Publications

IMPORTANCE: Asymptomatic blood pressure (BP) elevations are common in hospitalized older adults, and widespread heterogeneity in the clinical management of elevated inpatient BPs exists.

OBJECTIVE: To examine the association of intensive treatment of elevated inpatient BPs with in-hospital clinical outcomes of older adults hospitalized for noncardiac conditions.

DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study examined Veterans Health Administration data between October 1, 2015, and December 31, 2017, for patients aged 65 years or older hospitalized for noncardiovascular diagnoses and who experienced elevated BPs in the first 48 hours of hospitalization.

INTERVENTIONS: Intensive BP treatment following the first 48 hours of hospitalization, defined as receipt of intravenous antihypertensives or oral classes not used prior to admission.

MAIN OUTCOME AND MEASURES: The primary outcome was a composite of inpatient mortality, intensive care unit transfer, stroke, acute kidney injury, B-type natriuretic peptide elevation, and troponin elevation. Data were analyzed between October 1, 2021, and January 10, 2023, with propensity score overlap weighting used to adjust for confounding between those who did and did not receive early intensive treatment.

RESULTS: Among 66 140 included patients (mean [SD] age, 74.4 [8.1] years; 97.5% male and 2.6% female; 17.4% Black, 1.7% Hispanic, and 75.9% White), 14 084 (21.3%) received intensive BP treatment in the first 48 hours of hospitalization. Patients who received early intensive treatment vs those who did not continued to receive a greater number of additional antihypertensives during the remainder of their hospitalization (mean additional doses, 6.1 [95% CI, 5.8-6.4] vs 1.6 [95% CI, 1.5-1.8], respectively). Intensive treatment was associated with a greater risk of the primary composite outcome (1220 [8.7%] vs 3570 [6.9%]; weighted odds ratio [OR], 1.28; 95% CI, 1.18-1.39), with the highest risk among patients receiving intravenous antihypertensives (weighted OR, 1.90; 95% CI, 1.65-2.19). Intensively treated patients were more likely to experience each component of the composite outcome except for stroke and mortality. Findings were consistent across subgroups stratified by age, frailty, preadmission BP, early hospitalization BP, and cardiovascular disease history.

CONCLUSIONS AND RELEVANCE: The study's findings indicate that among hospitalized older adults with elevated BPs, intensive pharmacologic antihypertensive treatment was associated with a greater risk of adverse events. These findings do not support the treatment of elevated inpatient BPs without evidence of end organ damage, and they highlight the need for randomized clinical trials of inpatient BP treatment targets.

OBJECTIVE: Although animal models suggest a role for blood-brain barrier dysfunction in postoperative delirium-like behavior, its role in postoperative delirium and postoperative recovery in humans is unclear. Thus, we evaluated the role of blood-brain barrier dysfunction in postoperative delirium and hospital length of stay among older surgery patients.

METHODS: Cognitive testing, delirium assessment, and cerebrospinal fluid and blood sampling were prospectively performed before and after non-cardiac, non-neurologic surgery. Blood-brain barrier dysfunction was assessed using the cerebrospinal fluid-to-plasma albumin ratio (CPAR).

RESULTS: Of 207 patients (median age = 68 years, 45% female) with complete CPAR and delirium data, 26 (12.6%) developed postoperative delirium. Overall, CPAR increased from before to 24 hours after surgery (median change = 0.28, interquartile range [IQR] = -0.48 to 1.24, Wilcoxon p = 0.001). Preoperative to 24 hours postoperative change in CPAR was greater among patients who developed delirium versus those who did not (median [IQR] = 1.31 [0.004 to 2.34] vs 0.19 [-0.55 to 1.08], p = 0.003). In a multivariable model adjusting for age, baseline cognition, and surgery type, preoperative to 24 hours postoperative change in CPAR was independently associated with delirium occurrence (per CPAR increase of 1, odds ratio = 1.30, 95% confidence interval [CI] = 1.03-1.63, p = 0.026) and increased hospital length of stay (incidence rate ratio = 1.15, 95% CI = 1.09-1.22, p < 0.001).

INTERPRETATION: Postoperative increases in blood-brain barrier permeability are independently associated with increased delirium rates and postoperative hospital length of stay. Although these findings do not establish causality, studies are warranted to determine whether interventions to reduce postoperative blood-brain barrier dysfunction would reduce postoperative delirium rates and hospital length of stay. ANN NEUROL 2023;94:1024-1035.

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.

Delirium, an acute change in cognition, is common, morbid, and costly, particularly among hospitalized older adults. Despite growing knowledge of its epidemiology, far less is known about delirium pathophysiology. Initial work understanding delirium pathogenesis has focused on assaying single or a limited subset of molecules or genetic loci. Recent technological advances at the forefront of biomarker and drug target discovery have facilitated application of multiple "omics" approaches aimed to provide a more complete understanding of complex disease processes such as delirium. At its basic level, "omics" involves comparison of genes (genomics, epigenomics), transcripts (transcriptomics), proteins (proteomics), metabolites (metabolomics), or lipids (lipidomics) in biological fluids or tissues obtained from patients who have a certain condition (i.e., delirium) and those who do not. Multi-omics analyses of these various types of molecules combined with machine learning and systems biology enable the discovery of biomarkers, biological pathways, and predictors of delirium, thus elucidating its pathophysiology. This review provides an overview of the most recent omics techniques, their current impact on identifying delirium biomarkers, and future potential in enhancing our understanding of delirium pathogenesis. We summarize challenges in identification of specific biomarkers of delirium and, more importantly, in discovering the mechanisms underlying delirium pathophysiology. Based on mounting evidence, we highlight a heightened inflammatory response as one common pathway in delirium risk and progression, and we suggest other promising biological mechanisms that have recently emerged. Advanced multiple omics approaches coupled with bioinformatics methodologies have great promise to yield important discoveries that will advance delirium research.

BACKGROUND: Professional society guidelines recommend limiting the use of antipsychotics in older patients with postoperative delirium. How these recommendations affected the use of antipsychotics and other psychoactive drugs in the postoperative period has not been studied.

METHODS: This retrospective cohort study included patients 65 years or older without psychiatric diagnoses who underwent major surgery in community hospitals (CHs) and academic medical centers (AMCs) in the United States. The outcome was the rate of hospital days exposed to antipsychotics, antidepressants, antiepileptics, benzodiazepines, hypnotics, and selective alpha-2 receptor agonist dexmedetomidine in the postoperative period by hospital type.

RESULTS: The study included 4,098,431 surgical admissions from CHs (mean age 75.0 [standard deviation, 7.1] years; 50.8% female) during 2008-2018 and 2,310,529 surgical admissions from AMCs (75.0 [7.4] years; 49.4% female) during 2009-2018. In the intensive care unit (ICU) setting, the number of exposed days per 1000 hospital-days declined for haloperidol (CHs: 33-21 days [p < 0.01]; AMCs: 24-15 days [p < 0.01]) and benzodiazepines (CHs: 261-136 days [p < 0.01]; AMCs: 150-77 days [p < 0.01]). The use of atypical antipsychotics, antidepressants, antiepileptics, and dexmedetomidine increased, while hypnotic use varied by the hospital type. In the non-ICU setting, the rate declined for haloperidol in CHs but not in AMCs (CHs: 10-6 days [p < 0.01]; AMCs: 4-3 days [p = 0.52]) and for benzodiazepines in both settings (CHs: 126-56 days [p < 0.01]; AMCs: 30-27 days [p < 0.01]). The use of antiepileptics and antidepressants increased, while the use of atypical antipsychotics and hypnotics varied by the hospital type.

CONCLUSIONS: The use of haloperidol and benzodiazepines in the postoperative period declined at both CHs and AMCs. These trends coincided with the increasing use of other psychoactive drugs.

Delirium is a common postoperative complication among older patients with many adverse outcomes. Due to a lack of validated biomarkers, prediction and monitoring of delirium by biological testing is not currently feasible. Circulating proteins in cerebrospinal fluid (CSF) may reflect biological processes causing delirium. Our goal was to discover and investigate candidate protein biomarkers in preoperative CSF that were associated with the development of postoperative delirium in older surgical patients. We employed a nested case-control study design coupled with high multiplex affinity proteomics analysis to measure 1305 proteins in preoperative CSF. Twenty-four matched delirium cases and non-delirium controls were selected from the Healthier Postoperative Recovery (HiPOR) cohort, and the associations between preoperative protein levels and postoperative delirium were assessed using t-test statistics with further analysis by systems biology to elucidate delirium pathophysiology. Proteomics analysis identified 32 proteins in preoperative CSF that significantly associate with delirium (t-test p < 0.05). Due to the limited sample size, these proteins did not remain significant by multiple hypothesis testing using the Benjamini-Hochberg correction and q-value method. Three algorithms were applied to separate delirium cases from non-delirium controls. Hierarchical clustering classified 40/48 case-control samples correctly, and principal components analysis separated 43/48. The receiver operating characteristic curve yielded an area under the curve [95% confidence interval] of 0.91 [0.80-0.97]. Systems biology analysis identified several key pathways associated with risk of delirium: inflammation, immune cell migration, apoptosis, angiogenesis, synaptic depression and neuronal cell death. Proteomics analysis of preoperative CSF identified 32 proteins that might discriminate individuals who subsequently develop postoperative delirium from matched control samples. These proteins are potential candidate biomarkers for delirium and may play a role in its pathophysiology.

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.

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.

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.

Delirium is a debilitating medical condition that disproportionately affects hospitalized older adults and is associated with adverse health outcomes, increased mortality, and high medical costs. Efforts to understand delirium risk in hospitalized older adults have focused on examining medical comorbidities, pre-existing cognitive deficits, and other clinical and demographic factors present in the period proximate to the hospitalization. The contribution of social determinants of health (SDOH), including social circumstances, environmental characteristics, and early-life exposures, referred as the social exposome, to delirium risk is poorly understood. Increased knowledge about the influence of SDOH will offer a more comprehensive understanding of factors that may increase vulnerability to delirium and poor outcomes. Clinically, these efforts can guide the development and implementation of holistic preventive strategies to improve clinical outcomes. We propose a SDOH framework for delirium adapted for older adults. We provide the definition, description, and rationale for the domains and variables in our proposed model.