Methodology for the nocturnal cardiac arrhythmia ancillary study of the ADVENT-HF trial in patients with heart failure with reduced ejection fraction and sleep-disordered breathing
Bibliographic record
Abstract
Background: Sleep disordered breathing (SDB) may trigger nocturnal cardiac arrhythmias (NCA) in patients with heart failure with reduced ejection fraction (HFrEF). The NCA ancillary study of the ADVENT-HF trial will test whether, in HFrEF-patients with SDB, peak-flow-triggered adaptive servo-ventilation (ASVpf) reduces NCA. To this end, accurate scoring of NCA from polysomnography (PSG) is required. Objective: To develop a method to detect NCA accurately from a single-lead electrocardiogram (ECG) recorded during PSG and assess inter-observer agreement for NCA detection. Methods: Quality assurance of ECG analysis included training of the investigators, development of standardized technical quality, guideline-conforming semi-automated NCA-scoring via Holter-ECG software and implementation of an arrhythmia adjudication committee. To assess inter-observer agreement, the ECG was analysed by two independent investigators and compared for agreement on premature ventricular complexes (PVC) /h, premature atrial complexes/h (PAC) as well as for other NCA in 62 patients from two centers of the ADVENT-HF trial. Results: The intraclass correlation coefficients for PVC/h and PAC/h were excellent: 0.99 (95%- confidence interval [CI]: 0.99-0.99) and 0.99 (95%-CI: 0.97-0.99), respectively. No clinically relevant difference in inter-observer classification of other NCA was found. The detection of non-sustained ventricular tachycardia (18% versus 19%) and atrial fibrillation (10% versus 11%) was similar between the two investigators. No sustained ventricular tachycardia was detected. Conclusion: These findings indicate that our methods are very reliable for scoring NCAs and are adequate to apply for the entire PSG data set of the ADVENT-HF trial.
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 | 0.054 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".