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Record W1600274186 · doi:10.1111/jgs.13506

Outcomes of Cognitive Fluctuations in Dementia Patients

2015· letter· en· W1600274186 on OpenAlexaffabout
Gwen Li Sin, Brian J. Mainland, Jimmy Lee, Tisha J. Ornstein, Kenneth I. Shulman, Nathan Herrmann

Bibliographic record

VenueJournal of the American Geriatrics Society · 2015
Typeletter
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsToronto Metropolitan UniversitySunnybrook Health Science Centre
Fundersnot available
KeywordsDementiaMedicineDeliriumDementia with Lewy bodiesPsychiatryComorbidityGeriatric psychiatryCognitionVascular dementiaDiseaseGerontologyInternal medicine

Abstract

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To the Editor: Cognitive fluctuations (CFs) are defined as spontaneous alterations in cognition, attention, and arousal that can range from transient blackouts to a delirious state and stupor. CFs were originally associated with dementia with Lewy bodies (80–90%)1 but are also present in Alzheimer's disease,2 vascular dementia,3 and Parkinson's disease with dementia.4 Delirium and CFs in dementia share many similarities, yet it is important to differentiate between CFs in dementia and delirium because they have different effects on prognosis and treatment. In addition, delirium in dementia is commonly associated with death and institutionalization.5 This study aimed to assess the degree of CFs and the relationship with subsequent morbidity and mortality in nursing home residents with dementia. It was hypothesized that CFs would be associated with greater mortality and acute care hospitalizations. Residents with dementia at the Sunnybrook Health Sciences Centre Veteran Affairs Canada long-term care unit were recruited. Residents were excluded if they had severe visual or hearing impairment. The research ethics board of Sunnybrook Health Sciences Centre approved the study. Informed consent was obtained from a substitute decision-maker and the resident. Each subject underwent a diagnostic interview by a geriatric psychiatrist (NH) to ensure that Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, criteria for dementia were met. Comorbid diagnoses upon recruitment were used to calculate the Charlson Comorbidity Index. All participants were assessed using the Severe Impairment Battery.6 The presence of cognitive fluctuations was determined using the Dementia Cognitive Fluctuation Scale (DCFS).7 All participants were followed prospectively for 6 months. Outcomes of interest were death and hospitalizations. A geriatric psychiatrist (GLS) who was blinded to the presence of CFs obtained the information from the hospital database and chart reviews using a standardized data collection form. A Cox proportional hazards model was used to determine the relationship between CFs and subsequent hospitalization or death. Age, sex, Severe Impairment Battery Total score, and Charlson Comorbidity Index were included a priori in the model as covariates. All analyses were conducted using SPSS 20.0 (SPSS, Inc., Chicago, IL), and statistical significance was set at a two-tailed P-value < .05. The sample characteristics of the 55 participants are shown in Table 1. One participant was lost to follow-up, four required hospitalization (hip fracture (n = 2), pathological femur fracture (n = 1), community-acquired pneumonia (n = 1)), and four (7.3%) died during follow-up (metastatic lung cancer, sepsis, acute renal failure, end-stage Parkinson's disease). Because of the small numbers of participants, both outcomes were combined into a single outcome. Ratings on the DCFS were not significantly different between the groups with and without death and hospitalization (Mann–Whitney U = 134.00, z = −0.79, P = .45). There were also no significant differences in baseline demographic and clinical variables between the two groups. The a priori hypothesized model was not significant in predicting the events (−2 log likelihood = 53.833; chi-square = 1.314, P = .93). CF was not a significant predictor of events over 6 months (hazard ratio = 0.94, 95% confidence interval = 0.65–1.34, P = .71). This is the first study on the short-term prognosis of CFs in dementia. No association was found between CFs and mortality and morbidity, although the factors associated with mortality and morbidity for institutionalized individuals with end-stage dementia remains unclear. Factors such as age, sex, and type of dementia have been inconsistently identified as predictors of mortality.8 A limitation of this study was the small sample size and small number with death and hospitalization. Previous studies examining 6-month survival in nursing home residents with advanced dementia found 6-month mortality to range from 18.3%9 to 56.3%,10 but the current study's mortality over an identical time period was unexpectedly low. It is difficult to differentiate between CFs and delirium and what was rated as CFs could well have been symptoms of delirium, although the subjects were well known to the primary caregivers and were not reported to display any acute behavioral or cognitive change during recruitment. Although it is possible that CFs herald the onset of delirium and its consequences, if anything, this should have biased the study in favor of finding an association between CFs and negative outcomes. The original DCFS study involved younger community-dwelling individuals,7 and respondents for the DCFS in the current study were nursing home staff, rather than caretakers of individuals with dementia recruited through outpatient dementia referrals, as in the original study.7 These methodological differences may have limited the validity of using the DCFS to assess for CFs in the current study sample. CFs occur commonly in dementia of all types. Although similar in presentation to delirium, it is yet to be determined whether CFs are associated with the negative outcomes attributable to delirium in dementia. Conflict of Interest: The editor in chief has reviewed the conflict of interest checklist provided by the authors and has determined that the authors have no financial or any other kind of personal conflicts with this paper. J. Lee was supported by the Singapore Ministry of Health's National Medical Research Council (Grant NMRC/TA/002/2012). Author Contributions: Herrmann, Shulman, Ornstein, Mainlaind, Sin: study concept and design. Herrmann, Mainland, Sin: acquisition of subjects and data. Mainland, Herrmann, Lee, Sin: data analysis and interpretation. All authors were involved in preparation of the manuscript. Sponsor's Role: There was no sponsor.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.319
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2015
Admission routes2
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