Prevalence of Obstructive Sleep Apnea, Associated Risk Factors, and Quality of Life Among Indian Congestive Heart Failure Patients
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Stable congestive heart failure (CHF) patients frequently suffer from sleep apnea, which worsens the prognosis of heart failure and affects sleep quality of the patient. The present study was undertaken to assess the prevalence of obstructive sleep apnea (OSA) and associated quality of life (QOL) among CHF patients and to ascertain the relationship of OSA with excessive daytime sleepiness and selected demographic, clinical, and anthropometric characteristics. SUBJECTS AND METHODS: Fifty CHF patients and 50 healthy controls were conveniently recruited at CHF clinic, cardiothoracic and neurosciences center outpatient department, All India Institute of Medical Sciences, New Delhi, India. Participants were interviewed using Epworth Sleepiness Scale, Modified Berlin Questionnaire, Kansas City Cardiomyopathy Questionnaire, and Quebec Sleep Questionnaire. RESULTS: The present study revealed 18% and 8% prevalence of OSA in the CHF and control groups, respectively. The prevalence of OSA was comparable in males and females. Excessive daytime sleepiness was significantly associated with OSA in CHF patients (P = .02). Clinical severity (New York Heart Association class) and duration of illness were not significantly associated with OSA. Increased body mass index and neck circumference were the significant risk factors responsible for OSA. Quality of life of CHF patients was poor, and OSA had a significantly negative impact on the already compromised QOL in CHF patients as well as in individuals with no CHF. CONCLUSION: Prevalence of OSA is high in CHF patients as well as in the general population, and it has negative impact on the already compromised QOL of CHF patient; therefore, assessment of the risk of OSA and referral for appropriate treatment should be a part of routine nursing clinical workup. Nursing interventions should also be planned to improve sleep quality of CHF patients.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".