Cardiovascular disease risk prediction models: challenges and perspectives
Authors: Farshad Farzadfar
Journal: The Lancet Global Health (2019)

Cardiovascular disease is the leading cause of death worldwide and a major public health concern. Therefore, its risk assessment is crucial to many existing treatment guidelines.1National Institutes of HealthNational Heart, LungBlood InstituteThird report of the expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III).https://www.healthypeople.gov/2020/tools-resources/evidence-based-resource/third-report-expert-panel-detection-evaluation-andDate: 2002Date accessed: August 27, 2019Google Scholar Risk estimates are also being used to predict the magnitude of future cardiovascular disease mortality and morbidity at the population level and in specific subgroups to inform policymakers and health authorities about these risks. Additionally, risk prediction inspires individuals to change their lifestyle and behaviour and to adhere to medications.2WHOImplementation tools: package of essential noncommunicable (PEN) disease interventions for primary health care in low-resource settings. World Health Organization, Geneva2013Google Scholar Although several risk prediction models of cardiovascular disease have been developed for different populations in the past decade,3Hajifathalian K Ueda P Lu Y et al.A novel risk score to predict cardiovascular disease risk in national populations (Globorisk): a pooled analysis of prospective cohorts and health examination surveys.Lancet Diabetes Endocrinol. 2015; 3: 339-355Summary Full Text Full Text PDF PubMed Scopus (110) Google Scholar the validity of these models is a cause of concern. Most data for model formation and validation have been provided from a small set of populations, mostly from developed countries.4Lloyd-Jones DM Cardiovascular risk prediction: basic concepts, current status, and future directions.Circulation. 2010; 121: 1768-1777Crossref PubMed Scopus (458) Google Scholar Therefore, using this set for the classification of individuals from different risk groups of other populations might lead to risk overestimation. This, in turn, can result in increased costs of guidelines and health interventions. These models might also cause risk underestimation, which can lead to missing vulnerable cases. Consequently, providing a valid model for cardiovascular disease risk classification of each population has become a high priority for scientists and organisations working in this field.3Hajifathalian K Ueda P Lu Y et al.A novel risk score to predict cardiovascular disease risk in national populations (Globorisk): a pooled analysis of prospective cohorts and health examination surveys.Lancet Diabetes Endocrinol. 2015; 3: 339-355Summary Full Text Full Text PDF PubMed Scopus (110) Google Scholar In a systematic effort to provide a population-specific risk prediction model, WHO and the International Society of Hypertension (ISH) released cardiovascular disease risk prediction charts for all WHO regions in 2007.5WHOPrevention of cardiovascular disease: guidelines for assessment and management of cardiovascular risk. World Health Organization, Geneva2007Google Scholar However, further validation studies showed that the WHO–ISH risk prediction charts had validity issues when used in populations from different countries.6Selvarajah S Kaur G Haniff J et al.Comparison of the Framingham Risk Score, SCORE and WHO/ISH cardiovascular risk prediction models in an Asian population.Int J Cardiol. 2014; 176: 211-218Summary Full Text Full Text PDF PubMed Scopus (78) Google Scholar In The Lancet Global Health, the WHO CVD Risk Chart Working Group7The WHO CVD Risk Chart Working GroupWorld Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions.Lancet Glob Health. 2019; (published online Sept 2.)http://dx.doi.org/10.1016/S2214-109X(19)30318-3Google Scholar presented results of a study that aimed to assess the WHO–ISH prediction models for 21 global regions, by using individual participant data from the Emerging Risk Factors Collaboration (376 177 individuals from 85 cohort studies, and 19 333 incident cardiovascular events recorded during 10 years of follow-up). The group recalibrated the models for 21 global regions—using age-specific and sex-specific incidence data from the Global Burden of Diseases, Injuries, and Risk Factors Study and risk factor values from Non-Communicable Disease (NCD) Risk Factor Collaboration—and estimated the external validity of the models using 19 cohort studies (1 096 061 individuals, 25 950 cardiovascular disease events) that were not used in the model derivation. The group used the models for 79 countries (mostly low-income and middle-income countries) with the WHO STEPwise Approach to Surveillance survey. They explored a wide range of risk predictions for a specific risk-factor profile among different global regions, finding substantial variation across global regions. The group also detected a wide range in the proportion of individuals aged 40–64 years with an estimated risk higher than 20% in the inspected countries, ranging from less than 1% in Uganda to more than 16% in Egypt. The study by the WHO CVD Risk Chart Working Group has provided new charts for 21 global regions, which help risk prediction in clinics and public health interventions within countries. However, as the group correctly mentioned in the discussion, the models might overestimate risk scores because they use incidence at the population level, which might include recurrent cases. Nevertheless, a growing number of observational studies with considerable follow-up in middle-income countries are showing promising results on the development and validation of country-specific prediction models or validation of existing risk prediction models.8Poustchi H Eghtesad S Kamangar F et al.Prospective epidemiological research studies in Iran (the PERSIAN Cohort Study): rationale, objectives, and design.Am J Epidemiol. 2017; 187: 647-655Crossref Scopus (109) Google Scholar Moreover, the intrinsic specification of a 10-year risk prediction, which gives considerable weight to the age variable, does not allow the model to single out the long-term risk, specifically in younger age groups. The same problem exists for women at a smaller scale.9Cavanaugh-Hussey MW Berry JD Lloyd-Jones DM Who exceeds ATP-III risk thresholds? Systematic examination of the effect of varying age and risk factor levels in the ATP-III risk assessment tool.Prev Med. 2008; 47: 619-623Crossref PubMed Scopus (63) Google Scholar A risk prediction model that is age and sex specific might be the solution, but it would be an expensive one because it requires substantial long-term observational data. Lifetime risk prediction is another approach to address the problem, which has gained attention in cardiovascular disease literature.10Tolfrey K American Heart Association guidelines for preventing heart disease in women: 2007 update.Phys Sportsmed. 2010; 38: 162-164Crossref PubMed Scopus (1) Google Scholar Although the effect of primary health care in controlling NCDs has been demonstrated before,11Farzadfar F Murray CJ Gakidou E et al.Effectiveness of diabetes and hypertension management by rural primary health-care workers (Behvarz workers) in Iran: a nationally representative observational study.Lancet. 2012; 379: 47-54Summary Full Text Full Text PDF PubMed Scopus (132) Google Scholar controversies still exist on the benefits of applying risk prediction compared with those of the risk factor approach in population-based interventions.12Basu S Wagner RG Sewpaul R Reddy P Davies J Implications of scaling up cardiovascular disease treatment in South Africa: a microsimulation and cost-effectiveness analysis.Lancet Glob Health. 2019; 7: e270-e280Summary Full Text Full Text PDF PubMed Scopus (16) Google Scholar Furthermore, several population studies targeting populations by age or by risk prediction models alone are ongoing, mostly based on the polypill.13Yusuf S Pais P et al.Indian Polycap StudyEffects of a polypill (Polycap) on risk factors in middle-aged individuals without cardiovascular disease (TIPS): a phase II, double-blind, randomised trial.Lancet. 2009; 373: 1341-1351Summary Full Text Full Text PDF PubMed Scopus (399) Google Scholar These polypill studies address two main concerns: effectiveness and cost of preventive interventions. In conclusion, risk prediction methods are clearly in their prime, and further efforts and resources are needed to gather more observational data with lengthy follow-ups to be able to derive population-specific models that address all the concerns of existing risk prediction models. More advanced population-specific models, based on greater data and methods, could end up being personalised risk assessments in the future. This online publication has been corrected. The corrected version first appeared at thelancet.com/lancetgh on Oct 1, 2019 This online publication has been corrected. The corrected version first appeared at thelancet.com/lancetgh on Oct 1, 2019 I declare no competing interests. World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regionsWe have derived, calibrated, and validated new WHO risk prediction models to estimate cardiovascular disease risk in 21 Global Burden of Disease regions. The widespread use of these models could enhance the accuracy, practicability, and sustainability of efforts to reduce the burden of cardiovascular disease worldwide. Full-Text PDF Open AccessCorrection to Lancet Glob Health 2019; 7: e1288–89Farzadfar F. Cardiovascular disease risk prediction models: challenges and perspectives. Lancet Glob Health 2019; 7: e1288–89—In this Comment, the second sentence of the fifth paragraph should read "However, as the group correctly mentioned in the discussion, the models might overestimate risk scores". This correction has been made as of Oct 1, 2019. Full-Text PDF Open Access