Early diagnosis and treatment of acute heart failure in prehospital and emergency settings. Part 1 of the International Expert Opinion Series on acute heart failure management
Bibliographic record
Abstract
Acute heart failure (AHF) is diagnosed in about 0.5% of all patients seen by emergency medical systems (EMS) and represents about 1% of emergency department (ED) visits. Leg swelling and shortness of breath are the most frequent patient complaints. Despite significant advancements in patient care pathways, the proper diagnosis, treatment and disposition of AHF may be further improved in emergency settings. The present document is an expert consensus document outlining key points in diagnosis, treatment and decision-making of patients being diagnosed with AHF by EMS and in the ED. Pillars of correct diagnosis include detailed clinical assessment and accurate interpretation of natriuretic peptides, while chest X-ray is still the most frequent image test used in ED, that could be substituted by ultrasonography exploration in appropriate patients. Quick identification of the most severe cases needing intensive care is mandatory, most of them characterized by hemodynamic instability, ventilatory failure or acute coronary syndrome needing intervention. Treatment could be started in prehospital settings by EMS, and loop diuretics are still the cornerstone of decongestive therapy. Measurement of diuresis and natriuresis shortly after provision of the first diuretic bolus is recommended, as it can help in detecting patients with poor diuretic response for dose augmentation or drug escalation with the addition of acetazolamide or thiazides. For selected patients, vasodilators (especially for acute cardiogenic pulmonary edema phenotype) or inotropes/vasopressors (for those with cardiogenic shock) can be needed. Oxygen therapy should be provided to patients with air-room SpO 2 below 95%, and noninvasive ventilation is an option for patients with respiratory distress. After provision of ED care, a correct decision of patient discharge or hospitalization is paramount, and risk stratification can help in this regard. Other key points of AHF management in the ED include adequate diagnosis and management of triggers of the AHF episode; to take aspects of patient frailty into account; to avoid lines, catheters, and patient overstay in the ED where possible; and to ensure a proper follow-up plan after discharge from the hospital.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".