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Surgical patients with obstructive sleep apnea (OSA) have increased risk of perioperative complications. The primary objective is to determine the characteristics of surgical patients with unrecognized OSA requiring oxygen therapy for postoperative hypoxemia. The secondary objective is to investigate the characteristics of patients who were responsive to oxygen therapy. This was a post-hoc multicenter study involving patients with cardiovascular risk factors undergoing major non-cardiac surgery. Patients ≥45 years old underwent Type 3 sleep apnea testing and nocturnal oximetry preoperatively. Responders to oxygen therapy were defined as individuals with ≥50% reduction in oxygen desaturation index (ODI) on postoperative night 1 versus preoperative ODI. In total, 624 out of 823 patients with unrecognized OSA required oxygen therapy. These were mostly males, had larger neck circumferences, higher Revised Cardiac Risk Indices, higher STOP-Bang scores, and higher ASA physical status, undergoing intraperitoneal or vascular surgery. Multivariable regression analysis showed that the preoperative longer cumulative time SpO2 < 90% or CT90% (adjusted p = 0.03), and lower average overnight SpO2 (adjusted p < 0.001), were independently associated with patients requiring oxygen therapy. Seventy percent of patients were responders to oxygen therapy with ≥50% ODI reduction. Preoperative ODI (19.0 ± 12.9 vs. 14.1 ± 11.4 events/h, p < 0.001), CT90% (42.3 ± 66.2 vs. 31.1 ± 57.0 min, p = 0.038), and CT80% (7.1 ± 22.6 vs. 3.6 ± 8.7 min, p = 0.007) were significantly higher in the responder than the non-responder. Patients with unrecognized OSA requiring postoperative oxygen therapy were males with larger neck circumferences and higher STOP-Bang scores. Those responding to oxygen therapy were likely to have severe OSA and worse preoperative nocturnal hypoxemia. Preoperative overnight oximetry parameters may help in stratifying patients. Published version This research was funded by University Health Network Foundation; ResMed Foundation; Health and Medical Research Fund (09100351), Hong Kong; National Healthcare Group Small Innovative Grant (12019, 15201); University of Malaya, High Impact Research Grant (UM.C/625/1/HIR/067).

Aggression risk assessments are vital to prevent injuries and morbidities amongst patients and staff in psychiatric settings. More recent studies have harnessed artificial intelligence (AI) methods such as machine learning algorithms to determine factors associated with aggression in psychiatric treatment settings. In this review, using Cooper's five-stage review framework, we aimed to evaluate the: (1) predictive accuracy, and (2) clinical variables associated with AI-based aggression risk prediction amongst psychiatric inpatients. Databases including PubMed, Cochrane, Scopus, PsycINFO, CINAHL were searched for relevant articles until April 2022. The eight included studies were independently evaluated using critical appraisal tools for systematic review developed by Joanna Briggs Institute. Most of the studies (87.5%) examined health records in predicting aggression and reported acceptable to excellent accuracy with specific machine learning algorithms employed (area under curve range 0.75-0.87). No particular machine learning algorithm outperformed the others consistently across studies (area under curve range 0.61-0.87). Relevant factors identified with aggression related to demographic and social profile, past aggression, forensic history, other psychiatric history, psychopathology, challenging behaviors and management domains. The limited extant studies have highlighted a potential role for the use of AI methods to clarify factors associated with aggression in psychiatric inpatient treatment settings. Published version The study was funded by West Region, Institute of Mental Health.