A Study on the Effect of EECP on Clinical and Echocardiographic Profile of Patients with Angina and Heart Failure
Bibliographic record
Abstract
BACKGROUND: Heart Failure and Stable Angina are important cause of morbidity and poor quality of life among the Cardiovascular patients. Despite optimal medical therapy, few patients are symptomatic and require hospitalisations.Non-invasive techniques like EECP are increasingly used but sparsely studied. AIM OF THE STUDY: To observe the patients with angina and heart failure who undergo EECP and study its effect on clinical features and Echocardiographic profile, post procedure. METHODS: This study is a prospective study done on 50 patients with chronic Stable Angina & Heart Failure selected for EECP. Patients were assessed initially and after completion of 35 cycles of EECP. RESULTS: The Mean EF improved by 8.42 which was statistically significant (p < 0.0001). There was a reduction in pulse rate, which was statistically significant. Systolic BP decreased by 3 mm Hg on an average, with a p value of 0.05 and was not statistically significant. Diastolic BP reduced from 76.20 mm Hg, to 73.04 mm Hg and was statistically significant (p < 0.012). Female patients had significant improvement in EF(p < 0.05). Anginal symptoms decreased in 14 patients with symptomatic angina. 37 patients reverted back to Class I dyspnoea from Higher class of Dyspnoea after EECP. Few patients were optimised with a lower dose of Diuretics and Nitrates after careful assessment. 66% of patients enrolled had reported an Improvement in Quality of Life. CONCLUSION: We conclude that EECP significantly reduces anginal episodes, improves failure symptoms, reduces the dose of nitrates & diuretics, and improves the quality of life of the patients with only minimal risk of adverse events. Importantly, EECP is useful in a symptomatic TVD patient, unsuitable for revascularisation & also in patients with residual disease after PCI/CABG.
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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 | 0.001 |
| 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) | 0.001 | 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".