Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions.

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research.

Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research.

Digital health technologies (smartphones, smartwatches, and other body-worn sensors) can act as novel tools to aid in the diagnosis and remote objective monitoring of an individual's disease symptoms, both in clinical care and in research. Nonetheless, such digital health technologies have yet to widely demonstrate value in clinical research due to insufficient data interpretability and lack of regulatory acceptance. Metadata, i.e., data that accompany and describe the primary data, can be utilized to better understand the context of the sensor data and can assist in data management, data sharing, and subsequent data analysis. The need for data and metadata standards for digital health technologies has been raised in academic and industry research communities and has also been noted by regulatory authorities. Therefore, to address this unmet need, we here propose a metadata set that reflects regulatory guidelines and that can serve as a conceptual map to (1) inform researchers on the metadata they should collect in digital health studies, aiming to increase the interpretability and exchangeability of their data, and (2) direct standard development organizations on how to extend their existing standards to incorporate digital health technologies. The proposed metadata set is informed by existing standards pertaining to clinical trials and medical devices, in addition to existing schemas that have supported digital health technology studies. We illustrate this specifically in the context of Parkinson's disease, as a model for a wide range of other chronic conditions for which remote monitoring would be useful in both care and science. We invite the scientific and clinical research communities to apply the proposed metadata set to ongoing and planned research. Where the proposed metadata fall short, we ask users to contribute to its ongoing revision so that an adequate degree of consensus can be maintained in a rapidly evolving technology landscape.

The MSOAC approach to developing performance outcomes to measure and monitor multiple sclerosis disability.

BACKGROUND: The Multiple Sclerosis Outcome Assessments Consortium (MSOAC) was formed by the National MS Society to develop improved measures of multiple sclerosis (MS)-related disability. OBJECTIVES: (1) To assess the current literature and available data on functional performance outcome measures (PerfOs) and (2) to determine suitability of using PerfOs to quantify MS disability in MS clinical trials. METHODS: (1) Identify disability dimensions common in MS; (2) conduct a comprehensive literature review of measures for those dimensions; (3) develop an MS Clinical Data Interchange Standards Consortium (CDISC) data standard; (4) create a database of standardized, pooled clinical trial data; (5) analyze the pooled data to assess psychometric properties of candidate measures; and (6) work with regulatory agencies to use the measures as primary or secondary outcomes in MS clinical trials. CONCLUSION: Considerable data exist supporting measures of the functional domains ambulation, manual dexterity, vision, and cognition. A CDISC standard for MS ( http://www.cdisc.org/therapeutic#MS ) was published, allowing pooling of clinical trial data. MSOAC member organizations contributed clinical data from 16 trials, including 14,370 subjects. Data from placebo-arm subjects are available to qualified researchers. This integrated, standardized dataset is being analyzed to support qualification of disability endpoints by regulatory agencies.

Accelerating drug development for Alzheimer's disease through the use of data standards.

INTRODUCTION: The exceedingly high rate of failed trials in Alzheimer's disease (AD) calls for immediate attention to improve efficiencies and learning from past, ongoing, and future trials. Accurate, highly rigorous standardized data are at the core of meaningful scientific research. Data standards allow for proper integration of clinical data sets and represent the essential foundation for regulatory endorsement of drug development tools. Such tools increase the potential for success and accuracy of trial results. METHODS: The development of the Clinical Data Interchange Standards Consortium (CDISC) AD therapeutic area data standard was a comprehensive collaborative effort by CDISC and Coalition Against Major Diseases, a consortium of the Critical Path Institute. Clinical concepts for AD and mild cognitive impairment were defined and a data standards user guide was created from various sources of input, including data dictionaries used in AD clinical trials and observational studies. RESULTS: A comprehensive collection of AD-specific clinical data standards consisting of clinical outcome measures, leading candidate genes, and cerebrospinal fluid and imaging biomarkers was developed. The AD version 2.0 (V2.0) Therapeutic Area User Guide was developed by diverse experts working with data scientists across multiple consortia through a comprehensive review and revision process. The AD CDISC standard is a publicly available resource to facilitate widespread use and implementation. DISCUSSION: The AD CDISC V2.0 data standard serves as a platform to catalyze reproducible research, data integration, and efficiencies in clinical trials. It allows for the mapping and integration of available data and provides a foundation for future studies, data sharing, and long-term registries in AD. The availability of consensus data standards for AD has the potential to facilitate clinical trial initiation and increase sharing and aggregation of data across observational studies and among clinical trials, thereby improving our understanding of disease progression and treatment.

Therapeutic Area Data Standards for Autosomal Dominant Polycystic Kidney Disease: A Report From the Polycystic Kidney Disease Outcomes Consortium (PKDOC).

Data standards provide a structure for consistent understanding and exchange of data and enable the integration of data across studies for integrated analysis. There is no data standard applicable to kidney disease. We describe the process for development of the first-ever Clinical Data Interchange Standards Consortium (CDISC) data standard for autosomal dominant polycystic kidney disease (ADPKD) by the Polycystic Kidney Disease Outcomes Consortium (PKDOC). Definition of common data elements and creation of ADPKD-specific data standards from case report forms used in long-term ADPKD registries, an observational cohort (Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease [CRISP] 1 and 2), and a randomized clinical trial (Halt Progression of Polycystic Kidney Disease [HALT-PKD]) are described in detail. This data standard underwent extensive review, including a global public comment period, and is now available online as the first PKD-specific data standard (www.cdisc.org/therapeutic). Submission of clinical trial data that use standard data structures and terminology will be required for new electronic submissions to the US Food and Drug Administration for all disease areas by the end of 2016. This data standard will allow for the mapping and pooling of available data into a common data set in addition to providing a foundation for future studies, data sharing, and long-term registries in ADPKD. This data set will also be used to support the regulatory qualification of total kidney volume as a prognostic biomarker for use in clinical trials. The availability of consensus data standards for ADPKD has the potential to facilitate clinical trial initiation and increase sharing and aggregation of data across observational studies and among completed clinical trials, thereby improving our understanding of disease progression and treatment.

Development of a unified clinical trial database for Alzheimer's disease.

INTRODUCTION: Data obtained in completed Alzheimer's disease (AD) clinical trials can inform decision making for future trials. Recognizing the importance of sharing these data, the Coalition Against Major Diseases created an Online Data Repository for AD (CODR-AD) with the aim of supporting accelerated drug development. The aim of this study was to build an open access, standardized database from control arm data collected across many clinical trials. METHODS: Comprehensive AD-specific data standards were developed to enable the pooling of data from different sources. Nine member organizations contributed patient-level data from 24 clinical trials of AD treatments. RESULTS: CODR-AD consists of control arm pooled and standardized data from 24 trials currently numbered at 6500 subjects; Alzheimer's Disease Assessment Scale-cognitive subscale 11 is the main outcome and specific covariates are also included. DISCUSSION: CODR-AD represents a unique integrated standardized clinical trials database available to qualified researchers. The pooling of data across studies facilitates a more comprehensive understanding of disease heterogeneity.

Understanding placebo responses in Alzheimer's disease clinical trials from the literature meta-data and CAMD database.

BACKGROUND: The placebo response and the underlying disease progression is difficult to differentiate in longitudinal Alzheimer's disease (AD) studies, yet it is crucial to understand for designing clinical trials and interpreting results. OBJECTIVES: The placebo response in ADAS-cog11 from various studies was evaluated against model predictions derived from historical placebo data to demonstrate potential interpretation of study results using a prior understanding of expected disease progression. METHODS: The placebo response component from a previously published disease progression model was used to estimate the longitudinal placebo response, and the disease progression in the placebo group in various case studies were evaluated. In addition, placebo data from the Coalition Against Major Diseases (CAMD) database in mild to moderate AD patients is described. RESULTS: The case studies demonstrated potential different results in disease progression in a placebo group, and the impact on understanding the magnitude of drug effect. Baseline cognitive function is an important covariate of disease progression, therefore, it is important to evaluate the baseline severity and predict disease progression accordingly when comparing trial results. Furthermore, study duration, sample size, and study design may affect the placebo response, all of which have the potential to confound understanding of study results. CONCLUSION: The recent failures in Phase III AD studies are not likely due to insufficient cognitive decline in the control groups. A meta-analytic approach using all available data provides a robust understanding of placebo effect, disease progression, and potential interpretation of treatment effects, and offers a useful tool to aid in both trial design and interpretation.

Safe drug use in long QT syndrome and Brugada syndrome: comparison of website statistics.

AIMS: We sought to obtain insights into the efficacy of two websites, www.QTdrugs.org and www.BrugadaDrugs.org, that have the intention to prevent fatal arrhythmias due to unsafe drug use in Long QT syndrome and Brugada syndrome. METHODS AND RESULTS: Prospective web-use statistical analysis combined with online surveys were employed. Our main outcome measure was the percentage of Long QT syndrome patients and Brugada syndrome patients reporting refraining or discontinuation of possible unsafe drugs. QTdrugs.org has received >3 100 000 visitors from 180 countries. Most visitors originated from the Americas (87%), as compared with Europe (7%), Asia (3%), Oceania (2%), and Africa (1%). The QTdrugs.org survey yielded 340 respondents: 34% were patients and 50% medical professionals. Of the patients, 79% reported that they refrained from, and 61% reported discontinuing drugs due to the website. The website was very much appreciated by 65% of the respondents and 30% found it rather helpful. The BrugadaDrugs.org received >48 000 visitors from 154 countries. Most visitors originated from Europe (46%) and the Americas (39%), but less from Asia (10%), Oceania (4%), and Africa (<1%). The BrugadaDrugs.org survey yielded 178 respondents: 68% were patients and 21% medical professionals. Of the patients, 72% reported refraining from, and 48% discontinuing drugs due to the website. The website was very much appreciated by 72% of the respondents and 25% found it rather helpful. CONCLUSION: These websites are extensively used, they promote drug awareness, and they help patients to avoid possible pro-arrhythmic drugs. Visitors find the websites valuable but should note their limitations.