A comparative study of clinical trial and real-world data in patients with diabetic kidney disease

• Published in: Scientific reports Vol. 14; no. 1; p. 1731
• Main Authors: Kurki, Samu, Halla-aho, Viivi, Haussmann, Manuel, Lähdesmäki, Harri, Leinonen, Jussi V., Koskinen, Miika
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 19-01-2024; Nature Publishing Group; Nature Portfolio
• Subjects: 692/308; 692/308/153; 692/308/409; 692/699; Clinical trials; Cluster analysis; Comparative studies; Data Collection - methods; Diabetes; Diabetes Mellitus; Diabetic Nephropathies - epidemiology; Diabetic nephropathy; Electronic Health Records; Electronic medical records; Humanities and Social Sciences; Humans; Kidney diseases; Kidneys; multidisciplinary; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-51938-3

A growing body of research is focusing on real-world data (RWD) to supplement or replace randomized controlled trials (RCTs). However, due to the disparities in data generation mechanisms, differences are likely and necessitate scrutiny to validate the merging of these datasets. We compared the characteristics of RCT data from 5734 diabetic kidney disease patients with corresponding RWD from electronic health records (EHRs) of 23,523 patients. Demographics, diagnoses, medications, laboratory measurements, and vital signs were analyzed using visualization, statistical comparison, and cluster analysis. RCT and RWD sets exhibited significant differences in prevalence, longitudinality, completeness, and sampling density. The cluster analysis revealed distinct patient subgroups within both RCT and RWD sets, as well as clusters containing patients from both sets. We stress the importance of validation to verify the feasibility of combining RCT and RWD, for instance, in building an external control arm. Our results highlight general differences between RCT and RWD sets, which should be considered during the planning stages of an RCT-RWD study. If they are, RWD has the potential to enrich RCT data by providing first-hand baseline data, filling in missing data or by subgrouping or matching individuals, which calls for advanced methods to mitigate the differences between datasets.