Initial Care Pathway in Acute Heart Failure From Home to Hospital
Bibliographic record
Abstract
INTRODUCTION: The prognosis of acute heart failure (AHF) remains poor. Studies focusing on the time-sensitivity of early AHF management have reported controversial results. Thus, our aim is to review current studies focusing on AHF patients using emergency medical services (EMS), their early management, and patient outcomes. METHODS: We searched the recent literature in PubMed and Scopus for studies comparing AHF patients arriving at the hospital by EMS to those self-presenting (non-EMS) at ED (emergency department) from database inception until November 2022. RESULTS: The literature search found five studies fulfilling our inclusion criteria. The percentage of AHF patients using EMS varied in these studies: 11.5% (100/873) in Finnish FINN-AKVA II, 22.1% (236/1068) in Canadian ASCEND-HF, 35.5% (5129/14454) in a Pakistan Heart Failure-registry study, 52.8% (3224/6106) in Spanish SEMICA, and 61.8% (309/500) in the European EURODEM study. The pre-hospital management differed across the reviewed studies. The use of NIV was rare, ranging from zero to four percent. Vasodilators and diuretics were more commonly used. Although, the differences in the use were obvious (range from 7.1% to 22.0%, and 0.0% to 29.0% accordingly). Three of the studies reported significantly higher 30-day mortality among EMS patients compared to non-EMS patients: ranging from 5.6% versus 3.5%, p < 0.001% to 15.0% versus 6.9%, p < 0.001. CONCLUSION: The use of EMS, as well as pre-hospital management, varies between the international cohorts and registries. The pre-hospital AHF management is generally limited. Moreover, EMS patients tend to have worse outcomes compared to non-EMS patients.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".