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DEPRESSIVE SYMPTOMS AND COGNITIVE STATUS AFFECT HEALTH‐RELATED QUALITY OF LIFE IN OLDER PATIENTS WITH PARKINSON'S DISEASE

2007· letter· en· W1569943631 on OpenAlexaffabout
Tracy Greene, Richard Camicioli

Bibliographic record

VenueJournal of the American Geriatrics Society · 2007
Typeletter
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineGeriatric Depression ScaleMoodQuality of life (healthcare)Affect (linguistics)DementiaRating scaleDepression (economics)Parkinson's diseaseClinical Dementia RatingMontreal Cognitive AssessmentDiseaseCognitionGerontologyStroke (engine)Physical therapyPsychiatryInternal medicineDepressive symptomsPsychology

Abstract

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To the Editor: Parkinson's disease (PD) is a multidimensional disorder affecting motor function, mood, and cognitive function, all of which might affect health-related quality of life (HR-QoL). The EuroQoL is an instrument that has been validated in Parkinson's disease and has been shown to accurately reflect patients' perceptions of their health.1,2 Previous studies have shown that patients with PD with on/off fluctuations, falls, insomnia, depression, and cognitive impairment have lower HR-QoL than their counterparts without these problems.3 No study we know of has focused solely on older North American patients. We set out to examine the correlates of HR-QoL in patients with PD aged 65 and older. Participants aged 65 and older were recruited from the Movement Disorders Clinic at the University of Alberta or from the Parkinson's Society of Alberta from advertisement for a longitudinal study. Patients were from Edmonton, Alberta, or its surrounding rural areas. Patients with a history of unstable heart disease, ischemic changes (e.g., stroke or transient ischemic attack), active cancer, and dementia were excluded. Control participants matched for age, sex, and education were recruited by advertisement in local senior centers, and by word of mouth. The study design was cross-sectional. As described,4 subjects and their informants were interviewed, and standardized assessments were used. A neurologist (RC) administered the Unified Parkinson's Disease Rating Scale (UPDRS) and the Cumulative Illness Rating Scale (CIRS); a trained research assistant (TG) administered the Mini-Mental State Examination (MMSE), Geriatric Depression Scale (GDS), and EuroQoL 5-item questionnaire (EQ-5D), which measures participant problems in mobility, self-care, social activities, pain, anxiety, and depression. The participants were also asked to complete the EuroQoL visual analog rating, which asks participants to rate their health on a scale of 0 to 100. EQ-5D responses were categorized as no problems versus some or extreme problems for chi-square analysis. Multivariate linear regression was used to determine which factors most strongly contributed to HR-QoL on the EuroQoL visual analog. Fifty-one patients with PD and 50 age- and sex-matched controls participated in the study. Patients with PD had significantly higher (worse) GDS scores than matched controls and were more likely to take antidepressant medication. Controlling for age, sex, education, and CIRS score, GDS score was the strongest contributor to health rating based on the EuroQoL visual analog scale (beta=−0.425, P<.005). MMSE also significantly predicted HR-QoL (beta=0.296, P<.05) on the EuroQoL visual analog. Neither UPDRS III (beta=−0.075, P=.610) nor CIRS score (beta=−0.159, P=.219) were significant predictors of HR-QoL in patients with PD. Patients were more likely than control volunteers to report problems associated with mobility, self-care, social activities, anxiety, and depression, but not pain, on the EQ-5D. Depressive symptoms were the strongest predictor of HR-QoL in older people with PD, despite the fact that many people were treated for depression. The findings are similar to European studies,3,5–8 which have shown that patients with PD with depressive symptoms report lower HR-QoL but which did not focus on older patients. This suggests that HR-QoL of patients with PD might potentially be improved with proper management of depression. Depression may be underreported and often untreated in patients with PD. Effort should be made to detect, diagnose, and properly treat depression in patients with PD, although appropriate decision-making regarding treatment is difficult, given the paucity of clinical trials of depression in PD.9 The data suggest that minor deficits in cognitive functioning appear to affect HR-QoL. All participants were considered cognitively healthy, although three presented with mild cognitive impairment. Despite the overall high scores on the MMSE, it still significantly predicted HR-QoL visual analog ratings. The fact that the current study was not population-based limited it. Because of selection criteria, younger patients with PD are not represented in the results, although the sample may be representative of patients seeking subspecialty care. The results of our study are consistent with population-based studies and studies examining a broader age range of patients.3,10 The study was cross-sectional and therefore examined prevalence, not incidence of depressive symptoms and HR-QoL. Future studies should be longitudinal to address the question of whether depression is a precursor to or consequence of poor HR-QoL. The results reinforce the need for proper management of depressive symptoms in patients with PD. Healthcare providers for older people should be aware of the high prevalence and underreporting of depression in patients with PD. Understanding the link between depressive symptoms and HR-QoL might lead to improved quality of life for patients. We thank the staff at the Movement Disorders Clinic for help with recruitment of participants and Thomas Bouchard for help with data collection and follow-up of participants in the study. Financial Disclosure: Funded by the Canadian Institute of Health Research. The Editor has reviewed the submitted financial and personal conflicts list and determined that there are no conflicts with either of the authors in this letter. Author Contributions: Dr. Camicioli devised the study concept, designed the study, and obtained funding for its execution. Subjects were recruited from routine clinic visits with help from staff at the Movement Disorders Clinic. Dr. Camicioli neurologically assessed all participants, and Tracy Greene administered standardized cognitive tests. Tracy Greene performed data analysis and manuscript presentation with guidance from Dr. Camicioli. Thomas Bouchard has given written consent to be included in the acknowledgment section. Sponsor's Role: The sponsor had no role beyond funding the study.

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)0.002
metaresearch head score (Gemma)0.011
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: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.283
Teacher spread0.271 · 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
GenreCommentary

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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Citations20
Published2007
Admission routes2
Has abstractyes

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