Heart rate variability as a digital biomarker for frailty in cardiovascular patients
Bibliographic record
Abstract
BACKGROUND: Frailty is a syndrome associated with age-related impairments in multiple organ systems, of which the autonomic nervous system plays a fundamental role. Measurement of heart rate variability (HRV) is a non-invasive method to evaluate the autonomic activity and gain insights into cardiovascular health and potentially, frailty. A few small studies have explored the relationship between HRV and frailty, with promising but conflicting results. OBJECTIVE: To investigate the relationship between HRV and frailty among adult patients with cardiovascular disease. DESIGN: A cross-sectional study was conducted using clinical data. SETTING: Data were collected from an ambulatory cardiology clinic. PARTICIPANTS: The cohort comprised 155 patients with a mean age of 67 years (44 % female). MEASUREMENTS: HRV was assessed seated at rest for 2.5 min using a finger-based photoplethysmography (PPG) device. Frailty was assessed using the Clinical Frailty Scale (CFS), with a score ≥5 considered frail. Associations between HRV and frailty were examined using a Spearman correlation matrix and multivariable ordinal regression model. The LF/HF ratio (a frequency-domain measure reflecting imbalances between sympathetic and parasympathetic activity) was the primary HRV measure analyzed. RESULTS: The prevalence of frailty was 15 %. Among all HRV measures, the LF/HF ratio was most closely correlated with frailty (p < 0.001). In the multivariable model, each 1 standard deviation decrease in LF/HF ratio was associated with a 1.1-point increase in CFS (95 % CI 0.7-1.6, p < 0.001). The optimal ROC cutoff at which the LF/HF ratio was associated with frailty is ≤ 0.37. CONCLUSIONS: The LF/HF ratio is inversely correlated with the CFS and independently associated with frailty. Measurement of HRV is a promising technique to enrich existing frailty scales and assist in frailty assessments in an ambulatory cardiology clinic.
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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 | 0.002 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".