Cited by 366Health Promotion and Cardiovascular PreventionCardiovascular Health and Risk FactorsLipoproteins and Cardiovascular Health

The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.

Laboratory-based versus non-laboratory-based method for assessment of cardiovascular disease risk: the NHANES I Follow-up Study cohort

Open access

Gaziano, Thomas A, Young, Cynthia R, Fitzmaurice, Garrett, Atwood, Sidney, Gaziano, J Michael·2008·The Lancet

DOI 10.1016/s0140-6736(08)60418-3PMC PMC2864150

Open this paper in CoChat — free account

Abstract

Background Around 80% of all cardiovascular deaths occur in developing countries. Assessment of those patients at high risk is an important strategy for prevention. Since developing countries have limited resources for prevention strategies that require laboratory testing, we assessed if a risk prediction method that did not require any laboratory tests could be as accurate as one requiring laboratory information.Methods The National Health and Nutrition Examination Survey (NHANES) was a prospective cohort study of 14 407 US participants aged between 25-74 years at the time they were first examined (between 1971 and 1975). Our follow-up study population included participants with complete information on these surveys who did not report a history of cardiovascular disease (myocardial infarction, heart failure, stroke, angina) or cancer, yielding an analysis dataset N=6186. We compared how well either method could predict first-time fatal and non-fatal cardiovascular disease events in this cohort. For the laboratory-based model, which required blood testing, we used standard risk factors to assess risk of cardiovascular disease: age, systolic blood pressure, smoking status, total cholesterol, reported diabetes status, and current treatment for hypertension. For the non-laboratory-based model, we substituted body-mass index for cholesterol.Findings In the cohort of 6186, there were 1529 first-time cardiovascular events and 578 (38%) deaths due to cardiovascular disease over 21 years. In women, the laboratory-based model was useful for predicting events, with a c statistic of 0.829. The c statistic of the non-laboratory-based model was 0.831. In men, the results were similar (0.784 for the laboratory-based model and 0.783 for the non-laboratory-based model). Results were similar between the laboratory-based and non-laboratory-based models in both men and women when restricted to fatal events only.Interpretation A method that uses non-laboratory-based risk factors predicted cardiovascular events as accurately as one that relied on laboratory-based values. This approach could simplify risk assessment in situations where laboratory testing is inconvenient or unavailable.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1016/s0140-6736(08)60418-3

Cites

34 of 34 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Evaluation of Factors Related to Late Recurrence - Later than 10 Years after the Initial Treatment - in Primary Breast Cancer
Nishimura, R., Osako, T., Nishiyama, Y., Tashima, R., Nakano, M., Fujisue, M., Toyozumi, Y., & Arima, N. (2013) · Oncology · doi:10.1159/000353099 · Cited by 13,239
Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study
Yusuf, S., Hawken, S., Ôunpuu, S., Dans, T., Avezum, A., Lanas, F., McQueen, M., Budaj, A., Pais, P., Varigos, J., et al. (2004) · The Lancet · doi:10.1016/s0140-6736(04)17018-9 · Cited by 12,026
Prediction of coronary heart disease using risk factor categories
Wilson, P. W. F., D’Agostino, R. B., Levy, D., Belanger, A. M., Silbershatz, H., & Kannel, W. B. (1998) · Circulation · doi:10.1161/01.cir.97.18.1837 · Cited by 9,581
Global burden of hypertension: analysis of worldwide data
Kearney, P. M., Whelton, M., Reynolds, K., Muntner, P., Whelton, P. K., & He, J. (2005) · The Lancet · doi:10.1016/s0140-6736(05)17741-1 · Cited by 8,698
Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project
Conroy, R. (2003) · European Heart Journal · doi:10.1016/s0195-668x(03)00114-3 · Cited by 5,290
Mortality by cause for eight regions of the world: Global Burden of Disease Study
Murray, C. J., & Lopez, A. D. (1997) · The Lancet · doi:10.1016/s0140-6736(96)07493-4 · Cited by 4,043
Disease control priorities in developing countries
(2006) · Choice Reviews Online · doi:10.5860/choice.44-0343 · Cited by 3,636
Evaluating the Yield of Medical Tests
Harrell, F. E. (1982) · JAMA: The Journal of the American Medical Association · doi:10.1001/jama.1982.03320430047030 · Cited by 3,490
Global Burden of Disease and Risk Factors
López, A. D., Mathers, C. D., Ezzati, M., Jamison, D. T. T., & Murray, C. J. L. (2006) · World Bank, Washington, DC eBooks · doi:10.1596/978-0-8213-6262-4 · Cited by 2,714
Cardiovascular disease risk profiles
Anderson, K. M., Odell, P. M., Wilson, P. W. F., & Kannel, W. B. (1991) · American Heart Journal · doi:10.1016/0002-8703(91)90861-b · Cited by 2,163

Show all 34 references

Research works printed but not linked: 1

Conroy, 2003, Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project, Eur Heart J, 24, 987, 10.1016/S0195-668X(03)00114-3 · DOI · Paper page

Sorted by citations

Items 201–300 of 366

Usefulness of Diabetes Mellitus to Predict Long-Term Outcomes in Patients With Unstable Angina Pectoris
Farkouh, M. E., Aneja, A., Reeder, G. S., Smars, P. A., Lennon, R. J., Wiste, H. J., Traverse, K., Razzouk, L., Basu, A., Holmes, D. R., et al. (2009) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2009.04.007 · Cited by 8
Challenges Facing Successful Scaling Up of Effective Screening for Cardiovascular Disease by Community Health Workers in Mexico and South Africa: Policy Implications.
Abrahams‐Gessel, S., Ca, D., Ta, G., Levitt, N. S., & Puoane, T. R. (2016) · PubMed · Cited by 8
Knowledge, attitudes, and preventive practices regarding ischemic heart disease among HIV‐positive individuals in northern Tanzania
Prattipati, S., Mlangi, J. J., Tarimo, T. G., Kweka, G. L., Thielman, N. M., Bettger, J. P., Mmbaga, B. T., Sakita, F. M., & Hertz, J. T. (2021) · Tropical Medicine & International Health · doi:10.1111/tmi.13690 · Cited by 8
Predicting Coronary Heart Disease risk using health risk assessment data
Mohawish, A., Rathi, R., Abhishek, V., Lauritzen, T. Z., & Padman, R. (2015) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom.2015.7454479 · Cited by 8
Statin Eligibility, Coronary Artery Calcium, and Subsequent Cardiovascular Events According to the 2016 United States Preventive Services Task Force (USPSTF) Statin Guidelines: MESA (Multi‐Ethnic Study of Atherosclerosis)
Miedema, M. D., Dardari, Z., Kianoush, S., Virani, S. S., Yeboah, J., Knickelbine, T., Sandfort, V., Rodríguez, C. J., Nasir, K., & Blaha, M. J. (2018) · Journal of the American Heart Association · doi:10.1161/jaha.118.008920 · Cited by 8
Myocardial Biomarkers for Prediction of Cardiovascular Disease
Sundström, J. (2009) · Disease Markers · doi:10.1155/2009/436740 · Cited by 9
Association between psychosocial work-related factors at midlife and arterial stiffness at older age in a prospective cohort of 1736 white-collar workers
Massamba, V. K., Talbot, D. C., Milot, A., Trudel, X., Dionne, C. E., Vézina, M., Mâsse, B. R., Gilbert‐Ouimet, M., Dagenais, G. R., Pearce, N. E., et al. (2023) · BMJ Open · doi:10.1136/bmjopen-2023-073649 · Cited by 7
Reach and effectiveness of a HEARTS hypertension pilot project in Guatemala
Wellmann, I. A., Rodríguez, J. J., Batzin, B., Hegel, G., Ayala, L. F., Ozano, K., Fort, M. P., Flores, W., Ramírez, L., Palacios, E., et al. (2024) · Revista Panamericana de Salud Pública · doi:10.26633/rpsp.2024.100 · Cited by 7
Assessing the global risk of cardiovascular disease among a group of university students: population-based cross-sectional study in Yaoundé, Cameroon
Nansseu, J. R., Assah, F. K., Petnga, S.-J. N., Kameni, B. S., Tene, H. D. F., Terrenstra, F. N., Wouna, D. L. A., Noubiap, J. J., & Kamgno, J. (2019) · BMJ Open · doi:10.1136/bmjopen-2019-030594 · Cited by 7
Effect of a Triage‐Based Screening Protocol on Diagnosis and Treatment of Acute Coronary Syndrome in a Tanzanian Emergency Department: A Prospective Pre‐Post Study
Hertz, J. T., Sakita, F. M., Kweka, G. L., Bloomfield, G. S., Bartlett, J. A., Tarimo, T. G., Temu, G. A., Bettger, J. P., & Thielman, N. M. (2020) · Journal of the American Heart Association · doi:10.1161/jaha.120.016501 · Cited by 7
Global Cardiovascular Risk Profile and Cerebrovascular Abnormalities in Presymptomatic Individuals with CADASIL or Autosomal Dominant Alzheimer’s Disease
Schoemaker, D., Velilla-Jiménez, L. M., Zuluaga, Y., Baena, A. Y., Ospina, C., Bocanegra, Y., Álvarez, S., Ochoa‐Escudero, M., Guzmán‐Vélez, E., Martinez, J. E., et al. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-210313 · Cited by 7
An office-based cardiovascular prediction model developed and validated in cohort studies of a middle-income country
Fahimfar, N., Malekzadeh, R., Fotouhi, A., Mansournia, M. A., Sarrafzadegan, N., Azizi, F., Sepanlou, S. G., Mansourian, M., Hadaegh, F., Emamian, M. H., et al. (2021) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2021.12.017 · Cited by 7
Foundation/American Heart Association Task Force on Practice Guidelines Asymptomatic Adults : A Report of the American College of Cardiology 2010 ACCF/AHA Guideline for Assessment of Cardiovascular Risk in
Smith, S. C., Taylor, A. J., Weintraub, W. S., Kushner, F. G., Lauer, M. S., Benjamin, J. M., Budoff, M. J., Fayad, Z. A., Foster, E., Greenland, P., et al. (2011) · Cited by 7
Cardiovascular Risk Assessment in Developing World
Chamnan, P., & Aekplakorn, W. (2017) · InTech eBooks · doi:10.5772/intechopen.69041 · Cited by 7
Lipid profile prediction based on artificial neural networks
Vrbaški, M., Doroslovački, R., Kupusinac, A., Stokić, E., & Ivetić, D. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-019-01374-3 · Cited by 8
Investigation of the cardiovascular risk profile in a south Brazilian city: surveys from 2012 to 2016
Rissardo, J. P., Caprara, A. L. F., Prado, A. L. C., & Leite, M. T. B. (2018) · Arquivos de Neuro-Psiquiatria · doi:10.1590/0004-282x20180020 · Cited by 9
The Year in Atherothrombosis
Sanz, J., Moreno, P., & Fuster, V. (2009) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2008.12.047 · Cited by 8
Necesidad de integración de salubristas, epidemiólogos y clínicos en la atención de pacientes con enfermedades crónicas
Brito, A. E., & García, P. O. O. (2010) · Revista Cubana de Salud Pública · doi:10.1590/s0864-34662010000300011 · Cited by 7
Discrimination and calibration performances of non-laboratory-based and laboratory-based cardiovascular risk predictions: a systematic review
Alemu, M., Alemu, S. M., Bagheri, N., Wangdi, K., & Chateau, D. G. (2025) · Open Heart · doi:10.1136/openhrt-2024-003147 · Cited by 6
The Global Perspective of Ischemic Heart Disease
Lawlor, M. K., Gersh, B. J., Opie, L. H., & Gaziano, T. A. (2017) · Elsevier eBooks · doi:10.1016/b978-0-323-42880-4.00002-9 · Cited by 6
Self-citation
Cardiovascular Disease Risk Screening for Commercial Drivers Examined in Occupational Practice: Implementing Evidence-Based Practice to Champion the Health of Essential Workers
Barnes, A. (2023) · Workplace Health & Safety · doi:10.1177/21650799231184374 · Cited by 6
Subclinical Cardiovascular Disease Assessment in Persons with Diabetes
Warraich, H. J., & Nasir, K. (2013) · Current Cardiology Reports · doi:10.1007/s11886-013-0358-2 · Cited by 7
Integrated Preclinical Cardiovascular Prevention: A New Paradigm to Face Growing Challenges of Cardiovascular Disease
Volpe, M., Mastromarino, V., & Battistoni, A. (2015) · American Journal of Cardiovascular Drugs · doi:10.1007/s40256-015-0114-7 · Cited by 6
Framingham score and work-related variables for predicting cardiovascular disease in the working population
van der Zwaard, A.-J., Geraedts, A. S., Norder, G., Heymans, M. W., & Roelen, C. A. M. (2019) · European Journal of Public Health · doi:10.1093/eurpub/ckz008 · Cited by 7
Factors Associated with Coronary Heart Disease among Elderly People in Different Communities
Ferdushi, K. F., Kamil, A. A., Hasan, M. N., & Islam, T. (2020) · Springer eBooks · doi:10.1007/978-981-15-1735-8_16 · Cited by 6
Risk Assessment of Cardiovascular Diseases Using kNN and Decision Tree Classifier
Jadhav, A. D., & Chobe, S. V. (2022) · Revue d intelligence artificielle · doi:10.18280/ria.360118 · Cited by 6
Gender disparities in the association between socio-demographics and non-communicable disease risk factors among adults with disabilities in Shanghai, China
Zhang, Y., Chen, G., Zhang, Q., Lü, J., & Hui-jiong, Y. (2018) · PeerJ · doi:10.7717/peerj.4505 · Cited by 6
Patient-oriented evidence that matters
Boden, W. E., O’Rourke, R. A., & Teo, K. K. (2007) · JAAPA · doi:10.1097/01720610-200707000-00014 · Cited by 6
Body-mass index and mortality
Gaziano, T. A., & Opie, L. H. (2009) · The Lancet · doi:10.1016/s0140-6736(09)61279-4 · Cited by 6
Self-citation
Safety, tolerability and immunogenicity of a condensed, multi-dose prime regimen of PfSPZ Vaccine for the prevention of Plasmodium falciparum malaria infection
Berry, A. A., Richie, T. L., Church, L. W. P., Laurens, M. B., Boyce, C., Natasha, K. C., Joshi, S. N., Koudjra, A. R., Butler, L., Chen, M., et al. (2025) · Malaria Journal · doi:10.1186/s12936-025-05299-5 · Cited by 8
IMRAS—Immunization with radiation-attenuated Plasmodium falciparum sporozoites by mosquito bite: Cellular immunity to sporozoites, CSP, AMA1, TRAP and CelTOS
Sedegah, M., Hollingdale, M., Ganeshan, H., Belmonte, M., Huang, J., Belmonte, A., Inoue, S., Velasco, R., Hickey, B. W., Teneza-mora, N., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0256396 · Cited by 5
The Improved but Unfinished Business of Stroke Risk Stratification in Atrial Fibrillation
Banerjee, A., Marín, F. R., & Lip, G. Y. H. (2011) · Revista Española de Cardiología (English Edition) · doi:10.1016/j.rec.2011.04.004 · Cited by 5
Non-laboratory-based prediction of cardiovascular risk
Mendis, S., & Mohan, V. (2008) · The Lancet · doi:10.1016/s0140-6736(08)60392-x · Cited by 5
Degree of Agreement between Cardiovascular Risk Stratification Tools
Garcia, G. T., Stamm, A. M. N. D. F., Rosa, A. C., Marasciulo, A. C. E., Marasciulo, R. C., Battistella, C., & Remor, A. A. D. C. (2017) · Arquivos Brasileiros de Cardiologia · doi:10.5935/abc.20170057 · Cited by 5
Non-Laboratory-Based Risk Factors for Automated Heart Disease Detection
Mai, H., Pham, T. T. T., Nguyen, D. N., & Dutkiewicz, E. (2018) · International Symposium on Medical Information and Communication Technology · doi:10.1109/ismict.2018.8573706 · Cited by 5
Simple cardiovascular risk stratification by replacing total serum cholesterol with anthropometric measures: The MORGAM prospective cohort project
Rosberg, V., Vishram‐Nielsen, J. K. K., Kristensen, A. M. D., Pareek, M., Sehested, T. S. G., Nilsson, P. M., Linneberg, A. R., Palmieri, L., Giampaoli, S., Donfrancesco, C., et al. (2022) · Preventive Medicine Reports · doi:10.1016/j.pmedr.2022.101700 · Cited by 5
Hypertension, the changing pattern of drug usage.
Opie, L. H. (2009) · PubMed Central · Cited by 5
Cardiovascular risk estimation - making sense of the numbers
Viljoen, A. (2008) · International Journal of Clinical Practice · doi:10.1111/j.1742-1241.2008.01868.x · Cited by 5
BLUMENTHAL SCREENING FOR CARDIOVASCULAR RISK IN ASYMPTOMATIC PATIENTS
Berger, J. S., Jordan, C. O., Lloyd‐Jones, D. M., & Blumenthal, R. S. (2010) · Rational Pharmacotherapy in Cardiology · doi:10.20996/1819-6446-2010-6-3-381-390 · Cited by 5
Frequency oF cardIovascular rIsk Factors In volunteers attendIng a coMMunIty-based health educatIon InterventIon
de Paula, M. J. (2009) · Cited by 5
Family History of Heart Disease
Assimes, T. L. (2011) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2010.09.036 · Cited by 5
Using mHealth tools to improve access, coverage and treatment of uninsured people with high cardiovascular disease risk in Argentina: a study protocol for a pragmatic cluster randomised trial
Abrahams‐Gessel, S., Beratarrechea, A. G., Irazola, V. E., Gutiérrez, L., Moyano, D. L., Fernandez, A., Ciganda, Á., Celi, L. A., Winkler, E., Rubinstein, A., et al. (2018) · BMJ Innovations · doi:10.1136/bmjinnov-2017-000255 · Cited by 4
Self-citation
Cardiovascular event risk estimation among residents of a rural setting in Bayelsa state, Nigeria.
Okoro, T. E., & Jumbo, J. (2021) · PubMed · Cited by 5
Nomogram to predict risk of incident chronic kidney disease in high-risk population of cardiovascular disease in China: community-based cohort study
Zhang, Q., Zhang, J., Lei, L., Liang, H., Li, Y., Lu, J., Zhou, S., Li, G., Zhang, X., Chen, Y., et al. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-047774 · Cited by 4
A cardiovascular disease risk prediction algorithm for use with the Medicare current beneficiary survey
Fouayzi, H., Ash, A. S., & Rosen, A. K. (2020) · Health Services Research · doi:10.1111/1475-6773.13290 · Cited by 4
Surveillance of Cardiovascular Risk Factors in the Fifth Military Sector Health Center, Ngaoundéré, Cameroon: Observational Study
Ngan, W. B., Belinga, L. E. E., Nlo’o, A. S. P. E., Roche, F., Goethals, L., Mandenguè, S. H., & Bongué, B. (2020) · JMIR Formative Research · doi:10.2196/18567 · Cited by 4
CARDIOVASCULAR RISK PREDICTION IN CHILDREN - WITH FOCUS ON OBESITY
Bjelakovic, B. B. (2015) · Preventive Paediatrics · doi:10.46793/pp151102005b · Cited by 4
THE HEART OF STRENGTH: THE STRONG BLACK WOMAN SCHEMA AND CARDIOVASCULAR DISEASE RISK
Abrams, J. A. (2015) · VCU Scholars Compass (Virginia Commonwealth University) · doi:10.25772/zsdp-0x86 · Cited by 4
Combinatorial pattern-based survival analysis with applications in biology and medicine
Reddy, A. (2009) · Rutgers University Community Repository (Rutgers University) · doi:10.7282/t3pg1rxq · Cited by 4
Development of a CVD mortality risk score using nutritional predictors: A risk prediction model in the Golestan Cohort Study
Jabbari, M., Barati, M., Kalhori, A., Eini‐Zinab, H., Zayeri, F., Poustchi, H., Pourshams, A., Hekmatdoost, A., & Malekzadeh, R. (2024) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2024.10.008 · Cited by 3
Evaluating the Association of Anthropometric Indices With Total Cholesterol in a Large Population Using Data Mining Algorithms
Yousefabadi, S. A., Hafezi, S. G., Kooshki, A., Hosseini, M., Mansoori, A., Ghamsary, M. G., Esmaily, H., & Ghayour‐Mobarhan, M. (2024) · Journal of Clinical Laboratory Analysis · doi:10.1002/jcla.25095 · Cited by 3
Comparison of laboratory‐based and non‐laboratory‐based cardiovascular risk prediction tools in rural India
Birhanu, M. M., Zengin, A., Evans, R. G., Kim, J., Olaiya, M. T., Riddell, M. A., Kalyanram, K., Kartik, K., Suresh, O., Thomas, N., et al. (2024) · Tropical Medicine & International Health · doi:10.1111/tmi.14069 · Cited by 3
Social Network Lifestyle Interventions Reduce Non-Communicable Diseases Risk Factors in Financial Sector Employees
Hene, N., Wood, P., Schwellnus, M. P., Jordaan, E., & Laubscher, R. (2021) · Journal of Occupational and Environmental Medicine · doi:10.1097/jom.0000000000002474 · Cited by 3
Enhancing feedback by health coaching: the effectiveness of mixed methods approach to long-term physical activity changes in nurses. An intervention study
Nerek, A., Wesołowska-Górniak, K., & Czarkowska‐Pączek, B. (2024) · BMC Nursing · doi:10.1186/s12912-024-01815-1 · Cited by 3
Cardiovascular Risk Assessment—Where Are We Going?
Wilson, P. W. F. (2008) · Preventive Cardiology · doi:10.1111/j.1751-7141.2008.07770.x · Cited by 3
Harder-to-Treat Patients: Recognizing Them and Adapting Treatment Strategies
Miedema, M. D., & Virani, S. S. (2016) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2016.05.027 · Cited by 3
Bolstering the Business Case for Adoption of Shared Decision-Making Systems in Primary Care: Randomized Controlled Trial
Sperl‐Hillen, J. M., ANDERSON, J. P., Margolis, K. L., Rossom, R. C., Kopski, K. M., Averbeck, B. M., Rosner, J. A., Ekstrom, H. L., Dehmer, S. P., & O’Connor, P. J. (2022) · JMIR Formative Research · doi:10.2196/32666 · Cited by 4
Body-mass index and mortality – Authors' reply
Whitlock, G., Lewington, S., Sherliker, P., & Peto, R. (2009) · The Lancet · doi:10.1016/s0140-6736(09)61280-0 · Cited by 3
Assessment of cardiovascular risk in low resource settings “So much to do - So little done”
Hariram, V., & Arramraju, S. K. (2016) · Indian Heart Journal · doi:10.1016/j.ihj.2015.07.046 · Cited by 3
Quantifying cardiovascular autonomic aging with machine learning
Schumann, A., Gupta, Y., Geisler, M., de la Cruz, F., Gerstorf, D., Demuth, I., Olecka, M., Gaser, C., & Bär, K.-J. (2025) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00693.2025 · Cited by 2
Explaining population trends in cardiovascular risk: protocol for a comparative analysis of health transitions in South Africa and England using nationally representative survey data
Adjaye-Gbewonyo, K., & Cois, A. (2022) · BMJ Open · doi:10.1136/bmjopen-2022-061034 · Cited by 2
Cardio-Vascular Risk Detection System using different Machine Learning Techniques
Parmar, B., Patel, S., Kanani, J., Vaghasia, M., & Patel, K. (2022) · IEEE International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC) · doi:10.1109/isssc56467.2022.10051216 · Cited by 2
¿Podemos estratificar el riesgo cardiovascular en la población argentina?
Santos-Gallego, C. G., & Badimón, J. J. (2015) · Revista Argentina de Cardiología · doi:10.7775/rac.es.v82.i6.5473 · Cited by 2
Cardiovascular risk estimation tailored to different clinical settings – the Tromsø study
Borglykke, A., Jørgensen, T., Andreasen, A. H., Wilsgaard, T., Mathiesen, E. B., Løchen, M., & Njølstad, I. (2010) · Scandinavian Cardiovascular Journal · doi:10.3109/14017431.2010.483612 · Cited by 2
Prediction of Cardiovascular Disease
Wilson, P. W. F. (2011) · Elsevier eBooks · doi:10.1016/b978-1-4377-1366-4.00003-2 · Cited by 2
Big Data Predictive Analytics Model for Cardiovascular Risk Detection using Machine Learning Techniques
Kanani, J., Patel, S., Parmar, B., Vaghasia, M., & Patel, K. (2022) · IEEE International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC) · doi:10.1109/isssc56467.2022.10051293 · Cited by 2
Hyperlipidaemia and cardiovascular disease: apolipoprotein measurements and metabolic syndrome as predictors of cardiovascular risk
Deans, K. A., & Dominiczak, M. H. (2008) · Current Opinion in Lipidology · doi:10.1097/mol.0b013e328316c030 · Cited by 2
Development and application of statistical methods for prognosis research
Snell, K. I. (2015) · University of Birmingham Institutional Research Archive (University of Birmingham) · Cited by 2
Fixed-dose combination therapy for the prevention of cardiovascular disease
de Cates, A. N., Farr, M. R. B., Rees, K., Casas, J. P., & Huffman, M. D. (2012) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd009868 · Cited by 2
General Population and Global Cardiovascular Risk Prediction
Lloyd‐Jones, D. M. (2017) · Elsevier eBooks · doi:10.1016/b978-0-323-42973-3.00001-9 · Cited by 1
Evaluation of Cardiovascular Diseases Risk in the Iranian Population
Akhlaghdoust, M., Pirani, D., Nasiri, M., Ahangarani, S. L., Haghsetan, N., Karbalaiea, M., & Davoodi, P. (2021) · Precision Medicine and Clinical OMICS · doi:10.5812/pmco.121308 · Cited by 1
Inequalities in risk stratification and management of non-communicable diseases
Mocumbi, A. O. (2023) · The Lancet Global Health · doi:10.1016/s2214-109x(23)00364-9 · Cited by 1
Greenness, blueness, and whiteness evaluation of a quantitative nuclear magnetic resonance procedure for concurrent assay of aspirin and omeprazole in their single and fixed-dose combined tablets
El‐Masry, A. A., Zeid, A. M., & Abdallah, N. A. (2025) · BMC Chemistry · doi:10.1186/s13065-025-01477-3 · Cited by 1
Poor sleep health is associated with worse cardiometabolic risk among rural and urban schooled Nigerian adolescents
Olorunmoteni, O. E., Scheuermaier, K. D., Fatusi, A. O., Akande, J. I., & Gómez-Olivé, F. X. (2025) · Scientific Reports · doi:10.1038/s41598-025-25659-0 · Cited by 1
Implementation of cardiovascular disease risk prediction scores in Sub-Saharan Africa: a scoping review
Gouws, M., Araya, N., Harris, E., Gaziano, T. A., English, M., & Keene, C. (2026) · BMC Health Services Research · doi:10.1186/s12913-026-15378-1 · Cited by 1
Self-citation
Usefulness of Reynolds Risk Score in men with stable angina
Piętka, I., Sakowicz, A., Pietrucha, T., Cichocka‐Radwan, A., & Lelonek, M. (2013) · Open Medicine · doi:10.2478/s11536-013-0240-z · Cited by 1
CARDIOVASCULAR DISEASE RISK FACTOR ESTIMATION IN GUJARATI ASIAN INDIAN POPULATION USING FRAMINGHAM RISK EQUATION
Sahoo, S., Shah, K. H., Shukla, A., Prajapati, J., & Shah, P. (2014) · National Journal of Medical Research · Cited by 1
Predictive Value of Cardiovascular Risk Functions: Limitations and Future Potential
Baena-Díez, J. M., Ramos, R., & Marrugat, J. (2009) · Revista Española de Cardiología · Cited by 1
Cardiovascular risk calculation
Ker, J. A. (2014) · South African Family Practice · doi:10.1080/20786204.2014.932545 · Cited by 1
Every unhealthy population is unhealthy in its own way; population risk assessment: common and specific challenges
Vikhireva, O. (2012) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2012-201231 · Cited by 1
The discrepancy between perceived and estimated absolute risks of coronary heart disease in Middle Eastern women : implications for cardiac rehabilitation
Gholizadeh, L. (2008) · Cited by 1
ACCF/AHA Practice Guideline
Yancy, C. W., Jessup, M. L., Bozkurt, B., Butler, J., Drazner, M. H., Fonarow, G. C., Geraci, S. A., Horwich, T. B., Januzzi, J. L., Johnson, M. R., et al. (2013) · Cited by 1
Profile of vascular age and vascular response among elderly patients receiving antihypertensive therapy
Pathapati, R. M., Reddy, C. B., Madhavi, P., Abhishek, T. G., Kudagi, B. L., & Buchineni, M. (2017) · International Journal of Basic & Clinical Pharmacology · doi:10.18203/2319-2003.ijbcp20174137 · Cited by 1
Algorithm for assessing the total 10 years risk of death from cardiovascular diseases in women 25-64 years old in Tyumen (Tyumen risk scale)
Pushkarev, G. S., Мацкеплишвили, С. Т., Анатольевич, К. В., & Akimova, E. V. (2021) · Eurasian heart journal · doi:10.38109/2225-1685-2021-3-14-21 · Cited by 1
Psychological stress and cardiovascular risk among women in Brazilian communities: a cross-sectional study
Mattos, A. J. C., Avezum, Á., França, J. Í. D., Izar, M. C. D. O., Ferreira, J. F. M., Drager, L. F., Saraiva, J. F. K., & Fonseca, H. A. R. (2024) · Cadernos de Saúde Pública · doi:10.1590/0102-311xen234522 · Cited by 1
Closing gaps in evidence-based care for Acute Myocardial Infarction in northern Tanzania: insights from a prospective study
Adisa, O., Brown, E., Sakita, F. M., Temu, G. A., Rugakingira, A., Lameck, G., Shayo, F. M., Mmbaga, B. T., Thielman, N. M., Bloomfield, G. S., et al. (2026) · BMC Health Services Research · doi:10.1186/s12913-026-15434-w · Cited by 1
Predictive accuracy of a cardiovascular disease risk prediction model in rural South India - a community based retrospective cohort study.
Fathima, F. N., Raghunath, D., Hegde, S. K. B., Agrawal, T., Misquith, D., & Sreekantiah, P. (2015) · Indian Journal of Community Health (Indian Association of Preventive and Social Medicine) · Cited by 1
Edad vascular y disfunción endotelial en personas que viven a gran altura
Universidad Peruana Los Andes, F. D. M. H. H., Lazo, A. D., Barrientos-Huamani, C., Mori, E. R., & Instituto Nacional de Enfermedades Neoplásicas, S. D. C. L. (2019) · Horizonte Médico (Lima) · doi:10.24265/horizmed.2019.v19n2.05 · Cited by 1
Assessment of 5-year risk of cardiovascular events among adults residing in an urban underprivileged area of Bangalore city: A community-based cross-sectional study
Johnson, A., Gnanaselvam, N. A., Gomes, S., Jeskezia, J., Chandran, N., & Bajaj, S. (2020) · International Journal of Noncommunicable Diseases · doi:10.4103/jncd.jncd_60_20 · Cited by 1
Repeated Annual Health Risk Assessments With Intervention Did Not Reduce 10-year Cardiovascular Disease Risk
Hene, N., Wood, P. S., Schwellnus, M. P., Jordaan, E., & Laubscher, R. (2021) · Journal of Occupational and Environmental Medicine · doi:10.1097/jom.0000000000002251 · Cited by 1
Évaluation du risque cardiovasculaire chez les sujets asymptomatiques résidents au Sud du Maroc
Korrida, A., Bnin, M., Achablou, N., & Assahal, R. (2021) · Médecine des Maladies Métaboliques · doi:10.1016/j.mmm.2021.06.002 · Cited by 1
Is there evidence of social inequity in healthcare for coronary heart disease? an electronic-cohort analysis using record-linked, routine data
King, W. (2015) · ORCA Online Research @Cardiff (Cardiff University) · Cited by 1
Reach and effectiveness of a HEARTS hypertension control pilot project in GuatemalaPreprint
Wellmann, I. A., Rodríguez, J. J., Batzin, B., Hegel, G., Ayala, L. F., Ozano, K., Fort, M. P., Flores, W., Ramírez, L., Palacios, E., et al. (2024) · medRxiv · doi:10.1101/2024.04.03.24305304 · Cited by 1
The autonomic age gap: a machine learning approach to assess biological-calendar age deviationsPreprint
Schumann, A., Gupta, Y., Gerstorf, D., Demuth, I., Olecka, M., Gaser, C., & Bär, K. (2025) · medRxiv · doi:10.1101/2025.04.16.25325919 · Cited by 1
AN INCREASE IN RISK FACTORS FOR CARDIOVASCULAR DISEASE IN YOGYAKARTA, INDONESIA: A COMPARISON OF TWO CROSS-SECTIONAL SURVEYS.
Dewi, F. S. T., Stenlund, H. C., Hakimi, M., & Weinehall, L. (2015) · PubMed
Diagnostic and prognostic models: applications and methods
Zuithoff, N. P. A. (2012) · Utrecht University Repository (Utrecht University)
Coronary heart disease in the Netherlands : incidence, etiology and risk prediction
Merry, A. H. H. (2010) · Research Publications (Maastricht University) · doi:10.26481/dis.20101210am
Outdoor air pollution improves the validity of a screening scale for cardiovascular disease (CVD) in clinical settings
Nasser, Z., Salameh, P., Elias, E., Dakik, H. A., Abbas, L. A., & Lévêque, A. (2015) · Clinical Epidemiology and Global Health · doi:10.1016/j.cegh.2015.07.002
The Value of “More” Information: The Emerging Role of Novel Diagnostic Investigations in Cardiovascular Medicine
Chew, D. P., & Astley, C. M. (2009) · Heart Lung and Circulation · doi:10.1016/j.hlc.2008.11.005
Low-cost strategies to predict cardiovascular disease
Wilson, P. W. F., & Narayan, K. M. V. (2008) · Nature Clinical Practice Cardiovascular Medicine · doi:10.1038/ncpcardio1294

Authors: Gaziano, T. A., Young, C. R., Fitzmaurice, G., Atwood, S., & Gaziano, J. M.
Affiliations: Brigham and Women's Hospital (US)
Harvard University (US)
McLean Hospital (US)
Corresponding author: Gaziano, Thomas A
ORCID: Gaziano, Thomas A
Young, Cynthia R
Fitzmaurice, Garrett
Atwood, Sidney
Gaziano, J Michael
Venue: The Lancet
Publisher: Elsevier BV
Published: 2008-03
Volume: 371
Issue: 9616
Pages: 923-931
ISSN: 0099-5355 (ISSN-L)
0140-6736
DOI: 10.1016/s0140-6736(08)60418-3
PMC: PMC2864150
OpenAlex: W2114678398
Cited by: 366
References: 34
Citations by year: 2026: 8 · 2025: 15 · 2024: 18 · 2023: 6 · 2022: 14 · 2021: 23 · 2020: 27 · 2019: 17 · 2018: 17 · 2017: 21 · 2016: 13 · 2015: 33 · 2014: 19 · 2013: 23 · 2012: 19
FWCI: 30.34
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: The Lancet (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: Fogarty International Center (K01TW007141)
Topics: Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Risk Factors
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Lipoproteins and Cardiovascular Health
Keywords: cardiovascular risk prediction, body mass index, total cholesterol, laboratory testing, risk assessment, cardiovascular mortality, cardiovascular events, developing countries, resource-limited settings
MeSH terms: Adult, Aged, Cardiovascular Diseases, Female, Follow-Up Studies, Humans, Hypertension, Male, Middle Aged, Models, Biological, Nutrition Surveys, Predictive Value of Tests, ROC Curve, Smoking, United States, Risk Assessment, Diabetes Complications

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Laboratory-based versus non-laboratory-based method for assessment of cardiovascular disease risk: the NHANES I Follow-up Study cohort

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks