Activity levels influence frequency of premature atrial contractions
Bibliographic record
Abstract
Abstract Background Frequent premature atrial contractions (PACs) are associated with substantially increased risk of atrial fibrillation (AF) and stroke, but PAC count varies substantially day-to-day. With the emergence of potential therapies for primary prevention of AF reliable estimation of PAC frequency is increasingly relevant, as is an understanding of PAC determinants. Purpose To determine the effect of daily activity and heart rate on an individuals' daily PAC count. Methods We included a random sample of patients 18–85 years without AF who recorded an ambulatory ECG for 7–31 days in the U.S.A during 2019 using a full-disclosure mobile cardiac telemetry device, and who had ≥500PACs on at least one recording day. PACs were algorithmically detected and manually verified. PAC count and activity was sampled for each individual and each recording day during daytime (06–22h). The effect of activity on daily PAC count was assessed in a negative binomial regression model including age, sex and with a random effect for individual, to account for confounding due to inter-individual differences. Results The study population consisted of 2,094 patients, of which 48% were men (Fig 1). Mean time spent in activity was 32% (standard deviation (SD 10%) for men and women 31% (SD 10%) for women (Fig 2). The median PAC count was 592 (inter-quartile range 1280). Beyond age, sex and intra-individual differences PAC frequency was determined by activity levels, (intercept 629 PACs; incidence rate ratio per 10 minute increase in activity 1.03, p<0.0001). A 1-hour increase in daily activity was associated with a 20% increase in daily PACs count. Conclusions Physical activity is associated with increased PACs counts, implying both that a reliable diagnosis of PAC frequency needs to be conducted during a person's habitual level of activity and that PAC frequency is modifiable. In-hospital assessments of PACs while patients are mainly inactive may underestimate PAC frequency. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): the Swedish Society For Medical Researchthe Swedish Heart and Lung Foundation Age and sex distributionActivity levels by sex
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".