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Record W4388600956 · doi:10.1093/eurheartj/ehad655.834

Echocardiographic characterization of patients with supranormal ejection fraction: pooled core laboratory analysis of the TOPCAT and PARAGON-HF trials

2023· article· en· W4388600956 on OpenAlexaff
Xin Wang, Safia Chatur, Henri Lu, Brian Claggett, Hicham Skali, Muthiah Vaduganathan, Masatoshi Minamisawa, Riccardo M. Inciardi, Amil M. Shah, Milton Packer, Bertram Pitt, Jean L. Rouleau, Marc A. Pfeffer, John J.V. McMurray, Scott D. Solomon

Bibliographic record

VenueEuropean Heart Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsMontreal Heart Institute
FundersNational Heart, Lung, and Blood Institute
KeywordsEjection fractionMedicineCardiologyInternal medicineHeart failureAtrial fibrillationMyocardial infarctionStroke volumeDiastoleCardiac function curveBlood pressure

Abstract

fetched live from OpenAlex

Abstract Background Patients with heart failure and supranormal ejection fraction (HFsnEF) may be phenotypically distinct from those with normal or below normal left ventricular ejection fraction (LVEF), and may demonstrate different prognostic trajectories or treatment responses. Purpose To characterize clinical features, cardiac structure and function among patients with HF with preserved EF (defined as 45% ≤ LVEF ≤ 65%) and supranormal EF (defined as LVEF>65%). Methods Baseline clinical characteristics and key echocardiographic parameters from patients enrolled in the echocardiographic substudies of TOPCAT (Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist)-Americas and PARAGON-HF (Prospective Comparison of ARNI [angiotensin receptor–neprilysin inhibitor] with ARB [angiotensin-receptor blocker] Global Outcomes in Heart Failure with Preserved Ejection Fraction) were compared between patients with and without HFsnEF. Results Among 1,751 participants (mean age 73 years old, 51% women) in this pooled core laboratory analysis, 265 (15%) had HFsnEF. Participants in HFsnEF group were more likely to be women, and less likely to have prior myocardial infarction and atrial fibrillation (Table 1). Compared with those with LVEF ≤ 65%, those with LVEF > 65% had lower LV end-diastolic and end-systolic volume indices, lower LV mass index, higher LV relative wall thickness, and higher right ventricular fractional area change. LV geometry and measures of LV diastolic function were similar in both groups (Table 1). Global longitudinal strain (GLS) was abnormal (absolute GLS <16%) in 28% in those with HFsnEF compared with 53% in those with LVEF ≤ 65%. After adjusting for baseline LV ejection fraction and sex, and stratified by trial, abnormal GLS was associated with a higher risk of composite of first heart failure hospitalization or cardiovascular death (overall hazard ratio [HR] 1.56, 95% confidence interval [CI] 1.23 – 1.98, P < 0.001), cardiovascular death (overall HR 2.06, 95% CI 1.35 – 3.13, P = 0.001), and heart failure hospitalization (overall HR 1.49, 95% CI 1.15 – 1.93, P = 0.003) in both HFsnEF and non-HFsnEF groups (P-interaction > 0.05 for all, Figure 1). Conclusions Among participants in the TOPCAT-Americas and PARAGON-HF trials, those with LVEF > 65% were predominantly women, had less LV remodeling, and had better RV function based on echocardiographic measures. Nevertheless, despite supranormal LVEF, GLS was abnormal in over a quarter of patients and remained an important predictor of adverse clinical outcomes in this cohort.Table 1Figure 1

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)0.007
metaresearch head score (Gemma)0.008
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 score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.029
GPT teacher head0.271
Teacher spread0.242 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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