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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 (Aβ)42, Aβ40, 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, Aβ40 and Aβ42. 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. Key points: Delirium is a common complication of hospitalization associated with poor outcomes, but the underlying pathophsysiology of this condition is poorly understoodThere is a close interrelationship between delirium and dementia, therefore CSF and blood biomarkers for Alzheimer's Disease and neural injury might be associated with deliriumWe found that plasma and CSF glial fibrillary acid protein (GFAP), a marker of reactive gliosis known to be elevated in neurodegeneration, appears to be related to delirium risk, with a nearly two‐fold greater odds of deliriumFurther larger studies are needed to understand the potential role of GFAP as a predictive risk marker for delirium [ABSTRACT FROM AUTHOR]

The women suffering from bulimia nervosa were analyzed for self-ratings on the Maudsley Obsessional-Compulsive Inventory (MOCI) and dietary behavior related to weight concerns. Results show significant elevation of MOCI scores with scores on a control scale.

Research designed to establish the frequency of abnormalities in clinical laboratory tests in patients with bulimia nervosa who were otherwise healthy is presented. It was found that abnormalities can arise in large numbers in patients with bulimia nervosa who are otherwise healthy. The frequency of abnormalities will probably vary considerably across clinical environments.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

BACKGROUND/OBJECTIVES: Delirium manifests clinically in varying ways across settings. More than 40 instruments currently exist for characterizing the different manifestations of delirium. We evaluated all delirium identification instruments according to their psychometric properties and frequency of citation in published research. DESIGN: We conducted the systematic review by searching Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, Excerpta Medica Database (Embase), PsycINFO, PubMed, and Web of Science from January 1, 1974, to January 31, 2020, with the keywords "delirium" and "instruments," along with their known synonyms. We selected only systematic reviews, meta‐analyses, or narrative literature reviews including multiple delirium identification instruments. MEASUREMENTS: Two reviewers assessed the eligibility of articles and extracted data on all potential delirium identification instruments. Using the original publication on each instrument, the psychometric properties were examined using the Consensus‐based Standards for the Selection of Health Measurement Instruments (COSMIN) framework. RESULTS: Of 2,542 articles identified, 75 met eligibility criteria, yielding 30 different delirium identification instruments. A count of citations was determined using Scopus for the original publication for each instrument. Each instrument underwent methodological quality review of psychometric properties using COSMIN definitions. An expert panel categorized key domains for delirium identification based on criteria from the Diagnostic and Statistical Manual of Mental Disorders (DSM)‐III through DSM‐5. Four instruments were notable for having at least two of three of the following: citation count of 200 or more, strong validation methodology in their original publication, and fulfillment of DSM‐5 criteria. These were, alphabetically, Confusion Assessment Method, Delirium Observation Screening Scale, Delirium Rating Scale‐Revised‐98, and Memorial Delirium Assessment Scale. CONCLUSION: Four commonly used and well‐validated instruments can be recommended for clinical and research use. An important area for future investigation is to harmonize these measures to compare and combine studies on delirium. [ABSTRACT FROM AUTHOR]

OBJECTIVES We examined the association between delirium severity and outcomes of delirium among persons with and without Alzheimer's disease and related dementias (ADRD). DESIGN Prospective cohort study. SETTING Academic tertiary medical center. PARTICIPANTS A total of 352 medical and surgical patients. MEASUREMENTS Delirium incidence and severity were rated daily using the Confusion Assessment Method (CAM) and CAM‐Severity (CAM‐S) score during hospitalization. Severe delirium was defined as a CAM‐S Short Form score in the highest tertile (3‐7 points out of 7). ADRD status was determined by a clinical consensus process. Clinical outcomes included prolonged length of stay (>6 d), discharge to post‐acute nursing facility, any decline in activities of daily living (ADLs) at 1 month from prehospital baseline, ongoing nursing facility stay, and mortality. RESULTS: Patients with ADRD (n = 85 [24%]) had a significantly higher relative risk (RR) for incident delirium (RR = 2.31; 95% confidence interval [CI] = 1.64‐3.28) and higher peak CAM‐S scores (mean difference = 1.24 points; CI =.83‐1.65; P <.001). Among patients with ADRD, severe delirium significantly increased the RR for nursing facility stay (RR = 2.22; CI = 1.05‐4.69; P =.04) and increased the RR for mortality (RR = 2.10; CI =.89‐4.98; P =.09). Among patients without ADRD, severe delirium was associated with a significantly increased risk for all poor outcomes except mortality including prolonged length of stay in the hospital (RR = 1.47; CI = 1.18‐1.82) and discharge to a post‐acute nursing facility (RR = 2.17; CI = 1.58‐2.98) plus decline in ADLs (RR = 1.30; CI = 1.05‐1.60) and nursing facility stay at 1 month (RR = 1.93; CI = 1.31‐2.83). CONCLUSION: Severe delirium is associated with increased risk for poor clinical outcomes in patients with and without ADRD. In both groups, severe delirium increased risk of nursing home placement. In patients with ADRD, delirium was more severe and associated with a trend toward increased mortality at 1 month. Although the increased risk remains substantial by RR, the study had limited power to examine the rarer outcome of death. J Am Geriatr Soc 68:1722‐1730, 2020. [ABSTRACT FROM AUTHOR]

State Self-Esteem Scale is utilized in a study to evaluate self-esteem in recovered bulimia nervosa patients and compare it to the self-esteem in patients with active symptoms. Results show that patients in remission and free of major depression still remained low on self-esteem in comparison to healthy controls.

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. [ABSTRACT FROM AUTHOR]

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 ( R 2 ) 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., Competing Interests: The authors declare no competing interests., (© 2021 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer's Association.)