Abstract 18874: Beyond the Beat: Predicting Frailty in Cardiovascular Patients Using Heart Rate Variability
Bibliographic record
Abstract
Introduction: Frailty is the increased vulnerability to health issues due to age-related changes in body composition, mobility, and autonomic control. Heart rate variability (HRV) measures the oscillations between consecutive heartbeats. The LF/HF (low frequency/high frequency) ratio, a parameter of HRV, reflects the complex balance between sympathetic and parasympathetic control and cardiac autonomic regulation. Previous studies have linked the LF/HF ratio with frailty. Our goal was to determine if this association holds true in patients with cardiovascular disease (CVD) and if the LF/HF ratio can be used as a surrogate marker of frailty in these patients. Methods: A cross-sectional study was conducted at McGill University's Jewish General Hospital, involving adult patients at the ambulatory cardiology clinic. Resting HRV was evaluated for 2 minutes and 30 seconds using the Elite HRV CorSense monitor. Patients with electrical pacing, atrial fibrillation, or other arrhythmias during the visit were excluded. Frailty was assessed using the clinical frailty scale (CFS), with a CFS ≥ 5 indicating frailty. Routine comprehensive history and physical examinations were performed. Results: The cohort included 155 patients (66.9 ± 13 years, 68 females). Frailty prevalence was 15% (78.6 ± 10 years, 12 females), with a median LF/HF ratio of 0.37. The non-frail population (64.8 ± 12 years, 56 females) showed a significantly higher LF/HF ratio of 1.01 (p<0.001). This difference was driven by a decrease in LF power in the frail group (78.3 ms 2 ) compared to the non-frail group (231.9 ms 2 , p=0.01). The LF/HF ratio correlated with frailty, decreasing by 0.437 for each unit increase in CFS (p<0.001). An LF/HF ratio of 0.37 best predicted frailty (54% sensitivity, 82% specificity, 0.77 ROC). Adjustment for age, sex, and comorbidities did not alter the association between LF/HF ratio and frailty. Conclusions: The LF/HF ratio exhibits an inverse correlation with frailty, making it a predictive tool for identifying CVD patients who may benefit from further frailty evaluation. Additionally, HRV parameters show potential for monitoring frailty and guiding interventions. However, additional research is needed to assess their practical implementation fully.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".