Risk factors for premature ventricular contractions in young and healthy adults
Bibliographic record
Abstract
BACKGROUND: Premature ventricular contractions (PVCs) are associated with an increased risk of morbidity and mortality. Therefore, it was aimed to assess risk factors for the frequency of PVCs in young and healthy adults. METHODS: Our population-based study included 2048 healthy adults from the general population aged 25-41 years. PVC frequency was determined by 24-hour Holter ECG. We performed multivariable regression analysis using stepwise backward selection to identify factors independently associated with PVC frequency. RESULTS: Median age was 37 years, 953 (46.5%) were male. At least one PVC during the 24-hour monitoring period was observed in 69% of participants. Median number of detected PVCs was 2, the 95th percentile was 193. In multivariable regression analyses, we found 17 significant risk factors for PVC frequency. Low educational status (risk ratio (RR) 3.33; 95% CI 1.98 to 5.60), body height>median (1.58, 95% CI 1.11 to 2.24) and increasing levels of waist:hip ratio (2.15, 95% CI 1.77 to 2.61), N-terminal pro brain natriuretic peptide (1.52, 95% CI 1.30 to 1.76) and Sokolow-Lyon Index (1.38, 95% CI 1.15 to 1.66) (all p≤0.01) were associated with a higher PVC frequency. Physical activity (RR fourth vs first quartile 0.51, 95% CI 0.34 to 0.76) and increasing levels of haemoglobin (0.58, 95% CI 0.47 to 0.70) and glucagon-like peptide-1 (0.72, 95% CI 0.64 to 0.82) (all p<0.001) were related to a lower PVC frequency. CONCLUSIONS: PVC occurrence is common even in healthy low-risk individuals, and its frequency is associated with several covariates mainly related to cardiovascular risk factors, markers of cardiac structure and function and socioeconomic status.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".