Evidence map›Paper›PMID 34542603›Full record

ArticleBrain : a journal of neurology2021

Predictors of functional outcomes in patients with facioscapulohumeral muscular dystrophy.

Natalie K Katz, John Hogan, Ryan Delbango, Colin Cernik, Rabi Tawil, Jeffrey M Statland

Open access · bronzeAbstract read
In one paragraph

Article in Brain : a journal of neurology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.4field-weighted citation impact, top 19% 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

14 citing papers in PubMed, 29 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. Observational
  5. Review
  6. Article
  7. Review
  8. Article
  9. Review
  10. Observational
  11. Review
  12. Review
  13. Review
  14. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 1 country.

Natalie K KatzDepartment of Neurology, Children's Mercy Hospital, Kansas City, MO 64108, USA.ORCID 0000-0002-9998-8092
John HoganDepartment of Artificial Intelligence, AIbytes, LLC, Hurley, NY 12443, USA.
Ryan DelbangoDepartment of Artificial Intelligence, AIbytes, LLC, Hurley, NY 12443, USA.
Colin CernikDepartment of Biostatistics, University of Kansas Medical Center, Kansas City, KS 66160, USA.
Rabi TawilDepartment of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.
Jeffrey M StatlandDepartment of Neurology, University of Kansas Medical Center, Kansas City, KS 66160, USA.
University of Kansas Medical Center · USChildren's Mercy Hospital · USUniversity of Rochester Medical Center · US

Funding

Training and Educational CoreU54NS048843 · NINDS · UNIVERSITY OF ROCHESTER · PI THORNTON, CHARLES A · 2003 to 2017
$21.9M
Senator Paul D. Wellstone Muscular Dystrophy Specialized Research CenterP50NS048843 · NINDS · UNIVERSITY OF ROCHESTER · PI THORNTON, CHARLES A · 2018 to 2022
$7.1M
MINORITY PREDOCTORAL FELLOWSHIP PROGRAMF31AR052274 · NIAMS · MOUNT SINAI SCHOOL OF MEDICINE OF NYU · PI DELISSER, KEMESHA L · 2005 to 2005
$16k
NIAMS NIH HHS #N01-AR-5-2274NINDS NIH HHS P50 NS048843NINDS NIH HHS U54 NS048843
6 · The paper itself

Abstract

Facioscapulohumeral muscular dystrophy (FSHD) is one of the most prevalent muscular dystrophies characterized by considerable variability in severity, rates of progression and functional outcomes. Few studies follow FSHD cohorts long enough to understand predictors of disease progression and functional outcomes, creating gaps in our understanding, which impacts clinical care and the design of clinical trials. Efforts to identify molecularly targeted therapies create a need to better understand disease characteristics with predictive value to help refine clinical trial strategies and understand trial outcomes. Here we analysed a prospective cohort from a large, longitudinally followed registry of patients with FSHD in the USA to determine predictors of outcomes such as need for wheelchair use. This study analysed de-identified data from 578 individuals with confirmed FSHD type 1 enrolled in the United States National Registry for FSHD Patients and Family members. Data were collected from January 2002 to September 2019 and included an average of 9 years (range 0-18) of follow-up surveys. Data were analysed using descriptive epidemiological techniques, and risk of wheelchair use was determined using Cox proportional hazards models. Supervised machine learning analysis was completed using Random Forest modelling and included all 189 unique features collected from registry questionnaires. A separate medications-only model was created that included 359 unique medications reported by participants. Here we show that smaller allele sizes were predictive of earlier age at onset, diagnosis and likelihood of wheelchair use. Additionally, we show that females were more likely overall to progress to wheelchair use and at a faster rate as compared to males, independent of genetics. Use of machine learning models that included all reported clinical features showed that the effect of allele size on progression to wheelchair use is small compared to disease duration, which may be important to consider in trial design. Medical comorbidities and medication use add to the risk for need for wheelchair dependence, raising the possibility for better medical management impacting outcomes in FSHD. The findings in this study will require further validation in additional, larger datasets but could have implications for clinical care, and inclusion criteria for future clinical trials in FSHD.

Indexed as

Disease ProgressionMuscular Dystrophy, FacioscapulohumeralAdolescentAdultAgedAged, 80 and overChildCohort StudiesFemaleHumansLongitudinal StudiesMaleMiddle AgedPrognosisRisk FactorsYoung Adultartificial intelligencefacioscapulohumeral muscular dystrophyfunctional outcomesmachine learningwheelchair use

Identifiers

PMID34542603
PMCPMC8677548
OpenAlexW3199324431

What OpenQuestion holds

Textmetadata
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.