P-Wave Durations from Automated Electrocardiogram Analysis to Predict Atrial Fibrillation and Mortality in Heart Failure
Bibliographic record
Abstract
BACKGROUND: P-wave indices have been used to predict incident atrial fibrillation (AF), stroke, and mortality. However, such indices derived from automated ECG measurements have not been explored for their predictive values in heart failure (HF). We investigated whether automated P-wave indices can predict adverse outcomes in HF. METHODS: This study included consecutive Chinese patients admitted to a single tertiary centre, presenting with HF but without prior AF, and with at least one baseline ECG, between 1 January 2010 and 31 December 2016, with last follow-up of 31 December 2019. RESULTS: A total of 2718 patients were included [median age: 77.4, interquartile range (IQR): (66.9-84.3) years; 47.9 males]. After a median follow-up of 4.8 years (IQR: 1.9-9.0 years), 1150 patients developed AF (8.8/year), 339 developed stroke (2.6/year), 563 developed cardiovascular mortality (4.3/year), and 1972 had all-cause mortality (15.1/year). Compared with 101-120 ms as a reference, maximum P-wave durations predicted new-onset AF at ≤90 ms [HR: 1.17(1.11, 1.50), P < 0.01], 131-140 ms [HR: 1.29(1.09, 1.54), P < 0.001], and ≥141 ms [HR: 1.52(1.32, 1.75), P < 0.001]. Similarly, they predicted cardiovascular mortality at ≤90 ms [HR: 1.50(1.08, 2.06), P < 0.001] or ≥141 ms [HR: 1.18(1.15, 1.45), P < 0.001], and all-cause mortality at ≤90 ms [HR: 1.26(1.04, 1.51), P < 0.001], 131-140 ms [HR: 1.15(1.01, 1.32), P < 0.01], and ≥141 ms [HR: 1.31(1.18, 1.46), P < 0.001]. These remained significant after adjusting for significant demographics, past co-morbidities, P-wave dispersion, and maximum P-wave amplitude. CONCLUSIONS: Extreme values of maximum P-wave durations (≤90 ms and ≥141 ms) were significant predictors of new-onset AF, cardiovascular mortality, and all-cause mortality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".