Impact of a cardiovascular rehabilitation program on frailty indicators in elderly patients with heart disease
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: Private hospital(s). Main funding source(s): Hospital Cardio Pulmonar INTRODUCTION Frailty has been considered an important predictor of morbidity and mortality in elderly patients with cardiovascular disease. Cardiovascular Rehabilitation (CVR) has a direct and unequivocal effect on improving functional capacity in patients with heart disease, however, the effect of CVR on frailty indicators has not yet been well established. PURPOSE: To evaluate the association of the CVR program with frailty indicators in elderly patients with heart disease referred to a cardiovascular rehabilitation program and to identify possible predictors of improvement in frailty in this population. METHODS: Retrospective cohort with patients over 65 years old referred to an CVR program in Salvador-BA, Brazil from August / 2017 to March / 2020. Frailty was assessed using the Edmonton Frail Scale (EFS) at baseline and at least 3 months after the start of the program. Student"s t and Chi-square tests were used to compare continuous and categorical variables, respectively, logistic regression to analyze independent predictors of improvement in frailty and p <0.05 adopted as statistically significant. RESULTS: 51 patients were included, with a mean age of 75 ± 6 years, 65% men, 39 (77%) with coronary artery disease, 23 (50%) with heart failure, 21 (41%) with diabetes, 34 (67%) with hypertension and 41 (80%) dyslipidemia. According to the American Heart Association (AHA) risk stratification for exercise, 21 (49%) were risk B and 22 (51%) risk C. Regarding functional capacity, 12 (31%) were class I, 21 (41%) class II, 5 (13%) class III and 1 (3%) class IV according to the New York Heart Association (NYHA). The average initial ejection fraction was 53 ± 16%. The mean time between the two assessments was 5 ± 2 months and the improvement observed in maximum oxygen consumption (VO2 max) was from 15 ± 4 to 16 ± 4 mL.Kg-1.min-1 (p = 0.001). Regarding frailty, there was an improvement from 5.4 ± 2.0 to 4.8 ± 1.9 in the average of the EFS score (p = 0.034), with 25 patients (49%) being considered responders. This group was predominantly formed by men, non-diabetics, using statins, at risk B (AHA) and with a higher score on the quality of life score and on the EFS. However, in the multivariate analysis, only the highest score on the EFS (OR 1.8 CI 95% 1.06-3.3; p <0.05) and the lowest risk on the AHA scale (OR 0.18 CI 95% 0.03-0.97; p <0.05) remained as independent predictors of response. CONCLUSIONS: There was a significant improvement in the frailty of elderly patients referred for CVR, the higher the baseline frailty score, the greater the chance of response.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".