Obstructive sleep apnea as an independent predictor of postoperative delirium and pain: protocol for an observational study of a surgical cohort

Postoperative pain management in non-traumatic emergency general surgery: WSES-GAIS-SIAARTI-AAST guidelines

BACKGROUND

Non-traumatic emergency general surgery involves a heterogeneous population that may present with several underlying diseases. Timeous emergency surgical treatment should be supplemented with high-quality perioperative care, ideally performed by multidisciplinary teams trained to identify and handle complex postoperative courses. Uncontrolled or poorly controlled acute postoperative pain may result in significant complications. While pain management after elective surgery has been standardized in perioperative pathways, the traditional perioperative treatment of patients undergoing emergency surgery is often a haphazard practice. The present recommended pain management guidelines are for pain management after non-traumatic emergency surgical intervention. It is meant to provide clinicians a list of indications to prescribe the optimal analgesics even in the absence of a multidisciplinary pain team.

MATERIAL AND METHODS

An international expert panel discussed the different issues in subsequent rounds. Four international recognized scientific societies: World Society of Emergency Surgery (WSES), Global Alliance for Infection in Surgery (GAIS), Italian Society of Anesthesia, Analgesia Intensive Care (SIAARTI), and American Association for the Surgery of Trauma (AAST), endorsed the project and approved the final manuscript.

CONCLUSION

Dealing with acute postoperative pain in the emergency abdominal surgery setting is complex, requires special attention, and should be multidisciplinary. Several tools are available, and their combination is mandatory whenever is possible. Analgesic approach to the various situations and conditions should be patient based and tailored according to procedure, pathology, age, response, and available expertise. A better understanding of the patho-mechanisms of postoperative pain for short- and long-term outcomes is necessary to improve prophylactic and treatment strategies.

Automated Scoring of Respiratory Events in Sleep with a Single Effort Belt and Deep Neural Networks

OBJECTIVE

Automatic detection and analysis of respiratory events in sleep using a single respiratory effort belt and deep learning.

METHODS

Using 9,656 polysomnography recordings from the Massachusetts General Hospital (MGH), we trained a neural network (WaveNet) to detect obstructive apnea, central apnea, hypopnea and respiratory-effort related arousals. Performance evaluation included event-based analysis and apnea-hypopnea index (AHI) stratification. The model was further evaluated on a public dataset, the Sleep-Heart-Health-Study-1, containing 8,455 polysomnographic recordings.

RESULTS

For binary apnea event detection in the MGH dataset, the neural network obtained a sensitivity of 68%, a specificity of 98%, a precision of 65%, a F1-score of 67%, and an area under the curve for the receiver operating characteristics curve and precision-recall curve of 0.93 and 0.71, respectively. AHI prediction resulted in a mean difference of 0.417.8 and a r² of 0.90. For the multiclass task, we obtained varying performances: 84% of all labeled central apneas were correctly classified, whereas this metric was 51% for obstructive apneas, 40% for respiratory effort related arousals and 23% for hypopneas.

CONCLUSION

Our fully automated method can detect respiratory events and assess the AHI accurately. Differentiation of event types is more difficult and may reflect in part the complexity of human respiratory output and some degree of arbitrariness in the criteria used during manual annotation.

SIGNIFICANCE

The current gold standard of diagnosing sleep-disordered breathing, using polysomnography and manual analysis, is time-consuming, expensive, and only applicable in dedicated clinical environments. Automated analysis using a single effort belt signal overcomes these limitations.

The relationship between obstructive sleep apnoea and postoperative delirium and pain: an observational study of a surgical cohort

Patients with obstructive sleep apnoea are at increased risk of adverse postoperative outcomes, such as cardiac and respiratory complications. It has been hypothesised that obstructive sleep apnoea also increases the risk for postoperative delirium and acute postoperative pain. We conducted a retrospective, observational study investigating the relationship of obstructive sleep apnoea with postoperative delirium and acute postoperative pain severity. Patients were classified as being at high risk for obstructive sleep apnoea if they had been diagnosed with this condition, or if they were positive for more than four factors using the 'STOP-BANG' screening tool. Adjusted logistic regression was used to investigate the association between obstructive sleep apnoea and postoperative delirium, and multivariable linear regression to study the relationship between obstructive sleep apnoea and postoperative pain severity. The incidence of postoperative delirium was 307 in 1441 patients (21.3%; 95%CI 19.2-23.5%). In unadjusted analysis, high risk for obstructive sleep apnoea was associated with delirium, with an odds ratio (95%CI) of 1.77 (1.22-2.57; p = 0.003). After adjustment for pre-specified variables, the association was not statistically significant with odds ratio 1.34 (0.80-2.23; p = 0.27). The mean (SD) maximum pain (resting or provoked) reported for the entire cohort was 63.8 (27.9) mm on a 0-100 mm visual analogue scale. High risk for obstructive sleep apnoea was not associated with postoperative pain severity (β-coefficient 2.82; 95%CI, -2.34-7.97; p = 0.28). These findings suggest that obstructive sleep apnoea is unlikely to be a strong risk factor for postoperative delirium or acute postoperative pain severity.