Abstract
The management and analyses of large datasets is one of the grand challenges of modern biomedical research. Establishing methods to harmonise and standardise data collection, reporting, sharing and the employed data dictionaries, can support the resolution of these challenges whilst improving research quality, data quality and integrity, allowing sustainable knowledge transfer through re-usability, interoperability, reproducibility. The current project aimed to develop and propose a standardised reporting guideline for stroke research and clinical data reporting. Through systematic consolidation and harmonization of published data collection and reporting standards, several recommendations were drafted for the proposed guideline. These recommendations were reviewed by domain-researchers and clinicians using an online survey, developed in REDCap. The survey was completed by 20 international stroke-specialists, majority of respondents were based in Africa (10), followed by America, Europe and Australia (10). Of these respondents; the majority were working as dual clinician-researchers (57%) with more than 10 years’ experience in the field (78%). Data elements within the reporting standard were classified as participant-, study- and experiment-level information, further subdivided into essential or optional information, and defined using existing ontologies. The proposed reporting guideline can be employed for research utility and adapted for clinical utility as well. It is accompanied with an associated XML schema for REDCap implementation, to increase the user friendliness of data capturing, sharing, reporting and governance. Ultimately, the adoption of common reporting in stroke research has the potential to ensure that researchers gain the maximum benefit from their generated data and data collections.
Highlights
High-throughput technologies are increasingly being employed in biomedical- and healthcare-informatics research, producing large biological and clinical data sets at rapid speed (Luo et al, 2016)
Big data analytics are employed in stroke research to elucidate the genetic and environmental underpinnings of stroke (Akinyemi et al, 2015; Owolabi et al, 2018), as well as to research improved methods of stroke health care, such as revolutionising visual analytics, employing predictive analytics in hypertension patients and employing telecardiology as method of care (Wang and Alexander, 2016)
A list of reporting recommendations for the guideline was proposed based on the review of existing literature and online resources. These resources included data collection methods hosted on PhenX Toolkit, including the measure for collecting stroke history, and experimental reporting guidelines, hosted on FAIRsharing, including The Minimum Information About a Proteomics Experiment (MIAPE) (Taylor et al, 2007) and The Minimum Information required for DMET experiment (MIDE) (Kumuthini et al, 2016)
Summary
High-throughput technologies are increasingly being employed in biomedical- and healthcare-informatics research, producing large biological and clinical data sets at rapid speed (Luo et al, 2016). Modern biomedical and clinical research is characterised by the exponentially increasing volume of a variety of data types and structures, produced and processed at unprecedented velocity (Luo et al, 2016) Integrating these different sources of information holds great potential to elucidate the aetiologies of complex medical conditions, develop novel treatments for such conditions and revolutionise modern health care with the incorporation. Stroke, defined as an acute focal or global neurological deficit, results from spontaneous haemorrhage or infarction of the central nervous system with objective evidence of infarction or haemorrhage irrespective of duration of clinical symptoms (Sacco et al, 2013) It remains one of the primary causes of brain injury, disability and death, worldwide (Benjamin et al, 2017; Wang et al, 2016). Big data analytics are employed in stroke research to elucidate the genetic and environmental underpinnings of stroke (Akinyemi et al, 2015; Owolabi et al, 2018), as well as to research improved methods of stroke health care, such as revolutionising visual analytics, employing predictive analytics in hypertension patients and employing telecardiology as method of care (Wang and Alexander, 2016)
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