Evidence map›Paper›PMID 37363815›Full record

Observational studyMovement disorders : official journal of the Movement Disorder Society2023

Predicting Ambulatory Capacity in Parkinson's Disease to Analyze Progression, Biomarkers, and Trial Design.

Charles S Venuto, Greta Smith, Konnor Herbst, Robert Zielinski, Norman C W Yung, Donald G Grosset, E Ray Dorsey, Karl Kieburtz

Abstract readMulticenter StudyObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Movement disorders : official journal of the Movement Disorder Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
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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

8 authors.

Charles S VenutoCenter for Health + Technology, University of Rochester, Rochester, New York, USA.ORCID 0000-0002-2394-9513
Greta SmithCenter for Health + Technology, University of Rochester, Rochester, New York, USA.
Konnor HerbstCenter for Health + Technology, University of Rochester, Rochester, New York, USA.
Robert ZielinskiCenter for Health + Technology, University of Rochester, Rochester, New York, USA.
Norman C W YungCenter for Health + Technology, University of Rochester, Rochester, New York, USA.
Donald G GrossetSchool of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom.
E Ray DorseyCenter for Health + Technology, University of Rochester, Rochester, New York, USA.
Karl KieburtzCenter for Health + Technology, University of Rochester, Rochester, New York, USA.

Funding

The Clinical Core will support in-person and virtual research visits for three of the four Research Projects at the University of Rochester Udall CenterP50NS108676 · NINDS · UNIVERSITY OF ROCHESTER · PI DORSEY, EARL RAY · 2018 to 2022
$9.2M
NINDS NIH HHS P50 NS108676NINDS NIH HHS P50NS108676
6 · The paper itself

Abstract

backgroundIn Parkinson's disease (PD), gait and balance is impaired, relatively resistant to available treatment and associated with falls and disability. Predictive models of ambulatory progression could enhance understanding of gait/balance disturbances and aid in trial design.

objectivesTo predict trajectories of ambulatory abilities from baseline clinical data in early PD, relate trajectories to clinical milestones, compare biomarkers, and evaluate trajectories for enrichment of clinical trials.

methodsData from two multicenter, longitudinal, observational studies were used for model training (Tracking Parkinson's, n = 1598) and external testing (Parkinson's Progression Markers Initiative, n = 407). Models were trained and validated to predict individuals as having a "Progressive" or "Stable" trajectory based on changes of ambulatory capacity scores from the Movement Disorders Society Unified Parkinson's Disease Rating Scale parts II and III. Survival analyses compared time-to-clinical milestones and trial outcomes between predicted trajectories.

resultsOn external evaluation, a support vector machine model predicted Progressive trajectories using baseline clinical data with an accuracy, weighted-F1 (proportionally weighted harmonic mean of precision and sensitivity), and sensitivity/specificity of 0.735, 0.799, and 0.688/0.739, respectively. Over 4 years, the predicted Progressive trajectory was more likely to experience impaired balance, loss of independence, impaired function and cognition. Baseline dopamine transporter imaging and select biomarkers of neurodegeneration were significantly different between predicted trajectory groups. For an 18-month, randomized (1:1) clinical trial, sample size savings up to 30% were possible when enrollment was enriched for the Progressive trajectory versus no enrichment.

conclusionsIt is possible to predict ambulatory abilities from clinical data that are associated with meaningful outcomes in people with early PD. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Indexed as

Parkinson DiseaseBiomarkersDisease ProgressionHumansMental Status and Dementia TestsPhysical Therapy ModalitiesBiomarkersambulatory capacityclinical trial enrichmentParkinson's disease progressionpredictionprogression model

Identifiers

PMID37363815
PMCPMC10615710

What OpenQuestion holds

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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.