Harmonization of delirium severity instruments: a comparison of the DRS-R-98, MDAS, and CAM-S using item response theory

Abstract

BackgroundThis study aimed to describe the level of agreement of three commonly used delirium instruments: the Delirium Rating Scale-Revised-98 (DRS-R-98), Memorial Delirium Assessment Scale (MDAS), and Confusion Assessment Method-Severity (CAM-S).MethodsWe used data from a prospective clinical research study, in which a team of trained lay interviewers administered each instrument along with supporting interview and cognitive assessments in the same group of patients daily while in the hospital (N = 352). We used item response theory methods to co-calibrate the instruments.ResultsThe latent traits underlying the three measures, capturing the severity of a delirium assessment, had a high degree of correlation (r’s > .82). Unidimensional factor models fit well, facilitating co-calibration of the instruments. Across instruments, the less intense symptoms were generally items reflecting cognitive impairment. Although the intensity of delirium severity for most in the sample was relatively low, many of the item thresholds for the delirium severity scales are high (i.e., in the more severe range of the latent ability distribution). This indicates that even people with severe delirium may have a low probability of endorsing the highest severity categories for many items. Co-calibration enabled us to derive crosswalks to map delirium severity scores among the delirium instruments.ConclusionThese delirium instruments measure the same underlying construct of delirium severity. Relative locations of items may inform design of refined measurement instruments. Mapping of overall delirium severity scores across the delirium severity instruments enabled us to derive crosswalks, which allow scores to be translated across instruments, facilitating comparison and combination of delirium studies for integrative analysis.Electronic supplementary materialThe online version of this article (10.1186/s12874-018-0552-4) contains supplementary material, which is available to authorized users.

Item and domain network structures of the Resilience Scale for Adults in 675 university students

“…Network analysis has also been applied to several psychological constructs, such as personality (Costantini et al, 2015), empathy (Briganti et al, 2018), attitudes (Dalege et al, 2017, intelligence (Van Der Maas et al, 2006) and self-worth (Briganti et al, 2019). Other studies used innovative methods, including networks to harmonize rating scales (Gross et al, 2018;Purgato et al, 2018;Haroz et al, 2016) Learning the network structure of a given construct (such as resilience) or mental disorder (such as PTSD) is particularly relevant in clinical practice since it highlights potential clinical target that may affect multiple symptoms or elements composing the network (Fried et al, 2018); for instance, intervening on the connection between two components of the network is likely to modify the clinical presentations of said components (such as symptoms). In the Running head: NETWORK ANALYSIS OF RESILIENCE specific case of resilience, which is considered a protection against mental disorders, learning the network structure of resilience components may highlight potential targets to strengthen the overall mental health of a given individual.…”

New Delirium Severity Indicators: Generation and Internal Validation in the Better Assessment of Illness (BASIL) Study

“…We also show information functions for two comparator instruments: CAM-S and DRS-R-98. These information functions for comparator instruments have been described in Gross et al [32]. We also illustrate the distribution of estimated latent trait scores in our sample with a rug plot (shown along the x axis).…”

“…Our approach involved multiple iterative steps using confirmatory factor analysis methods for ordinal dependent variables [30], including bi-factor measurement models [31]. Details on how we parameterized our models for delirium severity indicators, addressed item level skew, and derived summary model fit and robust parameter estimates have been previously described in Gross et al [32]. Our analysis was supported by the Mplus software (version 8.2; Muthén & Muthén, Los Angeles, CA, USA) executed within the R environment (version 3.5; R Core Team, Vienna, Austria) and the Mplus Automation package [33].…”

State of the clinical science of perioperative brain health: report from the American Society of Anesthesiologists Brain Health Initiative Summit 2018

“…With the development of an online application to convert delirium severity scores by commonly used instruments, there now exists a possibility of forming meta-analyses on studies that have delirium severity and duration as secondary outcomes. 176 The most commonly used pharmacological treatments for delirium are typical and atypical antipsychotics. A recent landmark, randomised, double-blind, placebo-controlled trial compared the use of haloperidol and ziprasidone against placebo for the treatment of established delirium.…”