Evidence map›Paper›PMID 36536453›Full record

ArticleDiagnostic and prognostic research2022

Protocol for development and validation of postpartum cardiovascular disease (CVD) risk prediction model incorporating reproductive and pregnancy-related candidate predictors.

Steven Wambua, Francesca Crowe, Shakila Thangaratinam, Dermot O'Reilly, Colin McCowan, Sinead Brophy, Christopher Yau, Krishnarajah Nirantharakumar, Richard Riley, MuM-PreDiCT Group

Open access · goldAbstract read
In one paragraph

Article in Diagnostic and prognostic research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 37% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. 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

10 authors at 6 institutions in 1 country.

Steven WambuaInstitute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham, UK. ssw107@student.bham.ac.uk.ORCID http://orcid.org/0000-0003-2300-7670
Francesca CroweInstitute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham, UK.
Shakila ThangaratinamWHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK.
Dermot O'ReillyCentre for Public Health, Queen's University Belfast, Belfast, UK.
Colin McCowanSchool of Medicine, University of St Andrews, St Andrews, UK.
Sinead BrophyData Science, Medical School, Swansea University, Swansea, UK.
Christopher YauBig Data Institute, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Old Road Campus, Oxford, OX3 7LF, UK.
Krishnarajah NirantharakumarInstitute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham, UK.
Richard RileyInstitute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham, UK.
MuM-PreDiCT Group
University of Birmingham · GBBirmingham Women’s and Children’s NHS Foundation Trust · GBJohn Radcliffe Hospital · GBQueen's University Belfast · GBSwansea University · GBUniversity of St Andrews · GB

Funding

British Heart Foundation SP/19/3/34678Medical Research Council MR/V005243/1Medical Research Council MR/W014386/1Medical Research Council MR/W014432/1
6 · The paper itself

Abstract

backgroundCardiovascular disease (CVD) is a leading cause of death among women. CVD is associated with reduced quality of life, significant treatment and management costs, and lost productivity. Estimating the risk of CVD would help patients at a higher risk of CVD to initiate preventive measures to reduce risk of disease. The Framingham risk score and the QRISK® score are two risk prediction models used to evaluate future CVD risk in the UK. Although the algorithms perform well in the general population, they do not take into account pregnancy complications, which are well known risk factors for CVD in women and have been highlighted in a recent umbrella review. We plan to develop a robust CVD risk prediction model to assess the additional value of pregnancy risk factors in risk prediction of CVD in women postpartum.

methodsUsing candidate predictors from QRISK®-3, the umbrella review identified from literature and from discussions with clinical experts and patient research partners, we will use time-to-event Cox proportional hazards models to develop and validate a 10-year risk prediction model for CVD postpartum using Clinical Practice Research Datalink (CPRD) primary care database for development and internal validation of the algorithm and the Secure Anonymised Information Linkage (SAIL) databank for external validation. We will then assess the value of additional candidate predictors to the QRISK®-3 in our internal and external validations. DISCUSSION: The developed risk prediction model will incorporate pregnancy-related factors which have been shown to be associated with future risk of CVD but have not been taken into account in current risk prediction models. Our study will therefore highlight the importance of incorporating pregnancy-related risk factors into risk prediction modeling for CVD postpartum.

Indexed as

Cardiovascular diseasePrediction modelingPregnancy complicationsPregnant womenPrognosis

Identifiers

PMID36536453
PMCPMC9761974
OpenAlexW4312065703

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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.