham CHD prediction functions per a National Heart, Lung, and Blood Institute workshop organized for this purpose. Sex-specific CHD functions were derived from Framingham data for prediction of coronary death and myocardial infarction. These functions were applied to 6 prospectively studied, ethnically diverse cohorts (n = 23 424), including whites, blacks, Native Americans, Japanese American men, and Hispanic men: the Atherosclerosis Risk in Communities Study (1987-1988), Physicians' Health Study (1982), Honolulu Heart Program (1980-1982), Puerto Rico Heart Health Program (1965-1968), Strong Heart Study (1989-1991), and Cardiovascular Health Study (1989-1990). The performance, or ability to accurately predict CHD risk, of the Framingham functions compared with the performance of risk functions developed specifically from the individual cohorts' data. Comparisons included evaluation of the equality of relative risks for standard CHD risk factors, discrimination, and calibration. For white men and women and for black men and women the Framingham functions performed reasonably well for prediction of CHD events within 5 years of follow-up. Among Japanese American and Hispanic men and Native American women, the Framingham functions systematically overestimated the risk of 5-year CHD events. After recalibration, taking into account different prevalences of risk factors and underlying rates of developing CHD, the Framingham functions worked well in these populations. The sex-specific Framingham CHD prediction functions perform well among whites and blacks in different settings and can be applied to other ethnic groups after recalibration for differing prevalences of risk factors and underlying rates of CHD events.
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              A guide to systematic review and meta-analysis of prediction model performance

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                Author and article information

                Contributors
                Journal
                Glob Heart
                Glob Heart
                Global Heart
                Elsevier Ltd
                2211-8160
                2211-8179
                1 March 2019
                March 2019
                : 14
                : 1
                : 81-93
                Affiliations
                []Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
                []CRONICAS Centre of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru
                []Facultad de Medicina “Alberto Hurtado”, Universidad Peruana Cayetano Heredia, Lima, Peru
                [§ ]Department of Public Health and Advanced Center for Chronic Diseases (ACCDiS), Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
                []Institute for Clinical Effectiveness and Health Policy (IECS), Buenos Aires, Argentina
                []Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, USA
                [# ]Centre for Global Chronic Conditions, London School of Hygiene and Tropical Medicine, London, UK
                Author notes
                []Correspondence: R. M. Carrillo-Larco r.carrillo-larco@ 123456Imperial.ac.uk
                Article
                S2211-8160(19)30014-6
                10.1016/j.gheart.2019.03.001
                6499414
                31036306
                47d34d09-4a29-4e0e-8eed-9d7a81706533
                © 2019 The Authors

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

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