The prevention of delirium in elderly surgical patients with obstructive sleep apnea (PODESA): a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Obstructive sleep apnea (OSA) is associated with neurocognitive impairment - a known risk factor for postoperative delirium. However, it is unclear whether OSA increases the risk of postoperative delirium and whether treatment is protective. The objectives of this study were to identify OSA with a home sleep apnea test (HSAT) and to determine whether auto-titrating positive airway pressure (APAP) reduces postoperative delirium in older adults with newly diagnosed OSA undergoing elective hip or knee arthroplasty. METHODS: We conducted a multi-centre, randomized controlled trial at three academic hospitals in Canada. Research ethics board approval was obtained from the participating sites and informed consent was obtained from participants. Inclusion criteria were patients who were [Formula: see text]0 years and scheduled for elective hip or knee replacement. Patients with a STOP-Bang score of ≥ 3 had a HSAT. Patients were defined as having OSA if the apnea-hypopnea index was ≥ 10/h. These patients were randomized 1:1 to either: 1) APAP for 72 h postoperatively or until discharge, or 2) routine care after surgery. The primary outcome was postoperative delirium, assessed twice daily with the Confusion Assessment Method for 72 h or until discharge or by chart review. The secondary outcome measures included length of stay, and perioperative complications occurring within 30 days after surgery. RESULTS: Of 549 recruited patients, 474 completed a HSAT. A total of 234 patients with newly diagnosed OSA were randomized. The mean age was 68.2 (6.2) years and 58.6% were male. Analysis was performed on 220 patients. In total, 2.7% (6/220) patients developed delirium after surgery: 4.4% (5/114) patients in the routine care group, and 0.9% (1/106) patients in the treatment group (P = 0.21). The mean length of stay for the APAP vs. the routine care group was 2.9 (2.9) days vs. 3.5 (4.5) days (P = 0.24). On postoperative night 1, 53.5% of patients used APAP for 4 h/night or more, this decreased to 43.5% on night 2, and 24.6% on night 3. There was no difference in intraoperative and postoperative complications between the two groups. CONCLUSIONS: We had an unexpectedly low rate of postoperative delirium thus we were unable to determine if postoperative delirium was reduced in older adults with newly diagnosed OSA receiving APAP vs. those who did not receive APAP after elective knee or hip arthroplasty. TRIAL REGISTRATION: This trial was retrospectively registered in clinicaltrials.gov NCT02954224 on 03/11/2016.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".