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Changes that accompany older age can alter the pharmacokinetics (PK), pharmacodynamics (PD), and likelihood of adverse effects (AEs) of a drug. However, older adults, especially the oldest or those with multiple chronic health conditions, polypharmacy, or frailty, are often under-represented in clinical trials of new drugs. Deficits in the current conduct of clinical evaluation of drugs for older adults and potential steps to fill those knowledge gaps are presented in this communication. The most important step is to increase clinical trial enrollment of older adults who are representative of the target treatment population. Unnecessary eligibility criteria should be eliminated. Physical and financial barriers to participation should be removed. Incentives could be created for inclusion of older adults. Enrollment goals should be established based on intended treatment indications, prevalence of the condition, and feasibility. Relevant clinical pharmacology data need to be obtained early enough to guide dosing and reduce risk for participation of older adults. Relevant PK and PD data as well as patient-centered outcomes should be measured during trials. Trial data should be analyzed for differences in PK, PD, effectiveness, and safety arising from differences in age or from the presence of conditions common in older adults. Postmarket evaluations with real-world evidence and drug labeling updates throughout the product lifecycle reflecting new knowledge are also needed. A comprehensive plan is needed to ensure adequate evaluation of the safety and effectiveness of drugs in older adults.

Although declines in cognitive capacity are assumed to be a characteristic of aging, increasing evidence shows that it is age-related disease, rather than age itself, that causes cognitive impairment. Even so, older age is a primary risk factor for cognitive decline, and with individuals living longer as a result of medical advances, cognitive impairment and dementia are increasing in prevalence. On March 26 to 27, 2018, the American Geriatrics Society convened a conference in Bethesda, MD, to explore cognitive impairment across the subspecialties. Bringing together representatives from several subspecialties, this was the third of three conferences, supported by a U13 grant from the National Institute on Aging, to aid recipients of Grants for Early Medical/Surgical Specialists' Transition to Aging Research (GEMSSTAR) in integrating geriatrics into their subspecialties. Scientific sessions focused on the impact of cognitive impairment, sensory contributors, comorbidities, links between delirium and dementia, and issues of informed consent in cognitively impaired populations. Discussions highlighted the complexity not only of cognitive health itself, but also of the bidirectional relationship between cognitive health and the health of other organ systems. Thus, conference participants noted the importance of multidisciplinary team science in future aging research. This article summarizes the full conference report, "The Impact of Cognitive Impairment Across Specialties," and notes areas where GEMSSTAR scholars can contribute to progress as they embark on their careers in aging research. J Am Geriatr Soc 67:2011-2017, 2019.

Cognitive change affecting patients after anaesthesia and surgery has been recognised for more than 100 yr. Research into cognitive change after anaesthesia and surgery accelerated in the 1980s when multiple studies utilised detailed neuropsychological testing for assessment of cognitive change after cardiac surgery. This body of work consistently documented decline in cognitive function in elderly patients after anaesthesia and surgery, and cognitive changes have been identified up to 7.5 yr afterwards. Importantly, other studies have identified that the incidence of cognitive change is similar after non-cardiac surgery. Other than the inclusion of non-surgical control groups to calculate postoperative cognitive dysfunction, research into these cognitive changes in the perioperative period has been undertaken in isolation from cognitive studies in the general population. The aim of this work is to develop similar terminology to that used in cognitive classifications of the general population for use in investigations of cognitive changes after anaesthesia and surgery. A multispecialty working group followed a modified Delphi procedure with no prespecified number of rounds comprised of three face-to-face meetings followed by online editing of draft versions. Two major classification guidelines [Diagnostic and Statistical Manual for Mental Disorders, fifth edition (DSM-5) and National Institute for Aging and the Alzheimer Association (NIA-AA)] are used outside of anaesthesia and surgery, and may be useful for inclusion of biomarkers in research. For clinical purposes, it is recommended to use the DSM-5 nomenclature. The working group recommends that 'perioperative neurocognitive disorders' be used as an overarching term for cognitive impairment identified in the preoperative or postoperative period. This includes cognitive decline diagnosed before operation (described as neurocognitive disorder); any form of acute event (postoperative delirium) and cognitive decline diagnosed up to 30 days after the procedure (delayed neurocognitive recovery) and up to 12 months (postoperative neurocognitive disorder).

Preoperative risk biomarkers for delirium may aid in identifying high-risk patients and developing intervention therapies, which would minimize the health and economic burden of postoperative delirium. Previous studies have typically used single omics approaches to identify such biomarkers. Preoperative cerebrospinal fluid (CSF) from the Healthier Postoperative Recovery study of adults ≥ 63 years old undergoing elective major orthopedic surgery was used in a matched pair delirium case–no delirium control design. We performed metabolomics and lipidomics, which were combined with our previously reported proteomics results on the same samples. Differential expression, clustering, classification, and systems biology analyses were applied to individual and combined omics datasets. Probabilistic graph models were used to identify an integrated multi-omics interaction network, which included clusters of heterogeneous omics interactions among lipids, metabolites, and proteins. The combined multi-omics signature of 25 molecules attained an AUC of 0.96 [95% CI: 0.85–1.00], showing improvement over individual omics-based classification. We conclude that multi-omics integration of preoperative CSF identifies potential risk markers for delirium and generates new insights into the complex pathways associated with delirium. With future validation, this hypotheses-generating study may serve to build robust biomarkers for delirium and improve our understanding of its pathophysiology. [ABSTRACT FROM AUTHOR]

Key Points: Question: Does electroencephalography-guided anesthetic administration to minimize electroencephalography suppression decrease the incidence of postoperative delirium in older patients undergoing cardiac surgery? Findings: In this multicenter randomized clinical trial involving 1140 patients aged 60 years and older undergoing cardiac surgery, postoperative delirium occurred in 18.15% of participants in the electroencephalography-guided anesthetic group and 18.10% in the usual care group, a difference that was not statistically significant. Meaning: These findings do not support the use of electroencephalography-guided anesthetic administration for the prevention of postoperative delirium among older adults undergoing cardiac surgery. 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 This randomized clinical trial examines whether electroencephalogram -guided anesthesia decreases the incidence of delirium after cardiac surgery among adults 60 years or older. [ABSTRACT FROM AUTHOR]

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. See related Editorial by Callahan and Kuchel in this issue. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]