Personalized cardiac rehabilitation in older geriatric patients: an analysis of age, heart failure subtypes, and physical metrics
Bibliographic record
Abstract
Abstract Background While cardiac rehabilitation (CR) is recognized for enhancing physical fitness, psychological well-being, and overall quality of life in cardiac patients, its universal application often overlooks the heterogeneity within the geriatric population. This study delves into the effectiveness of CR among older geriatric patients, aiming to uncover the disparities in response across various demographics, heart failure subtypes, and angina classifications, thereby underscoring the imperative for personalized CR programs. Methods We retrospectively analyzed 438 CR participants from December 2020 to December 2023, stratifying them by age (median: 65.14 years, IQR: 57.23-72.64), sex (23.74% female), heart failure subtype, and CCS angina class to identify distinct response patterns to CR. The analysis included a comprehensive assessment of physical metrics pre- and post-12-week CR program following cardiac surgeries, transcatheter procedures, coronary events, and/or interventions. We employed the Wilcoxon Rank Sum test for statistical analysis, focusing on identifying non-responders and quantifying effect sizes (r) to determine the magnitude of change across various physical metrics. Results Notable improvements were observed in metabolic equivalents (METs) (+1.82), oxygen uptake (VO2 peak) (+6.36 mL/kg/min), Duke Activity Status Index (DASI) score (+14.79), moderate to vigorous physical activity (MVPA) (+131.28 minutes/week), sitting time (-1.21 hours/day), total activity (+145.65 minutes/week) and sit-to-stand repetitions (+2.65). The greatest effect sizes were observed in DASI score (r=0.750) and sit-to-stand (r=SIS) repetitions (r=0.610), consistent across demographics, heart failure, and CCS categories. Other metrics noted variable changes, with handgrip strength showing no consistent trend. Sensitivity analyses revealed that older geriatric patients (≥ 80 years) with HFpEF experienced significant improvements in DASI score (r=0.638), MVPA (r=0.636), and SIS (r=0.504), while those with HFrEF and HFmrEF showed no significant improvements in any physical metrics. CCS angina classification, common comorbidities, and initial referral events did not significantly influence outcomes. Conclusion This study reaffirms the efficacy of CR but crucially identifies the gaps in its universal application, particularly among older geriatric patients with diverse heart failure subtypes. Our analysis advocates for developing personalized CR programs tailored to patients' individual profiles to enhance effectiveness of rehabilitation. By addressing these specific needs, we can ensure more inclusive and optimized patient care, ultimately supporting the expansion of CR indications to foster a more holistic approach to cardiac health in the geriatric population.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".