Improving data quality in observational research studies: Report of the Cure Glomerulonephropathy (CureGN) network

Abstract

Background High data quality is of crucial importance to the integrity of research projects. In the conduct of multi-center observational cohort studies with increasing types and quantities of data, maintaining data quality is challenging, with few published guidelines. Methods The Cure Glomerulonephropathy (CureGN) Network has established numerous quality control procedures to manage the 70 participating sites in the United States, Canada, and Europe. This effort is supported and guided by the activities of several committees, including Data Quality, Recruitment and Retention, and Central Review, that work in tandem with the Data Coordinating Center to monitor the study. We have implemented coordinator training and feedback channels, data queries of questionable or missing data, and developed performance metrics for recruitment, retention, visit completion, data entry, recording of patient-reported outcomes, collection, shipping and accessing of biological samples and pathology materials, and processing, cataloging and accessing genetic data and materials. Results We describe the development of data queries and site Report Cards, and their use in monitoring and encouraging excellence in site performance. We demonstrate improvements in data quality and completeness over 4 years after implementing these activities. We describe quality initiatives addressing specific challenges in collecting and cataloging whole slide images and other kidney pathology data, and novel methods of data quality assessment. Conclusions This paper reports the CureGN experience in optimizing data quality and underscores the importance of general and study-specific data quality initiatives to maintain excellence in the research measures of a multi-center observational study.

Immunosuppression Exposure and Risk of Infection-Related Acute Care Events in Patients With Glomerular Disease: An Observational Cohort Study

“…Detailed methods for the CureGN study, including approaches to ensuring data quality, have been previously published. 12 , 13 …”

The promise of artificial intelligence for kidney pathophysiology

“…The Observational Medical Outcomes Partnership (OMOP) common data model provides one potential method for combining data from disparate sources [9]. Several rich databases are available for research (Table 1) [10][11][12][13][14][15][16][17][18][19][20][21].…”

“…Machine learning algorithms work best when developed in large, diverse and representative cohorts, yet this is often limited by the abilities to share data across institutions. To address this need, some health systems have de-identified their data and made them freely and publicly available [61], and others have created programmes that collect data nationally [10,11,17,20,21]. Another approach is federated learning, which allows for the training of a prediction model, although all data remain at their respective institutions.…”

Data Collection Theory in Healthcare Research: The Minimum Dataset in Quantitative Studies

“…For some research questions, new high-quality data may be required to derive meaningful results, but this may be expensive and labor-intensive work. To increase the quality of observational data, various quality control procedures and guidelines for data acquisition, quality and curation have been developed in various healthcare settings [ 7 , 8 , 9 ].…”