Evidence map›Paper›PMID 31713893›Full record

ArticleClinical cardiology2020

Predicting risk of cardiovascular events 1 to 3 years post-myocardial infarction using a global registry.

Stuart J Pocock, David Brieger, John Gregson, Ji Y Chen, Mauricio G Cohen, Shaun G Goodman, Christopher B Granger, Richard Grieve, Jose C Nicolau, Tabassome Simon and 6 more

Abstract read
In one paragraph

Article in Clinical cardiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 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

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  3. Article
  4. Observational
  5. Article
  6. Article
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  8. Review
  9. Article
  10. Observational
  11. A critical review of chronic kidney disease as a risk factor for coronary artery disease.International journal of cardiology. Heart & vasculature · 2021
    Review
  12. Observational
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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

16 authors.

Stuart J PocockDepartment of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.ORCID https://orcid.org/0000-0003-2212-4007
David BriegerDivision of Cardiology, Concord Hospital and University of Sydney, Sydney, Australia.ORCID https://orcid.org/0000-0001-6115-0326
John GregsonDepartment of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.
Ji Y ChenDepartment of Cardiology, Guangdong General Hospital, Provincial Key Laboratory of Coronary Disease, Guangzhou, China.
Mauricio G CohenCardiovascular Division, University of Miami Miller School of Medicine, Miami, Florida.
Shaun G GoodmanAlready given, Terrence Donnelly Heart Centre, St Michael's Hospital, University of Toronto, Toronto, Canada.
Christopher B GrangerCardiac Intensive Care Unit, Duke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina.
Richard GrieveDepartment of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.
Jose C NicolauInstituto do Coração (InCor), Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil.
Tabassome SimonAssistance Publique-Hopitaux de Paris (APHP) Department of Clinical Pharmacology and Clinical Research Platform of East of Paris, Paris, France.
Dirk WestermannDepartment of General and Interventional Cardiology, University Heart Center Eppendorf, Hamburg, Germany.ORCID https://orcid.org/0000-0002-7542-1956
Satoshi YasudaDepartment of Cardiovascular Medicine, National Cerebral and Cardiovascular Center, Osaka, Japan.
Katarina HedmanGlobal Medical Affairs Cardiovascular, Renal and Metabolic, AstraZeneca, Gothenburg, Sweden.
Kirsten L RennieDepartment of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.
Karolina Andersson SundellGlobal Medical Affairs Cardiovascular, Renal and Metabolic, AstraZeneca, Gothenburg, Sweden.
TIGRIS Study Investigators

Funding

AstraZeneca
6 · The paper itself

Abstract

backgroundRisk prediction tools are lacking for patients with stable disease some years after myocardial infarction (MI). HYPOTHESIS: A practical long-term cardiovascular risk index can be developed.

methodsThe long-Term rIsk, Clinical manaGement and healthcare Resource utilization of stable coronary artery dISease in post-myocardial infarction patients prospective global registry enrolled patients 1 to 3 years post-MI (369 centers; 25 countries), all with ≥1 risk factor (age ≥65 years, diabetes mellitus requiring medication, second prior MI, multivessel coronary artery disease, or chronic non-end-stage kidney disease [CKD]). Self-reported health was assessed with EuroQoL-5 dimensions. Multivariable Poisson regression models were used to determine key predictors of the primary composite outcome (MI, unstable angina with urgent revascularization [UA], stroke, or all-cause death) over 2 years.

resultsThe primary outcome occurred in 621 (6.9%) of 9027 eligible patients: death 295 (3.3%), MI 195 (2.2%), UA 103 (1.1%), and stroke 58 (0.6%). All events accrued linearly. In a multivariable model, 11 significant predictors of primary outcome (age ≥65 years, diabetes, second prior MI, CKD, history of major bleed, peripheral arterial disease, heart failure, cardiovascular hospitalization (prior 6 months), medical management (index MI), on diuretic, and poor self-reported health) were identified and combined into a user-friendly risk index. Compared with lowest-risk patients, those in the top 16% had a rate ratio of 6.9 for the primary composite, and 18.7 for all-cause death (overall c-statistic; 0.686, and 0.768, respectively). External validation was performed using the Australian Cooperative National Registry of Acute Coronary Care, Guideline Adherence and Clinical Events registry (c-statistic; 0.748, and 0.849, respectively).

conclusionsIn patients >1-year post-MI, recurrent cardiovascular events and deaths accrue linearly. A simple risk index can stratify patients, potentially helping to guide management.

Indexed as

AgedAged, 80 and overCardiovascular DiseasesFemaleHumansMaleMiddle AgedModels, CardiovascularMyocardial InfarctionPrognosisProspective StudiesRegistriesRisk AssessmentRisk Factorscardiac risk factors and preventioncoronary artery diseasemyocardial infarction

Identifiers

PMID31713893
PMCPMC6954378

What OpenQuestion holds

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Registered trials

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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.