Evidence map›Paper›PMID 30629157›Full record

ArticleEuropean heart journal2019

Prediction of individual life-years gained without cardiovascular events from lipid, blood pressure, glucose, and aspirin treatment based on data of more than 500 000 patients with Type 2 diabetes mellitus.

Gijs F N Berkelmans, Soffia Gudbjörnsdottir, Frank L J Visseren, Sarah H Wild, Stefan Franzen, John Chalmers, Barry R Davis, Neil R Poulter, Annemieke M Spijkerman, Mark Woodward and 8 more

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In one paragraph

Article in European heart journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 2 pooled it
7.2field-weighted citation impact, top 2% of its field
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

43 citing papers in PubMed, 2 syntheses or guidelines pooled it, 98 citations in OpenAlex.

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  14. Italian Association of Hospital Cardiologists position paper-obesity in adults: a clinical primer.European heart journal supplements : journal of the European Society of Cardiology · 2024
    Article
  15. Cardio-renal-metabolic disease in primary care setting.Diabetes/metabolism research and reviews · 2024
    Review
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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

18 authors at 8 institutions in 5 countries.

Gijs F N BerkelmansDepartment of Vascular Medicine, University Medical Center Utrecht, GA Utrecht, the Netherlands.
Soffia GudbjörnsdottirSwedish National Diabetes Register, Center of Registers in Region, Medicinaregatan 18C, Gothenburg, Sweden.
Frank L J VisserenDepartment of Vascular Medicine, University Medical Center Utrecht, GA Utrecht, the Netherlands.
Sarah H WildUsher Institute of Population Health Sciences and Informatics, University of Edinburgh, Old Medical School, Teviot place, EH89AG Edinburgh, UK and the Scottish Diabetes Research Network Epidemiology Group.
Stefan FranzenSwedish National Diabetes Register, Center of Registers in Region, Medicinaregatan 18C, Gothenburg, Sweden.
John ChalmersThe George Institute for Global Health, University of New South Wales, Sydney, Level 5, 1 King Street, Newtown NSW, Australia.
Barry R DavisDepartment of Biostatistics, University of Texas School of Public Health, Houston, TX, USA.
Neil R PoulterICCH, Imperial College London, Level 2 Faculty building, South Kensington campus, London, UK.
Annemieke M SpijkermanNational Institute for Public Health and the Environment (RIVM), 3720 BA, Bilthoven, the Netherlands.
Mark WoodwardThe George Institute for Global Health, University of New South Wales, Sydney, Level 5, 1 King Street, Newtown NSW, Australia.
Sara L PresselDepartment of Biostatistics, University of Texas School of Public Health, Houston, TX, USA.
Ajay K GuptaICCH, Imperial College London, Level 2 Faculty building, South Kensington campus, London, UK.
Yvonne T van der SchouwJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, HP: str 6.131, GA Utrecht, the Netherlands.
Ann-Marie SvenssonSwedish National Diabetes Register, Center of Registers in Region, Medicinaregatan 18C, Gothenburg, Sweden.
Yolanda van der GraafJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, HP: str 6.131, GA Utrecht, the Netherlands.
Stephanie H ReadUsher Institute of Population Health Sciences and Informatics, University of Edinburgh, Old Medical School, Teviot place, EH89AG Edinburgh, UK and the Scottish Diabetes Research Network Epidemiology Group.
Bjorn EliassonSwedish National Diabetes Register, Center of Registers in Region, Medicinaregatan 18C, Gothenburg, Sweden.
Jannick A N DorresteijnDepartment of Vascular Medicine, University Medical Center Utrecht, GA Utrecht, the Netherlands.
University Medical Center Utrecht · NLCentre of Registers Vastra Gotaland · SEImperial College London · GBThe University of Texas Health Science Center at Houston · USUniversity of Edinburgh · GBJohns Hopkins University · USNational Institute for Public Health and the Environment · NLUNSW Sydney · AU

Funding

CLINICAL TRIALS CENTER FOR ALLHAT-N01HC35130-268035130N01HC035130 · HC · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 1993 to 2005
$6.3M
Chief Scientist Office PDF/15/07NHLBI NIH HHS N01 HC035130
6 · The paper itself

Abstract

aimsAlthough group-level effectiveness of lipid, blood pressure, glucose, and aspirin treatment for prevention of cardiovascular disease (CVD) has been proven by trials, important differences in absolute effectiveness exist between individuals. We aim to develop and validate a prediction tool for individualizing lifelong CVD prevention in people with Type 2 diabetes mellitus (T2DM) predicting life-years gained without myocardial infarction or stroke. METHODS AND

resultsWe developed and validated the Diabetes Lifetime-perspective prediction (DIAL) model, consisting of two complementary competing risk adjusted Cox proportional hazards functions using data from people with T2DM registered in the Swedish National Diabetes Registry (n = 389 366). Competing outcomes were (i) CVD events (vascular mortality, myocardial infarction, or stroke) and (ii) non-vascular mortality. Predictors were age, sex, smoking, systolic blood pressure, body mass index, haemoglobin A1c, estimated glomerular filtration rate, non- high-density lipoprotein cholesterol, albuminuria, T2DM duration, insulin treatment, and history of CVD. External validation was performed using data from the ADVANCE, ACCORD, ASCOT and ALLHAT-LLT-trials, the SMART and EPIC-NL cohorts, and the Scottish diabetes register (total n = 197 785). Predicted and observed CVD-free survival showed good agreement in all validation sets. C-statistics for prediction of CVD were 0.83 (95% confidence interval: 0.83-0.84) and 0.64-0.65 for internal and external validation, respectively. We provide an interactive calculator at www.U-Prevent.com that combines model predictions with relative treatment effects from trials to predict individual benefit from preventive treatment.

conclusionCardiovascular disease-free life expectancy and effects of lifelong prevention in terms of CVD-free life-years gained can be estimated for people with T2DM using readily available clinical characteristics. Predictions of individual-level treatment effects facilitate translation of trial results to individual patients.

Indexed as

AgedAntihypertensive AgentsAspirinCardiovascular DiseasesCohort StudiesDiabetes ComplicationsDiabetes Mellitus, Type 2Disease-Free SurvivalFemaleHumansHypoglycemic AgentsHypolipidemic AgentsMaleMiddle AgedMyocardial InfarctionPrognosisAntihypertensive AgentsAspirinHypoglycemic AgentsHypolipidemic AgentsCardiovascularLifelong preventionLifetime predictionType 2 diabetes mellitus

Identifiers

PMID30629157
PMCPMC7963127
OpenAlexW2909864871

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.