Obstructive sleep apnoea and open heart surgery: a review of its incidence and impact to patients
Bibliographic record
Abstract
BACKGROUND: Obstructive sleep apnea (OSA) is a serious health disorder which contributes to cardiovascular complications, decreased work productivity, automobile accidents, and death. This condition is characterized by a temporary cessation of breathing resulting due to upper airway closure during a person's sleep. Strain to the heart caused by this repetitive hypoxic insult can lead to postoperative complications for patients undergoing heart surgery. Recognizing cardiac surgical patients with OSA is important. Early recognition and intervention such as use of BiPAP device can reduce the postoperative complications due to OSA. METHODS: The aim of this study is to identify the incidence of unrecognized OSA in cardiac surgical patients. This is a retrospective analysis of a prospective data of the study which was done between July 2012 and July 2013 in University Malaya Medical Center. All patients undergoing elective cardiac surgery were recruited and the demographic data, questionnaire and the portable sleep study results were obtained. Patients were followed up till 30 days to record any complications. Data were entered in SPSS version 17 and analysis was done. RESULTS: The incidence of OSA was 61.4% and 43.5% were moderate to severe OSA. There was male predominance of OSA (79%) with a mean age of 60 years. OSA subjects had shorter inter-incisor distance (4.18±0.6 cm) and larger waistline (94.1±12.1 cm). The STOP-Bang questionnaire has a sensitivity of 75.8% in predicting apnoa-hypopnea index (AHI) ≥5/hour. CONCLUSIONS: OSA can be diagnosed with a simple screening questionnaire and a bedside portable sleep study. Cardiac patients diagnosed with OSA can be pre-emptively given extra attention in managing their postoperative care.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".