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Open Access Green as soon as Postprint is submitted to ZB.
Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression.
Nat. Biotechnol. 33, 51-57 (2015)
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with substantial heterogeneity in its clinical presentation. This makes diagnosis and effective treatment difficult, so better tools for estimating disease progression are needed. Here, we report results from the DREAM-Phil Bowen ALS Prediction Prize4Life challenge. In this crowdsourcing competition, competitors developed algorithms for the prediction of disease progression of 1,822 ALS patients from standardized, anonymized phase 2/3 clinical trials. The two best algorithms outperformed a method designed by the challenge organizers as well as predictions by ALS clinicians. We estimate that using both winning algorithms in future trial designs could reduce the required number of patients by at least 20%. The DREAM-Phil Bowen ALS Prediction Prize4Life challenge also identified several potential nonstandard predictors of disease progression including uric acid, creatinine and surprisingly, blood pressure, shedding light on ALS pathobiology. This analysis reveals the potential of a crowdsourcing competition that uses clinical trial data for accelerating ALS research and development.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Uric-acid Levels; Body-mass Index; Functional Rating-scale; Alsfrs-r Score; Disease Progression; Prognostic-factors; Parkinson-disease; Blood-pressure; Survival; Population
ISSN (print) / ISBN
1087-0156
e-ISSN
1546-1696
Journal
Nature Biotechnology
Quellenangaben
Volume: 33,
Issue: 1,
Pages: 51-57
Publisher
Nature Publishing Group
Publishing Place
New York, NY
Non-patent literature
Publications
Reviewing status
Peer reviewed