Heart Rate Variability Triangular Index as a Predictor of Cardiovascular Mortality in Patients With Atrial Fibrillation
Bibliographic record
Abstract
Background Impaired heart rate variability (HRV) is associated with increased mortality in sinus rhythm. However, HRV has not been systematically assessed in patients with atrial fibrillation (AF). We hypothesized that parameters of HRV may be predictive of cardiovascular death in patients with AF. Methods and Results From the multicenter prospective Swiss‐AF (Swiss Atrial Fibrillation) Cohort Study, we enrolled 1922 patients who were in sinus rhythm or AF. Resting ECG recordings of 5‐minute duration were obtained at baseline. Standard parameters of HRV (HRV triangular index, SD of the normal‐to‐normal intervals, square root of the mean squared differences of successive normal‐to‐normal intervals and mean heart rate) were calculated. During follow‐up, an end point committee adjudicated each cause of death. During a mean follow‐up time of 2.6±1.0 years, 143 (7.4%) patients died; 92 deaths were attributable to cardiovascular reasons. In a Cox regression model including multiple covariates (age, sex, body mass index, smoking status, history of diabetes mellitus, history of hypertension, history of stroke/transient ischemic attack, history of myocardial infarction, antiarrhythmic drugs including β blockers, oral anticoagulation), a decreased HRV index ≤ median (14.29), but not other HRV parameters, was associated with an increase in the risk of cardiovascular death (hazard ratio, 1.7; 95% CI, 1.1–2.6; P =0.01) and all‐cause death (hazard ratio, 1.42; 95% CI, 1.02–1.98; P =0.04). Conclusions The HRV index measured in a single 5‐minute ECG recording in a cohort of patients with AF is an independent predictor of cardiovascular mortality. HRV analysis in patients with AF might be a valuable tool for further risk stratification to guide patient management. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02105844.
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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 | 0.000 | 0.000 |
| 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".