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Record W4283787464 · doi:10.1101/2022.06.30.22277083

An independent external validation of the QRISK3 cardiovascular risk prediction model applied to UK Biobank participants

2022· preprint· en· W4283787464 on OpenAlexaff
Ruth E. Parsons, Xiaonan Liu, Jennifer A. Collister, David A. Clifton, Benjamin J. Cairns, Lei Clifton

Bibliographic record

VenuemedRxiv · 2022
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsFuture Earth
FundersMedical Research CouncilNorthwest Regional Development AgencyUniversity of OxfordNational Institute for Health and Care ResearchBritish Heart FoundationWellcome TrustGlaxoSmithKline
KeywordsBiobankMedicineCohortEpidemiologyStroke (engine)PopulationCohort studyDiseaseProspective cohort studyDemographyGerontologyInternal medicineEnvironmental healthBioinformatics

Abstract

fetched live from OpenAlex

ABSTRACT Background The QRISK3 cardiovascular disease (CVD) risk prediction model was derived using primary care data; however, it is frequently used outside of clinical settings. The use of QRISK3 in epidemiological studies without external validation may lead to inaccurate results, however it has been used multiple times on data from UK Biobank. We aimed to externally evaluate the performance of QRISK3 for predicting 10-year risk of cardiovascular events in the UK Biobank cohort. Methods We used data from the UK Biobank, a large-scale prospective cohort study of 403,370 participants aged 40-69 years recruited between 2006 and 2010 in the United Kingdom (UK). We included participants with no previous history of CVD or statin treatment and the outcome was the first occurrence of coronary heart disease, ischaemic stroke or transient ischaemic attack, derived from linked hospital episode statistics (HES) and death registration data (DRD). Results Our study population included 233,233 females and 170,137 males, with 9295 and 13,028 incident cardiovascular events, respectively. The overall median follow-up time after recruitment was 11.7 years. The discrimination measure of QRISK3 in the overall population was reasonable (Harrell’s C-Index 0.722 in females and 0.697 in males), this was poorer in older participants (<0.62 in all participants aged 65 or older). QRISK3 had systematic over-prediction of CVD risk in UK Biobank, particularly in older participants, by as much as 20%. Conclusions QRISK3 had reasonable overall discrimination for the whole study population, which was best in younger participants. The observed CVD risk in UK Biobank participants was lower than that predicted by QRISK3, particularly for older participants. The UK Biobank cohort is known to be healthier than the general population and therefore it is necessary to recalibrate QRISK3 before using it to predict absolute CVD risk in the UK Biobank cohort.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.064
metaresearch head score (Gemma)0.121
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.337

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0640.121
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.281
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2022
Admission routes1
Has abstractyes

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