Electroencephalography-Guided Anesthesia and Delirium in Older Adults After Cardiac Surgery: The ENGAGES-Canada Randomized Clinical Trial.

Importance: Intraoperative electroencephalogram (EEG) waveform suppression, suggesting excessive general anesthesia, has been associated with postoperative delirium. Objective: To assess whether EEG-guided anesthesia decreases the incidence of delirium after cardiac surgery. Design, Setting, and Participants: Randomized, parallel-group clinical trial of 1140 adults 60 years or older undergoing cardiac surgery at 4 Canadian hospitals. Recruitment was from December 2016 to February 2022, with follow-up until February 2023. Interventions: Patients were randomized in a 1:1 ratio (stratified by hospital) to receive EEG-guided anesthesia (n = 567) or usual care (n = 573). Patients and those assessing outcomes were blinded to group assignment. Main Outcomes and Measures: The primary outcome was delirium during postoperative days 1 through 5. Intraoperative measures included anesthetic concentration and EEG suppression time. Secondary outcomes included intensive care and hospital length of stay. Serious adverse events included intraoperative awareness, medical complications, and 30-day mortality. Results: Of 1140 randomized patients (median [IQR] age, 70 [65-75] years; 282 [24.7%] women), 1131 (99.2%) were assessed for the primary outcome. Delirium during postoperative days 1 to 5 occurred in 102 of 562 patients (18.15%) in the EEG-guided group and 103 of 569 patients (18.10%) in the usual care group (difference, 0.05% [95% CI, -4.57% to 4.67%]). In the EEG-guided group compared with the usual care group, the median volatile anesthetic minimum alveolar concentration was 0.14 (95% CI, 0.15 to 0.13) lower (0.66 vs 0.80) and there was a 7.7-minute (95% CI, 10.6 to 4.7) decrease in the median total time spent with EEG suppression (4.0 vs 11.7 min). There were no significant differences between groups in median length of intensive care unit (difference, 0 days [95% CI, -0.31 to 0.31]) or hospital stay (difference, 0 days [95% CI, -0.94 to 0.94]). No patients reported intraoperative awareness. Medical complications occurred in 64 of 567 patients (11.3%) in the EEG-guided group and 73 of 573 (12.7%) in the usual care group. Thirty-day mortality occurred in 8 of 567 patients (1.4%) in the EEG-guided group and 13 of 573 (2.3%) in the usual care group. Conclusions and Relevance: Among older adults undergoing cardiac surgery, EEG-guided anesthetic administration to minimize EEG suppression, compared with usual care, did not decrease the incidence of postoperative delirium. This finding does not support EEG-guided anesthesia for this indication. Trial Registration: ClinicalTrials.gov Identifier: NCT02692300.

Relationship between cortical brain atrophy, delirium, and long-term cognitive decline in older surgical patients.

In older patients, delirium after surgery is associated with long-term cognitive decline (LTCD). The neural substrates of this association are unclear. Neurodegenerative changes associated with dementia are possible contributors. We investigated the relationship between brain atrophy rates in Alzheimer's disease (AD) and cognitive aging signature regions from magnetic resonance imaging before and one year after surgery, LTCD assessed by the general cognitive performance (GCP) score over 6 years post-operatively, and delirium in 117 elective surgery patients without dementia (mean age = 76). The annual change in cortical thickness was 0.2(1.7) % (AD-signature p = 0.09) and 0.4(1.7) % (aging-signature p = 0.01). Greater atrophy was associated with LTCD (AD-signature: beta(CI) = 0.24(0.06-0.42) points of GCP/mm of cortical thickness; p < 0.01, aging-signature: beta(CI) = 0.55(0.07-1.03); p = 0.03). Atrophy rates were not significantly different between participants with and without delirium. We found an interaction with delirium severity in the association between atrophy and LTCD (AD-signature: beta(CI) = 0.04(0.00-0.08), p = 0.04; aging-signature: beta(CI) = 0.08(0.03-0.12), p < 0.01). The rate of cortical atrophy and severity of delirium are independent, synergistic factors determining postoperative cognitive decline in the elderly.

Characterizing medical patients with delirium: A cohort study comparing ICD-10 codes and a validated chart review method.

BACKGROUND: Delirium is a major cause of preventable mortality and morbidity in hospitalized adults, but accurately determining rates of delirium remains a challenge. OBJECTIVE: To characterize and compare medical inpatients identified as having delirium using two common methods, administrative data and retrospective chart review. METHODS: We conducted a retrospective study of 3881 randomly selected internal medicine hospital admissions from six acute care hospitals in Toronto and Mississauga, Ontario, Canada. Delirium status was determined using ICD-10-CA codes from hospital administrative data and through a previously validated chart review method. Baseline sociodemographic and clinical characteristics, processes of care and outcomes were compared across those without delirium in hospital and those with delirium as determined by administrative data and chart review. RESULTS: Delirium was identified in 6.3% of admissions by ICD-10-CA codes compared to 25.7% by chart review. Using chart review as the reference standard, ICD-10-CA codes for delirium had sensitivity 24.1% (95%CI: 21.5-26.8%), specificity 99.8% (95%CI: 99.5-99.9%), positive predictive value 97.6% (95%CI: 94.6-98.9%), and negative predictive value 79.2% (95%CI: 78.6-79.7%). Age over 80, male gender, and Charlson comorbidity index greater than 2 were associated with misclassification of delirium. Inpatient mortality and median costs of care were greater in patients determined to have delirium by ICD-10-CA codes (5.8% greater mortality, 95% CI: 2.0-9.5 and $6824 greater cost, 95%CI: 4713-9264) and by chart review (11.9% greater mortality, 95%CI: 9.5-14.2% and $4967 greater cost, 95%CI: 4415-5701), compared to patients without delirium. CONCLUSIONS: Administrative data are specific but highly insensitive, missing most cases of delirium in hospital. Mortality and costs of care were greater for both the delirium cases that were detected and missed by administrative data. Better methods of routinely measuring delirium in hospital are needed.

The Delphi Delirium Management Algorithms. A practical tool for clinicians, the result of a modified Delphi expert consensus approach.

Delirium is common in hospitalised patients, and there is currently no specific treatment. Identifying and treating underlying somatic causes of delirium is the first priority once delirium is diagnosed. Several international guidelines provide clinicians with an evidence-based approach to screening, diagnosis and symptomatic treatment. However, current guidelines do not offer a structured approach to identification of underlying causes. A panel of 37 internationally recognised delirium experts from diverse medical backgrounds worked together in a modified Delphi approach via an online platform. Consensus was reached after five voting rounds. The final product of this project is a set of three delirium management algorithms (the Delirium Delphi Algorithms), one for ward patients, one for patients after cardiac surgery and one for patients in the intensive care unit.

Trajectories of Postoperative Depressive Symptoms in Older Patients Undergoing Major Surgery.

This cohort study examines the trajectories of postoperative depressive symptoms in older patients undergoing major surgery and the differences in patient characteristics between the trajectory groups.

Biomarkers of neurodegeneration and neural injury as potential predictors for delirium.

OBJECTIVES: Determine if biomarkers of Alzheimer's disease and neural injury may play a role in the prediction of delirium risk. METHODS: In a cohort of older adults who underwent elective surgery, delirium case-no delirium control pairs (N = 70, or 35 matched pairs) were matched by age, sex and vascular comorbidities. Biomarkers from CSF and plasma samples collected prior to surgery, including amyloid beta (Aß)42 , Aß40 , total (t)-Tau, phosphorylated (p)-Tau181 , neurofilament-light (NfL), and glial fibrillary acid protein (GFAP) were measured in cerebrospinal fluid (CSF) and plasma using sandwich enzyme-linked immunosorbent assays (ELISAs) or ultrasensitive single molecule array (Simoa) immunoassays. RESULTS: Plasma GFAP correlated significantly with CSF GFAP and both plasma and CSF GFAP values were nearly two-fold higher in delirium cases. The median paired difference between delirium case and control without delirium for plasma GFAP was not significant (p = 0.074) but higher levels were associated with a greater risk for delirium (odds ratio 1.52, 95% confidence interval 0.85, 2.72 per standard deviation increase in plasma GFAP concentration) in this small study. No matched pair differences or associations with delirium were observed for NfL, p-Tau 181, Aß40 and Aß42 . CONCLUSIONS: These preliminary findings suggest that plasma GFAP, a marker of astroglial activation, may be worth further investigation as a predictive risk marker for delirium.

Social determinants of health and incident postoperative delirium: Exploring key relationships in the SAGES study.

BACKGROUND: Examining the associations of social determinants of health (SDOH) with postoperative delirium in older adults will broaden our understanding of this potentially devastating condition. We explored the association between SDOH factors and incident postoperative delirium. METHODS: A retrospective study of a prospective cohort of patients enrolled from June 18, 2010, to August 8, 2013, across two academic medical centers in Boston, Massachusetts. Overall, 560 older adults age ≥70 years undergoing major elective non-cardiac surgery were included in this analysis. Exposure variables included income, lack of private insurance, and neighborhood disadvantage. Our main outcome was incident postoperative delirium, measured using the Confusion Assessment Method long form. RESULTS: Older age (odds ratio, OR: 1.01, 95% confidence interval, CI: 1.00, 1.02), income <20,000 a year (OR: 1.12, 95% CI: 1.00, 1.26), lack of private insurance (OR: 1.19, 95% CI: 1.04, 1.38), higher depressive symptomatology (OR: 1.02, 95% CI: 1.01, 1.04), and the Area Deprivation Index (OR: 1.02, 95% CI: 1.01, 1.04) were significantly associated with increased risk of postoperative delirium in bivariable analyses. In a multivariable model, explaining 27% of the variance in postoperative delirium, significant independent variables were older age (OR 1.01, 95% CI 1.00, 1.02), lack of private insurance (OR 1.18, 95% CI 1.02, 1.36), and depressive symptoms (OR 1.02, 95% CI 1.00, 1.03). Household income was no longer a significant independent predictor of delirium in the multivariable model (OR:1.02, 95% CI: 0.90, 1.15). The type of medical insurance significantly mediated the association between household income and incident delirium. CONCLUSIONS: Lack of private insurance, a social determinant of health reflecting socioeconomic status, emerged as a novel and important independent risk factor for delirium. Future efforts should consider targeting SDOH factors to prevent postoperative delirium in older adults.

Optimizing delirium care in the era of Age-Friendly Health System.

Delirium is a significant geriatric condition associated with adverse clinical and economic outcomes. The cause of delirium is usually multifactorial, and person-centered multicomponent approaches for proper delirium management are required. In 2017, the John A. Hartford Foundation and the Institute for Healthcare Improvement (IHI) launched a national initiative, Age-Friendly Health System (AFHS), promoting the use of a framework called 4Ms (what matters, medication, mentation, and mobility). The 4Ms framework's primary goal is to provide comprehensive and practical person-centered care for older adults and it aligns with the core concepts of optimal delirium management. In this special article, we demonstrate how a traditional delirium prevention and management model can be assessed from the perspective of AFHS. An example is the crosswalk with the Hospital Elder Life Program (HELP) Core Interventions and the 4MS, which demonstrates alignment in delirium management. We also introduce useful tools to create an AFHS environment in delirium management. Although much has been written about delirium management, there is a need to identify the critical steps in advancing the overall delirium care in the context of the AFHS. In this article, we suggest future directions, including the need for more prospective and comprehensive research to assess the impact of AFHS on delirium care, the need for more innovative and sustainable education platforms, fundamental changes in the healthcare payment system for proper adoption of AFHS in any healthcare setting, and application of AFHS in the community for continuity of care for older adults with delirium.

Successful aging after elective surgery II: Study cohort description.

BACKGROUND: The Successful Aging after Elective Surgery (SAGES) II Study was designed to examine the relationship between delirium and Alzheimer's disease and related dementias (AD/ADRD), by capturing novel fluid biomarkers, neuroimaging markers, and neurophysiological measurements. The goal of this paper is to provide the first complete description of the enrolled cohort, which details the baseline characteristics and data completion. We also describe the study modifications necessitated by the COVID-19 pandemic, and lay the foundation for future work using this cohort. METHODS: SAGES II is a prospective observational cohort study of community-dwelling adults age 65 and older undergoing major non-cardiac surgery. Participants were assessed preoperatively, throughout hospitalization, and at 1, 2, 6, 12, and 18 months following discharge to assess cognitive and physical functioning. Since participants were enrolled throughout the COVID-19 pandemic, procedural modifications were designed to reduce missing data and allow for high data quality. RESULTS: About 420 participants were enrolled with a mean (standard deviation) age of 73.4 (5.6) years, including 14% minority participants. Eighty-eight percent of participants had either total knee or hip replacements; the most common surgery was total knee replacement with 210 participants (50%). Despite the challenges posed by the COVID-19 pandemic, which required the use of novel procedures such as video assessments, there were minimal missing interviews during hospitalization and up to 1-month follow-up; nearly 90% of enrolled participants completed interviews through 6-month follow-up. CONCLUSION: While there are many longitudinal studies of older adults, this study is unique in measuring health outcomes following surgery, along with risk factors for delirium through the application of novel biomarkers-including fluid (plasma and cerebrospinal fluid), imaging, and electrophysiological markers. This paper is the first to describe the characteristics of this unique cohort and the data collected, enabling future work using this novel and important resource.

A Novel Approach to Monitoring Cognitive Adverse Events for Interventional Studies Involving Advanced Dementia Patients: Insights From the Electroconvulsive Therapy for Agitation in Dementia Study.

OBJECTIVE: To develop an individualized method for detecting cognitive adverse events (CAEs) in the context of an ongoing trial of electroconvulsive therapy for refractory agitation and aggression for advanced dementia (ECT-AD study). METHODS: Literature search aimed at identifying (a) cognitive measures appropriate for patients with advanced dementia, (b) functional scales to use as a proxy for cognitive status in patients with floor effects on baseline cognitive testing, and (c) statistical approaches for defining a CAE, to develop CAEs monitoring plan specifically for the ECT-AD study. RESULTS: Using the Severe Impairment Battery-8 (SIB-8), baseline floor effects are defined as a score of ≤5/16. For patients without floor effects, a decline of ≥6 points is considered a CAE. For patients with floor effects, a decline of ≥30 points from baseline on the Barthel Index is considered a CAE. These values were derived using the standard deviation index (SDI) approach to measuring reliable change. CONCLUSIONS: The proposed plan accounts for practical and statistical challenges in detecting CAEs in patients with advanced dementia. While this protocol was developed in the context of the ECT-AD study, the general approach can potentially be applied to other interventional neuropsychiatric studies that carry the risk of CAEs in patients with advanced dementia.

Factors associated with disagreement between clinician app-based ultra-brief Confusion Assessment Method and reference standard delirium assessments.

BACKGROUND: Recently, the Ultra-Brief Confusion Assessment Method (UB-CAM), designed to help physicians and nurses to recognize delirium, showed high, but imperfect, accuracy compared with Research Reference Standard Delirium Assessments (RRSDAs). The aim of this study is to identify factors associated with disagreement between clinicians' app-based UB-CAM assessments and RRSDAs. METHODS: This is a secondary analysis of a prospective diagnostic test study. The study was conducted at two hospitals and included 527 inpatients (≥70 years old) and 289 clinicians (53 physicians, 236 nurses). Trained research associates performed RRSDAs and determined delirium presence using the CAM. Clinicians administered the UB-CAM using an iPad app. Disagreement factors considered were clinician, patient, and delirium characteristics. We report odds ratios and 95% confidence intervals. RESULTS: One thousand seven hundred and ninety-five clinician UB-CAM assessments paired with RRSDAs were administered. The prevalence of delirium was 17%. The rate of disagreement between clinician UB-CAM assessments and RRSDAs was 12%. Significant factors associated with disagreement between clinician UB-CAM assessments and RRSDAs (OR [95% CI]) included: presence of dementia (2.7 [1.8-4.1]), patient education high school or less (1.9 [1.3-2.9]), psychomotor retardation (2.5 [1.4-4.2]), and the presence of mild delirium or subsyndromal delirium (5.5 [3.5-8.7]). Significant risk factors for false negatives were patient age less than 80 (2.2 [1.1-4.3]) and mild delirium (3.5 [1.6-7.4]). Significant risk factors for false positives were presence of dementia (4.0 [2.3-7.0]), subsyndromal delirium (5.1 [2.9-9.1]), and patient education high school or less (2.0 [1.2-3.6]). Clinician characteristics were not significantly associated with disagreement. CONCLUSIONS: The strongest factors associated with disagreement between clinician UB-CAM screens and RRSDAs were the presence of dementia and subsyndromal delirium as risk factors for false positives, and mild delirium and younger age as a risk factor for false negatives. These disagreement factors contrast with previous studies of risk factors for incorrect clinician delirium screening, and better align screening results with patient outcomes.

Comparison of the frailty index and frailty phenotype and their associations with postoperative delirium incidence and severity.

BACKGROUND: Recent studies have reported an association between presurgical frailty and postoperative delirium. However, it remains unclear whether the frailty-delirium relationship differs by measurement tool (e.g., frailty index vs. frailty phenotype) and whether frailty is associated with delirium, independent of preoperative cognition. METHODS: We used the successful aging after elective surgery (SAGES) study, a prospective cohort of older adults age ≥70 undergoing major non-cardiac surgery (N = 505). Preoperative measurement of the modified mini-mental (3MS) test, frailty index and frailty phenotype were obtained. The confusion assessment method (CAM), supplemented by chart review, identified postoperative delirium. Delirium feature severity was measured by the sum of CAM-severity (CAM-S) scores. Generalized linear models were used to determine the relative risk of each frailty measure with delirium incidence and severity. Subsequent models adjusted for age, sex, surgery type, Charlson comorbidity index, and 3MS. RESULTS: On average, patients were 76.7 years old (standard deviation 5.22), 58.8% of women. For the frailty index, the incidence of delirium was 14% in robust, 17% in prefrail, and 31% in frail patients (p < 0.001). For the frailty phenotype, delirium incidence was 13% in robust, 21% in prefrail, and 27% in frail patients (p = 0.016). Frailty index, but not phenotype, was independently associated with delirium after adjustment for comorbidities (relative risk [RR] 2.13, 95% confidence interval [CI] 1.23-3.70; RR 1.61, 95% CI 0.77-3.37, respectively). Both frailty measures were associated with delirium feature severity. After adjustment for preoperative cognition, only the frailty index was associated with delirium incidence; neither index nor phenotype was associated with delirium feature severity. CONCLUSION: Both the frailty index and phenotype were associated with the development of postoperative delirium. The index showed stronger associations that remained significant after adjusting for baseline comorbidities and preoperative cognition. Measuring frailty prior to surgery can assist in identifying patients at risk for postoperative delirium.

Association of Loneliness With Change in Physical and Emotional Health of Older Adults During the COVID-19 Shutdown.

OBJECTIVES: To examine factors influencing loneliness and the effect of loneliness on physical and emotional health, in the context of the COVID-19 pandemic. DESIGN: Prospective, observational cohort. SETTING: Community-dwelling participants. PARTICIPANTS: Older adults (n = 238) enrolled in a longitudinal study. MEASUREMENTS: Interviews were completed July-December 2020. Loneliness was measured with the UCLA 3-item loneliness scale. Data including age, marriage, education, cognitive functioning, functional impairment, vision or hearing impairment, depression, anxiety, medical comorbidity, social network size, technology use, and activity engagement were collected. Health outcomes included self-rated health, and physical and mental composites from the 12-item Short Form Survey. Physical function was measured by a PROMIS-scaled composite score. RESULTS: Thirty-nine (16.4%) participants reported loneliness. Vulnerability factors for loneliness included age (RR = 1.08, 95% CI 1.02-1.14); impairment with instrumental activities of daily living (RR = 2.08, 95% CI 1.14-3.80); vision impairment (RR = 2.09, 95% CI 1.10-3.97); depression (RR = 1.34, 95% CI 1.25-1.43); and anxiety (RR = 1.92, 95% CI 1.55-2.39). Significant resilience factors included high cognitive functioning (RR = 0.88, 95% CI 0.83-0.94); large social network size (RR = 0.92, 95% CI 0.88-0.96); technology use (RR = 0.81, 95% CI 0.73-0.90); and social and physical activity engagement (RR = 0.91, 95% CI 0.85-0.98). Interaction analyses showed that larger social network size moderated the effect of loneliness on physical function (protective interaction effect, RR = 0.64, 95% CI 0.15-1.13, p <.01), and activity engagement moderated the effect of loneliness on mental health (protective interaction effect, RR = 0.65, 95% CI 0.25-1.05, p <.001). CONCLUSIONS: Resilience factors may mitigate the adverse health outcomes associated with loneliness. Interventions to enhance resilience may help to diminish the detrimental effects of loneliness and hold great importance for vulnerable older adults.