Phenomapping in Heart Failure With Reduced Ejection Fraction to Identify Subpopulations With High Residual Risk: A VICTORIA Substudy
Bibliographic record
Abstract
BACKGROUND: Patients with heart failure and reduced ejection fraction (HFrEF) have a high residual risk for heart failure hospitalizations and cardiovascular death. We aimed to use multimodality data to identify unique HFrEF subgroups with high residual risk. METHODS: In this VICTORIA substudy (Vericiguat Global Study in Subjects With Heart Failure With Reduced Ejection Fraction), clinical, electrocardiographic, echocardiographic, quantitative biomarker, and targeted proteomics data were collected. Agglomerative hierarchical clustering was performed using 105 variables to define HFrEF phenogroups. Cox regression estimated the relationship between the HFrEF phenogroups and the primary composite outcome of cardiovascular death or heart failure hospitalization. External validation of the phenogroups was performed in the BIOSTAT-CHF cohort (Biology Study to Tailored Treatment in Chronic Heart Failure). Multinomial logistic regression identified the most important variables in defining the HFrEF phenogroups. RESULTS: There were 564 participants; after clustering, the optimal number of HFrEF phenogroups was 3. Phenogroup 1 was young, well-treated with guideline-directed medical therapy, and least likely to have an implantable cardioverter defibrillator. Phenogroup 2 had the highest prevalence of atrial fibrillation and pathological Q-waves on electrocardiography. Phenogroup 3 was older, had more biventricular dysfunction, and had advanced renal disease. A stepwise increase in the risk of the primary composite outcome was observed from HFrEF phenogroup 1 to 3 (hazard ratio, 7.0 [95% CI, 4.1–12.0]; P ≤0.01). The phenogroups were externally validated in BIOSTAT-CHF, and phenogroup 3 had similar patient characteristics (eg, older with more significant renal dysfunction) and had the highest event rate at 1 year (41% [95% CI, 38%–45%]). After multinomial regression, GDF-15 (growth differentiation factor 15) was the most important variable in discriminating the 3 HFrEF phenogroups in both VICTORIA and BIOSTAT-CHF. CONCLUSIONS: We identified and externally validated unique HFrEF subpopulations with shared biological characteristics that differentiated residual risk for cardiovascular death or heart failure hospitalization. GDF-15 was the most important protein used to distinguish the 3 HFrEF phenogroups. These findings may inform study entry criteria for future HFrEF trials focused on the development of novel therapeutics. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02861534.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".