Detailed Assessment of the “I Need Help” Criteria in Patients With Heart Failure: Insights From the HELP-HF Registry
Bibliographic record
Abstract
BACKGROUND: The “I Need Help” markers have been proposed to identify patients with advanced heart failure (HF). We evaluated the prognostic impact of these markers on clinical outcomes in a real-world, contemporary, multicenter HF population. METHODS: We included consecutive patients with HF and at least 1 high-risk “I Need Help” marker from 4 centers. The impact of the cumulative number of “I Need Help” criteria and that of each individual “I Need Help” criterion was evaluated. The primary end point was the composite of all-cause mortality or first HF hospitalization. RESULTS: Among 1149 patients enrolled, the majority had 2 (30.9%) or 3 (22.6%) “I Need Help” criteria. A higher cumulative number of “I Need Help” criteria was independently associated with a higher risk of the primary end point (adjusted hazard ratio for each criterion increase, 1.19 [95% CI, 1.11–1.27]; P <0.001), and patients with >5 criteria had the worst prognosis. Need of inotropes, persistently high New York Heart Association classes III and IV or natriuretic peptides, end-organ dysfunction, >1 HF hospitalization in the last year, persisting fluid overload or escalating diuretics, and low blood pressure were the individual criteria independently associated with a higher risk of the primary end point. CONCLUSIONS: In our HF population, a higher number of “I Need Help” criteria was associated with a worse prognosis. The individual criteria with an independent impact on mortality or HF hospitalization were need of inotropes, New York Heart Association class or natriuretic peptides, end-organ dysfunction, multiple HF hospitalizations, persisting edema or escalating diuretics, and low blood pressure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".