Multimorbidity Is Associated With Symptom Severity and Disease Progression in Patients with Paroxysmal Atrial Fibrillation—Data From the RACE V Study
Bibliographic record
Abstract
Background Multimorbidity is common among patients with atrial fibrillation (AF) and is associated with worse outcomes. We aimed to investigate the association between multimorbidity, AF progression and AF symptom severity in patients with paroxysmal AF. Methods and Results The RACE V (Reappraisal of AF: Interaction Between Hypercoagulability, Electrical Remodeling, and Vascular Destabilization in the Progression of AF) study included patients with paroxysmal AF and continuous rhythm monitoring. Multimorbidity was defined as ≥2 comorbidities (heart failure, hypertension, diabetes, coronary heart disease, kidney dysfunction, moderate or severe mitral valve regurgitation, or obesity). AF symptom severity was assessed via the University of Toronto AF Severity Scale questionnaire. The associations between multimorbidity, AF progression, and AF symptom severity were determined using logistic regression analyses. Median age was 65 (58–71) years and 179 of 417 patients (43%) were women, with a median of 1 (1–2) comorbidities. Median follow‐up was 2.2 (1.6–2.8) years. Multimorbidity was associated with AF progression (odds ratio [OR], 2.02 [95% CI, 1.10–3.72], P =0.024) and increased AF symptom severity (OR, 2.67 [95% CI, 1.79–3.99], P <0.001). There was a positive dose–response relation between the number of comorbidities and AF progression (OR, 1.40 [95% CI, 1.09–1.79], P =0.008), as well as AF symptom severity (OR, 1.64 [95% CI, 1.35–1.99], P <0.001). These results remained significant after adjusting for age. Conclusions In patients with paroxysmal AF, multimorbidity was associated with AF progression and AF symptom severity. The risk of AF progression and AF symptom severity increased with every additional comorbidity. Registration URL: clinicaltrials.gov . Unique Identifier: NCT02726698.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".