Dataset Information

Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge.

ABSTRACT: Consumer wearables and sensors are a rich source of data about patients' daily disease and symptom burden, particularly in the case of movement disorders like Parkinson's disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95).

SUBMITTER: Sieberts SK

PROVIDER: S-EPMC7979931 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge.

Sieberts Solveig K SK Schaff Jennifer J Duda Marlena M Pataki Bálint Ármin BÁ Sun Ming M Snyder Phil P Daneault Jean-Francois JF Parisi Federico F Costante Gianluca G Rubin Udi U Banda Peter P Chae Yooree Y Chaibub Neto Elias E Dorsey E Ray ER Aydın Zafer Z Chen Aipeng A Elo Laura L LL Espino Carlos C Glaab Enrico E Goan Ethan E Golabchi Fatemeh Noushin FN Görmez Yasin Y Jaakkola Maria K MK Jonnagaddala Jitendra J Klén Riku R Li Dongmei D McDaniel Christian C Perrin Dimitri D Perumal Thanneer M TM Rad Nastaran Mohammadian NM Rainaldi Erin E Sapienza Stefano S Schwab Patrick P Shokhirev Nikolai N Venäläinen Mikko S MS Vergara-Diaz Gloria G Zhang Yuqian Y Wang Yuanjia Y Guan Yuanfang Y Brunner Daniela D Bonato Paolo P Mangravite Lara M LM Omberg Larsson L

NPJ digital medicine 20210319 1

Consumer wearables and sensors are a rich source of data about patients' daily disease and symptom burden, particularly in the case of movement disorders like Parkinson's disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from acc ...[more]

PMID: 33742069

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Project description:Therapeutic trials of disease-modifying agents in neurodegenerative disease typically require several hundred participants and long durations for clinical endpoints. Trials of this size are not feasible for prion diseases, rare dementia disorders associated with misfolding of prion protein. In this situation, biomarkers are particularly helpful. On diagnostic imaging, prion diseases demonstrate characteristic brain signal abnormalities on diffusion-weighted MRI. The aim of this study was to determine whether cerebral water diffusivity could be a quantitative imaging biomarker of disease severity. We hypothesized that the basal ganglia were most likely to demonstrate functionally relevant changes in diffusivity. Seventy-one subjects (37 patients and 34 controls) of whom 47 underwent serial scanning (23 patients and 24 controls) were recruited as part of the UK National Prion Monitoring Cohort. All patients underwent neurological assessment with the Medical Research Council Scale, a functionally orientated measure of prion disease severity, and diffusion tensor imaging. Voxel-based morphometry, voxel-based analysis of diffusion tensor imaging and regions of interest analyses were performed. A significant voxel-wise correlation of decreased Medical Research Council Scale score and decreased mean, radial and axial diffusivities in the putamen bilaterally was observed (P < 0.01). Significant decrease in putamen mean, radial and axial diffusivities over time was observed for patients compared with controls (P = 0.01), and there was a significant correlation between monthly decrease in putamen mean, radial and axial diffusivities and monthly decrease in Medical Research Council Scale (P < 0.001). Step-wise linear regression analysis, with dependent variable decline in Medical Research Council Scale, and covariates age and disease duration, showed the rate of decrease in putamen radial diffusivity to be the strongest predictor of rate of decrease in Medical Research Council Scale (P < 0.001). Sample size calculations estimated that, for an intervention study, 83 randomized patients would be required to provide 80% power to detect a 75% amelioration of decline in putamen radial diffusivity. Putamen radial diffusivity has potential as a secondary outcome measure biomarker in future therapeutic trials in human prion diseases.

Dataset Information

Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge.

Publications

Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge.

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