Using Bayesian item response theory for multicohort repeated measure design to estimate individual latent change scores.

Repeated measure data design has been used extensively in a wide range of fields, such as brain aging or developmental psychology, to answer important research questions exploring relationships between trajectory of change and external variables. In many cases, such data may be collected from multiple study cohorts and harmonized, with the intention of gaining higher statistical power and enhanced external validity. When psychological constructs are measured using survey scales, a fundamental psychometric challenge for data harmonization is to create commensurate measures for the constructs of interest across studies. Traditional analysis may fit a unidimensional item response theory model to data from one time point and one cohort to obtain item parameters and fix the same parameters in subsequent analyses. Such a simplified approach ignores item residual dependencies in the repeated measure design on one hand, and on the other hand, it does not exploit accumulated information from different cohorts. Instead, two alternative approaches should serve such data designs much better: an integrative approach using multiple-group two-tier model via concurrent calibration, and if such calibration fails to converge, a Bayesian sequential calibration approach that uses informative priors on common items to establish the scale. Both approaches use a Markov chain Monte Carlo algorithm that handles computational complexity well. Through a simulation study and an empirical study using Alzheimer's diseases neuroimage initiative cognitive battery data (i.e., language and executive functioning), we conclude that latent change scores obtained from these two alternative approaches are more precisely recovered. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Linking self-perceived cognitive functioning questionnaires using item response theory: The subjective cognitive decline initiative.

OBJECTIVE: Self-perceived cognitive functioning, considered highly relevant in the context of aging and dementia, is assessed in numerous ways-hindering the comparison of findings across studies and settings. Therefore, the present study aimed to link item-level self-report questionnaire data from international aging studies. METHOD: We harmonized secondary data from 24 studies and 40 different questionnaires with item response theory (IRT) techniques using a graded response model with a Bayesian estimator. We compared item information curves to identify items with high measurement precision at different levels of the self-perceived cognitive functioning latent trait. Data from 53,030 neuropsychologically intact older adults were included, from 13 English language and 11 non-English (or mixed) language studies. RESULTS: We successfully linked all questionnaires and demonstrated that a single-factor structure was reasonable for the latent trait. Items that made the greatest contribution to measurement precision (i.e., "top items") assessed general and specific memory problems and aspects of executive functioning, attention, language, calculation, and visuospatial skills. These top items originated from distinct questionnaires and varied in format, range, time frames, response options, and whether they captured ability and/or change. CONCLUSIONS: This was the first study to calibrate self-perceived cognitive functioning data of geographically diverse older adults. The resulting item scores are on the same metric, facilitating joint or pooled analyses across international studies. Results may lead to the development of new self-perceived cognitive functioning questionnaires guided by psychometric properties, content, and other important features of items in our item bank. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Six-Year Cognitive Trajectory in Older Adults Following Major Surgery and Delirium.

Importance: The 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. Objective: To 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 Participants: This 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. Exposure: Development of incident delirium following major elective surgery. Main Outcomes and Measures: Delirium 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. Results: The 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 Relevance: This 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.

Successful aging after elective surgery II: Study design and methods.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Sex differences in the genetic architecture of cognitive resilience to Alzheimer's disease.

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, ß (females) = 0.08, P (females) = 5.76 × 10-09, ß (males) = -0.01, P(males) = 0.70, ß (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.

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.

Development and internal validation of a predictive model of cognitive decline 36 months following elective surgery.

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.

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.

Development and validation of language and visuospatial composite scores in ADNI.

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.