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.

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.

Comparative Safety Analysis of Oral Antipsychotics for In-Hospital Adverse Clinical Events in Older Adults After Major Surgery : A Nationwide Cohort Study.

BACKGROUND: Antipsychotics are commonly used to manage postoperative delirium. Recent studies reported that haloperidol use has declined, and atypical antipsychotic use has increased over time. OBJECTIVE: To compare the risk for in-hospital adverse events associated with oral haloperidol, olanzapine, quetiapine, and risperidone in older patients after major surgery. DESIGN: Retrospective cohort study. SETTING: U.S. hospitals in the Premier Healthcare Database. PATIENTS: 17 115 patients aged 65 years and older without psychiatric disorders who were prescribed an oral antipsychotic drug after major surgery from 2009 to 2018. INTERVENTIONS: Haloperidol (≤4 mg on the day of initiation), olanzapine (≤10 mg), quetiapine (≤150 mg), and risperidone (≤4 mg). MEASUREMENTS: The risk ratios (RRs) for in-hospital death, cardiac arrhythmia events, pneumonia, and stroke or transient ischemic attack (TIA) were estimated after propensity score overlap weighting. RESULTS: The weighted population had a mean age of 79.6 years, was 60.5% female, and had in-hospital death of 3.1%. Among the 4 antipsychotics, quetiapine was the most prescribed (53.0% of total exposure). There was no statistically significant difference in the risk for in-hospital death among patients treated with haloperidol (3.7%, reference group), olanzapine (2.8%; RR, 0.74 [95% CI, 0.42 to 1.27]), quetiapine (2.6%; RR, 0.70 [CI, 0.47 to 1.04]), and risperidone (3.3%; RR, 0.90 [CI, 0.53 to 1.41]). The risk for nonfatal clinical events ranged from 2.0% to 2.6% for a cardiac arrhythmia event, 4.2% to 4.6% for pneumonia, and 0.6% to 1.2% for stroke or TIA, with no statistically significant differences by treatment group. LIMITATION: Residual confounding by delirium severity; lack of untreated group; restriction to oral low-to-moderate dose treatment. CONCLUSION: These results suggest that atypical antipsychotics and haloperidol have similar rates of in-hospital adverse clinical events in older patients with postoperative delirium who receive an oral low-to-moderate dose antipsychotic drug. PRIMARY FUNDING SOURCE: National Institute on Aging.

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.

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.

Patterns and Persistence of Perioperative Plasma and Cerebrospinal Fluid Neuroinflammatory Protein Biomarkers After Elective Orthopedic Surgery Using SOMAscan.

BACKGROUND: The neuroinflammatory response to surgery can be characterized by peripheral acute plasma protein changes in blood, but corresponding, persisting alterations in cerebrospinal fluid (CSF) proteins remain mostly unknown. Using the SOMAscan assay, we define acute and longer-term proteome changes associated with surgery in plasma and CSF. We hypothesized that biological pathways identified by these proteins would be in the categories of neuroinflammation and neuronal function and define neuroinflammatory proteome changes associated with surgery in older patients. METHODS: SOMAscan analyzed 1305 proteins in blood plasma (n = 14) and CSF (n = 15) samples from older patients enrolled in the Role of Inflammation after Surgery for Elders (RISE) study undergoing elective hip and knee replacement surgery with spinal anesthesia. Systems biology analysis identified biological pathways enriched among the surgery-associated differentially expressed proteins in plasma and CSF. RESULTS: Comparison of postoperative day 1 (POD1) to preoperative (PREOP) plasma protein levels identified 343 proteins with postsurgical changes ( P < .05; absolute value of the fold change [|FC|] > 1.2). Comparing postoperative 1-month (PO1MO) plasma and CSF with PREOP identified 67 proteins in plasma and 79 proteins in CSF with altered levels ( P < .05; |FC| > 1.2). In plasma, 21 proteins, primarily linked to immune response and inflammation, were similarly changed at POD1 and PO1MO. Comparison of plasma to CSF at PO1MO identified 8 shared proteins. Comparison of plasma at POD1 to CSF at PO1MO identified a larger number, 15 proteins in common, most of which are regulated by interleukin-6 (IL-6) or transforming growth factor beta-1 (TGFB1) and linked to the inflammatory response. Of the 79 CSF PO1MO-specific proteins, many are involved in neuronal function and neuroinflammation. CONCLUSIONS: SOMAscan can characterize both short- and long-term surgery-induced protein alterations in plasma and CSF. Acute plasma protein changes at POD1 parallel changes in PO1MO CSF and suggest 15 potential biomarkers for longer-term neuroinflammation that warrant further investigation.

Comparative Implementation of a Brief App-Directed Protocol for Delirium Identification by Hospitalists, Nurses, and Nursing Assistants : A Cohort Study.

BACKGROUND: Systematic screening improves delirium identification among hospitalized older adults. Little data exist on how to implement such screening. OBJECTIVE: To test implementation of a brief app-directed protocol for delirium identification by physicians, nurses, and certified nursing assistants (CNAs) in real-world practice relative to a research reference standard delirium assessment (RSDA). DESIGN: Prospective cohort study. SETTING: Large urban academic medical center and small rural community hospital. PARTICIPANTS: 527 general medicine inpatients (mean age, 80 years; 35% with preexisting dementia) and 399 clinicians (53 hospitalists, 236 nurses, and 110 CNAs). MEASUREMENTS: On 2 study days, enrolled patients had an RSDA. Subsequently, CNAs performed an ultra-brief 2-item screen (UB-2) for delirium, whereas physicians and nurses performed a 2-step protocol consisting of the UB-2 followed in those with a positive screen result by the 3-Minute Diagnostic Assessment for the Confusion Assessment Method. RESULTS: Delirium was diagnosed in 154 of 924 RSDAs (17%) and in 114 of 527 patients (22%). The completion rate for clinician protocols exceeded 97%. The CNAs administered the UB-2 in a mean of 62 seconds (SD, 51). The 2-step protocols were administered in means of 104 seconds (SD, 99) by nurses and 106 seconds (SD, 105) by physicians. The UB-2 had sensitivities of 88% (95% CI, 72% to 96%), 87% (CI, 73% to 95%), and 82% (CI, 65% to 91%) when administered by CNAs, nurses, and physicians, respectively, with specificities of 64% to 70%. The 2-step protocol had overall accuracy of 89% (CI, 83% to 93%) and 87% (CI, 81% to 91%), with sensitivities of 65% (CI, 48% to 79%) and 63% (CI, 46% to 77%) and specificities of 93% (CI, 88% to 96%) and 91% (CI, 86% to 95%), for nurses and physicians, respectively. Two-step protocol sensitivity for moderate to severe delirium was 78% (CI, 54% to 91%). LIMITATION: Two sites; limited diversity. CONCLUSION: An app-directed protocol for delirium identification was feasible, brief, and accurate, and CNAs and nurses performed as well as hospitalists. PRIMARY FUNDING SOURCE: National Institute on Aging.

One-year Medicare costs associated with delirium in older hospitalized patients with and without Alzheimer's disease dementia and related disorders.

INTRODUCTION: One-year health-care costs associated with delirium in older hospitalized patients with and without Alzheimer's disease and related dementias (ADRD) have not been examined previously. METHODS: Medicare costs were determined prospectively at discharge, and at 30, 90, and 365 days in a cohort (n = 311) of older adults after hospital admission. RESULTS: Seventy-six (24%) patients had ADRD and were more likely to develop delirium (51% vs. 24%, P < 0.001) and die within 1 year (38% vs. 21%, P = 0.002). In ADRD patients with versus without delirium, adjusted mean difference in costs associated with delirium were $34,828; most of the excess costs were incurred between 90 and 365 days (P = 0.03). In non-ADRD patients, delirium was associated with increased costs at all timepoints. Excess costs associated with delirium in ADRD patients increased progressively over 1 year, whereas in non-ADRD patients the increase was consistent across time periods. DISCUSSION: Our findings highlight the complexity of health-care costs for ADRD patients who develop delirium, a potentially preventable source of expenditures. HIGHLIGHTS: Novel examination of health-care costs of delirium in persons with and without Alzheimer's disease and related dementias (ADRD). Increased 1-year costs of $34,828 in ADRD patients with delirium (vs. without). Increased costs for delirium in ADRD occur later during the 365-day study period. For ADRD patients, cost differences between those with and without delirium increased over 1 year. For non-ADRD patients, the parallel cost differences were consistent over time.

Preclinical and translational models for delirium: Recommendations for future research from the NIDUS delirium network.

Delirium is a common, morbid, and costly syndrome that is closely linked to Alzheimer's disease (AD) and AD-related dementias (ADRD) as a risk factor and outcome. Human studies of delirium have advanced our knowledge of delirium incidence and prevalence, risk factors, biomarkers, outcomes, prevention, and management. However, understanding of delirium neurobiology remains limited. Preclinical and translational models for delirium, while challenging to develop, could advance our knowledge of delirium neurobiology and inform the development of new prevention and treatment approaches. We discuss the use of preclinical and translational animal models in delirium, focusing on (1) a review of current animal models, (2) challenges and strategies for replicating elements of human delirium in animals, and (3) the utility of biofluid, neurophysiology, and neuroimaging translational markers in animals. We conclude with recommendations for the development and validation of preclinical and translational models for delirium, with the goal of advancing awareness in this important field.

Predicting hospital mortality and length of stay: A prospective cohort study comparing the Intensive Care Delirium Screening Checklist versus Confusion Assessment Method for the Intensive Care Unit.

OBJECTIVE: The objective of this study was to compare two tools, the Intensive Care Delirium Screening Checklist (ICDSC) and Confusion Assessment Method for the intensive care unit (ICU) (CAM-ICU), for their predictive validity for outcomes related to delirium, hospital mortality, and length of stay (LOS). METHODS: The prospective study conducted in six medical ICUs at a tertiary care hospital in Taiwan enrolled consecutive patients (≥20 years) without delirium at ICU admission. Delirium was screened daily using the ICDSC and CAM-ICU in random order. Arousal was assessed by the Richmond Agitation-Sedation Scale (RASS). Participants with any one positive result were classified as ICDSC- or CAM-ICU-delirium groups. RESULTS: Delirium incidence evaluated by the ICDSC and CAM-ICU were 69.1% (67/97) and 50.5% (49/97), respectively. Although the ICDSC identified 18 more cases as delirious, substantial concordance (κ = 0.63; p < 0.001) was found between tools. Independent of age, Acute Physiology and Chronic Health Evaluation II score, and Charlson Comorbidity Index, both ICDSC- and CAM-ICU-rated delirium significantly predicted hospital mortality (adjusted odds ratio: 4.93; 95% confidence interval [CI]:1.56 to 15.63 vs. 2.79; 95% CI: 1.12 to 6.97, respectively), and only the ICDSC significantly predicted hospital LOS with a mean of 17.59 additional days compared with the no-delirium group. Irrespective of delirium status, a sensitivity analysis of normal-to-increased arousal (RASS≥0) test results did not alter the predictive ability of ICDSC- or CAM-ICU-delirium for hospital mortality (adjusted odds ratio: 2.97; 95% CI: 1.06 to 8.37 vs. 3.82; 95% CI: 1.35 to 10.82, respectively). With reduced arousal (RASS<0), neither tool significantly predicted mortality or LOS. CONCLUSIONS: The ICDSC identified more delirium cases and may have higher predictive validity for mortality and LOS than the CAM-ICU. However, arousal substantially affected performance. Future studies may want to consider patients' arousal when deciding which tool to use to maximise the effects of delirium identification on patient mortality.

Parental Education and Delirium Risk after Surgery in Older Adults.

OBJECTIVES: Efforts to conceptualize risk factors for postoperative delirium in older adults have focused on the time proximate to the episode, but how early-life exposures influence delirium risk is poorly understood. METHODS: An observational cohort of 547 patients aged 70+undergoing major non-cardiac surgery at two academic medical centers in Boston. Demographic characteristics, cognition, parental education, health, and participation in cognitively stimulating activities were assessed prior to surgery. Delirium incidence and severity were measured daily during hospitalization. RESULTS: Higher paternal education was associated with significantly lower incidence of delirium (X2(1, N =547)=8.35, p <.001; odds ratio OR=.93, 95% CI, .87 to .98) and inversely associated with delirium severity (r(545)=-.13, p <.001). Higher maternal education was associated with lower delirium incidence but did not reach statistical significance. The effect of paternal education on delirium incidence was independent of the patient's education, estimated premorbid intelligence, medical comorbidities, neighborhood disadvantage, and participation in cognitively stimulating activities (X2(2, N =547)=31.22, p <.001). CONCLUSIONS: Examining early-life exposures may yield unique insights into the risks and pathogenesis of delirium. CLINICAL IMPLICATIONS: Evaluating long-term factors that increase vulnerability to delirium may improve our ability to calculate risk. It may guide clinical decision-making and inform pre- and post-operative recommendations.

Harmonization of Four Delirium Instruments: Creating Crosswalks and the Delirium Item-Bank (DEL-IB).

OBJECTIVES: Over 30 instruments are in current, active use for delirium identification. In a recent systematic review, we recommended 4 commonly used and well-validated instruments for clinical and research use. The goal of this study is to harmonize the four instruments on the same metric using modern methods in psychometrics. DESIGN: Secondary data analysis from 3 studies, and a simulation study based on the observed data. SETTING: Hospitalized (non-ICU) adults over 65 years old in the United States, Ireland, and Belgium. PARTICIPANTS: The total sample comprised 600 participants, contributing 1,623 assessments. MEASUREMENTS: Confusion Assessment Method (long-form and short-form), Delirium Observation Screening Scale, Delirium Rating Scale-Revised-98 (DRS-R-98) (total and severity scores), and Memorial Delirium Assessment Scale. RESULTS: Using item response theory, we linked scores across instruments, placing all four instruments and their separate scorings on the same metric (the propensity to delirium). Kappa statistics comparing agreement in delirium identification among the instruments ranged from 0.37 to 0.75, with the highest agreement between the DRS-R-98 total score and MDAS. After linking scores, we created a harmonized item bank, called the Delirium Item Bank (DEL-IB), consisting of 50 items. The DEL-IB allowed us to create six crosswalks, to allow scores to be translated across instruments. CONCLUSIONS: With our results, individual instrument scores can be directly compared to aid in clinical decision-making, and quantitatively combined in meta-analyses.

Identifying Delirium in Persons With Moderate or Severe Dementia: Review of Challenges and an Illustrative Approach.

Delirium and dementia are common causes of cognitive impairment among older adults, which often coexist. Delirium is associated with poor clinical outcomes, and is more frequent and more severe in patients with dementia. Identifying delirium in the presence of dementia, also described as delirium superimposed on dementia (DSD), is particularly challenging, as symptoms of delirium such as inattention, cognitive dysfunction, and altered level of consciousness, are also features of dementia. Because DSD is associated with poorer clinical outcomes than dementia alone, detecting delirium is important for reducing morbidity and mortality in this population. We review a number of delirium screening instruments that have shown promise for use in DSD, including the 4-DSD, combined Six Item Cognitive Impairment Test (6-CIT) and 4 'A's Test (4AT), Confusion Assessment Method (CAM), and the combined UB2 and 3D-CAM (UB-CAM). Each has advantages and disadvantages. We then describe the operationalization of a CAM-based approach in a current ECT in dementia project as an example of modifying an existing instrument for patients with moderate to severe dementia. Ultimately, any instrument modified will need to be validated against a standard clinical reference, in order to fully establish its sensitivity and specificity in the moderate to severe dementia population. Future work is greatly needed to advance the challenging area of accurate identification of delirium in moderate or severe dementia.

Delirium Item Bank: Utilization to Evaluate and Create Delirium Instruments.

INTRODUCTION: The large number of heterogeneous instruments in active use for identification of delirium prevents direct comparison of studies and the ability to combine results. In a recent systematic review we performed, we recommended four commonly used and well-validated instruments and subsequently harmonized them using advanced psychometric methods to develop an item bank, the Delirium Item Bank (DEL-IB). The goal of the present study was to find optimal cut-points on four existing instruments and to demonstrate use of the DEL-IB to create new instruments. METHODS: We used a secondary analysis and simulation study based on data from three previous studies of hospitalized older adults (age 65+ years) in the USA, Ireland, and Belgium. The combined dataset included 600 participants, contributing 1,623 delirium assessments, and an overall incidence of delirium of about 22%. The measurements included the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition diagnostic criteria for delirium, Confusion Assessment Method (long form and short form), Delirium Observation Screening Scale, Delirium Rating Scale-Revised-98 (total and severity scores), and Memorial Delirium Assessment Scale (MDAS). RESULTS: We identified different cut-points for each existing instrument to optimize sensitivity or specificity, and compared instrument performance at each cut-point to the author-defined cut-point. For instance, the cut-point on the MDAS that maximizes both sensitivity and specificity was at a sum score of 6 yielding 89% sensitivity and 79% specificity. We then created four new example instruments (two short forms and two long forms) and evaluated their performance characteristics. In the first example short form instrument, the cut-point that maximizes sensitivity and specificity was at a sum score of 3 yielding 90% sensitivity, 81% specificity, 30% positive predictive value, and 99% negative predictive value. DISCUSSION/CONCLUSION: We used the DEL-IB to better understand the psychometric performance of widely used delirium identification instruments and scorings, and also demonstrated its use to create new instruments. Ultimately, we hope that the DEL-IB might be used to create optimized delirium identification instruments and to spur the development of a unified approach to identify delirium.

Association Between Perioperative Medication Use and Postoperative Delirium and Cognition in Older Adults Undergoing Elective Noncardiac Surgery.

BACKGROUND: Postoperative delirium is frequent in older adults and is associated with postoperative neurocognitive disorder (PND). Studies evaluating perioperative medication use and delirium have generally evaluated medications in aggregate and been poorly controlled; the association between perioperative medication use and PND remains unclear. We sought to evaluate the association between medication use and postoperative delirium and PND in older adults undergoing major elective surgery. METHODS: This is a secondary analysis of a prospective cohort study of adults ≥70 years without dementia undergoing major elective surgery. Patients were interviewed preoperatively to determine home medication use. Postoperatively, daily hospital use of 7 different medication classes listed in guidelines as risk factors for delirium was collected; administration before delirium was verified. While hospitalized, patients were assessed daily for delirium using the Confusion Assessment Method and a validated chart review method. Cognition was evaluated preoperatively and 1 month after surgery using a neurocognitive battery. The association between prehospital medication use and postoperative delirium was assessed using a generalized linear model with a log link function, controlling for age, sex, type of surgery, Charlson comorbidity index, and baseline cognition. The association between daily postoperative medication use (when class exposure ≥5%) and time to delirium was assessed using time-varying Cox models adjusted for age, sex, surgery type, Charlson comorbidity index, Acute Physiology and Chronic Health Evaluation (APACHE)-II score, and baseline cognition. Mediation analysis was utilized to evaluate the association between medication use, delirium, and cognitive change from baseline to 1 month. RESULTS: Among 560 patients enrolled, 134 (24%) developed delirium during hospitalization. The multivariable analyses revealed no significant association between prehospital benzodiazepine (relative risk [RR], 1.44; 95% confidence interval [CI], 0.85-2.44), beta-blocker (RR, 1.38; 95% CI, 0.94-2.05), NSAID (RR, 1.12; 95% CI, 0.77-1.62), opioid (RR, 1.22; 95% CI, 0.82-1.82), or statin (RR, 1.34; 95% CI, 0.92-1.95) exposure and delirium. Postoperative hospital benzodiazepine use (adjusted hazard ratio [aHR], 3.23; 95% CI, 2.10-4.99) was associated with greater delirium. Neither postoperative hospital antipsychotic (aHR, 1.48; 95% CI, 0.74-2.94) nor opioid (aHR, 0.82; 95% CI, 0.62-1.11) use before delirium was associated with delirium. Antipsychotic use (either presurgery or postsurgery) was associated with a 0.34 point (standard error, 0.16) decrease in general cognitive performance at 1 month through its effect on delirium (P = .03), despite no total effect being observed. CONCLUSIONS: Administration of benzodiazepines to older adults hospitalized after major surgery is associated with increased postoperative delirium. Association between inhospital, postoperative medication use and cognition at 1 month, independent of delirium, was not detected.

Identification of Plasma Proteome Signatures Associated With Surgery Using SOMAscan.

OBJECTIVES: To characterize the proteomic signature of surgery in older adults and association with postoperative outcomes. SUMMARY OF BACKGROUND DATA: Circulating plasma proteins can reflect the physiological response to and clinical outcomes after surgery. METHODS: Blood plasma from older adults undergoing elective surgery was analyzed for 1305 proteins using SOMAscan. Surgery-associated proteins underwent Ingenuity Pathways Analysis. Selected surgery-associated proteins were independently validated using Luminex or enzyme-linked immunosorbent assay methods. Generalized linear models estimated correlations with postoperative outcomes. RESULTS: Plasma from a subcohort (n = 36) of the Successful Aging after Elective Surgery (SAGES) study was used for SOMAscan. Systems biology analysis of 110 proteins with Benjamini-Hochberg (BH) corrected P value ≤0.01 and an absolute foldchange (|FC|) ≥1.5 between postoperative day 2 (POD2) and preoperative (PREOP) identified functional pathways with major effects on pro-inflammatory proteins. Chitinase-3-like protein 1 (CHI3L1), C-reactive protein (CRP), and interleukin-6 (IL-6) were independently validated in separate validation cohorts from SAGES (n = 150 for CRP, IL-6; n = 126 for CHI3L1). Foldchange CHI3L1 and IL-6 were associated with increased postoperative complications [relative risk (RR) 1.50, 95% confidence interval (95% CI) 1.21-1.85 and RR 1.63, 95% CI 1.18-2.26, respectively], length of stay (RR 1.35, 95% CI 0.77-1.92 and RR 0.98, 95% CI 0.52-1.45), and risk of discharge to postacute facility (RR 1.15, 95% CI 1.04-1.26 and RR 1.11, 95% CI 1.04-1.18); POD2 and PREOP CRP difference was associated with discharge to postacute facility (RR 1.14, 95% CI 1.04-1.25). CONCLUSION: SOMAscan can identify novel and clinically relevant surgery-induced protein changes. Ultimately, proteomics may provide insights about pathways by which surgical stress contributes to postoperative outcomes.

Plasma and cerebrospinal fluid inflammation and the blood-brain barrier in older surgical patients: the Role of Inflammation after Surgery for Elders (RISE) study.

BACKGROUND: Our understanding of the relationship between plasma and cerebrospinal fluid (CSF) remains limited, which poses an obstacle to the identification of blood-based markers of neuroinflammatory disorders. To better understand the relationship between peripheral and central nervous system (CNS) markers of inflammation before and after surgery, we aimed to examine whether surgery compromises the blood-brain barrier (BBB), evaluate postoperative changes in inflammatory markers, and assess the correlations between plasma and CSF levels of inflammation. METHODS: We examined the Role of Inflammation after Surgery for Elders (RISE) study of adults aged ≥ 65 who underwent elective hip or knee surgery under spinal anesthesia who had plasma and CSF samples collected at baseline and postoperative 1 month (PO1MO) (n = 29). Plasma and CSF levels of three inflammatory markers previously identified as increasing after surgery were measured using enzyme-linked immunosorbent assay: interleukin-6 (IL-6), C-reactive protein (CRP), and chitinase 3-like protein (also known as YKL-40). The integrity of the BBB was computed as the ratio of CSF/plasma albumin levels (Qalb). Mean Qalb and levels of inflammation were compared between baseline and PO1MO. Spearman correlation coefficients were used to determine the correlation between biofluids. RESULTS: Mean Qalb did not change between baseline and PO1MO. Mean plasma and CSF levels of CRP and plasma levels of YKL-40 and IL-6 were higher on PO1MO relative to baseline, with a disproportionally higher increase in CRP CSF levels relative to plasma levels (CRP tripled in CSF vs. increased 10% in plasma). Significant plasma-CSF correlations for CRP (baseline r = 0.70 and PO1MO r = 0.89, p < .01 for both) and IL-6 (PO1MO r = 0.48, p < .01) were observed, with higher correlations on PO1MO compared with baseline. CONCLUSIONS: In this elective surgical sample of older adults, BBB integrity was similar between baseline and PO1MO, plasma-CSF correlations were observed for CRP and IL-6, plasma levels of all three markers (CRP, IL-6, and YKL-40) increased from PREOP to PO1MO, and CSF levels of only CRP increased between the two time points. Our identification of potential promising plasma markers of inflammation in the CNS may facilitate the early identification of patients at greatest risk for neuroinflammation and its associated adverse cognitive outcomes.

Association of Plasma Neurofilament Light with Postoperative Delirium.

OBJECTIVE: To examine the association of the plasma neuroaxonal injury markers neurofilament light (NfL), total tau, glial fibrillary acid protein, and ubiquitin carboxyl-terminal hydrolase L1 with delirium, delirium severity, and cognitive performance. METHODS: Delirium case-no delirium control (n = 108) pairs were matched by age, sex, surgery type, cognition, and vascular comorbidities. Biomarkers were measured in plasma collected preoperatively (PREOP), and 2 days (POD2) and 30 days postoperatively (PO1MO) using Simoa technology (Quanterix, Lexington, MA). The Confusion Assessment Method (CAM) and CAM-S (Severity) were used to measure delirium and delirium severity, respectively. Cognitive function was measured with General Cognitive Performance (GCP) scores. RESULTS: Delirium cases had higher NfL on POD2 and PO1MO (median matched pair difference = 16.2pg/ml and 13.6pg/ml, respectively; p < 0.05). Patients with PREOP and POD2 NfL in the highest quartile (Q4) had increased risk for incident delirium (adjusted odds ratio [OR] = 3.7 [95% confidence interval (CI) = 1.1-12.6] and 4.6 [95% CI = 1.2-18.2], respectively) and experienced more severe delirium, with sum CAM-S scores 7.8 points (95% CI = 1.6-14.0) and 9.3 points higher (95% CI = 3.2-15.5). At PO1MO, delirium cases had continued high NfL (adjusted OR = 9.7, 95% CI = 2.3-41.4), and those with Q4 NfL values showed a -2.3 point decline in GCP score (-2.3 points, 95% CI = -4.7 to -0.9). INTERPRETATION: Patients with the highest PREOP or POD2 NfL levels were more likely to develop delirium. Elevated NfL at PO1MO was associated with delirium and greater cognitive decline. These findings suggest NfL may be useful as a predictive biomarker for delirium risk and long-term cognitive decline, and once confirmed would provide pathophysiological evidence for neuroaxonal injury following delirium. ANN NEUROL 2020;88:984-994.

Machine Learning to Develop and Internally Validate a Predictive Model for Post-operative Delirium in a Prospective, Observational Clinical Cohort Study of Older Surgical Patients.

BACKGROUND: Our objective was to assess the performance of machine learning methods to predict post-operative delirium using a prospective clinical cohort. METHODS: We analyzed data from an observational cohort study of 560 older adults (≥ 70 years) without dementia undergoing major elective non-cardiac surgery. Post-operative delirium was determined by the Confusion Assessment Method supplemented by a medical chart review (N = 134, 24%). Five machine learning algorithms and a standard stepwise logistic regression model were developed in a training sample (80% of participants) and evaluated in the remaining hold-out testing sample. We evaluated three overlapping feature sets, restricted to variables that are readily available or minimally burdensome to collect in clinical settings, including interview and medical record data. A large feature set included 71 potential predictors. A smaller set of 18 features was selected by an expert panel using a consensus process, and this smaller feature set was considered with and without a measure of pre-operative mental status. RESULTS: The area under the receiver operating characteristic curve (AUC) was higher in the large feature set conditions (range of AUC, 0.62-0.71 across algorithms) versus the selected feature set conditions (AUC range, 0.53-0.57). The restricted feature set with mental status had intermediate AUC values (range, 0.53-0.68). In the full feature set condition, algorithms such as gradient boosting, cross-validated logistic regression, and neural network (AUC = 0.71, 95% CI 0.58-0.83) were comparable with a model developed using traditional stepwise logistic regression (AUC = 0.69, 95% CI 0.57-0.82). Calibration for all models and feature sets was poor. CONCLUSIONS: We developed machine learning prediction models for post-operative delirium that performed better than chance and are comparable with traditional stepwise logistic regression. Delirium proved to be a phenotype that was difficult to predict with appreciable accuracy.

Association of Depressive Symptoms With Postoperative Delirium and CSF Biomarkers for Alzheimer's Disease Among Hip Fracture Patients.

OBJECTIVES: While there is growing evidence of an association between depressive symptoms and postoperative delirium, the underlying pathophysiological mechanisms remain unknown. The goal of this study was to explore the association between depression and postoperative delirium in hip fracture patients, and to examine Alzheimer's disease (AD) pathology as a potential underlying mechanism linking depressive symptoms and delirium. METHODS: Patients 65 years old or older (N = 199) who were undergoing hip fracture repair and enrolled in the study "A Strategy to Reduce the Incidence of Postoperative Delirium in Elderly Patients" completed the 15-item Geriatric Depression Scale (GDS-15) preoperatively. Cerebrospinal fluid (CSF) was obtained during spinal anesthesia and assayed for amyloid-beta (Aß) 40, 42, total tau (t-tau), and phosphorylated tau (p-tau)181. RESULTS: For every one point increase in GDS-15, there was a 13% increase in odds of postoperative delirium, adjusted for baseline cognition (MMSE), age, sex, race, education and CSF AD biomarkers (OR = 1.13, 95%CI = 1.02-1.25). Both CSF Aß42/t-tau (ß = -1.52, 95%CI = -2.1 to -0.05) and Aß42/p-tau181 (ß = -0.29, 95%CI = -0.48 to -0.09) were inversely associated with higher GDS-15 scores, where lower ratios indicate greater AD pathology. In an analysis to identify the strongest predictors of delirium out of 18 variables, GDS-15 had the highest classification accuracy for postoperative delirium and was a stronger predictor of delirium than both cognition and AD biomarkers. CONCLUSIONS: In older adults undergoing hip fracture repair, depressive symptoms were associated with underlying AD pathology and postoperative delirium. Mild baseline depressive symptoms were the strongest predictor of postoperative delirium, and may represent a dementia prodrome.