Evidence map›Paper›PMID 21148212›Full record

ArticleBMJ (Clinical research ed.)2010

Derivation, validation, and evaluation of a new QRISK model to estimate lifetime risk of cardiovascular disease: cohort study using QResearch database.

Julia Hippisley-Cox, Carol Coupland, John Robson, Peter Brindle

Registry-linked trialAbstract readEvaluation StudyMulticenter StudyValidation Study
In one paragraph

Article in BMJ (Clinical research ed.), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03453398 (Effects of Shift Work on Health), which is not on this map. Cited by 148 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
148citing papers in PubMed, 5 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.

NCT03453398 nacompletednot on this mapstarted 2018, after this paper: background citation

Effects of Shift Work on Health: Assessment of Sleep Quality, Motor Control and Cardiovascular Risk.

TypeinterventionalSponsorI.R.C.C.S Ospedale Galeazzi-Sant'AmbrogioRan2018 to 2020Enrolled71ConditionsShift Work Type Circadian Rhythm Sleep Disorder, Chronobiology Disorders, Mood Swing, Physical ActivityArmsShifts working hours with different schedules
3 · Its place in the literature

Who cites it

148 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Julia Hippisley-CoxDivision of Primary Care, University Park, Nottingham NG2 7RD, UK. juliahippisleycox@gmail.com
Carol Coupland
John Robson
Peter Brindle

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo develop, validate, and evaluate a new QRISK model to estimate lifetime risk of cardiovascular disease.

designProspective cohort study with routinely collected data from general practice. Cox proportional hazards models in the derivation cohort to derive risk equations accounting for competing risks. Measures of calibration and discrimination in the validation cohort.

setting563 general practices in England and Wales contributing to the QResearch database. SUBJECTS: Patients aged 30-84 years who were free of cardiovascular disease and not taking statins between 1 January 1994 and 30 April 2010: 2 343 759 in the derivation dataset, and 1 267 159 in the validation dataset. Main outcomes measures Individualised estimate of lifetime risk of cardiovascular disease accounting for smoking status, ethnic group, systolic blood pressure, ratio of total cholesterol:high density lipoprotein cholesterol, body mass index, family history of coronary heart disease in first degree relative aged <60 years, Townsend deprivation score, treated hypertension, rheumatoid arthritis, chronic renal disease, type 2 diabetes, and atrial fibrillation. Age-sex centile values for lifetime cardiovascular risk compared with 10 year risk estimated using QRISK2 (2010).

resultsAcross all the 1 267 159 patients in the validation dataset, the 50th, 75th, 90th, and 95th centile values for lifetime risk were 31%, 39%, 50%, and 57% respectively. Of the 10% of patients in the validation cohort classified at highest risk with either the lifetime risk model or the 10 year risk model, only 18 385(14.5%) were at high risk on both measures. Patients identified as high risk with the lifetime risk approach were more likely to be younger, male, from ethnic minority groups, and have a positive family history of premature coronary heart disease than those identified with the 10 year QRISK2 score. The lifetime risk calculator is available at www.qrisk.org/lifetime/.

conclusionsCompared with using a 10 year QRISK2 score, a lifetime risk score will tend to identify patients for intervention at a younger age. Although lifestyle interventions at an earlier age could be advantageous, there would be small gains under the age of 65, and medical interventions carry risks as soon as they are initiated. Research is needed to examine closely the cost effectiveness and acceptability of such an approach.

Indexed as

AdultAgedAged, 80 and overAge DistributionCardiovascular DiseasesCohort StudiesEnglandFemaleHumansMaleMiddle AgedRisk AssessmentRisk FactorsSex DistributionWales

Identifiers

PMID21148212
PMCPMC2999889

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Registered trials

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.