scholarly journals The Harmonization of Four Delirium Instruments

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 520-520
Author(s):  
Benjamin Helfand ◽  
Elke Detroyer ◽  
Koen Milisen ◽  
Dimitrios Adamis ◽  
Richard Jones
Keyword(s):  
Structural Equation ◽  
Structural Equation Models ◽  
Rating Scale ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Clinical Syndrome ◽  
Kappa Statistics ◽  
Highly Correlated ◽  
Sum Scores ◽  
Meta Analyses

Abstract Delirium is a clinical syndrome characterized by acute cognitive dysfunction, which is pervasive in older persons. Delirium affects over 2.6 million Americans over age 65 annually. One major problem in detection of delirium is that over 40 different instruments have been created to identify delirium in different clinical settings. There is no single agreed upon reference standard instrument. In previous work, we performed a systematic review to identify the four most commonly cited and well-validated instruments for delirium identification. The aim of this study is to harmonize these four commonly used instruments: Confusion Assessment Method (CAM), Delirium Observation Screening Scale (DOSS), Delirium Rating Scale-Revised-98 (DRS-R-98), and Memorial Delirium Assessment Scale (MDAS). We used data from three separate sources (N = 1623). Participants were rated by multiple and overlapping instruments across studies, allowing us to apply item response theory linking procedures. We fit generalized structural equation models, and found unidimensional factor models fit well. We found the instruments were highly correlated (all r > 0.9) and kappa statistics for delirium case identification were high (range: 0.89 to 0.95). We generated crosswalks to map sum scores on one instrument to another. Our results suggest the same underlying construct, propensity to delirium, is measured across the four instruments. The crosswalks facilitate comparison and combination for immediate clinical use or for future meta-analyses. In future steps, we will use our results to find the optimal cut-points for use across all instruments to identify delirium.

scholarly journals ShareSEM: Example and Template R Scripts for SEM, Bayesian, and More

2019 ◽  
Author(s):  
Daniel John Phipps
Keyword(s):  
Path Analysis ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Bayesian Modelling ◽  
Meta Analyses

The ShareSEM project is a simple depository of R scripts for running structural equation models and Bayesian modelling in R. The scripts are designed such that, if measurement items are named consistently, models can be ran with no extra scripting. If models or items differ from the template file, the scripts are designed to be easily adaptable. Bayesian path analysis scripts are also available, with pre-specified priors from meta-analyses of the models.

Evaluation of point-of-care thumb-size bispectral electroencephalography device to quantify delirium severity and predict mortality

2021 ◽  
pp. 1-8
Author(s):  
Takehiko Yamanashi ◽  
Kaitlyn J. Crutchley ◽  
Nadia E. Wahba ◽  
Eleanor J. Sullivan ◽  
Katie R. Comp ◽  
...  
Keyword(s):  
Rating Scale ◽  
Point Of Care ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Negative Group ◽  
Positive Group ◽  
Prediction Of Mortality ◽  
Eeg Data ◽  
Poor Outcomes ◽  
Screening Scale

Background We have developed the bispectral electroencephalography (BSEEG) method for detection of delirium and prediction of poor outcomes. Aims To improve the BSEEG method by introducing a new EEG device. Method In a prospective cohort study, EEG data were obtained and BSEEG scores were calculated. BSEEG scores were filtered on the basis of standard deviation (s.d.) values to exclude signals with high noise. Both non-filtered and s.d.-filtered BSEEG scores were analysed. BSEEG scores were compared with the results of three delirium screening scales: the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU), the Delirium Rating Scale-Revised-98 (DRS) and the Delirium Observation Screening Scale (DOSS). Additionally, the 365-day mortalities and the length of stay (LOS) in the hospital were analysed. Results We enrolled 279 elderly participants and obtained 620 BSEEG recordings; 142 participants were categorised as BSEEG-positive, reflecting slower EEG activity. BSEEG scores were higher in the CAM-ICU-positive group than in the CAM-ICU-negative group. There were significant correlations between BSEEG scores and scores on the DRS and the DOSS. The mortality rate of the BSEEG-positive group was significantly higher than that of the BSEEG-negative group. The LOS of the BSEEG-positive group was longer compared with that of the BSEEG-negative group. BSEEG scores after s.d. filtering showed stronger correlations with delirium screening scores and more significant prediction of mortality. Conclusions We confirmed the usefulness of the BSEEG method for detection of delirium and of delirium severity, and prediction of patient outcomes with a new EEG device.

Delirium classification by the diagnostic and statistical manual – a moving target

2015 ◽  
Vol 27 (6) ◽  
pp. 881-882 ◽  
Author(s):  
Karin J. Neufeld
Keyword(s):  
Rating Scale ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Classification Systems ◽  
Diagnostic Systems ◽  
Delirium Assessment ◽  
Diagnostic And Statistical Manual ◽  
Dsm 5 ◽  
Medical Inpatients ◽  
Dsm Iv

The following paper, entitled “A Comparison of Delirium Diagnosis in Elderly Medical Inpatients using the CAM, DRS-R98, DSM-IV and DSM-5 Criteria” by Adamis and colleagues, reports the results of a single delirium assessment of 200 medical inpatients, aged 70 years and older. The aim was to compare the prevalence of delirium using two different diagnostic classification systems (DSM-5 and DSM-IV) and two commonly used research tools (Confusion Assessment Method and the Delirium Rating Scale-Revised ‘98). This editorial focuses on the comparison of the two versions of the DSM. The authors conclude that, while both diagnostic systems identify a core concept of delirium, the DSM-IV criteria are the most inclusive of the four approaches and the DSM-5, the most restrictive, identifying a prevalence of 19.5% and 13%, respectively in this sample. Furthermore, they conclude that these two systems do not appear to detect the same patients: only 14 of 26 (54%) individuals identified as delirious by the more exclusive DSM-5 criteria were also identified as such by DSM-IV.

scholarly journals Structural Equation Modeling (SEM)-Based Meta-Analysis

2021 ◽  
Author(s):  
Mike W.-L. Cheung
Keyword(s):  
Social Sciences ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Meta Analysis ◽  
Equation Model ◽  
Equation Modeling ◽  
Future Directions ◽  
Software Packages ◽  
Meta Analyses

Structural equation modeling (SEM) and meta-analysis are two popular techniques in the behavioral, medical, and social sciences. They have their own research communities, terminologies, models, software packages, and even journals. This chapter introduces SEM-based meta-analysis, an approach to conduct meta-analyses using the SEM framework. By conceptualizing studies in a meta-analysis as subjects in a structural equation model, univariate, multivariate, and three-level meta-analyses can be fitted as structural equation models using definition variables. We will review fixed-, random-, and mixed-effects models using the SEM framework. Examples will be used to illustrate the procedures using the metaSEM and OpenMx packages in R. This chapter closes with a discussion of some future directions for research.

scholarly journals Advanced Age Is Associated With Catatonia in Critical Illness: Results From the Delirium and Catatonia Prospective Cohort Investigation

2021 ◽  
Vol 12 ◽  
Author(s):  
Jennifer Connell ◽  
Ahra Kim ◽  
Nathan E. Brummel ◽  
Mayur B. Patel ◽  
Simon N. Vandekar ◽  
...  
Keyword(s):  
Intensive Care ◽  
Critical Illness ◽  
Critically Ill ◽  
Rating Scale ◽  
Medical Center ◽  
Adult Population ◽  
Academic Medical Center ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Advanced Age

Introduction: Catatonia, characterized by motor, behavioral and affective abnormalities, frequently co-occurs with delirium during critical illness. Advanced age is a known risk factor for development of delirium. However, the association between age and catatonia has not been described. We aim to describe the occurrence of catatonia, delirium, and coma by age group in a critically ill, adult population.Design: Convenience cohort, nested within two clinical trials and two observational cohort studies.Setting: Intensive care units in an academic medical center in Nashville, TN.Patients: 378 critically ill adult patients on mechanical ventilation and/or vasopressors.Measurements and Main Results: Patients were assessed for catatonia, delirium, and coma by independent and blinded personnel, the Bush Francis Catatonia Rating Scale, the Confusion Assessment Method for the Intensive Care Unit (ICU) and the Richmond Agitation and Sedation Scale. Of 378 patients, 23% met diagnostic criteria for catatonia, 66% experienced delirium, and 52% experienced coma during the period of observation. There was no relationship found between age and catatonia severity or age and presence of specific catatonia items. The prevalence of catatonia was strongly associated with age in the setting of critical illness (p < 0.05). Delirium and comas' association with age was limited to the setting of catatonia.Conclusion: Given the significant relationship between age and catatonia independent of coma and delirium status, these data demonstrate catatonia's association with advanced age in the setting of critical illness. Future studies can explore the causative factors for this association and further elucidate the risk factors for acute brain dysfunction across the age spectrum.

scholarly journals A Comparative Assessment of Graphic and 0–10 Rating Scales Used to Measure Entrepreneurial Competences

Axioms ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 21 ◽  
Author(s):  
Laura Vall-Llosera ◽  
Salvador Linares-Mustarós ◽  
Andrea Bikfalvi ◽  
Germà Coenders
Keyword(s):  
Structural Equation ◽  
Rating Scales ◽  
Structural Equation Models ◽  
Rating Scale ◽  
Business Students ◽  
Comparative Assessment ◽  
Computer Assisted ◽  
Measurement Instruments ◽  
Production Scale ◽  
Line Production

This article presents an empirical comparative assessment of the measurement quality of two instruments commonly used to measure fuzzy characteristics in computer-assisted questionnaires: a graphic scale (a line production scale using a slider bar) and an endecanary scale (a 0–10 rating scale using radio buttons). Data are analyzed by means of multitrait–multimethod models estimated as structural equation models with a mean and covariance structure. For the first time in such research, the results include bias, valid variance, method variance, and random error variance. The data are taken from a program that assesses entrepreneurial competences in undergraduate Economics and Business students by means of questionnaires administered on desktop computers. Neither of the measurement instruments was found to be biased with respect to the other, meaning that their scores are comparable. While both instruments achieve valid and reliable measurements, the reliability and validity are higher for the endecanary scale. This study contributes to the still scarce literature on fuzzy measurement instruments and on the comparability and relative merits of graphic and discrete rating scales on computer-assisted questionnaires.

The Protective Role of Childcare Quality for Behavioral Adjustment in 3- to 5-Year-Old Children

2017 ◽  
Vol 49 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Corina Wustmann Seiler ◽  
Eva Müller ◽  
Heidi Simoni
Keyword(s):  
Structural Equation ◽  
Structural Equation Models ◽  
Rating Scale ◽  
Risk Index ◽  
Cumulative Risk ◽  
Process Quality ◽  
Family Risk Factors ◽  
High Quality ◽  
Family Risks

Abstract. This study examined the role of childcare process quality regarding the relation between family risks and preschoolers’ social–emotional problems. The study included 24 childcare centers with 42 groups in the German-speaking part of Switzerland. The 162 children in the sample were aged 3 – 5 years. Parents and teachers completed the Strengths and Difficulties Questionnaire (SDQ-Deu). Eight family risk factors were subsumed into a cumulative risk index. Childcare process quality was assessed by various observation instruments, for example, the Infant/Toddler Environment Rating Scale-Revised (ITERS-R) and the Early Childhood Environment Rating Scale-Revised (ECERS–R). The calculated structural equation models show that high-quality teaching and interaction, and provisions for learning, can mitigate the negative effects of family risks on children’s internalizing problems. High process quality can provide the chance of considerable attention, encouragement, and new learning opportunities for children at risk; these high-quality centers thereby contribute to protective processes.

scholarly journals A Municipal Audit Fee Model Using Structural Equation Modeling

2011 ◽  
Vol 24 (3) ◽  
Author(s):  
Gary Giroux ◽  
Andrew McLelland
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Audit Fees ◽  
Governance Structure ◽  
Equation Modeling ◽  
Financial Risks ◽  
Large Cities ◽  
Audit Fee ◽  
Highly Correlated

<p class="MsoNormal" style="text-align: justify; margin: 0in 0.5in 0pt; mso-outline-level: 1;"><span style="font-size: 10pt;"><span style="font-family: Times New Roman;">The purpose of this project is to model municipal audit fees using an audit economics framework and then analyze this conceptual framework empirically using structural equation modeling, because structural equation models are excellent for examining complex and interdependent environments.<span style="mso-spacerun: yes;">&nbsp; </span>The sample is large cities using 1996 data.<span style="mso-spacerun: yes;">&nbsp; </span>The theoretical model uses five constructs to explain audit fees:<span style="mso-spacerun: yes;">&nbsp; </span>(1) client size, (2) complexity of client operations, (3) financial risks including demographic characteristics, (4) auditing factors, and (5) governance structure.<span style="mso-spacerun: yes;">&nbsp; </span>The final model includes six variables directly related to audit fee plus five mediating variables.<span style="mso-spacerun: yes;">&nbsp; </span>The results demonstrate that SEM modeling can explain audit fees and provides more information on how the highly correlated independent variables are interrelated in the context of explaining audit fee levels.</span></span></p>

scholarly journals Derivation of a simple postoperative delirium incidence and severity prediction model

2018 ◽  
Author(s):  
Lindroth H. ◽  
Bratzke L. ◽  
Twadell S. ◽  
Rowley P. ◽  
Kildow J. ◽  
...  
Keyword(s):  
Prediction Model ◽  
Postoperative Delirium ◽  
Rating Scale ◽  
Factor Model ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Improvement Program ◽  
Risk Of Death ◽  
Framingham Risk ◽  
Severity Prediction

SummaryBackgroundDelirium is an important postoperative complication, yet a simple and effective delirium prediction model remains elusive. We hypothesized that the combination of the National Surgical Quality Improvement Program (NSQIP) risk calculator for serious complications (NSQIP-SC) or risk of death (NSQIP-D), and cognitive tests of executive function (Trail Making Test A and B [TMTA, TMTB]), could provide a parsimonious model to predict postoperative delirium incidence or severity.MethodsData were collected from 100 adults (≥65yo) undergoing major non-cardiac surgery. In addition to NSQIP-SC, NSQIP-D, TMTA and TMTB, we collected participant age, sex, ASA score, tobacco use, type of surgery, depression, Framingham risk score, and preoperative blood pressure. Delirium was diagnosed with the Confusion Assessment Method (CAM), and the Delirium Rating Scale-R-98 (DRS) was used to assess symptom severity. LASSO and Best Subsets logistic and linear regression were employed in line with TRIPOD guidelines.ResultsThree participants were excluded due to intraoperative deaths (2) and alcohol withdrawal (1). Ninety-seven participants with a mean age of 71.68±4.55, 55% male (31/97 CAM+, 32%) and a mean Peak DRS of 21.5±6.40 were analyzed. Of the variables included, only NSQIP-SC and TMTB were identified to be predictors of postoperative delirium incidence (p<0.001, AUROC 0.81, 95% CI: 0.72, 0.90) and severity (p<0.001, Adj. R2: 0.30).ConclusionsIn this cohort, preoperative NSQIP-SC and TMTB were identified as predictors of postoperative delirium incidence and severity. Future studies should verify whether this two-factor model could be used for accurate delirium prediction.

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