Effects of long-term continuous screening for atrial fibrillation on heart failure events - results from the randomized LOOP Study
Bibliographic record
Abstract
Abstract Background Previous research established a solid link between atrial fibrillation (AF) and heart failure (HF). However, while two landmark trials have assessed AF screening for stroke prevention, the potential impact of AF screening on HF remains unclear. Purpose This secondary analysis of the LOOP Study aimed to investigate the effects of AF screening on HF events. Methods The LOOP Study was a randomized clinical trial evaluating long-term continuous AF screening with implantable loop recorder (ILR) in individuals aged 70-90 years and with ≥1 of arterial hypertension, diabetes mellitus, HF, and/or prior stroke. At inclusion, study participants were randomized 1:3 to either ILR screening or usual care. Anticoagulation was recommended upon AF detection, whereas other treatment was left to the discretion of the treating physician. In this secondary analysis, a cause-specific Cox regression model was applied to explore the effects of AF screening vs usual care on 1) time-to-first HF event and 2) total HF events, with the latter performed using the Andersen-and-Gill extension. HF event was defined as newly diagnosed HF with left ventricular ejection fraction ≤40% or any HF hospitalization in participants with prior diagnosis of HF with reduced ejection fraction. Results In total, 6004 participants were enrolled in the LOOP Study: 1501 in the ILR group vs 4503 in the Control group. The study population had a mean age of 75 years and slightly more males than females (53% vs 47%), whereas 266 (4.4%) were previously diagnosed with HF at baseline. During a median follow-up of 5.4 years, a total of 208 HF events were reported in 175 (2.9%) participants: 37 events among 34 (2.3%) participants in the ILR group vs 171 events among 141 (3.1%) participants in the Control group. For time-to-first HF event, the annualized event rate was 0.44%/year [95% confidence interval (CI): 0.30-0.62] vs 0.61%/year [95% CI: 0.51-0.72] for ILR vs Control, corresponding to a hazard ratio (HR) of 0.72 [95% CI: 0.50-1.05]. When assessing total HF events, a significantly lower event rate was observed in the ILR group (0.47%/year [95% CI: 0.33-0.65]) compared with the Control group (0.73%/year [95% CI: 0.62-0.84]), resulting in a HR of 0.65 [95% CI: 0.44-0.97]. Conclusions Among elderly individuals with additional stroke risk factors, long-term continuous AF screening led to a significantly reduced risk of total HF events, compared with usual care. This should be considered as hypothesis-generating and warrants further investigation.Cumulative incidence of HF event
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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 | 0.002 |
| 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) | 0.000 | 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".