Prevalence of Frequent Premature Ventricular Contractions and Left-ventricular systolic dysfunction In Patients Receiving Holter Monitoring
Bibliographic record
Abstract
Background : Premature ventricular contractions (PVCs) are frequently observed with left ventricular (LV) systolic dysfunction, although the prevalence of these associated conditions in the general population remains unknown. Objective: We sought to understand the prevalence of frequent PVCs (defined PVCs>5%) and high burden PVCs (defined PVCs>10%) and LV systolic dysfunction in patients receiving ambulatory Holter monitors (HM). Methods: A prospective multicenter (8 US medical centers) cross-sectional study collected demographic and PVC burden data from consecutive patients undergoing 24-hour, 48-hour, and 14-day HM (July 2018-June 2020). Left ventricle ejection fraction (LVEF) data was collected if obtained within 6 months of HM. Four PVC burden groups were analyzed (<1%, 1-5%, 5.1-10% and >10% burden) and stratified by normal LVEF (≥50%) or presence LVEF<50%. Results: The prevalence of PVC burden of 5.1-10% and >10% was 4% and 5%, respectively in the population undergoing HM (n=6,529). Age was significantly different between PVC groups (p<0.001). In those with LVEF assessment (n=3,713), the prevalence of LVEF<50% and both LVEF<50% and PVC>5% was 16.4% and 4.2%, respectively. The prevalence of PVC>5% and PVC>10% in patients with LVEF<50% was 26% and 16%, respectively. PVC>5% were more prevalent in older, male, and Caucasians (p<0.001). Females had a lower prevalence of PVC>5% than males (6% vs. 11%; p<0.001), but not among those with LVEF<50% (24% vs. 26%, p=0.10). Conclusion: PVC>5% and PVC>10% and LVEF<50% are prevalent in patients undergoing HM. PVC>5% are associated with older age. Females have a lower prevalence of PVC>5% than males but similar combined PVC>5% and LVEF<50%.
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 | 0.003 |
| 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".