Abstract
BackgroundThe Canadian Inherited Metabolic Diseases Research Network (CIMDRN) is a pan-Canadian practice-based research network of 14 Hereditary Metabolic Disease Treatment Centres and over 50 investigators. CIMDRN aims to develop evidence to improve health outcomes for children with inherited metabolic diseases (IMD). We describe the development of our clinical data collection platform, discuss our data quality management plan, and present the findings to date from our data quality assessment, highlighting key lessons that can serve as a resource for future clinical research initiatives relating to rare diseases.MethodsAt participating centres, children born from 2006 to 2015 who were diagnosed with one of 31 targeted IMD were eligible to participate in CIMDRN’s clinical research stream. For all participants, we collected a minimum data set that includes information about demographics and diagnosis. For children with five prioritized IMD, we collected longitudinal data including interventions, clinical outcomes, and indicators of disease management. The data quality management plan included: design of user-friendly and intuitive clinical data collection forms; validation measures at point of data entry, designed to minimize data entry errors; regular communications with each CIMDRN site; and routine review of aggregate data.ResultsAs of June 2019, CIMDRN has enrolled 798 participants of whom 764 (96%) have complete minimum data set information. Results from our data quality assessment revealed that potential data quality issues were related to interpretation of definitions of some variables, participants who transferred care across institutions, and the organization of information within the patient charts (e.g., neuropsychological test results). Little information was missing regarding disease ascertainment and diagnosis (e.g., ascertainment method – 0% missing).DiscussionUsing several data quality management strategies, we have established a comprehensive clinical database that provides information about care and outcomes for Canadian children affected by IMD. We describe quality issues and lessons for consideration in future clinical research initiatives for rare diseases, including accurately accommodating different clinic workflows and balancing comprehensiveness of data collection with available resources. Integrating data collection within clinical care, leveraging electronic medical records, and implementing core outcome sets will be essential for achieving sustainability.
Highlights
The Canadian Inherited Metabolic Diseases Research Network (CIMDRN) is a pan-Canadian practicebased research network of 14 Hereditary Metabolic Disease Treatment Centres and over 50 investigators
Integrating data collection within clinical care, leveraging electronic medical records, and implementing core outcome sets will be essential for achieving sustainability
We describe the design and development of our clinical data collection platform, discuss our data quality management plan, and present the findings to date from our data quality assessment, highlighting key lessons that can serve as a resource for future clinical research initiatives relating to rare diseases
Summary
The Canadian Inherited Metabolic Diseases Research Network (CIMDRN) is a pan-Canadian practicebased research network of 14 Hereditary Metabolic Disease Treatment Centres and over 50 investigators. The overall global birth prevalence of IMD has been estimated as 50.9 per 100, 000 live births [3], representing a significant impact on population health Advancements such as generation sequencing, metabolomics, and newborn screening have led to earlier detection of IMD, improved understanding of the underlying biological mechanism of disease, and subsequent development of new therapeutics [4, 5]. Population-based cohort studies, patient registries, and practice-based evidence networks are important tools for investigating natural history, evaluating disease management practices, establishing effectiveness of interventions, and assessing both short- and long- term outcomes [9,10,11,12] To successfully achieve these goals, collection of high quality observational data is imperative [13, 14]
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