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Int Psychogeriatr ; 26(4): 693-702, 2014 Apr.
Article in English | MEDLINE | ID: mdl-24429062

ABSTRACT

BACKGROUND: Delirium is a common neuropsychiatric syndrome with considerable heterogeneity in clinical profile. Identification of clinical subtypes can allow for more targeted clinical and research efforts. We sought to develop a brief method for clinical subtyping in clinical and research settings. METHODS: A multi-site database, including motor symptom assessments conducted in 487 patients from palliative care, adult and old age consultation-liaison psychiatry services was used to document motor activity disturbances as per the Delirium Motor Checklist (DMC). Latent class analysis (LCA) was used to identify the class structure underpinning DMC data and also items for a brief subtyping scale. The concordance of the abbreviated scale was then compared with the original Delirium Motor Subtype Scale (DMSS) in 375 patients having delirium as per the American Psychiatric Association's Diagnostic and Statistical Manual (4th edition) criteria. RESULTS: Latent class analysis identified four classes that corresponded closely with the four recognized motor subtypes of delirium. Further, LCA of items (n = 15) that loaded >60% to the model identified four features that reliably identified the classes/subtypes, and these were combined as a brief motor subtyping scale (DMSS-4). There was good concordance for subtype attribution between the original DMSS and the DMSS-4 (κ = 0.63). CONCLUSIONS: The DMSS-4 allows for rapid assessment of clinical subtypes in delirium and has high concordance with the longer and well-validated DMSS. More consistent clinical subtyping in delirium can facilitate better delirium management and more focused research effort.


Subject(s)
Delirium/classification , Motor Activity , Psychomotor Disorders/diagnosis , Aged , Delirium/diagnosis , Diagnostic and Statistical Manual of Mental Disorders , Female , Humans , Male , Middle Aged , Psychiatric Status Rating Scales , Psychomotor Disorders/psychology , ROC Curve , Reproducibility of Results , Severity of Illness Index
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