Literature DB >> 34542603

Predictors of functional outcomes in patients with facioscapulohumeral muscular dystrophy.

Natalie K Katz1, John Hogan2, Ryan Delbango2, Colin Cernik3, Rabi Tawil4, Jeffrey M Statland5.   

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.
© The Author(s) (2021). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  artificial intelligence; facioscapulohumeral muscular dystrophy; functional outcomes; machine learning; wheelchair use

Mesh:

Year:  2021        PMID: 34542603      PMCID: PMC8677548          DOI: 10.1093/brain/awab326

Source DB:  PubMed          Journal:  Brain        ISSN: 0006-8950            Impact factor:   15.255


  33 in total

1.  Pregnancy and birth outcomes in women with facioscapulohumeral muscular dystrophy.

Authors:  E Ciafaloni; E K Pressman; A M Loi; A M Smirnow; D J Guntrum; N Dilek; R Tawil
Journal:  Neurology       Date:  2006-11-28       Impact factor: 9.910

2.  Estrogens enhance myoblast differentiation in facioscapulohumeral muscular dystrophy by antagonizing DUX4 activity.

Authors:  Emanuela Teveroni; Marsha Pellegrino; Sabrina Sacconi; Patrizia Calandra; Isabella Cascino; Stefano Farioli-Vecchioli; Angela Puma; Matteo Garibaldi; Roberta Morosetti; Giorgio Tasca; Enzo Ricci; Carlo Pietro Trevisan; Giuliana Galluzzi; Alfredo Pontecorvi; Marco Crescenzi; Giancarlo Deidda; Fabiola Moretti
Journal:  J Clin Invest       Date:  2017-03-06       Impact factor: 14.808

Review 3.  Multigenic modeling of complex disease by random forests.

Authors:  Yan V Sun
Journal:  Adv Genet       Date:  2010       Impact factor: 1.944

4.  MRI change metrics of facioscapulohumeral muscular dystrophy: Stir and T1.

Authors:  Mark R Ferguson; Sandra L Poliachik; Christopher B Budech; Nancy E Gove; Gregory T Carter; Leo H Wang; Daniel G Miller; Dennis W W Shaw; Seth D Friedman
Journal:  Muscle Nerve       Date:  2018-03-03       Impact factor: 3.217

5.  A standardized clinical evaluation of patients affected by facioscapulohumeral muscular dystrophy: The FSHD clinical score.

Authors:  Costanza Lamperti; Greta Fabbri; Liliana Vercelli; Roberto D'Amico; Roberto Frusciante; Emanuela Bonifazi; Chiara Fiorillo; Carlo Borsato; Michelangelo Cao; Maura Servida; Francesca Greco; Rita Di Leo; Leda Volpi; Claudia Manzoli; Paola Cudia; Ebe Pastorello; Leopoldo Ricciardi; Gabriele Siciliano; Giuliana Galluzzi; Carmelo Rodolico; Lucio Santoro; Giuliano Tomelleri; Corrado Angelini; Enzo Ricci; Laura Palmucci; Maurizio Moggio; Rossella Tupler
Journal:  Muscle Nerve       Date:  2010-08       Impact factor: 3.217

6.  Genetic characterization of a large, historically significant Utah kindred with facioscapulohumeral dystrophy.

Authors:  K M Flanigan; C M Coffeen; L Sexton; D Stauffer; S Brunner; M F Leppert
Journal:  Neuromuscul Disord       Date:  2001-09       Impact factor: 4.296

7.  Respiratory pattern in an adult population of dystrophic patients.

Authors:  M G D'Angelo; M Romei; A Lo Mauro; E Marchi; S Gandossini; S Bonato; G P Comi; F Magri; A C Turconi; A Pedotti; N Bresolin; A Aliverti
Journal:  J Neurol Sci       Date:  2011-05-06       Impact factor: 3.181

8.  Risk of functional impairment in Facioscapulohumeral muscular dystrophy.

Authors:  Jeffrey M Statland; Rabi Tawil
Journal:  Muscle Nerve       Date:  2014-02-10       Impact factor: 3.217

9.  Progress in the molecular diagnosis of facioscapulohumeral muscular dystrophy and correlation between the number of KpnI repeats at the 4q35 locus and clinical phenotype.

Authors:  E Ricci; G Galluzzi; G Deidda; S Cacurri; L Colantoni; B Merico; N Piazzo; S Servidei; E Vigneti; V Pasceri; G Silvestri; M Mirabella; F Mangiola; P Tonali; L Felicetti
Journal:  Ann Neurol       Date:  1999-06       Impact factor: 10.422

10.  Predicting Diabetes Mellitus With Machine Learning Techniques.

Authors:  Quan Zou; Kaiyang Qu; Yamei Luo; Dehui Yin; Ying Ju; Hua Tang
Journal:  Front Genet       Date:  2018-11-06       Impact factor: 4.599

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  3 in total

1.  Random forest: random results or meaningful insights for patients with facioscapulohumeral muscular dystrophy?

Authors:  Lindsay N Alfano; Tahseen Mozaffar
Journal:  Brain       Date:  2021-12-16       Impact factor: 15.255

Review 2.  Outcome Measures in Facioscapulohumeral Muscular Dystrophy Clinical Trials.

Authors:  Mehdi Ghasemi; Charles P Emerson; Lawrence J Hayward
Journal:  Cells       Date:  2022-02-16       Impact factor: 6.600

Review 3.  Update on the Molecular Aspects and Methods Underlying the Complex Architecture of FSHD.

Authors:  Valerio Caputo; Domenica Megalizzi; Carlo Fabrizio; Andrea Termine; Luca Colantoni; Carlo Caltagirone; Emiliano Giardina; Raffaella Cascella; Claudia Strafella
Journal:  Cells       Date:  2022-08-29       Impact factor: 7.666

  3 in total

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