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External Validation of the H <sub>2</sub> F-PEF Model in Diagnosing Patients With Heart Failure and Preserved Ejection Fraction

2019· letter· en· W2944378578 on OpenAlexaffabout
Nariman Sepehrvand, Wendimagegn Alemayehu, Garrison J. B. Dyck, Jason R.B. Dyck, Todd J. Anderson, Jonathan G. Howlett, D. Ian Paterson, Finlay A. McAlister, Justin A. Ezekowitz

Bibliographic record

VenueCirculation · 2019
Typeletter
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsAlberta HealthVanguard CollegeLibin Cardiovascular Institute of AlbertaInstitute of Health EconomicsUniversity of AlbertaCanadian VIGOUR CentreUniversity of CalgaryUniversity of Alberta Hospital
Fundersnot available
KeywordsMedicineEjection fractionHeart failureCardiologyInternal medicine

Abstract

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Patients with heart failure (HF) and reduced ejection fraction often have a more straightforward diagnostic pathway compared with patients with heart failure with preserved ejection fraction (HFpEF). 1 As a consequence, patients with HFpEF, a very heterogenous population without a single clear defining feature (ie, low ejection fraction as in heart failure with reduced ejection fraction), may be misdiagnosed, leading to additional testing and clinical uncertainty.This imprecision has also led to difficulty in clinical trial design.Several diagnostic criteria and algorithms have been proposed to aid clinicians with the diagnosis of HFpEF but lacked sensitivity and had reasonable performance metrics. 2 A recent diagnostic model (H 2 FPEF) may have the potential to overcome the hurdle of HFpEF diagnosis. 3he H 2 FPEF diagnostic model was developed in a cohort of patients with unexplained dyspnea who were referred for invasive hemodynamic exercise testing as the gold standard. 3The H 2 FPEF score had good discriminatory performance with an area under the operating curve of 0.88 in a validation cohort where the prevalence of HFpEF was 64%. 3 It uses 6 clinical and echocardiographic variables including age >60 years, body mass index >30 kg/m 2 , hypertension with ≥2 antihypertensive medications, atrial fibrillation, echocardiographic E/e' >9, and pulmonary artery systolic pressure >35 mm Hg.The total score ranges from 0 to 9 with the scores <2 and scores ≥6, respectively, reflecting low and high likelihoods of HFpEF.Patients with intermediate scores between 2 and 5 require further (invasive) evaluations. 3lthough this model is helpful, it has not been evaluated across other patient populations with suspected HFpEF.Using data collected from the modest-sized Alberta HEART (Alberta Heart Failure Etiology and Analysis Research Team) cohort, we evaluated the performance of the H 2 FPEF model across the spectrum of cardiovascular disease, 2,4 including: (1) patients at-risk for heart failure (n=115); (2) patients at-risk for HF with symptoms of other diseases (eg, lung diseases, coronary artery disease, atrial fibrillation, and others; n=48); (3) HFpEF (n=191, including 46 with previous history of low ejection fraction); (4) reduced ejection fraction (n=169); and (5) age-and sex-matched healthy controls (n=98).The study was approved by the Health Research Ethics Boards, and informed consent was obtained from participants. 4All patients' diagnosis was independently adjudicated by 2 cardiologists after review of all previous information and assessment of echo parameters without using an explicit scoring system.Patients in group 3 (adjudicated HFpEF) were defined as HFpEF, and the other 4 groups were pooled as not-HFpEF.We further explored performance of the H 2 FPEF model in those presenting with dyspnea at the baseline visit, and other details on the cohort are as previously reported. 2,4ge ≥60 years, body mass index >30 kg/m 2 , atrial fibrillation, and hypertension were reported respectively in 83%, 53%, 49%, and 94% of patients with HFpEF.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.006
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

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

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 head0.012
GPT teacher head0.214
Teacher spread0.201 · 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 teacher head, 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".

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Citations64
Published2019
Admission routes2
Has abstractyes

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