Evidence map›Paper›PMID 30586759›Full record

ArticleCirculation2019

Quantifying Importance of Major Risk Factors for Coronary Heart Disease.

Michael J Pencina, Ann Marie Navar, Daniel Wojdyla, Robert J Sanchez, Irfan Khan, Joseph Elassal, Ralph B D'Agostino, Eric D Peterson, Allan D Sniderman

Abstract read
In one paragraph

Article in Circulation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 103 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
103citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

103 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Trial
  5. Trial
  6. Trial
  7. Trial
  8. Article
  9. Review
  10. Cardiovascular risk and hippocampal-cognitive coupling in Alzheimer's disease.medRxiv : the preprint server for health sciences · 2026
    Article
  11. Article
  12. Review
  13. Review
  14. Review
  15. Article
  16. Article
  17. Review
  18. Review
  19. Article
  20. Article

43 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Michael J PencinaDuke Clinical Research Institute, Duke University School of Medicine, Durham, NC (M.J.P., A.M.N., D.W., E.D.P.).
Ann Marie NavarDuke Clinical Research Institute, Duke University School of Medicine, Durham, NC (M.J.P., A.M.N., D.W., E.D.P.).
Daniel WojdylaDuke Clinical Research Institute, Duke University School of Medicine, Durham, NC (M.J.P., A.M.N., D.W., E.D.P.).
Robert J SanchezRegeneron Pharmaceuticals Inc, Tarrytown, NY (R.J.S., J.E.).
Irfan KhanReal-World Evidence and Clinical Outcomes, Sanofi, Bridgewater, NJ (I.K.).
Joseph ElassalRegeneron Pharmaceuticals Inc, Tarrytown, NY (R.J.S., J.E.).
Ralph B D'AgostinoDepartment of Mathematics and Statistics, Boston University, MA (R.B.D.).
Eric D PetersonDuke Clinical Research Institute, Duke University School of Medicine, Durham, NC (M.J.P., A.M.N., D.W., E.D.P.).
Allan D SnidermanMike Rosenbloom Laboratory for Cardiovascular Research, McGill University Health Centre, Royal Victoria Hospital, Montreal, Quebec, Canada (A.D.S.).

Funding

Preventing Cardiovascular Disease through Improved Risk Prediction and CommunicationK01HL133416 · NHLBI · DUKE UNIVERSITY · PI NAVAR, ANN MARIE · 2016 to 2020
$707k
NHLBI NIH HHS K01 HL133416
6 · The paper itself

Abstract

backgroundTo optimize preventive strategies for coronary heart disease (CHD), it is essential to understand and appropriately quantify the contribution of its key risk factors. Our objective was to compare the associations of key modifiable CHD risk factors-specifically lipids, systolic blood pressure (SBP), diabetes mellitus, and smoking-with incident CHD events based on their prognostic performance, attributable risk fractions, and treatment benefits, overall and by age.

methodsPooled participant-level data from 4 observational cohort studies sponsored by the National Heart, Lung, and Blood Institute were used to create a cohort of 22 626 individuals aged 45 to 84 years who were initially free of cardiovascular disease. Individuals were followed for 10 years from baseline evaluation for incident CHD. Proportional hazards regression was used to estimate metrics of prognostic model performance (likelihood ratio, C index, net reclassification, discrimination slope), hazard ratios, and population attributable fractions for SBP, non-high-density lipoprotein cholesterol (non-HDL-C), diabetes mellitus, and smoking. Expected absolute risk reductions for antihypertensive and lipid-lowering treatment were assessed.

resultsAge, sex, and race capture 63% to 80% of the prognostic performance of cardiovascular risk models. In contrast, adding either SBP, non-HDL-C, diabetes mellitus, or smoking to a model with other risk factors increases the C index by only 0.004 to 0.013. However, primordial prevention could have a substantial effect as demonstrated by population attributable fractions of 28% for SBP≥130 mm Hg and 17% for non-HDL-C≥130 mg/dL. Similarly, lowering the SBP of all individuals to <130 mm Hg or lowering low-density lipoprotein cholesterol by 30% would be expected to lower a baseline 10-year CHD risk of 10.7% to 7.0 and 8.0, respectively (absolute risk reductions: 3.7% and 2.7%, respectively). Prognostic performance decreases with age (C indices for age groups 45-54, 55-64, 65-74, 75-84 are 0.75, 0.72, 0.66, and 0.62, respectively), whereas absolute risk reductions increase (SBP: 1.1%, 2.3%, 5.4%, 10.3%, respectively; non-HDL-C: 1.1%, 2.0%, 3.7%, 5.9%, respectively).

conclusionsAlthough individual modifiable CHD risk factors contribute only modestly to prognostic performance, our models indicate that eliminating or controlling these individual factors would lead to substantial reductions in total population CHD events. Metrics used to judge importance of risk factors should be tailored to the research objectives.

Indexed as

Blood PressureAgedAged, 80 and overCholesterol, HDLCholesterol, LDLCoronary DiseaseDiabetes MellitusFemaleHumansMaleMiddle AgedRisk FactorsCholesterol, HDLCholesterol, LDLblood pressurecholesterol, LDLcoronary diseaselipoproteins, HDL2populationrisk factors

Identifiers

PMID30586759
PMCPMC6433489

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.