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Record W3201531986 · doi:10.1161/jaha.120.017735

LVS‐HARMED Risk Score for Incident Heart Failure in Patients With Atrial Fibrillation Who Present to the Emergency Department: Data from a World‐Wide Registry

2021· article· en· W3201531986 on OpenAlexafffund
Linda Johnson, Jonas Oldgren, Tyler W. Barrett, Candace D. McNaughton, Jorge Wong, William F. McIntyre, Clifford L. Freeman, Laura Murphy, Gunnar Engström, Michael D. Ezekowitz, Stuart J. Connolly, Lizhen Xu, Juliet Nakamya, David Conen, Shrikant I. Bangdiwala, Salim Yusuf, Jeff S. Healey

Bibliographic record

VenueJournal of the American Heart Association · 2021
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
FundersNational Center for Advancing Translational SciencesNational Institute on Drug AbuseCenters for Disease Control and PreventionSvenska LäkaresällskapetNational Institutes of HealthPfizerNational Heart, Lung, and Blood InstituteCanadian Institutes of Health ResearchRiksförbundet HjärtLungMcMaster UniversityHamilton Health SciencesU.S. Department of Veterans Affairs
KeywordsMedicineAtrial fibrillationHeart failureInternal medicineEmergency departmentMyocardial infarctionCardiologyOdds ratioHeart disease

Abstract

fetched live from OpenAlex

Background Heart failure (HF) is a common complication to atrial fibrillation (AF), leading to rehospitalization and death. Early identification of patients with AF at risk for HF might improve outcomes. We aimed to derive a score to predict 1-year risk of new-onset HF after an emergency department (ED) visit with AF. Methods and Results The RE-LY AF (Randomized Evaluation of Long-Term Anticoagulant Therapy) registry enrolled patients with AF presenting to an ED in 47 countries, and followed them for a year. The end point was HF hospitalization and/or HF death. Among 15 400 ED patients, 9765 had no prior HF (mean age, 64.9±14.9 years). Within 1 year, new-onset HF developed in 6.8% of patients, of whom 21% died of HF. Independent predictors of HF included left ventricular hypertrophy (odds ratio [OR], 1.47; 95% CI, 1.19-1.82), valvular heart disease (OR, 1.55; 95% CI, 1.18-2.04), smoking (OR, 1.42; 95% CI, 1.12-1.78), height (OR, 0.93; 95% CI, 0.90-0.95 per 3 cm), age (OR, 1.11; 95% CI, 1.07-1.15 per 5 years), rheumatic heart disease (OR, 1.77, 95% CI, 1.24-2.51), prior myocardial infarction (OR, 1.85; 95% CI, 1.45-2.36), remaining in AF at ED discharge (OR, 1.86; 95% CI, 1.46-2.36), and diabetes (OR, 1.33; 95% CI, 1.09-1.64). A continuous risk prediction score (LVS-HARMED [left ventricular, valvular heart disease, smoking or other tobacco use, height, age, rheumatic heart disease, myocardial infarction, emergency department discharge rhythm, and diabetes]) had good discrimination (C statistic, 0.735; 95% CI, 0.716-0.755). Validation was conducted internally using bootstrapping (optimism-corrected C statistic, 0.705) and externally (C statistic, 0.699). The 1-year incidence of HF hospitalization and/or HF death across quartile groups of the score was 1.1%, 4.5%, 6.9%, and 14.4%, respectively. LVS-HARMED also predicted incident stroke (C statistic, 0.753; 95% CI, 0.728-0.778). Conclusions The LVS-HARMED score predicts new-onset HF after an ED visit for AF. Preventative strategies should be considered in patients with high LVS-HARMED HF risk.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.324
Teacher spread0.289 · 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

Citations9
Published2021
Admission routes2
Has abstractyes

Explore more

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