Association and prediction of subjective sleep quality and postoperative delirium during major non-cardiac surgery: a prospective observational study

Postoperative Delirium and the Older Adult: Untangling the Confusion

Postoperative delirium is one of the most prevalent postoperative complications, affecting mostly older adults. Its incidence is expected to rise because of surgical advances, shifting demographics, and increased life expectancy. Although an acute alteration in brain function, postoperative delirium is associated with adverse outcomes, including progressive cognitive decline and dementia, that place significant burdens on patients' lives and healthcare systems. This has prompted efforts to understand the mechanisms of postoperative delirium to provide effective prevention and treatment. There are multiple mechanisms involved in the etiology of postoperative delirium that share similarities with the physiological changes associated with the aging brain. In addition, older patients often have multiple comorbidities including increased cognitive impairment that is also implicated in the genesis of delirium. These tangled connections pinpointed a shift toward creation of a holistic model of the pathophysiology of postoperative delirium. Scientific advancements integrating clinical risk factors, possible postoperative delirium biomarkers, genetic features, digital platforms, and other biotechnical and information technological innovations, will become available in the near future. Advances in artificial intelligence, for example, will aggregate cognitive testing platforms with patient-specific postoperative delirium risk stratification studies, panels of serum and cerebrospinal fluid molecules, electroencephalogram signatures, and gut microbiome features, along with the integration of novel polygenetic variants of sleep and cognition. These advances will allow for the enrollment of high-risk patients into prevention programs and help uncover new pharmacologic targets.

Association between cerebrovascular disease and perioperative neurocognitive disorders: a retrospective cohort study

BACKGROUND

Previous studies have shown that patients with cerebrovascular disease (CVD) have a significantly increased risk of cognitive decline or dementia; however, the association between preoperative CVD and perioperative neurocognitive disorders (PNDs) remains unclear. This study aimed to explore the correlation between preoperative CVD and PNDs, as well as combine logistic regression and receiver operating characteristic (ROC) curves to construct a clinical prediction PND model.

MATERIALS AND METHODS

This retrospective cohort study evaluated 13 899 surgical patients of a large-scale comprehensive hospital between January 2021 and January 2022 to explore the association between preoperative CVD and PNDs, with follow-up to monitor postoperative survival until 28 February 2023, unless the patient died. The study participants comprised all inpatients from the Bone and Joint Surgery, Spine Surgery, Urology, Hepatobiliary Surgery, Gastrointestinal Surgery, and Thoracic Surgery departments. Patients were classified into two groups: the CVD group with a confirmed diagnosis and the noncerebrovascular disease group. The incidence of PNDs was measured, and potential associations between patient demographic information, preoperative comorbidities, and CVD, as well as the correlation between preoperative CVD and PNDs, were investigated by multivariate logistic regression analysis. Next, the authors constructed a clinical prediction PND model by drawing the ROC curve. The postoperative survival of all patients was tracked, and a survival curve was constructed and incorporated into the Cox proportional hazard regression model to analyze the relationship between preoperative CVD and the overall postoperative survival rate.

RESULTS

Of the included 13 899 patients, propensity score matching yielded 1006 patient pairs. Multivariate logistic regression analysis revealed that CVD was an independent risk factor for PNDs [odds ratio: 10.193; 95% CI: 7.454-13.938; P <0.001]. Subsequently, the authors developed a clinical prediction model for PNDs by multivariate logistic regression analysis. The area under the ROC curve was 0.798 (95% CI: 0.765-0.830). The survival of 11 702 patients was followed up. Multivariate Cox hazard ratio regression analysis revealed that CVD affected the overall postoperative survival rate (hazard ratio, 1.398; 95% CI: 1.112-1.758; P <0.001).

CONCLUSION

CVD was an independent risk factor for PNDs and affected the overall postoperative survival rate of surgical patients with preoperative CVD.

Impact of Preoperative Sleep Disturbances on Postoperative Delirium in Patients with Intracranial Tumors: A Prospective, Observational, Cohort Study

Background

Postoperative delirium (POD) is prevalent in craniotomy patients and is associated with high mortality. Sleep disturbances are receiving increasing attention from clinicians as associated risk factors for postoperative complications. This study aimed to determine the impact of preoperative sleep disturbances on POD in craniotomy patients.

Methods

We recruited 130 patients undergoing elective craniotomy for intracranial tumors between May 1st and December 30th, 2022. Preoperative subjective sleep disturbances were assessed using the Pittsburgh Sleep Quality Index on the day of admission. We also measured objective perioperative sleep patterns using a dedicated sleep monitoring device 3 days before and 3 days after the surgery. POD was assessed twice daily using the Confusion Assessment Model for the Intensive Care Unit within the first week after craniotomy.

Results

Preoperative sleep disturbances were diagnosed in 49% of the study patients, and POD was diagnosed in 22% of all the study patients. Sleep disturbances were an independent risk factor for POD (OR: 2.709, 95% CI: 1.020-7.192, P = 0.045). Other risk factors for POD were age (OR: 3.038, 95% CI: 1.195-7.719, P = 0.020) and the duration of urinary catheterization (OR: 1.246, 95% CI: 1.025-1.513, P = 0.027). Perioperative sleep patterns (including sleep latency, deep sleep duration, frequency of awakenings, apnea-hypopnea index, and sleep efficiency) were significantly associated with POD.

Conclusion

This study demonstrated that preoperative sleep disturbances predispose patients undergoing craniotomy to POD, also inferred a correlation between perioperative sleep patterns and POD. The targeted screening and intervention specifically for sleep disturbances during the perioperative period are immensely required.