MétaCan
Menu
Back to cohort

Health-Related Quality of Life and Mortality in Heart Failure: The Global Congestive Heart Failure Study of 23 000 Patients From 40 Countries

2021· article· en· W3159134391 on OpenAlexaff
Isabelle Johansson, Philip Joseph, Kumar Balasubramanian, John J.V. McMurray, Lars H. Lund, Justin A. Ezekowitz, Deepak Kamath, Khalid F. AlHabib, Antoni Bayés‐Genís, Andrzej Budaj, Antonio L. Dans, Anastase Dzudié, Jefferey L. Probstfield, Keith A.A. Fox, Kamilu M. Karaye, Abel Makubi, Bianca Fukakusa, Koon Teo, Ahmet Temizhan, Thomas Wittlinger, Aldo P. Maggioni, Fernando Laņas, Patricio López‐Jaramillo, José Silva‐Cardoso, Karen Sliwa, Hisham Dokainish, Alex Grinvalds, Tara McCready, Salim Yusuf

Bibliographic record

VenueCirculation · 2021
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsCanadian VIGOUR CentreHamilton Health SciencesMcMaster UniversityPopulation Health Research Institute
Fundersnot available
KeywordsMedicineHeart failureEjection fractionHazard ratioInternal medicineQuality of life (healthcare)Proportional hazards modelHealth related quality of lifeCardiologyConfidence intervalDisease

Abstract

fetched live from OpenAlex

Background: Poor health-related quality of life (HRQL) is common in heart failure (HF), but there are few data on HRQL in HF and the association between HRQL and mortality outside Western countries. Methods: We used the Kansas City Cardiomyopathy Questionnaire–12 (KCCQ-12) to record HRQL in 23 291 patients with HF from 40 countries in 8 different world regions in the G-CHF study (Global Congestive Heart Failure). We compared standardized KCCQ-12 summary scores (adjusted for age, sex, and markers of HF severity) among regions (scores range from 0 to 100, with higher score indicating better HRQL). We used multivariable Cox regression with adjustment for 15 variables to assess the association between KCCQ-12 summary scores and the composite of all-cause death, HF hospitalization, and each component over a median follow-up of 1.6 years. Results: The mean age of participants was 65 years; 61% were men; 40% had New York Heart Association class III or IV symptoms; and 46% had left ventricular ejection fraction ≥40%. Average HRQL differed between regions (lowest in Africa [mean± SE, 39.5±0.3], highest in Western Europe [62.5±0.4]). There were 4460 (19%) deaths, 3885 (17%) HF hospitalizations, and 6949 (30%) instances of either event. Lower KCCQ-12 summary score was associated with higher risk of all outcomes; the adjusted hazard ratio (HR) for each 10-unit KCCQ-12 summary score decrement was 1.18 (95% CI, 1.17–1.20) for death. Although this association was observed in all regions, it was less marked in South Asia, South America, and Africa (weakest association in South Asia: HR, 1.08 [95% CI, 1.03–1.14]; strongest association in Eastern Europe: HR, 1.31 [95% CI, 1.21–1.42]; interaction P <0.0001). Lower HRQL predicted death in patients with New York Heart Association class I or II and III or IV symptoms (HR, 1.17 [95% CI, 1.14–1.19] and HR, 1.14 [95% CI, 1.12–1.17]; interaction P =0.13) and was a stronger predictor for the composite outcome in New York Heart Association class I or II versus class III or IV (HR 1.15 [95% CI, 1.13–1.17] versus 1.09 [95% CI, [1.07–1.11]; interaction P <0.0001). HR for death was greater in ejection fraction ≥40 versus <40% (HR, 1.23 [95% CI, 1.20–1.26] and HR, 1.15 [95% CI, 1.13–1.17]; interaction P <0.0001). Conclusion: HRQL is a strong and independent predictor of all-cause death and HF hospitalization across all geographic regions, in mildly and severe symptomatic HF, and among patients with preserved and reduced ejection fraction. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT03078166.

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)0.002
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.033
GPT teacher head0.316
Teacher spread0.283 · 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

Citations292
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueCirculationSame topicHeart Failure Treatment and ManagementFrench-language works237,207