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Record W2981638414 · doi:10.1093/eurheartj/ehz748.0941

P2618Association of left ventricular ejection fraction with mortality and hospitalizations

2019· article· en· W2981638414 on OpenAlexaffabout
Paul Angaran, Paul Dorian, Andrew C.T. Ha, Paaladinesh Thavendiranathan, Wendy Tsang, Howard Leong‐Poi, Anna Woo, B Dias, Xin⁃ping WANG, Paul E Austin, D Lee

Bibliographic record

VenueEuropean Heart Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsInstitute for Clinical Evaluative SciencesLondon Health Sciences CentreUniversity Health NetworkSt. Michael's Hospital
Fundersnot available
KeywordsMedicineEjection fractionHazard ratioCardiologyHeart failureInternal medicineAmbulatoryProportional hazards modelCohortEmergency medicineConfidence interval

Abstract

fetched live from OpenAlex

Abstract Background Although 2-dimensional echocardiography (2DE) is widely used to measure left ventricular ejection fraction (LVEF), the prognostic value of 2DE-derived LVEF has not been clearly demonstrated in a broad range of patients, including those acutely hospitalized as well as ambulatory patients. In particular, the prognostic value of echocardiographic LVEF has not been demonstrated for cardiovascular and heart failure hospitalizations. Purpose To determine if greater degrees of LV dysfunction are associated with progressively increasing risks of death or cardiovascular hospitalizations among patients undergoing echocardiography in hospital or outpatient settings. Methods We examined quantitative LVEFs from patient-level echocardiographic reports at 3 large hospital laboratories, which were linked to the Canadian Institute for Health Information hospitalization database and to death registries in Ontario, Canada. LVEF was categorized as <25%, 25–35%, 36–45%, or 46–55% (reference). Analyses were performed using cause-specific hazard competing risk models and stratified by: a) outpatient vs. inpatient echocardiogram, and b) if inpatient study, whether the reason for hospitalization was cardiac or noncardiac in nature. Results In the echocardiographic cohort of 27,323 patients (median age 68 [IQR: 58–77], 14,828 women [31.7%]), greater reductions in LVEF were associated with higher rates of all-cause mortality, with adjusted hazard ratios (95% CI) of 1.67 (1.57, 1.77) for LVEF <25%, 1.30 (1.24, 1.36) for LVEF 25–35%, and 1.17 (1.11, 1.23) for LVEF 36–45%, compared to LVEF 46–55% (all p<0.001). The cumulative incidence of cardiovascular death was higher as LVEF progressively worsened (Figure). The rate of heart failure hospitalizations was also increased with hazard ratios of 1.71 (1.59, 1.85) for LVEF <25%, 1.39 (1.31, 1.48) for LVEF 25–35%, and 1.21 (1.13, 1.29) for LVEF 36–45%, compared to LVEF 46–55% (all p<0.001). Cardiovascular hospitalizations were also increased with hazard ratios of 1.35 (1.27, 1.42), 1.21 (1.16, 1.27), and 1.13 (1.07, 1.18) for LVEFs <25%, 25–35%, and 36–45%, respectively (all p<0.001). The risk of mortality and hospitalizations increased comparably with greater reductions in LVEF during both inpatient cardiac or noncardiac admissions (p<0.001). Cumulative incidence of CV death Conclusions Quantitative LVEF assessed by 2DE is potent prognostically and was able to stratify the risk of both death and hospitalization outcomes in a wide range of clinical settings. Patients with reduced LVEF measured on inpatient or outpatient echocardiograms, and even in the context of non-cardiac admission, should be considered an at-risk group in whom quality of care metrics could be evaluated in future studies. Acknowledgement/Funding Canadian Institutes of Health Research, Heart and Stroke Foundation, and the Ted Rogers Centre for Heart Research

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)0.001
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 score0.058
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.033
GPT teacher head0.286
Teacher spread0.254 · 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".

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Citations0
Published2019
Admission routes2
Has abstractyes

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