Elena Rossi1, Manuela Rosa2, Lorenzo Rossi2, Alberto Priori2, Sara Marceglia3. 1. Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, piazza Leonardo da Vinci 32, 20133 Milan, Italy. 2. Centro Clinico per la Neurostimolazione, le Neurotecnologie ed i Disordini del Movimento, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy. 3. Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, piazza Leonardo da Vinci 32, 20133 Milan, Italy. Electronic address: sara.marceglia@polimi.it.
Abstract
BACKGROUND: The web-based systems available for multi-centre clinical trials do not combine clinical data collection (Electronic Health Records, EHRs) with signal processing storage and analysis tools. However, in pathophysiological research, the correlation between clinical data and signals is crucial for uncovering the underlying neurophysiological mechanisms. A specific example is the investigation of the mechanisms of action for Deep Brain Stimulation (DBS) used for Parkinson's Disease (PD); the neurosignals recorded from the DBS target structure and clinical data must be investigated. OBJECTIVE: The aim of this study is the development and testing of a new system dedicated to a multi-centre study of Parkinson's Disease that integrates biosignal analysis tools and data collection in a shared and secure environment. METHODS: We designed a web-based platform (WebBioBank) for managing the clinical data and biosignals of PD patients treated with DBS in different clinical research centres. Homogeneous data collection was ensured in the different centres (Operative Units, OUs). The anonymity of the data was preserved using unique identifiers associated with patients (ID BAC). The patients' personal details and their equivalent ID BACs were archived inside the corresponding OU and were not uploaded on the web-based platform; data sharing occurred using the ID BACs. The system allowed researchers to upload different signal processing functions (in a .dll extension) onto the web-based platform and to combine them to define dedicated algorithms. RESULTS: Four clinical research centres used WebBioBank for 1year. The clinical data from 58 patients treated using DBS were managed, and 186 biosignals were uploaded and classified into 4 categories based on the treatment (pharmacological and/or electrical). The user's satisfaction mean score exceeded the satisfaction threshold. CONCLUSIONS: WebBioBank enabled anonymous data sharing for a clinical study conducted at multiple centres and demonstrated the capabilities of the signal processing chain configuration as well as its effectiveness and efficiency for integrating the neurophysiological results with clinical data in multi-centre studies, which will allow the future collection of homogeneous data in large cohorts of patients.
BACKGROUND: The web-based systems available for multi-centre clinical trials do not combine clinical data collection (Electronic Health Records, EHRs) with signal processing storage and analysis tools. However, in pathophysiological research, the correlation between clinical data and signals is crucial for uncovering the underlying neurophysiological mechanisms. A specific example is the investigation of the mechanisms of action for Deep Brain Stimulation (DBS) used for Parkinson's Disease (PD); the neurosignals recorded from the DBS target structure and clinical data must be investigated. OBJECTIVE: The aim of this study is the development and testing of a new system dedicated to a multi-centre study of Parkinson's Disease that integrates biosignal analysis tools and data collection in a shared and secure environment. METHODS: We designed a web-based platform (WebBioBank) for managing the clinical data and biosignals of PDpatients treated with DBS in different clinical research centres. Homogeneous data collection was ensured in the different centres (Operative Units, OUs). The anonymity of the data was preserved using unique identifiers associated with patients (ID BAC). The patients' personal details and their equivalent ID BACs were archived inside the corresponding OU and were not uploaded on the web-based platform; data sharing occurred using the ID BACs. The system allowed researchers to upload different signal processing functions (in a .dll extension) onto the web-based platform and to combine them to define dedicated algorithms. RESULTS: Four clinical research centres used WebBioBank for 1year. The clinical data from 58 patients treated using DBS were managed, and 186 biosignals were uploaded and classified into 4 categories based on the treatment (pharmacological and/or electrical). The user's satisfaction mean score exceeded the satisfaction threshold. CONCLUSIONS: WebBioBank enabled anonymous data sharing for a clinical study conducted at multiple centres and demonstrated the capabilities of the signal processing chain configuration as well as its effectiveness and efficiency for integrating the neurophysiological results with clinical data in multi-centre studies, which will allow the future collection of homogeneous data in large cohorts of patients.
Authors: Sara Marceglia; Elena Rossi; Manuela Rosa; Filippo Cogiamanian; Lorenzo Rossi; Laura Bertolasi; Alberto Vogrig; Francesco Pinciroli; Sergio Barbieri; Alberto Priori Journal: JMIR Res Protoc Date: 2015-03-06