MétaCan
Menu
← Back to cohort
Record W4306255595 · doi:10.1093/eurheartj/ehac544.891

Prognostic implications of NYHA class and NT-proBNP levels in mild heart failure: a PARADIGM-HF analysis

2022· article· en· W4306255595 on OpenAlexaff
L E Rohde, A Zimerman, B Claggett, Milton Packer, Akshay S. Desai, Michael R. Zile, Jean L. Rouleau, Karl Swedberg, Marty Lefkowitz, V. Shi, John J.V. McMurray, M Vaduganathan, Scott D. Solomon

Bibliographic record

VenueEuropean Heart Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsMontreal Heart Institute
Fundersnot available
KeywordsMedicineHeart failureEjection fractionInternal medicineCardiologyAsymptomaticNatriuretic peptideRandomizationLogistic regressionReceiver operating characteristicClinical endpointRandomized controlled trial

Abstract

fetched live from OpenAlex

Abstract Background Treatment recommendations for heart failure (HF) with reduced ejection fraction are primarily centered on New York Heart Association (NYHA) classification, such that apparently asymptomatic patients might not be eligible for disease-modifying therapies. NYHA classification, however, may be particularly limited to discriminate mild forms of HF. Purpose The present study aimed to determine the relationship between NYHA classification and an objective measure of HF severity (N-terminal pro–B-type natriuretic peptide [NT pro-BNP]), and their association with long-term prognosis in the PARADIGM-HF trial. Methods We compared PARADIGM-HF patients classified as NYHA class I, II, and III at randomization (NYHA class IV patients or with unavailable NYHA class were excluded [n=73]). We present kernel density estimation (KDE) plots–a non-parametric way to describe the underlying distribution of a variable–to compare NT-proBNP levels across NYHA classes. Logistic regression and the area under the receiver operating characteristic curve (AUC) were used to assess the ability to predict a patient's NYHA class using NT-proBNP levels. Time-to-event data were calculated with Kaplan–Meier estimates and NYHA class were further stratified by median baseline NT-proBNP (< or ≥1600 pg/ml). The primary outcome was cardiovascular death or first HF hospitalization. Results 8326 patients were included in this analysis (median age, 64 years; women, 22%; and median left ventricular ejection fraction, 30%). Of 389 patients classified as NYHA class I at randomization, 228 (59%) changed functional class during the first year after randomization. For log-transformed NT-proBNP, KDE overlapped substantially across NYHA classes (Figure 1A). NT-proBNP levels were a poor predictor of NYHA classification: for NYHA class I vs. II, AUC (95% confidence interval [CI]) was 0.51 (0.48–0.54); for NHYA I vs. III, 0.57 (0.54–0.60); and for NYHA II vs. III, 0.56 (0.54–0.57). NYHA class III patients displayed a distinctively higher rate of cardiovascular deaths or first HF hospitalizations (Figure 1B). NYHA class I and II patients revealed lower event rates that were not significantly different (NYHA II vs. I, HR 1.24 [0.97–1.58]). Stratification by NT-proBNP levels identified subgroups with distinctive risk, such that NYHA I patients with high NT-proBNP levels (n=175) had a higher event rate than patients with low NT-proBNP with any NYHA class (Figure 1C). Conclusion NYHA class I and II patients overlapped substantially in objective HF measures and long-term prognosis. NYHA classification remains a powerful predictor of cardiovascular events but might be limited to differentiate mild forms of HF, as apparently asymptomatic patients based on physician-defined functional class might become symptomatic within a year and conceal subjects at substantial risk for adverse outcomes. Funding Acknowledgement Type of funding sources: None.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.059
GPT teacher head0.310
Teacher spread0.252 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Heart Journal→Same topicHeart Failure Treatment and Management→French-language works237,207→