Multimorbidity and risk of atrial fibrillation in the Lifelines cohort
Bibliographic record
Abstract
Abstract Aims Associations of individual comorbidities with incident atrial fibrillation (AF) are well-studied. However, the impact of multimorbidity and potentially clustering of comorbidities on incident AF remains unclear. This study investigated the number and clustering of (non-)cardiovascular comorbidities with incident AF. Methods and results We studied 25 (non-)cardiovascular comorbidities in 76 648 participants from the Lifelines cohort. Logistic regression was used to study the association between the number of comorbidities and incident AF. Latent class analysis was used to identify comorbidity clusters. Mean age was 46.4 ± 2.6 years and 59.3% were women. In this population, 56 034 (73.1%) participants had ≥2 comorbidities, 42 575 (55.5%) ≥ 2 cardiovascular comorbidities, and 14 612 (19.1%) ≥ 2 non-cardiovascular comorbidities. After a mean follow-up of 3.70 ± 0.95 years, 188 (0.2%) participants developed incident AF. After adjusting for age and sex, the total number of comorbidities (OR 1.10 [1.01–1.19], P = 0.022) and number of cardiovascular comorbidities (OR 1.18 [1.06–1.31], P = 0.002) were associated with incident AF, but not the number of non-cardiovascular comorbidities. We identified 12 comorbidity clusters carrying different risks of incident AF (AF incidence rate range 0.00 to 0.58 per 100 person-years, P < 0.001) with the median number of comorbidities ranging from one to seven. However, the clusters did not demonstrate specific combinations of comorbidities. Conclusion There was a dose-dependent relationship between the number of total comorbidities and cardiovascular comorbidities and risk of incident AF, but not for non-cardiovascular comorbidities. We identified 12 comorbidity clusters with different risks of incident AF; however, these clusters were determined by the number of comorbidities rather than specific combinations.
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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 | 0.002 |
| 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) | 0.002 | 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 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".