Evidence map›Paper›PMID 33645912›Full record

ArticleBrain and behavior2021

Nonmotor symptom burden grading as predictor of cognitive impairment in Parkinson's disease.

Panteleimon Oikonomou, Daniel J van Wamelen, Daniel Weintraub, Dag Aarsland, Dominic Ffytche, Pablo Martinez-Martin, Carmen Rodriguez-Blazquez, Valentina Leta, Corinne Borley, Carolina Sportelli and 6 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Brain and behavior, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
0.7field-weighted citation impact, top 29% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
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  8. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors at 6 institutions in 6 countries.

Panteleimon OikonomouDepartment of Neurosciences, King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK.ORCID 0000-0002-3973-2861
Daniel J van WamelenDepartment of Neurosciences, King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK.
Daniel WeintraubPerelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Dag AarslandDepartment of Old Age Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
Dominic FfytcheDepartment of Old Age Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
Pablo Martinez-MartinCenter for Networked Biomedical Research in Neurodegenerative Diseases (CIBERNED), Carlos III Institute of Health, Madrid, Spain.
Carmen Rodriguez-BlazquezCenter for Networked Biomedical Research in Neurodegenerative Diseases (CIBERNED), Carlos III Institute of Health, Madrid, Spain.
Valentina LetaDepartment of Neurosciences, King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK.
Corinne BorleyDepartment of Neurosciences, King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK.
Carolina SportelliDepartment of Neurosciences, King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK.
Dhaval TrivediDepartment of Neurosciences, King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK.
Aleksandra M PodlewskaDepartment of Neurosciences, King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK.
Katarina RukavinaDepartment of Neurosciences, King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK.
Alexandra RizosDepartment of Neurosciences, King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK.
Claudia Lazcano-OcampoDepartment of Neurosciences, King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK.
Kallol Ray ChaudhuriDepartment of Neurosciences, King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK.
King's College London · GBBiomedical Research Networking Center on Neurodegenerative Diseases · ESInstituto de Salud Carlos III · ESRadboud University Nijmegen · NLUniversity of Freiburg · DEVeterans Health Administration · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIdentifying predictors of incident cognitive impairment (CI), one of the most problematic long-term outcomes, in Parkinson's disease (PD) is highly relevant for personalized medicine and prognostic counseling. The Nonmotor Symptoms Scale (NMSS) provides a global clinical assessment of a range of NMS, reflecting NMS burden (NMSB), and thus may assist in the identification of an "at-risk" CI group based on overall NMSB cutoff scores.

methodsTo investigate whether specific patterns of PD NMS profiles predict incident CI, we performed a retrospective longitudinal study on a convenience sample of 541 nondemented PD patients taking part in the Nonmotor Longitudinal International Study (NILS) cohort, with Mini-Mental State Examination (MMSE), NMSS, and Scales for Outcomes in PD Motor Scale (SCOPA Motor) scores at baseline and last follow-up (mean 3.2 years) being available.

resultsPD patients with incident CI (i.e., MMSE score ≤ 25) at last follow-up (n = 107) had severe overall NMSB level, significantly worse NMSS hallucinations/perceptual problems and higher NMSS attention/memory scores at baseline. Patients with CI also were older and with more advanced disease, but with no differences in disease duration, dopamine replacement therapy, sex, and comorbid depression, anxiety, and sleep disorders.

conclusionsOur findings suggest that a comprehensive baseline measure of NMS and in particular hallucinations and perceptual problems assessed with a validated single instrument can be used to predict incident CI in PD. This approach provides a simple, holistic strategy to predict future CI in this population.

Indexed as

Cognitive DysfunctionParkinson DiseaseHumansLongitudinal StudiesRetrospective StudiesSeverity of Illness Indexcognitive impairmentMMSENonmotor symptom burden gradingNonmotor symptomsParkinson's disease

Identifiers

PMID33645912
PMCPMC8119808
OpenAlexW3134868065

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.