Predictors of acute hospital mortality and length of stay in patients with new‐onset atrial fibrillation: a first‐hand experience from a medical emergency team response provider
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) occurs frequently following cardiothoracic surgery and treatment decisions are informed by evidence-based clinical guidelines. Outside this setting there are few data to guide clinical management. AIM: To describe the characteristics, management and outcomes of hospitalised adult patients with new-onset AF. METHODS: The medical emergency team (MET) database was utilised to identify patients who had a 'MET call' activated for tachycardia between 2015 and 2016. Patients with sinus tachycardia, pre-existing AF/atrial flutter or other known tachyarrhythmia were excluded. Primary outcomes were length of hospital stay and in-hospital mortality. RESULTS: New-onset AF was identified in 137 patients: 68 medically managed; 38 non-cardiothoracic post-operative; and 31 cardiothoracic post-operative. Mean age was 74 ± 11.6 years and 72 (53%) were male. Of 79 patients who underwent echocardiography, 80% had left atrial dilatation and 14% had reduced left ventricular ejection fraction (LVEF). Mean length of stay (LOS) was 12 days and in-hospital mortality rate was 11%. On multivariable analysis, the odds of death during acute hospitalisation was 7.4 times higher in patients with heart failure with reduced LVEF (odds ratio 7.4, 95% confidence interval (CI) 1.23-44.8, P = 0.028). Length of acute hospital stay increased by 36% if the duration of AF was longer than 48 h (beta coefficient 0.36, 95% CI -0.015 to 0.74, P = 0.059). CONCLUSION: Left ventricular systolic dysfunction in hospitalised patients with new-onset AF is associated with increased all-cause mortality whereas lower serum potassium levels are associated with an increased LOS. A prospective study is planned to compare outcomes based on in-hospital treatment strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".