Acute Dyspnoea in Cancer Patients: Prevalence of Acute Heart Failure, Resource Use and Diagnostic Accuracy of Natriuretic Peptides
Bibliographic record
Abstract
AIMS: Among cancer patients presenting with acute dyspnoea, the prevalence of acute heart failure (AHF), resource use and diagnostic accuracy of natriuretic peptides remain unknown. This study aimed to address these knowledge gaps. METHODS AND RESULTS: Patients presenting with acute dyspnoea to the emergency department (ED) were prospectively enrolled in a multicentre diagnostic study. AHF was centrally adjudicated by two independent cardiologists based on current guidelines. B-type natriuretic peptide (BNP) and N-terminal proBNP (NT-proBNP) concentrations were measured at ED presentation. Cancer status, resource use, and long-term outcomes were prospectively assessed. Among 2153 patients, 473 (22.0%) had an active or past cancer. AHF was the most common final diagnosis in both cancer and non-cancer patients (44.4% vs. 51.0%, p = 0.01). Among the alternative diagnoses, pneumonia and cancer-related dyspnoea were more frequent in patients with cancer, while anxiety disorder/hyperventilation was frequent in patients without cancer. Hospitalization rate and length of hospital stay were both higher in cancer patients (p < 0.01). Among AHF-related signs, rales and pleural effusion showed a significant interaction with cancer status and had lower diagnostic accuracy in cancer patients. The area under the curve (AUC) of NT-proBNP was lower in cancer than in non-cancer patients (0.89 vs. 0.93, p = 0.01), while that of BNP was similar (0.93 vs. 0.95, p = ns). This difference was mainly due to active cancers. CONCLUSIONS: Acute heart failure was the most common diagnosis in cancer patients presenting with acute dyspnoea. Rales, pleural effusion, and NT-proBNP had lower diagnostic accuracy versus patients without cancer, while that of BNP remained robust.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".