Development of a Thai Parkinson's Disease Screening Tool and the Prevalence of Parkinsonism and Parkinson's Disease, Based on a Community Survey in Bangkok

2017 ◽  
Vol 49 (1-2) ◽  
pp. 74-81 ◽  
Author(s):  
Weerasak Muangpaisan ◽  
Pitiporn Siritipakorn ◽  
Prasert Assantachai
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Screening Tool ◽  
Disease Screening ◽  
Community Survey

scholarly journals Assessing Diet Quality of People with Parkinson's Disease: Dietary Screening Tool Versus HEI-2015

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 517-517
Author(s):  
Sarah Dobiszewski ◽  
Leslie Mahler ◽  
Ingrid Lofgren
Keyword(s):  
Parkinson’S Disease ◽  
At Risk ◽  
Parkinson's Disease ◽  
Diet Quality ◽  
Screening Tool ◽  
Healthy Eating Index ◽  
Assessment Tools ◽  
Nutritional Risk ◽  
Not At Risk

Abstract Objectives To assess the diet quality of people with Parkinson's Disease (PwPD) using two assessment tools: Healthy Eating Index (HEI)-2015 and the Dietary Screening Tool (DST). HEI-2015 and DST scores were compared for alignment to explore associations between HEI-2015 and DST total scores and sub-scores. Methods This secondary data analysis utilized a total of 28 participants from two studies examining PwPD and their informal caregivers. Dietary data was collected with two 24-hour recalls using the multiple pass method and entered into the Nutrition Data System for Research (NDSR). SAS Version 9.4 was used to compute HEI-2015 scores to assess diet quality. The HEI-2015 score is a total of 100 points and is based on the Dietary Guidelines for Americans for 2015–2020. Total HEI-2015 scores and 13 component scores were interpreted using the graded approach (grades A-F). Radar graphs provide a visual representation. Participants also completed the DST. The DST is a 25-item questionnaire used to identify dietary patterns and assess nutritional risk. Total scores were categorized into at risk <60, possibly risk 60–75, and not at risk >75. Sub-scores were calculated with point classifications for specific food categories. These sub-scores were divided into tertials of recommended intakes less than 50%, 50–80% and 80–100%, and compared for alignment between DST sub-scores and HEI-2015 sub-scores by assessing total number of pairs per category. Results The mean total score was 59.4 ± 28.6 for DST and 58.7 ± 22.2 for HEI-2015. HEI-2015 scores fell into grades of A (n = 0) B (n = 3) C (n = 3) D (n = 7) F (n = 14). PwPD who were categorized at risk from the DST scores (n = 18) received an HEI grade of D/F. PwPD who were at possible risk (n = 7) received a grade of B/C/D. PwPD who were not at risk (n = 3) received a grade of B/C/D for sub-scores, vegetable intake had a 50% alignment in scores, fat/sugar 43% alignment, whole grains and dairy 46% alignment and fruit 39% alignment. Conclusions There appeared to be partial alignment between the total HEI-2015 scores and DST nutritional risk scores. Sub-scores differ 50% of the time. Due to differences between the assessment tools, more research is needed to validate the DST in PwPD as this can be a less taxing assessment compared with 24-hour recalls. Funding Sources There was no external funding for this study.

Using Polar Expression Features and Nonlinear Machine Learning Classifier for Automated Parkinson’s Disease Screening

2020 ◽  
Vol 20 (1) ◽  
pp. 501-514 ◽  
Author(s):  
Tsung-Lung Yang ◽  
Ping-Ju Kan ◽  
Chia-Hung Lin ◽  
Hsin-Yu Lin ◽  
Wei-Ling Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Screening ◽  
Learning Classifier

scholarly journals Is the MDS-UPDRS a Good Screening Tool for Detecting Sleep Problems and Daytime Sleepiness in Parkinson’s Disease?

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Krisztina Horváth ◽  
Zsuzsanna Aschermann ◽  
Péter Ács ◽  
Edit Bosnyák ◽  
Gabriella Deli ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Daytime Sleepiness ◽  
Screening Tool ◽  
Rating Scale ◽  
Sleep Problems ◽  
Rank Correlation ◽  
Correlation Coefficients ◽  
Moderate Correlation ◽  
Cross Sectional

Movement Disorder Society-sponsored Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) has separate items for measuring sleep problems (item 1.7) and daytime sleepiness (1.8). The aim of our study was to evaluate the screening sensitivity and specificity of these items to the PD Sleep Scale 2nd version (PDSS-2) and Epworth Sleepiness Scale (ESS). In this nationwide, cross-sectional study 460 PD patients were enrolled. Spearman’s rank correlation coefficients were calculated between the individual items, domains, and the total score of PDSS-2 and item 1.7 of MDS-UPDRS. Similarly, the items and the total score of ESS were contrasted to item 1.8 of MDS-UPDRS. After developing generalized ordinal logistic regression models, the transformed and observed scores were compared by Lin’s Concordance Correlation Coefficient. Only item 3 difficulties staying asleep and the “disturbed sleep” domain of PDSS-2 showed high correlation with “sleep problems” item 1.7 of the MDS-UPDRS. Total score of PDSS-2 had moderate correlation with this MDS-UPRDS item. The total score of ESS showed the strongest, but still moderate, correlation with “daytime sleepiness” item 1.8 of MDS-UPDRS. As intended, the MDS-UPDRS serves as an effective screening tool for both sleep problems and daytime sleepiness and identifies subjects whose disabilities need further investigation.

scholarly journals Timely referral for advanced therapy in Parkinson’s disease. Development of a screening tool

2021 ◽  
Author(s):  
Harmen R. Moes ◽  
Jolien ten Kate ◽  
Axel T. Portman ◽  
Barbera van Harten ◽  
Mirjam E. van Kesteren ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Screening Tool ◽  
External Validation ◽  
Daily Practice ◽  
Subsequent Treatment ◽  
Multivariable Logistic Regression Analysis ◽  
Cross Sectional ◽  
Advanced Therapy ◽  
Timely Referral

AbstractObjectivesTo develop a screening tool for timely referral for advanced therapy (AT) in patients with Parkinson’s disease (PD), and to compare the newly-developed tool with the published 5-2-1 criteria.DesignCross-sectional, diagnostic, observational study and multivariable logistic regression analysis for item selection.Setting8 hospitals in the Northern part of the Netherlands situated in the catchment area of a specialized movement disorder centre.Participants259 consecutive PD patients not yet on AT visiting the outpatient clinic of participating hospitals from February 2017 to July 2018.Predictors24 patient and disease characteristics as assessed by the treating neurologists, and scores on the NMS questionnaire.OutcomeApparent eligibility for referral for AT based on consensus by a panel of 5 experts in the field of AT.Results17 patients were deemed eligible for referral for AT (point prevalence: 6.6%). Presence of response fluctuations and troublesome dyskinesias were the strongest independent predictors of being eligible for referral. Both variables were included in the final model, as well as levodopa equivalent daily dose. Decision curve analysis showed that the new model outperformed the 5-2-1 criteria. A simple chart was constructed to provide guidance for referral. Discrimination of this simplified scoring system was good (AUC after bootstrapping: 0.97).ConclusionThe screening tool may improve efficiency of referral and subsequent treatment with AT in patients with PD. External validation is required prior to application in daily practice.Anyone who wishes to share, reuse, remix, or adapt this material must obtain permission from the corresponding author.

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