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Approximately 30% of elderly adults are cognitively unimpaired at time of death despite the presence of Alzheimer’s disease neuropathology at autopsy. Studying individuals who are resilient to the cognitive consequences of Alzheimer’s disease neuropathology may uncover novel therapeutic targets to treat Alzheimer’s disease. It is well established that there are sex differences in response to Alzheimer’s disease pathology, and growing evidence suggests that genetic factors may contribute to these differences. Taken together, we sought to elucidate sex-specific genetic drivers of resilience. We extended our recent large scale genomic analysis of resilience in which we harmonized cognitive data across four cohorts of cognitive ageing, in vivo amyloid PET across two cohorts, and autopsy measures of amyloid neuritic plaque burden across two cohorts. These data were leveraged to build robust, continuous resilience phenotypes. With these phenotypes, we performed sex-stratified [n (males) = 2093, n (females) = 2931] and sex-interaction [n (both sexes) = 5024] genome-wide association studies (GWAS), gene and pathway-based tests, and genetic correlation analyses to clarify the variants, genes and molecular pathways that relate to resilience in a sex-specific manner. Estimated among cognitively normal individuals of both sexes, resilience was 20–25% heritable, and when estimated in either sex among cognitively normal individuals, resilience was 15–44% heritable. In our GWAS, we identified a female-specific locus on chromosome 10 [rs827389, β (females) = 0.08, P (females) = 5.76 × 10−09, β (males) = −0.01, P(males) = 0.70, β (interaction) = 0.09, P (interaction) = 1.01 × 10−04] in which the minor allele was associated with higher resilience scores among females. This locus is located within chromatin loops that interact with promoters of genes involved in RNA processing, including GATA3. Finally, our genetic correlation analyses revealed shared genetic architecture between resilience phenotypes and other complex traits, including a female-specific association with frontotemporal dementia and male-specific associations with heart rate variability traits. We also observed opposing associations between sexes for multiple sclerosis, such that more resilient females had a lower genetic susceptibility to multiple sclerosis, and more resilient males had a higher genetic susceptibility to multiple sclerosis. Overall, we identified sex differences in the genetic architecture of resilience, identified a female-specific resilience locus and highlighted numerous sex-specific molecular pathways that may underly resilience to Alzheimer’s disease pathology. This study illustrates the need to conduct sex-aware genomic analyses to identify novel targets that are unidentified in sex-agnostic models. Our findings support the theory that the most successful treatment for an individual with Alzheimer’s disease may be personalized based on their biological sex and genetic context.

The Successful Aging after Elective Surgery (SAGES) II study was designed to increase knowledge of the pathophysiology and linkages between delirium and dementia. We examine novel biomarkers potentially associated with delirium, including inflammation, Alzheimer's disease (AD) pathology and neurodegeneration, neuroimaging markers, and neurophysiologic markers. The goal of this paper is to describe the study design and methods for the SAGES II study.The SAGES II study is a 5-year prospective observational study of 400-420 community dwelling persons, aged 65 years and older, assessed prior to scheduled surgery and followed daily throughout hospitalization to observe for development of delirium and other clinical outcomes. Delirium is measured with the Confusion Assessment Method (CAM), long form, after cognitive testing. Cognitive function is measured with a detailed neuropsychologic test battery, summarized as a weighted composite, the General Cognitive Performance (GCP) score. Other key measures include magnetic resonance imaging (MRI), transcranial magnetic stimulation (TMS)/electroencephalography (EEG), and Amyloid positron emission tomography (PET) imaging. We describe the eligibility criteria, enrollment flow, timing of assessments, and variables collected at baseline and during repeated assessments at 1, 2, 6, 12, and 18 months.This study describes the hospital and surgery-related variables, delirium, long-term cognitive decline, clinical outcomes, and novel biomarkers. In inter-rater reliability assessments, the CAM ratings (weighted kappa = 0.91, 95% confidence interval, CI = 0.74-1.0) in 50 paired assessments and GCP ratings (weighted kappa = 0.99, 95% CI 0.94-1.0) in 25 paired assessments. We describe procedures for data quality assurance and Covid-19 adaptations.This complex study presents an innovative effort to advance our understanding of the inter-relationship between delirium and dementia via novel biomarkers, collected in the context of major surgery in older adults. Strengths include the integration of MRI, TMS/EEG, PET modalities, and high-quality longitudinal data.

ImportanceThe study results suggest that delirium is the most common postoperative complication in older adults and is associated with poor outcomes, including long-term cognitive decline and incident dementia.ObjectiveTo examine the patterns and pace of cognitive decline up to 72 months (6 years) in a cohort of older adults following delirium.Design, Setting, and ParticipantsThis was a prospective, observational cohort study with long-term follow-up including 560 community-dwelling older adults (older than 70 years) in the ongoing Successful Aging after Elective Surgery study that began in 2010. The data were analyzed from 2021 to 2022.ExposureDevelopment of incident delirium following major elective surgery.Main Outcomes and MeasuresDelirium was assessed daily during hospitalization using the Confusion Assessment Method, which was supplemented with medical record review. Cognitive performance using a comprehensive battery of neuropsychological tests was assessed preoperatively and across multiple points postoperatively to 72 months of follow-up. We evaluated longitudinal cognitive change using a composite measure of neuropsychological performance called the general cognitive performance (GCP), which is scaled so that 10 points on the GCP is equivalent to 1 population SD. Retest effects were adjusted using cognitive test results in a nonsurgical comparison group.ResultsThe 560 participants (326 women [58%]; mean [SD] age, 76.7 [5.2] years) provided a total of 2637 person-years of follow-up. One hundred thirty-four participants (24%) developed postoperative delirium. Cognitive change following surgery was complex: we found evidence for differences in acute, post–short-term, intermediate, and longer-term change from the time of surgery that were associated with the development of postoperative delirium. Long-term cognitive change, which was adjusted for practice and recovery effects, occurred at a pace of about −1.0 GCP units (95% CI, −1.1 to −0.9) per year (about 0.10 population SD units per year). Participants with delirium showed significantly faster long-term cognitive change with an additional −0.4 GCP units (95% CI, −0.1 to −0.7) or −1.4 units per year (about 0.14 population SD units per year).Conclusions and RelevanceThis cohort study found that delirium was associated with a 40% acceleration in the slope of cognitive decline out to 72 months following elective surgery. Because this is an observational study, we cannot be sure whether delirium directly causes subsequent cognitive decline, or whether patients with preclinical brain disease are more likely to develop delirium. Future research is needed to understand the causal pathway between delirium and cognitive decline.

Background: Delirium is a common and preventable geriatric syndrome. Moving beyond the binary classification of delirium present/absent, delirium severity represents a potentially important outcome for evaluating preventive and treatment interventions and tracking the course of patients. Although several delirium severity assessment tools currently exist, most have been developed in the absence of advanced measurement methodology and have not been evaluated with rigorous validation studies. Objective: We aimed to report our development of new delirium severity items and the results of item reduction and selection activities guided by psychometric analysis of data derived from a field study. Methods: Building on our literature review of delirium instruments and expert panel process to identify domains of delirium severity, we adapted items from existing delirium severity instruments and generated new items. We then fielded these items among a sample of 352 older hospitalized patients. Results: We used an expert panel process and psychometric data analysis techniques to narrow a set of 303 potential items to 17 items for use in a new delirium severity instrument. The 17-item set demonstrated good internal validity and favorable psychometric characteristics relative to comparator instruments, including the Confusion Assessment Method – Severity (CAM-S) score, the Delirium Rating Scale Revised 98, and the Memorial Delirium Assessment Scale. Conclusion: We more fully conceptualized delirium severity and identified characteristics of an ideal delirium severity instrument. These characteristics include an instrument that is relatively quick to administer, is easy to use by raters with minimal training, and provides a severity rating with good content validity, high internal consistency reliability, and broad domain coverage across delirium symptoms. We anticipate these characteristics to be represented in the subsequent development of our final delirium severity instrument.

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.

Editor’s PerspectiveWhat We Already Know about This TopicWhat This Article Tells Us That Is NewBackgroundPostoperative delirium and postoperative cognitive dysfunction share risk factors and may co-occur, but their relationship is not well established. The primary goals of this study were to describe the prevalence of postoperative cognitive dysfunction and to investigate its association with in-hospital delirium. The authors hypothesized that delirium would be a significant risk factor for postoperative cognitive dysfunction during follow-up.MethodsThis study used data from an observational study of cognitive outcomes after major noncardiac surgery, the Successful Aging after Elective Surgery study. Postoperative delirium was evaluated each hospital day with confusion assessment method–based interviews supplemented by chart reviews. Postoperative cognitive dysfunction was determined using methods adapted from the International Study of Postoperative Cognitive Dysfunction. Associations between delirium and postoperative cognitive dysfunction were examined at 1, 2, and 6 months.ResultsOne hundred thirty-four of 560 participants (24%) developed delirium during hospitalization. Slightly fewer than half (47%, 256 of 548) met the International Study of Postoperative Cognitive Dysfunction-defined threshold for postoperative cognitive dysfunction at 1 month, but this proportion decreased at 2 months (23%, 123 of 536) and 6 months (16%, 85 of 528). At each follow-up, the level of agreement between delirium and postoperative cognitive dysfunction was poor (kappa less than .08) and correlations were small (r less than .16). The relative risk of postoperative cognitive dysfunction was significantly elevated for patients with a history of postoperative delirium at 1 month (relative risk = 1.34; 95% CI, 1.07–1.67), but not 2 months (relative risk = 1.08; 95% CI, 0.72–1.64), or 6 months (relative risk = 1.21; 95% CI, 0.71–2.09).ConclusionsDelirium significantly increased the risk of postoperative cognitive dysfunction in the first postoperative month; this relationship did not hold in longer-term follow-up. At each evaluation, postoperative cognitive dysfunction was more common among patients without delirium. Postoperative delirium and postoperative cognitive dysfunction may be distinct manifestations of perioperative neurocognitive deficits.

Introduction Our goal was to determine if features of surgical patients, easily obtained from the medical chart or brief interview, could be used to predict those likely to experience more rapid cognitive decline following surgery. Methods We analyzed data from an observational study of 560 older adults (≥70 years) without dementia undergoing major elective non‐cardiac surgery. Cognitive decline was measured using change in a global composite over 2 to 36 months following surgery. Predictive features were identified as variables readily obtained from chart review or a brief patient assessment. We developed predictive models for cognitive decline (slope) and predicting dichotomized cognitive decline at a clinically determined cut. Results In a hold‐out testing set, the regularized regression predictive model achieved a root mean squared error (RMSE) of 0.146 and a model r‐square (R2) of .31. Prediction of “rapid” decliners as a group achieved an area under the curve (AUC) of .75. Conclusion Some of our models could predict persons with increased risk for accelerated cognitive decline with greater accuracy than relying upon chance, and this result might be useful for stratification of surgical patients for inclusion in future clinical trials.

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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11606-020-06238-7) contains supplementary material, which is available to authorized users.

Introduction Composite scores may be useful to summarize overall language or visuospatial functioning in studies of older adults. Methods We used item response theory to derive composite measures for language (ADNI‐Lan) and visuospatial functioning (ADNI‐VS) from the cognitive battery administered in the Alzheimer's Disease Neuroimaging Initiative (ADNI). We evaluated the scores among groups of people with normal cognition, mild cognitive impairment (MCI), and Alzheimer's disease (AD) in terms of responsiveness to change, association with imaging findings, and ability to differentiate between MCI participants who progressed to AD dementia and those who did not progress. Results ADNI‐Lan and ADNI‐VS were able to detect change over time and predict conversion from MCI to AD. They were associated with most of the pre‐specified magnetic resonance imaging measures. ADNI‐Lan had strong associations with a cerebrospinal fluid biomarker pattern. Discussion ADNI‐Lan and ADNI‐VS may be useful composites for language and visuospatial functioning in ADNI.

Background: Practice effects (PEs) are improvements in performance after repeated exposure to test materials, and typically viewed as a source of bias in repeated cognitive assessments. We aimed to determine whether characterizing PEs could also provide a useful marker of early cognitive decline. Methods: We conducted a systematic review of the literature, searching PsycInfo (Ebsco) and PubMed databases for articles studying PEs in aging and dementia populations. Articles published between 1920 and 2019 were included. Result: We identified 259 articles, of which 27 studied PEs as markers of cognitive performance. These studies consistently showed that smaller, less-robust PEs were associated with current diagnostic status and/or future cognitive decline. In addition, lower PEs were associated with Alzheimer's disease risk factors and neurodegeneration biomarkers. Conclusion: PEs provide a potentially useful marker of cognitive decline, and could prove valuable as part of a cost-effective strategy to select individuals who are at-risk for dementia for future interventions.

INTRODUCTION: Older adults, including those with mild cognitive impairment (MCI), are increasingly undergoing surgery. METHODS: Relative risks of MCI alone or with delirium on adverse outcomes were estimated in an ongoing prospective, observational cohort study of 560 non-demented adults ≥70 years old. RESULTS: MCI (n=61, 11%) was associated with increased relative risk (RR) of delirium (RR=1.9, p

The Autism Diagnostic Observation Schedule (ADOS) is widely used to assess symptoms of autism spectrum disorder (ASD). Given well-documented differences in social behaviors across cultures, this study examined whether item-level biases exist in ADOS scores across sociodemographic groups (race, ethnicity, and gender). We examined a subset of ten ADOS items among participants (N = 2458). Holding level of overall ADOS behavioral symptoms constant, we found significant item level bias (measurement noninvariance) for race and ethnicity on three ADOS items. Item-level bias was not apparent across gender. Although the magnitude of bias was small, our findings highlight the need to reevaluate norms and operational definitions used in assessments to increase ASD diagnostic accuracy among culturally-diverse groups.

Objectives To examine the effect of hospital readmission on functional recovery after elective surgery in older adults. Design Prospective cohort of individuals aged 70 and older undergoing elective surgery, enrolled from June 2010 to August 2013. Setting Two academic medical centers. Participants Community-dwelling older adults (N = 566; mean age ± standard deviation 77 ± 5) undergoing major elective surgery and expected to be admitted for at least 3 days. Measurements Readmission was assessed in multiple interviews with participants and family members over 18 months and validated against medical record review. Physical function was assessed according to ability to perform instrumental activities of daily living (IADLs) and activities of daily living (ADL), Medical Outcomes Study 12-item Short-Form Survey Physical Component Summary score, and a standardized functional composite. Results Two hundred fifty-five (45%) participants experienced 503 readmissions. Readmissions were associated with delays in functional recovery in all measures of physical function. Having two or more readmissions over 18 months was associated with persistent and significantly greater risk of IADL dependence (relative risk (RR) = 1.8, 95% confidence interval (CI) = 1.5–2.3) and ADL dependence (RR = 3.3, 95% CI = 1.7–6.4). Degree of functional impairment increased progressively with number of readmissions. Readmissions within 2 months resulted in delayed functional recovery to baseline by 18 months, and readmissions between 12 and 18 months after surgery resulted in loss of functional recovery previously achieved. Conclusion Readmission after elective surgery may contribute to delays in functional recovery and persistent functional deficits in older adults.

Introduction As the relationship between delirium and long-term cognitive decline has not been well-explored, we evaluated this association in a prospective study. Methods SAGES is an ongoing study involving 560 adults age 70 years or more without dementia scheduled for major surgery. Delirium was assessed daily in the postoperative period using the Confusion Assessment Method. General Cognitive Performance (GCP) and the Informant Questionnaire for Cognitive Decline in the Elderly were assessed preoperatively then repeatedly out to 36 months. Results On average, patients with postoperative delirium had significantly lower preoperative cognitive performance, greater immediate (1 month) impairment, equivalent recovery at 2 months, and significantly greater long-term cognitive decline relative to the nondelirium group. Proxy reports corroborated the clinical significance of the long-term cognitive decline in delirious patients. Discussion Cognitive decline after surgery is biphasic and accelerated among persons with delirium. The pace of long-term decline is similar to that seen with mild cognitive impairment.

Background Delirium has been associated with more rapid cognitive decline. However, it is unknown whether increased delirium severity is associated with a higher rate of long-term cognitive decline. Objective To evaluate delirium severity and the presence and rate of cognitive decline over 36 months following surgery. Methods We examined patients from the Successful Aging after Elective Surgery Study, who were age ≥70 years undergoing major elective surgery (N = 560). Delirium severity was determined by the peak Confusion Assessment Method-Severity (CAM-S) score for each patient's hospitalization and grouped based on the sample distribution: scores of 0-2, 3-7, and 8-19. A neuropsychological composite, General Cognitive Performance (GCP), and proxy-reported Informant Questionnaire for Cognitive Decline (IQCODE) were used to examine cognitive outcomes following surgery at 0, 1, and 2 months, and then every 6 months for up to 3 years. Results No significant cognitive decline was observed for patients with peak CAM-S scores 0-2 (-0.17 GCP units/year, 95% confidence interval [CI] -0.35, 0.01). GCP scores decreased significantly in the group with peak CAM-S scores 3-7 (-0.30 GCP units/year, 95% CI -0.51, -0.09), and decreased almost three times faster in the highest delirium severity group (peak CAM-S scores 8-19; -0.82 GCP units/year, 95% CI -1.28, -0.37). A similar association was found for delirium severity and the proportion of patients who developed IQCODE impairment over time. Conclusion Patients with the highest delirium severity experienced the greatest rate of cognitive decline, which exceeds the rate previously observed for patients with dementia, on serial neuropsychological testing administered over 3 years, with a dose-response relationship between delirium severity and long-term cognitive decline.

Prevention of postoperative delirium and long-term cognitive decline (LTCD) requires risk quantification that is valid in the general clinical setting. Existing prediction rules were primarily developed using stepwise regression, which tends to be overly influenced by specific data sets. We explored multiple different machine learning (ML) algorithms to predict delirium and LTCD (2–36 months). ML better protects against overfitting. We used model development and validation samples from the SAGES cohort (N = 560 adults ≥70 years). A cross-validated logistic regression approach generated the optimal predictive model for delirium (sensitivity 73%), while a random forest optimally prediction of magnitude of LTCD (R2 .20). Pre-operative cognitive level and comorbidity were important in predicting both outcomes, but the final models were otherwise distinct. These results demonstrate the utility of ML for generalizable prediction in this setting and underscores the contribution of baseline vulnerability to risk of delirium and cognitive decline.

BACKGROUND Pap test (PT) interpretations of low-grade squamous intraepithelial lesion (LSIL), cannot exclude high-grade squamous intraepithelial lesion (HSIL), or LSIL-H, are used in many laboratories; however monitoring its usage for quality assurance purposes is understudied. METHODS PTs from 2005 to 2010 were collected, and yearly frequencies of LSIL, HSIL, LSIL-H, and atypical squamous cells, cannot exclude HSIL (ASC-H) as a function of total PTs and total squamous intraepithelial lesions (SILs) were calculated. Two-year risk of cervical intraepithelial neoplasia 2 (CIN2) or worse (CIN2+) and CIN 3 or worse (CIN3+) was calculated. RESULTS A total of 352,220 PTs were identified including 17,301 abnormal PTs. LSIL-H usage increased from 2005 to 2010 (from 0.28% of total PTs in 2005 to 0.61% in 2010, P

Background:Cognitive impairment is a well-recognized risk factor for delirium. Our goal was to determine whether the level of cognitive performance across the nondemented cognitive ability spectrum is correlated with delirium risk and to gauge the importance of cognition relative to other known risk factors for delirium.Methods:The Successful Aging after Elective Surgery study enrolled 566 adults aged ≥70 years scheduled for major surgery. Patients were assessed preoperatively and daily during hospitalization for the occurrence of delirium using the Confusion Assessment Method. Cognitive function was assessed preoperatively with an 11-test neuropsychological battery combined into a composite score for general cognitive performance (GCP). We examined the risk for delirium attributable to GCP, as well as demographic factors, vocabulary ability, and informant-rated cognitive decline, and compared the strength of association with risk factors identified in a previously published delirium prediction rule for delirium.Results:Delirium occurred in 135 (24%) patients. Lower GCP score was strongly and linearly predictive of delirium risk (relative risk = 2.0 per each half standard deviation difference in GCP score, 95% confidence interval, 1.5-2.5). This effect was not attenuated by statistical adjustment for demographics, vocabulary ability, and informant-rated cognitive decline. The effect was stronger than, and largely independent from, both standard delirium risk factors and comorbidity.Conclusion:Risk of delirium is linearly and strongly related to presurgical cognitive performance level even at levels above the population median, which would be considered unimpaired.

Delirium is associated with accelerated long-term cognitive decline and risk for disability, dementia, and death. These two empirically-based but seemingly contradictory statements underscore that delirium is poorly understood and that broad descriptive categories such as DSM diagnostic labels -- originally intended for actuarial and service planning purposes -- contribute little to (and perhaps detract from) our understanding of a complex, heterogeneous, and potentially devastating geriatric syndrome. In this presentation, we review existing literature and report on new results from the Successful Aging after Elective Surgery (SAGES) cohort that support that delirium is both transient / reversible and associated with long-term decline, and provide new research results that highlight the differential symptom and clinical outcome profiles of persons with delirium complicated by accelerated long-term cognitive decline. We provide evidence to support the new conceptualization of “complicated” delirium. This new conceptual model holds important implications for research, prevention, and clinical management.

The objective of this analysis was to develop a measure of neuropsychological performance for cardiac surgery and assess its psychometric properties. Older patients (n=210) underwent a neuropsychological battery using nine assessments. The number of factors was identified with variable reduction methods. Item response theory-based factor analysis methods were used to evaluate the measure. Modified parallel analysis supported a single factor, and the battery formed an internally consistent set (coefficient alpha=0.82). The developed measure provided a reliable, continuous measure (reliability >0.90) across a broad range of performance (−1.5 SD units to +1.0 SD units) with minimal ceiling and floor effects.

Delirium is common among hospitalized older patients and is associated with poor outcomes. Reliable methods to quantify the severity of delirium are scarce, but this study validates a novel scale f...

Background Delirium is characterized by acute cognitive impairment. We examined the association of delirium with long-term cognitive trajectories in older adults with Alzheimer disease (AD). Methods We evaluated prospectively collected data from a nested cohort of hospitalized patients with AD (n = 263) in the Massachusetts Alzheimer Disease Research Center patient registry between January 1, 1991, and June 30, 2006 (median follow-up duration, 3.2 years). Cognitive function was measured using the information-memory-concentration (IMC) section of the Blessed Dementia Rating Scale. Delirium was identified using a validated medical record review method. The rate of cognitive deterioration was contrasted using random-effects regression models. Results Fifty-six percent of patients with AD developed delirium during hospitalization. The rate of cognitive deterioration before hospitalization did not differ significantly between patients who developed delirium (1.4 [95% CI, 0.7-2.1] IMC points per year) and patients who did not develop delirium (0.8 [95% CI, 0.3-1.3] IMC points per year) (P = .24). After adjusting for dementia severity, comorbidity, and demographic characteristics, patients who had developed delirium experienced greater cognitive deterioration in the year following hospitalization (3.1 [95% CI, 2.1-4.1] IMC points per year) relative to patients who had not developed delirium (1.4 [95% CI, 0.2-2.6] IMC points per year). The ratio of these changes suggests that cognitive deterioration following delirium proceeds at twice the rate in the year after hospitalization compared with patients who did not develop delirium. Patients who had developed delirium maintained a more rapid rate of cognitive deterioration throughout a 5-year period following hospitalization. Sensitivity analyses that excluded rehospitalized patients and included matching on baseline cognitive function and baseline rate of cognitive deterioration produced essentially identical results. Conclusions Delirium is highly prevalent among persons with AD who are hospitalized and is associated with an increased rate of cognitive deterioration that is maintained for up to 5 years. Strategies to prevent delirium may represent a promising avenue to explore for ameliorating cognitive deterioration in AD.

Network accounts of the progression of Alzheimer's disease (AD), based on cross-sectional brain imaging observations, postulate that the biological course of the disease is characterized by coordinated spatial patterns of brain change to distributed cognitive networks. This study tests this conjecture by quantifying inter-regional covariance in cortical gray matter atrophy rates in 317 Alzheimer's Disease Neuroimaging Initiative participants who were clinically diagnosed with amnestic mild cognitive impairment at baseline and underwent serial MRI at 6-month intervals over the course of 2 years. A factor analysis model identified five factors (i.e. groupings of regions) that exhibited highly correlated rates of atrophy. Four groupings approximately corresponded to coordinated change within the posterior default mode network, prefrontal cortex, medial temporal lobe, and regions largely spared by the early pathological course of AD (i.e., sensorimotor and occipital cortex), while the fifth grouping represented diffuse, global atrophy. The data-driven observation of “frontal aging” superimposed upon medial temporal atrophy typical of early AD and default mode network changes supports the view that in individuals at high risk of eventual clinical AD, multiple patterns of distributed neuronal death corresponding to multiple biological substrates may be active.

Hospitalization, frequently complicated by delirium, can be a life-changing event for patients with Alzheimer disease (AD).To determine risks for institutionalization, cognitive decline, or death associated with hospitalization and delirium in patients with AD.Prospective cohort enrolled between 1991 and 2006 into the Massachusetts Alzheimer's Disease Research Center (MADRC) patient registry.Community-based.771 persons aged 65 years or older with a clinical diagnosis of AD.Hospitalization, delirium, death, and institutionalization were identified through administrative databases. Cognitive decline was defined as a decrease of 4 or more points on the Blessed Information-Memory-Concentration test score. Multivariate analysis was used to calculate adjusted relative risks (RRs).Of 771 participants with AD, 367 (48%) were hospitalized and 194 (25%) developed delirium. Hospitalized patients who did not have delirium had an increased risk for death (adjusted RR, 4.7 [95% CI, 1.9 to 11.6]) and institutionalization (adjusted RR, 6.9 [CI, 4.0 to 11.7]). With delirium, risk for death (adjusted RR, 5.4 [CI, 2.3 to 12.5]) and institutionalization (adjusted RR, 9.3 [CI, 5.5 to 15.7]) increased further. With hospitalization and delirium, the adjusted RR for cognitive decline for patients with AD was 1.6 (CI, 1.2 to 2.3). Among hospitalized patients with AD, 21% of the incidences of cognitive decline, 15% of institutionalization, and 6% of deaths were associated with delirium.Cognitive outcome was missing in 291 patients. Sensitivity analysis was performed to test the effect of missing data, and a composite outcome was used to decrease the effect of missing data.Approximately 1 in 8 hospitalized patients with AD who develop delirium will have at least 1 adverse outcome, including death, institutionalization, or cognitive decline, associated with delirium. Delirium prevention may represent an important strategy for reducing adverse outcomes in this population.

Background: studies of cognitive ageing at the group level suggest that age is associated with cognitive decline; however, there may be individual differences such that not all older adults will experience cognitive decline. Objective: to evaluate patterns of cognitive decline in a cohort of older adults initially free of dementia. Design, setting and subjects: elderly Catholic clergy members participating in the Religious Orders Study were followed for up to 15 years. Cognitive performance was assessed annually. Methods: performance on a composite global measure of cognition was analysed using random effects models for baseline performance and change over time. A profile mixture component was used to identify subgroups with different cognitive trajectories over the study period. Results: from a sample of 1,049 participants (mean age 75 years), three subgroups were identified based on the distribution of baseline performance and change over time. The majority (65%) of participants belonged to a slow decline class that did not experience substantial cognitive decline over the observation period [−0.04 baseline total sample standard deviation (SD) units/year]. About 27% experienced moderate decline (−0.19 SD/year), and 8% belonged to a class experiencing rapid decline (−0.57 SD/year). A subsample analysis revealed that when substantial cognitive decline does occur, the magnitude and rate of decline is correlated with neuropathological processes. Conclusions: in this sample, the most common pattern of cognitive decline is extremely slow, perceptible on a time scale measured by decades, not years. While in need of cross validation, these findings suggest that cognitive changes associated with ageing may be minimal and emphasise the importance of understanding the full range of age-related pathologies that may diminish brain function.

Cognitive impairment is often unrecognized among older adults. Meanwhile, current assessment instruments are underused, lack sensitivity, or may be restricted by copyright laws. To address these limitations, we created a new brief cognitive assessment tool: the Sweet 16.The Sweet 16 was developed in a cohort from a large post-acute hospitalization study (n=774) and compared with the Mini-Mental State Examination (MMSE). Equipercentile equating identified Sweet 16 cut points that correlated with widely used MMSE cut points. Sweet 16 performance characteristics were independently validated in a cohort from the Aging, Demographics, and Memory Study (n=709) using clinical consensus diagnosis, the modified Blessed Dementia Rating Scale, and the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE).The Sweet 16 correlated highly with the MMSE (Spearman r, 0.94; P.001). Validated against the IQCODE, the area under the curve was 0.84 for the Sweet 16 and 0.81 for the MMSE (P=.06). A Sweet 16 score of less than 14 (approximating an MMSE score24) demonstrated a sensitivity of 80% and a specificity of 70%, whereas an MMSE score of less than 24 showed a sensitivity of 64% and a specificity of 86% against the IQCODE. When compared with clinical diagnosis, a Sweet 16 score of less than 14 showed a sensitivity of 99% and a specificity of 72% in contrast to an MMSE score with a sensitivity of 87% and a specificity of 89%. For education of 12 years or more, the area under the curve was 0.90 for the Sweet 16 and 0.84 for the MMSE (P=.03).The Sweet 16 is simple, quick to administer, and will be available open access. The performance of the Sweet 16 is equivalent or superior to that of the MMSE.

Self modeling regression (SEMOR) is an approach for modeling sets of observed curves that have a common shape (or sequence of features) but have variability in the amplitude (y-axis) and/or timing (x-axis) of the features across curves. SEMOR assumes the x and y axes for each observed curve can be separately transformed in a parametric manner so that the features across curves are aligned with the common shape, usually represented by non-parametric function. We show that when the common shape is modeled with a regression spline and the transformational parameters are modeled as random with the traditional distribution (normal with mean zero), the SEMOR model may surprisingly suffer from lack of fit and the variance components may be over-estimated. A random effects distribution that restricts the predicted random transformational parameters to have mean zero or the inclusion of a fixed transformational parameter improves estimation. Our work is motivated by arterial pulse pressure waveform data where one of the variance components is a novel measure of short-term variability in blood pressure.

Background: Delirium (acute confusion), is a common, morbid, and costly complication of acute illness in older adults. Yet, researchers and clinicians lack short, efficient, and sensitive case identification tools for delirium. Though the Confusion Assessment Method (CAM) is the most widely used algorithm for delirium, the existing assessments that operationalize the CAM algorithm may be too long or complicated for routine clinical use. Item response theory (IRT) models help facilitate the development of short screening tools for use in clinical applications or research studies. This study utilizes IRT to identify a reduced set of optimally performing screening indicators for the four CAM features of delirium. Methods: Older adults were screened for enrollment in a large scale delirium study conducted in Boston-area postacute facilities (n = 4,598). Trained interviewers conducted a structured delirium assessment that culminated in rating the presence or absence of four features of delirium based on the CAM. A pool of 135 indicators from established cognitive testing and delirium assessment tools were assigned by an expert panel into two indicator sets per CAM feature representing (a) direct interview questions, including cognitive testing, and (b) interviewer observations. We used IRT models to identify the best items to screen for each feature of delirium. Results: We identified 10 dimensions and chose up to five indicators per dimension. Preference was given to items with peak psychometric information in the latent trait region relevant for screening for delirium. The final set of 48 indicators, derived from 39 items, maintains fidelity to clinical constructs of delirium and maximizes psychometric information relevant for screening. Conclusions: We identified optimal indicators from a large item pool to screen for delirium. The selected indicators maintain fidelity to clinical constructs of delirium while maximizing psychometric information important for screening. This reduced item set facilitates development of short screening tools suitable for use in clinical applications or research studies. This study represents the first step in the establishment of an item bank for delirium screening with potential questions for clinical researchers to select from and tailor according to their research objectives.