Insights into the Importance of the Electrocardiogram in Patients with Acute Heart Failure
Bibliographic record
Abstract
BACKGROUND: Patients presenting to the emergency department (ED) with acute heart failure (AHF) are at an increased risk of morbidity and mortality. The electrocardiogram (ECG) is a routine investigation in patients with AHF used to identify potential causes and/or complications. It is unclear whether 12-lead ECG characteristics can serve as a prognostic indicator in this population. METHODS AND RESULTS: Patients with AHF from four hospital EDs were prospectively enrolled into the AHF - Emergency Management cohort. In addition to baseline data collection, the first available ECG was read in a core laboratory. Clinical outcomes (all-cause mortality and readmission) were recorded and risk models were developed. Of 937 enrolled patients, 816 had a diagnosis of AHF and an available ECG. Median age of the population was 77 [interquartile range (IQR) 67-85], 47% were female and median ejection fraction was 45% (IQR 30-55). Abnormalities were common, with only 7.5% of patients having a normal ECG. During the median follow-up of 25.7 months, there were 379 (46%) all-cause deaths and 328 (40%) hospital readmissions. Sinus rhythm was associated with better outcomes [hazard ratio (HR) 0.76; 95% confidence interval (CI) 0.62, 0.94], while paced rhythms (HR 1.51, 95% CI 1.11, 2.05), a wide QRS (HR 1.29, 95% CI 1.04, 1.59) and an ECG with any abnormality (HR 1.57, 95% CI 1.01, 2.44) was associated with poorer outcomes. Other individual ECG characteristics were not related to clinical outcomes after risk adjustment. CONCLUSIONS: Certain ECG abnormalities are common in patients with AHF and associated with poor outcomes. Used in conjunction with other clinical variables, the ECG may be a useful tool in long-term risk stratifying patients.
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 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 | 0.013 |
| 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 | 0.001 |
| 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".