Cited by 4,945Health Promotion and Cardiovascular PreventionCoronary Interventions and DiagnosticsClinical practice guidelines implementation

The DOI matches its Crossref record, with a small difference in title or year; 86 of the 87 references it cites were found in a registry or an index.

2013 ACC/AHA Guideline on the Assessment of Cardiovascular Risk

Open access

Goff, David C., Lloyd-Jones, Donald M., Bennett, Glen, Coady, Sean, D’Agostino, Ralph B., Gibbons, Raymond, Greenland, Philip, Lackland, Daniel T., Levy, Daniel, O’Donnell, Christopher J., Robinson, Jennifer G., Schwartz, J. Sanford, Shero, Susan T., Smith, Sidney C., Sorlie, Paul, Stone, Neil J., Wilson, Peter W.F.·2014·Journal of the American College of Cardiology

DOI 10.1016/j.jacc.2013.11.005PMC PMC4700825

Open this paper in CoChat — free account

Abstract

Harmon S. Jordan, ScD, Lev Nevo, MD, Janusz Wnek, PhD Jeffrey L. Anderson, MD, FACC, FAHA, Chair , Jonathan L. Halperin, MD, FACC, FAHA, Chair-Elect , Nancy M. Albert, PhD, CCNS, CCRN, FAHA, Biykem Bozkurt, MD, PhD, FACC, FAHA, Ralph G. Brindis, MD, MPH, MACC, Lesley H. Curtis, PhD, FAHA, David

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/j.jacc.2013.11.005

Cites

86 of 86 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.

Magnesium Replacement Improves the Metabolic Profile in Obese and Pre-Diabetic Patients with Mild-to-Moderate Chronic Kidney Disease: A 3-Month, Randomised, Double-Blind, Placebo-Controlled Study
Toprak, Ö., Kurt, H., Sarı, Y., Şarkış, C., Us, H., & Kırık, A. (2017) · Kidney & Blood Pressure Research · doi:10.1159/000468530 · Cited by 29,772
Summary of the second report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel II)
Grundy, S. M., Bilheimer, D. W., Chait, A., Clark, L. T., Denke, M. A., Havel, R. J., Hazzard, W. R., Hulley, S. B., Hunninghake, D. B., Kreisberg, R. A., et al. (1993) · JAMA · doi:10.1001/jama.269.23.3015 · Cited by 27,944
A New Equation to Estimate Glomerular Filtration Rate
Levey, A. S., Stevens, L. A., Schmid, C. H., Zhang, Y. (., Castro, A. F., Feldman, H. I., Kusek, J. W., Eggers, P., Van Lente, F., Greene, T., et al. (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-150-9-200905050-00006 · Cited by 25,603
Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report.
(2002) · Circulation · doi:10.1161/circ.106.25.3143 · Cited by 14,092
Detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III)
Grundy, S. M., Becker, D. M., Clark, L. T., Cooper, R., Denke, M. A., Howard, J. L., Hunninghake, D. B., Illingworth, D. R., Luepker, R. V., McBride, P. E., et al. (2002) · Civil War Book Review · Cited by 12,198
Self-citation
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
Self-citation
European Guidelines on cardiovascular disease prevention in clinical practice (version 2012)
Authors/Task Force Members:, Perk, J., De Backer, G., Gohlke, H., Graham, I., Reiner, Ž., Verschuren, M., Albus, C., Benlian, P., Boysen, G., et al. (2012) · European Heart Journal · doi:10.1093/eurheartj/ehs092 · Cited by 8,490
Heart Disease and Stroke Statistics—2023 Update: A Report From the American Heart Association
Tsao, C. W., Aday, A. W., Almarzooq, Z. I., Anderson, C. A. M., Arora, P., Avery, C. L., Baker-Smith, C. M., Beaton, A. Z., Boehme, A. K., Buxton, A. E., et al. (2023) · Circulation · doi:10.1161/cir.0000000000001123 · Cited by 8,300
General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study
D’Agostino, R. B., Vasan, R. S., Pencina, M. J., Wolf, P. A., Cobain, M., Massaro, J. M., & Kannel, W. B. (2008) · Circulation · doi:10.1161/circulationaha.107.699579 · Cited by 7,491
Self-citation
Heart Disease and Stroke Statistics—2012 Update: A Report From the American Heart Association
Writing Group Members, Roger, V. L., Go, A. S., Lloyd-Jones, D. M., Benjamin, E. J., Berry, J. D., Borden, W. B., Bravata, D. M., Dai, S., Ford, E. S., et al. (2012) · Circulation · doi:10.1161/cir.0b013e31823ac046 · Cited by 7,333
Self-citation

Flagged first, then by citations

Show all 87 references

1 of the 87 references couldn’t be matched to a known paper.

Sorted by citations

Items 601–700 of 1,030

Lipid management in contemporary community practice: Results from the Provider Assessment of Lipid Management (PALM) Registry
Navar, A. M., Wang, T. Y., Li, S., Robinson, J. G., Goldberg, A. C., Virani, S., Roger, V. L., Wilson, P. W. F., Elassal, J., Lee, L. V., et al. (2017) · American Heart Journal · doi:10.1016/j.ahj.2017.08.005 · Cited by 94
Self-citation
Representation of cardiovascular magnetic resonance in the AHA / ACC guidelines
von Knobelsdorff‐Brenkenhoff, F., Pilz, G., & Schulz‐Menger, J. (2016) · Journal of Cardiovascular Magnetic Resonance · doi:10.1186/s12968-017-0385-z · Cited by 96
Interim results of a prospective, randomized, open-label, Phase 3 study of the long-term safety and efficacy of lasmiditan for acute treatment of migraine (the GLADIATOR study)
Brandes, J. L., Klise, S. R., Krege, J. H., Case, M. G., Khanna, R., Vasudeva, R., Raskin, J., Pearlman, E. M., & Kudrow, D. (2019) · Cephalalgia · doi:10.1177/0333102419864132 · Cited by 94
Cardio-ankle vascular index predicts for the incidence of cardiovascular events in obese patients: A multicenter prospective cohort study (Japan Obesity and Metabolic Syndrome Study: JOMS)
Satoh‐Asahara, N., Kotani, K., Yamakage, H., Yamada, T., Araki, R., Okajima, T., Adachi, M., Oishi, M., & Shimatsu, A. (2015) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2015.08.003 · Cited by 95
Pharmacological treatment and therapeutic perspectives of metabolic syndrome
Lim, S. K., & Eckel, R. H. (2014) · Reviews in Endocrine and Metabolic Disorders · doi:10.1007/s11154-014-9298-4 · Cited by 98
Sex Differences in Cardiorespiratory Fitness and All-Cause Mortality
Al-Mallah, M. H., Juraschek, S. P., Whelton, S. P., Dardari, Z., Ehrman, J. K., Michos, E. D., Blumenthal, R. S., Nasir, K., Qureshi, W., Brawner, C. A., et al. (2016) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2016.04.002 · Cited by 100
Prediction of Cardiovascular Mortality by Estimated Cardiorespiratory Fitness Independent of Traditional Risk Factors: The HUNT Study
Nauman, J., Nes, B. M., Lavie, C. J., Jackson, A. S., Sui, X., Coombes, J. S., Blair, S. N., & Wisløff, U. (2016) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2016.10.007 · Cited by 92
Validation of Risk Equations for Complications of Type 2 Diabetes (RECODe) Using Individual Participant Data From Diverse Longitudinal Cohorts in the U.S.
Basu, S., Sussman, J. B., Berkowitz, S. A., Hayward, R. A., Bertoni, A. G., Correa, A., Mwasongwe, S. E., & Yudkin, J. S. (2017) · Diabetes Care · doi:10.2337/dc17-2002 · Cited by 94
Estimated Life Expectancy Without Recurrent Cardiovascular Events in Patients With Vascular Disease: The SMART‐REACH Model
Kaasenbrood, L., Bhatt, D. L., Dorresteijn, J. A. N., Wilson, P. W. F., D’Agostino, R. B., Massaro, J. M., van der Graaf, Y., Cramer, M. J., Kappelle, L. J., de Borst, G. J., et al. (2018) · Journal of the American Heart Association · doi:10.1161/jaha.118.009217 · Cited by 102
Self-citation
Differences in Progression to Obstructive Lesions per High-Risk Plaque Features and Plaque Volumes With CCTA
Lee, S., Sung, J. M., Andreini, D., Al-Mallah, M. H., Budoff, M. J., Cademartiri, F., Chinnaiyan, K. M., Choi, J. H., Chun, E. J., Conte, E., et al. (2019) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2019.09.011 · Cited by 93
Comparing Risk Scores in the Prediction of Coronary and Cardiovascular Deaths
Blaha, M. J., Whelton, S. P., Rifai, M. A., Dardari, Z., Shaw, L. J., Al-Mallah, M. H., Matsushita, K., Rozanski, A., Rumberger, J. A., Berman, D. S., et al. (2020) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2019.12.010 · Cited by 93
Validation of the pooled cohort risk score in an Asian population – a retrospective cohort study
Chia, Y. C., Lim, H. M., & Ching, S. M. (2014) · BMC Cardiovascular Disorders · doi:10.1186/1471-2261-14-163 · Cited by 92
Aspirin for the Primary Prevention of Cardiovascular Disease and Colorectal Cancer: A Decision Analysis for the U.S. Preventive Services Task Force
Dehmer, S. P., Maciosek, M. V., Flottemesch, T. J., LaFrance, A. B., & Whitlock, E. P. (2016) · Annals of Internal Medicine · doi:10.7326/m15-2129 · Cited by 93
Use of Long-term Cumulative Blood Pressure in Cardiovascular Risk Prediction Models
Pool, L. R., Ning, H., Wilkins, J. T., Lloyd‐Jones, D. M., & Allen, N. B. (2018) · JAMA Cardiology · doi:10.1001/jamacardio.2018.2763 · Cited by 92
Self-citation
A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes
Jamthikar, A. D., Gupta, D., Khanna, N. N., Saba, L., Araki, T., Višković, K., Suri, H. S., Gupta, A. K., Mavrogeni, S. I., Turk, M., et al. (2019) · Cardiovascular Diagnosis and Therapy · doi:10.21037/cdt.2019.09.03 · Cited by 93
Age-related trends in lipid levels: a large-scale cross-sectional study of the general Chinese population
Feng, L., Nian, S., Tong, Z., Zhu, Y. Y., Li, Y., Zhang, C., Bai, X., Luo, X., Wu, M., & Yan, Z. (2020) · BMJ Open · doi:10.1136/bmjopen-2019-034226 · Cited by 103
An Update on Coronary Artery Calcium Interpretation at Chest and Cardiac CT
Obisesan, O. H., Osei, A. D., Uddin, S. M. I., Dzaye, O. D. A., & Blaha, M. J. (2021) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.2021200484 · Cited by 97
Framingham Heart Study
Andersson, C., Nayor, M. G., Tsao, C. W., Levy, D. A., & Vasan, R. S. (2021) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2021.01.059 · Cited by 107
Self-citation
Longitudinal Plasma Measures of Trimethylamine N‐Oxide and Risk of Atherosclerotic Cardiovascular Disease Events in Community‐Based Older Adults
Lee, Y., Nemet, I., Wang, Z., Lai, H. T. M., Otto, M. C. D. O., Lemaître, R. N., Fretts, A. M., Sotoodehnia, N., Budoff, M. J., DiDonato, J. A., et al. (2021) · Journal of the American Heart Association · doi:10.1161/jaha.120.020646 · Cited by 95
Electrocardiogram-Based Heart Age Estimation by a Deep Learning Model Provides More Information on the Incidence of Cardiovascular Disorders
Chang, C., Lin, C., Luo, Y., Lee, Y., & Lin, C. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.754909 · Cited by 96
Performance of the Atherosclerotic Cardiovascular Disease Pooled Cohort Risk Equations by Social Deprivation Status
Colantonio, L. D., Richman, J., Carson, A. P., Lloyd‐Jones, D. M., Howard, G., Deng, L., Howard, V. J., Safford, M. M., Muntner, P., & Goff, D. C. (2017) · Journal of the American Heart Association · doi:10.1161/jaha.117.005676 · Cited by 93
Self-citation
Predictive Value of Age- and Sex-Specific Nomograms of Global Plaque Burden on Coronary Computed Tomography Angiography for Major Cardiac Events
Naoum, C., Berman, D. S., Ahmadi, A. A., Blanke, P., Gransar, H., Narula, J., Shaw, L. J., Kritharides, L., Achenbach, S. S., Al-Mallah, M. H., et al. (2017) · Circulation Cardiovascular Imaging · doi:10.1161/circimaging.116.004896 · Cited by 91
Relative Prognostic Value of Cardiac Troponin I and C-Reactive Protein in the General Population (from the Nord-Trøndelag Health [HUNT] Study)
Sigurdardottir, F. D., Lyngbakken, M. N., Holmen, O. L., Dalen, H., Hveem, K., Røsjø, H. R., & Omland, T. (2018) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2018.01.004 · Cited by 94
Association of 10-Year C-Reactive Protein Trajectories With Markers of Healthy Aging: Findings From the English Longitudinal Study of Aging
Lassale, C. M., Batty, G. D., Steptoe, A., Cadar, D., Akbaraly, T. N., Mika Kivimaki, B., & Zaninotto, P. (2018) · The Journals of Gerontology Series A · doi:10.1093/gerona/gly028 · Cited by 95
Constructing the Immune Signature of Schizophrenia for Clinical Use and Research; An Integrative Review Translating Descriptives Into Diagnostics
Kroken, R. A., Sommer, I. E., Steen, V. M., Dieset, I., & Johnsen, E. (2019) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2018.00753 · Cited by 95
Pathophysiology and management of cardiovascular disease in patients with HIV
Nou, E., Lo, J., Hadigan, C. M., & Grinspoon, S. K. (2016) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(15)00388-5 · Cited by 95
Prevalence of the American College of Cardiology/American Heart Association statin eligibility groups, statin use, and low-density lipoprotein cholesterol control in US adults using the National Health and Nutrition Examination Survey 2011–2012
Wong, N. D., Young, D., Zhao, Y., Nguyen, H. A., Caballes, J., Khan, I., & Sanchez, R. J. (2016) · Journal of clinical lipidology · doi:10.1016/j.jacl.2016.06.011 · Cited by 93
LPA Variants Are Associated With Residual Cardiovascular Risk in Patients Receiving Statins
Wei, W., Li, X., Feng, Q., Kubo, M., Kullo, I. J., Peissig, P. L., Karlson, E. W., Jarvik, G. P., Lee, M. T. M., Shang, N., et al. (2018) · Circulation · doi:10.1161/circulationaha.117.031356 · Cited by 93
Lasmiditan for acute treatment of migraine in patients with cardiovascular risk factors: post-hoc analysis of pooled results from 2 randomized, double-blind, placebo-controlled, phase 3 trials
Shapiro, R. E., Hochstetler, H. M., Dennehy, E. B., Khanna, R., Doty, E. G., Berg, P. H., & Starling, A. J. (2019) · The Journal of Headache and Pain · doi:10.1186/s10194-019-1044-6 · Cited by 90
Patient and provider perspectives on polygenic risk scores: implications for clinical reporting and utilization
Lewis, A. C. F., Perez, E. F., Prince, A. E. R., Flaxman, H. R., Gomez, L., Brockman, D., Chandler, P. D., Kerman, B. J., Lebo, M. S., Smoller, J. W., et al. (2022) · Genome Medicine · doi:10.1186/s13073-022-01117-8 · Cited by 89
Management of Traditional Cardiovascular Risk Factors in CKD: What Are the Data?
Gregg, L. P., & Hedayati, S. S. (2018) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2017.12.007 · Cited by 93
Unmet Needs in LDL-C Lowering: When Statins Won’t Do!
Krähenbühl, S., Pavik-Mezzour, I., & von Eckardstein, A. (2016) · Drugs · doi:10.1007/s40265-016-0613-0 · Cited by 89
Aerobic, resistance, or combined exercise training and cardiovascular risk profile in overweight or obese adults: the CardioRACE trial
Lee, D.-C., Brellenthin, A. G., Lanningham-Foster, L. M., Kohut, M. L., & Li, Y. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehad827 · Cited by 108
NLA Task Force on Statin Safety - 2014 update
Jacobson, T. A. (2014) · Journal of clinical lipidology · doi:10.1016/j.jacl.2014.03.003 · Cited by 91
HIV Infection Itself May Not Be Associated With Subclinical Coronary Artery Disease Among African Americans Without Cardiovascular Symptoms
Lai, H. C., Moore, R. D., Celentano, D. D., Gerstenblith, G., Treisman, G. J., Keruly, J. C., Kickler, T. S., Li, J., Chen, S., Lai, S., et al. (2016) · Journal of the American Heart Association · doi:10.1161/jaha.115.002529 · Cited by 89
Unstable carotid artery plaque: new insights and controversies in diagnostics and treatment
Skagen, K. R., Skjelland, M., Zamani, M., & Russell, D. (2016) · Croatian Medical Journal · doi:10.3325/cmj.2016.57.311 · Cited by 86
Carotid plaque-thickness and common carotid IMT show additive value in cardiovascular risk prediction and reclassification
Amato, M., Veglia, F., Fairé, U. D., Giral, P., Rauramaa, R., Smit, A. J., Kurl, S., Ravani, A. L., Frigerio, B., Sansaro, D., et al. (2017) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2017.05.023 · Cited by 89
Precision prognostics for the development of complications in diabetes
Schiborn, C., & Schulze, M. B. (2022) · Diabetologia · doi:10.1007/s00125-022-05731-4 · Cited by 103
Atherosclerotic Cardiovascular Disease Risk Estimates Using the Predicting Risk of Cardiovascular Disease Events Equations
Anderson, T. S., Wilson, L. M., & Sussman, J. B. (2024) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2024.1302 · Cited by 89
The Montreal-FH-SCORE: A new score to predict cardiovascular events in familial hypercholesterolemia
Paquette, M., Dufour, R., & Baass, A. W. (2016) · Journal of clinical lipidology · doi:10.1016/j.jacl.2016.10.004 · Cited by 89
Cardiac computed tomography in current cardiology guidelines
Al-Mallah, M. H., Aljizeeri, A. H., Villines, T. C., Srichai, M. B., & Alsaileek, A. A. (2015) · Journal of cardiovascular computed tomography · doi:10.1016/j.jcct.2015.09.003 · Cited by 88
Association of high-density lipoprotein subclasses and incident coronary heart disease: The Jackson Heart and Framingham Offspring Cohort Studies
Joshi, P. H., Toth, P. P., Lirette, S. T., Griswold, M. E., Massaro, J. M., Martin, S. S., Blaha, M. J., Kulkarni, K. R., Khokhar, A. A., Correa, A., et al. (2014) · European Journal of Preventive Cardiology · doi:10.1177/2047487314543890 · Cited by 91
Diagnosis and treatment of high density lipoprotein deficiency
Schaefer, E. J., Anthanont, P., Diffenderfer, M. R., Polisecki, E. Y., & Asztalos, B. F. (2016) · Progress in Cardiovascular Diseases · doi:10.1016/j.pcad.2016.08.006 · Cited by 89
Rationale and design of the coronary artery calcium consortium: A multicenter cohort study
Blaha, M. J., Whelton, S. P., Rifai, M. A., Dardari, Z., Shaw, L. J., Al-Mallah, M. H., Matsushita, K., Rumberger, J. A., Berman, D. S., Budoff, M. J., et al. (2016) · Journal of cardiovascular computed tomography · doi:10.1016/j.jcct.2016.11.004 · Cited by 88
Assessment and Management of Cardiovascular Risk Factors Among US Veterans With Prostate Cancer
Sun, L., Parikh, R. B., Hubbard, R. A., Cashy, J., Takvorian, S. U., Vaughn, D. J., Robinson, K. W., Narayan, V., & Ky, B. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.0070 · Cited by 89
All-cause mortality by age and gender based on coronary artery calcium scores
Nakanishi, R., Li, D., Blaha, M. J., Whelton, S. P., Darabian, S., Flores, F. R., Dailing, C., Blumenthal, R. S., Nasir, K., Berman, D. S., et al. (2015) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jev328 · Cited by 88
Long-Term Prognosis After Coronary Artery Calcium Scoring Among Low-Intermediate Risk Women and Men
Kelkar, A. A., Schultz, W. M., Khosa, F., Schulman‐Marcus, J., O’Hartaigh, B. W. J., Gransar, H., Blaha, M. J., Knapper, J. T., Berman, D. S., Quyyumi, A. A., et al. (2016) · Circulation Cardiovascular Imaging · doi:10.1161/circimaging.115.003742 · Cited by 88
Impact of Cardiovascular Risk on the Relative Benefit and Harm of Intensive Treatment of Hypertension
Phillips, R. A., Xu, J., Peterson, L. E., Arnold, R. M., Diamond, J. A., & Schussheim, A. E. (2018) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.01.074 · Cited by 88
The relationship of blood glucose with cardiovascular disease is mediated over time by traditional risk factors in type 1 diabetes: the DCCT/EDIC study
Bebu, I., Braffett, B. H., Pop‐Busui, R., Orchard, T. J., Nathan, D. M., & Lachin, J. M. (2017) · Diabetologia · doi:10.1007/s00125-017-4374-4 · Cited by 87
PM2.5 concentration in the ambient air is a risk factor for the development of high-risk coronary plaques
Yang, S., Lee, S., Park, J., Lee, H., Kang, S., Lee, S.-E., Kim, J. B., Choi, S., Kim, Y.-J., & Chang, H. (2019) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jez209 · Cited by 87
The Relationship Between Coronary Calcification and the Natural History of Coronary Artery Disease
Jin, H., Weir-McCall, J. R., Leipsic, J. A., Son, J., Sellers, S., Shao, M., Blanke, P., Ahmadi, A. A., Hadamitzky, M., Kim, Y.-J., et al. (2020) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2020.08.036 · Cited by 87
Cardiovascular Risk Is Elevated in Lean Subjects with Nonalcoholic Fatty Liver Disease
Kim, Y., Han, E., Lee, J. S., Lee, H. W., Kim, B. K., Kim, M. K., Kim, H. S., Park, J. Y., Kim, D. Y., Ahn, S. H., et al. (2021) · Gut and Liver · doi:10.5009/gnl210084 · Cited by 87
Management of Obesity in Cardiovascular Practice
Després, J.-P., Carpentier, A. C., Tchernof, A., Neeland, I. J., & Poirier, P. (2021) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2021.05.035 · Cited by 87
Risk assessment of cardiovascular disease based on SOLSSA-CatBoost model
Wei, X., Rao, C., Xiao, X., Chen, L., & Goh, M. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119648 · Cited by 87
Update on the NCEP ATP-III emerging cardiometabolic risk factors
Eckel, R. H., & Cornier, M. (2014) · BMC Medicine · doi:10.1186/1741-7015-12-115 · Cited by 96
Ranking of psychosocial and traditional risk factors by importance for coronary heart disease: the Copenhagen City Heart Study
Schnohr, P., Marott, J. L., Kristensen, T. S., Gyntelberg, F., Grønbæk, M., Lange, P., Jensen, M. T., Jensen, G. B., & Prescott, E. (2015) · European Heart Journal · doi:10.1093/eurheartj/ehv027 · Cited by 86
Relationship of Hypertension to Coronary Atherosclerosis and Cardiac Events in Patients With Coronary Computed Tomographic Angiography
Nakanishi, R., Baskaran, L., Gransar, H., Budoff, M. J., Achenbach, S. S., Al-Mallah, M. H., Cademartiri, F., Callister, T. Q., Chang, H., Chinnaiyan, K. M., et al. (2017) · Hypertension · doi:10.1161/hypertensionaha.117.09402 · Cited by 94
Prediction of cardiovascular disease among hematopoietic cell transplantation survivors
Armenian, S. H., Yang, D., Teh, J. B., Atencio, L., Gonzales, A., Wong, F. L., Leisenring, W. M., Forman, S. J., Nakamura, R., & Chow, E. J. (2018) · Blood Advances · doi:10.1182/bloodadvances.2018019117 · Cited by 87
Cardiovascular Risk for Patients With and Without Schizophrenia, Schizoaffective Disorder, or Bipolar Disorder
Rossom, R. C., Hooker, S. A., O’Connor, P. J., Crain, A. L., & Sperl‐Hillen, J. M. (2022) · Journal of the American Heart Association · doi:10.1161/jaha.121.021444 · Cited by 86
Utility of Nontraditional Risk Markers in Individuals Ineligible for Statin Therapy According to the 2013 American College of Cardiology/American Heart Association Cholesterol Guidelines
Yeboah, J., Polonsky, T. S., Young, R. L., McClelland, R. L., Delaney, J. A., Dawood, F. Z., Blaha, M. J., Miedema, M. D., Sibley, C. T., Carr, J. J., et al. (2015) · Circulation · doi:10.1161/circulationaha.115.016846 · Cited by 85
Self-citation
Diagnosis and management of cardiovascular risk in nonalcoholic fatty liver disease
Lonardo, A., Ballestri, S., Targher, G., & Loria, P. (2014) · Expert Review of Gastroenterology & Hepatology · doi:10.1586/17474124.2015.965143 · Cited by 86
Individualized Statin Benefit for Determining Statin Eligibility in the Primary Prevention of Cardiovascular Disease
Thanassoulis, G., Williams, K., Altobelli, K. K., Pencina, M. J., Cannon, C. P., & Sniderman, A. D. (2016) · Circulation · doi:10.1161/circulationaha.115.018383 · Cited by 86
Sex Differences in Functional and CT Angiography Testing in Patients With Suspected Coronary Artery Disease
Pagidipati, N. J., Hemal, K., Coles, A., Mark, D. B., Dolor, R. J., Pellikka, P. A., Hoffmann, U., Litwin, S. E., Udelson, J. E., Daubert, M. A., et al. (2016) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2016.03.523 · Cited by 85
Assessing Cardiovascular Risk in Patients with Diabetes: An Update
Damaskos, C., Garmpis, N., Kollia, P., Mitsiopoulos, G., Barlampa, D., Drosos, A., Patsouras, A., Gravvanis, N., Antoniou, V., Litos, A., et al. (2019) · Current Cardiology Reviews · doi:10.2174/1573403x15666191111123622 · Cited by 85
Pre-existing and machine learning-based models for cardiovascular risk prediction
Cho, S.-Y., Kim, S., Kang, S., Lee, K. J., Choi, D., Kang, S., Park, S. J., Kim, T., Yoon, C., Youn, T., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-88257-w · Cited by 88
Association of 10‐Year and Lifetime Predicted Cardiovascular Disease Risk With Subclinical Atherosclerosis in South Asians: Findings From the Mediators of Atherosclerosis in South Asians Living in America (MASALA) Study
Kandula, N. R., Kanaya, A. M., Liu, K., Lee, J. Y., Herrington, D. M., Hulley, S. B., Persell, S. D., Lloyd‐Jones, D. M., & Huffman, M. D. (2014) · Journal of the American Heart Association · doi:10.1161/jaha.114.001117 · Cited by 85
Self-citation
The Treatment of Disorders of Lipid Metabolism
Parhofer, K. G. (2016) · Deutsches Ärzteblatt international · doi:10.3238/arztebl.2016.0261 · Cited by 86
Grip Strength and Walking Pace and Cardiovascular Disease Risk Prediction in 406,834 UK Biobank Participants
Welsh, C. E., Celis-Morales, C. A., Ho, F. K., Brown, R., Mackay, D. F., Lyall, D. M., Anderson, J. J., Pell, J. P., Gill, J. M. R., Sattar, N., et al. (2020) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2019.12.032 · Cited by 85
Cardiovascular toxicities of BCR-ABL tyrosine kinase inhibitors in chronic myeloid leukemia: preventive strategies and cardiovascular surveillance
Aghel, N., Delgado, D., & Lipton, J. H. (2017) · Vascular Health and Risk Management · doi:10.2147/vhrm.s108874 · Cited by 84
Evaluation of the Pooled Cohort Risk Equations for Cardiovascular Risk Prediction in a Multiethnic Cohort From the Women’s Health Initiative
Mora, S., WENGER, N. K., Cook, N. R., Liu, J., Howard, B. V., Limacher, M. C., Liu, S., Margolis, K. L., Martin, L. W., Paynter, N. P., et al. (2018) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2018.2875 · Cited by 86
Self-citation
Association of Low‐Density Lipoprotein Cholesterol Levels with More than 20‐Year Risk of Cardiovascular and All‐Cause Mortality in the General Population
Rong, S., Li, B., Chen, L., Sun, Y., Du, Y., Liu, B., Robinson, J. G., & Bao, W. (2022) · Journal of the American Heart Association · doi:10.1161/jaha.121.023690 · Cited by 88
Self-citation
Association between vegetarian diets and cardiovascular risk factors in non-Hispanic white participants of the Adventist Health Study-2
Matsumoto, S., Beeson, W. L., Shavlik, D. J., Segovia‐Siapco, G., Jaceldo‐Siegl, K., Fraser, G. E., & Knutsen, S. F. (2019) · Journal of Nutritional Science · doi:10.1017/jns.2019.1 · Cited by 85
Extreme Atherosclerotic Cardiovascular Disease (ASCVD) Risk Recognition
Rosenblit, P. D. (2019) · Current Diabetes Reports · doi:10.1007/s11892-019-1178-6 · Cited by 120
Cardiovascular/stroke risk predictive calculators: a comparison between statistical and machine learning models
Jamthikar, A. D., Gupta, D., Saba, L., Khanna, N. N., Araki, T., Višković, K., Mavrogeni, S. I., Laird, J. R., Pareek, G., Miner, M. M., et al. (2020) · Cardiovascular Diagnosis and Therapy · doi:10.21037/cdt.2020.01.07 · Cited by 85
Cardiovascular risk stratification by coronary computed tomography angiography imaging: current state-of-the-art
Antonopoulos, A. S., Angelopoulos, A., Tsioufis, K. P., Antoniades, C. A., & Tousoulis, D. (2021) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwab067 · Cited by 83
Stroke risk prediction using machine learning: a prospective cohort study of 0.5 million Chinese adults
Chun, M., Clarke, R., Cairns, B. J., Clifton, D. A., Bennett, D., Chen, Y., Guo, Y., Pei, P., Lv, J., Yu, C., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab068 · Cited by 89
Cohort design and natural language processing to reduce bias in electronic health records research
Khurshid, S. S., Reeder, C., Harrington, L. X., Singh, P., Sarma, G., Friedman, S. F., Achille, P. D., Diamant, N., Cunningham, J. W., Turner, A. C., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00590-0 · Cited by 82
Evaluating and mitigating bias in machine learning models for cardiovascular disease prediction
Li, F., Wu, P., Ong, H. H., Peterson, J. F., Wei, W., & Zhao, J. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104294 · Cited by 91
Influence of Subclinical Atherosclerosis Burden and Progression on Mortality
Fuster, V., García‐Álvarez, A., Devesa, A., Mass, V., Owen, R., Quesada, A. J., Fuster, J. J., García‐Lunar, I., Pocock, S., Sánchez‐Gonzalez, J., et al. (2024) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2024.06.045 · Cited by 83
New Strategy to Reduce the Global Burden of Stroke
Feigin, V. L., Krishnamurthi, R., Bhattacharjee, R., Parmar, P. G. K., Theadom, A., Hussein, T., Purohit, M., Hume, P. A., Abbott, M., Rush, E. C., et al. (2015) · Stroke · doi:10.1161/strokeaha.115.008222 · Cited by 83
Carotid Intima‐Media Thickness and Prediction of Cardiovascular Disease
Øygarden, H. (2017) · Journal of the American Heart Association · doi:10.1161/jaha.116.005313 · Cited by 86
External Validations of Cardiovascular Clinical Prediction Models: A Large-Scale Review of the Literature
Wessler, B. S., Nelson, J., Park, J. G., McGinnes, H., Gulati, G., Brazil, R., Van Calster, B., van Klaveren, D., Venema, E., Steyerberg, E., et al. (2021) · Circulation: Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.121.007858 · Cited by 83
Patient-reported adherence to statin therapy, barriers to adherence, and perceptions of cardiovascular risk
Fung, V., Graetz, I., Reed, M., & Jaffe, M. G. (2018) · PLoS ONE · doi:10.1371/journal.pone.0191817 · Cited by 88
Potential need for expanded pharmacologic treatment and lifestyle modification services under the 2017 ACC/AHA Hypertension Guideline
Ritchey, M. D., Gillespie, C., Wozniak, G., Shay, C. M., Thompson‐Paul, A. M., Loustalot, F. V., & Hong, Y. (2018) · Journal of Clinical Hypertension · doi:10.1111/jch.13364 · Cited by 84
Interplay of Coronary Artery Calcium and Risk Factors for Predicting CVD/CHD Mortality
Grandhi, G. R., Mirbolouk, M., Dardari, Z., Al-Mallah, M. H., Rumberger, J. A., Shaw, L. J., Blankstein, R., Miedema, M. D., Berman, D. S., Budoff, M. J., et al. (2019) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2019.08.024 · Cited by 82
The Effects of Nano-Curcumin Supplementation on Risk Factors for Cardiovascular Disease: A GRADE-Assessed Systematic Review and Meta-Analysis of Clinical Trials
Ashtary‐Larky, D., Kelishadi, M. R., Bagheri, R., Moosavian, S. P., Wong, A., Davoodi, S. H., Khalili, P., Dutheil, F., Suzuki, K., & Asbaghi, O. (2021) · Antioxidants · doi:10.3390/antiox10071015 · Cited by 87
Association of low muscle mass and obesity with increased all‐cause and cardiovascular disease mortality in US adults
Kim, D., Lee, J., Park, R., Oh, C., & Moon, S. (2023) · Journal of Cachexia Sarcopenia and Muscle · doi:10.1002/jcsm.13397 · Cited by 85
Cardiovascular Risk Associated With Social Determinants of Health at Individual and Area Levels
Xia, M., An, J., Safford, M. M., Colantonio, L. D., Sims, M., Reynolds, K., Moran, A. E., & Zhang, Y. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.8584 · Cited by 86
Consensus statement on management of dyslipidemia in Indian subjects
Chandra, K. S., Bansal, M., Nair, T., Iyengar, S. S., Gupta, R., Manchanda, S. C., Mohanan, P. P., Rao, V. D., Manjunath, C. N., Sawhney, J. P. S., et al. (2014) · Indian Heart Journal · doi:10.1016/j.ihj.2014.12.001 · Cited by 86
Cardiovascular risk assessment in kidney transplantation.
Hart, A., Weir, M., & Kasiske, B. (2015) · Kidney International · doi:10.1038/ki.2014.335 · Cited by 83
Socioeconomic deprivation: an important, largely unrecognized risk factor in primary prevention of cardiovascular disease
Kimenai, D. M., Pirondini, L., Gregson, J., Prieto, D., Pocock, S. J., Perel, P., Hamilton, T., Welsh, P., Campbell, A., Porteous, D. J., et al. (2022) · Circulation · doi:10.1161/circulationaha.122.060042 · Cited by 76
How Low to Go With Glucose, Cholesterol, and Blood Pressure in Primary Prevention of CVD
Hong, K. N., Fuster, V., Rosenson, R. S., Rosendorff, C., & Bhatt, D. L. (2017) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2017.09.001 · Cited by 87
Performance of the Pooled Cohort Equations to Estimate Atherosclerotic Cardiovascular Disease Risk by Body Mass Index
Khera, R., Pandey, A., Ayers, C. R., Carnethon, M. R., Greenland, P., Ndumele, C. E., Nambi, V., Seliger, S. L., Chaves, P. H. M., Safford, M. M., et al. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.23242 · Cited by 79
Self-citation
US Trends in Cholesterol Screening, Lipid Levels, and Lipid‐Lowering Medication Use in US Adults, 1999 to 2018
Gao, Y., Shah, L. M., Ding, J., & Martin, S. S. (2023) · Journal of the American Heart Association · doi:10.1161/jaha.122.028205 · Cited by 82
Polygenic risk score adds to a clinical risk score in the prediction of cardiovascular disease in a clinical setting
Samani, N. J., Beeston, E., Greengrass, C., Riveros-Mckay, F., Dębiec, R., Lawday, D., Wang, Q., Budgeon, C. A., Braund, P. S., Bramley, R., et al. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehae342 · Cited by 86
Coronary Artery Calcium Improves Risk Classification in Younger Populations
Paixao, A. R. M., Ayers, C. R., Sabbagh, A. E., Sanghavi, M., Berry, J. D., Rohatgi, A. K., Kumbhani, D. J., McGuire, D. K., Das, S. R., de Lemos, J. A., et al. (2015) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2015.06.015 · Cited by 83
The emergence of proton nuclear magnetic resonance metabolomics in the cardiovascular arena as viewed from a clinical perspective
Rankin, N., Preiss, D., Welsh, P. I., Burgess, K. E., Nelson, S. M., Lawlor, D. A., & Sattar, N. A. (2014) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2014.09.024 · Cited by 85
Intersectionality and risk for ischemic heart disease in Sweden: Categorical and anti-categorical approaches
Wemrell, M., Mulinari, S., & Merlo, J. (2017) · Social Science & Medicine · doi:10.1016/j.socscimed.2017.01.050 · Cited by 88
Short Telomere Length is Associated with Aging, Central Obesity, Poor Sleep and Hypertension in Lebanese Individuals
Zgheib, N. K., Sleiman, F., Nasreddine, L., Nasrallah, M., Nakhoul, N., Isma’eel, H., & Tamim, H. (2018) · Aging and Disease · doi:10.14336/ad.2017.0310 · Cited by 81
Awareness of racial and ethnic bias and potential solutions to address bias with use of health care algorithms.
Jain, A., Brooks, J. R., Alford, C. C., Chang, C. S., Mueller, N. M., Umscheid, C. A., & Bierman, A. S. (2023) · JAMA Health Forum · doi:10.1001/jamahealthforum.2023.1197 · Cited by 79

Authors: Goff, D. C., Lloyd-Jones, D. M., Bennett, G., Coady, S., D’Agostino, R. B., Gibbons, R., Greenland, P., Lackland, D. T., Levy, D., O’Donnell, C. J., Robinson, J. G., Schwartz, J. S., Shero, S. T., Smith, S. C., Sorlie, P., Stone, N. J., & Wilson, P. W. F.
Affiliations: General Department of Preventive Medicine (VN)
ORCID: Goff, David C.
D’Agostino, Ralph B.
Gibbons, Raymond
Greenland, Philip
Lackland, Daniel T.
Levy, Daniel
O’Donnell, Christopher J.
Robinson, Jennifer G.
Shero, Susan T.
Smith, Sidney C.
Stone, Neil J.
Wilson, Peter W.F.
Venue: Journal of the American College of Cardiology
Publisher: Elsevier BV
Published: 2014-07
Volume: 63
Issue: 25
Pages: 2935-2959
ISSN: 0735-1097 (ISSN-L)
DOI: 10.1016/j.jacc.2013.11.005
PMC: PMC4700825
OpenAlex: W2168536722
Cited by: 4,945
References: 87
Citations by year: 2026: 122 · 2025: 207 · 2024: 294 · 2023: 375 · 2022: 443 · 2021: 504 · 2020: 440 · 2019: 469 · 2018: 474 · 2017: 484 · 2016: 439 · 2015: 391 · 2014: 285
FWCI: 383.50
Type: journal article
Language: en
Update notices: erratum (2014-07-01)
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Journal of the American College of Cardiology (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Institutes of Health (Z99 HL999999)
Topics: Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Coronary Interventions and Diagnostics
Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Clinical practice guidelines implementation
Keywords: cardiovascular risk assessment, cardiovascular risk, risk stratification, risk prediction
MeSH terms: American Heart Association, Blood Pressure, Cardiology, Cardiovascular Diseases, Diet, Humans, Morbidity, Risk Factors, Societies, Medical, Sodium, Dietary, United States, Risk Assessment

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

2013 ACC/AHA Guideline on the Assessment of Cardiovascular Risk

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