Review no. 1: designing clinical kidney research using real-world data: research questions, data sources, and analytical skills

This review series provided methodological guidance for clinical kidney research using real-world data, building on the "Hands-on R Seminar for Clinical Research: acute kidney injury (AKI) Detection and estimated glomerular filtration rate (eGFR) Slope Estimation from Creatinine Data," held at the 68th Annual Meeting of the Japanese Society of Nephrology in 2025. The seminar offered participants mock datasets, R scripts, and practical exercises to set up analysis environments and conduct data analyses, alongside brief lectures on conducting clinical research on AKI and eGFR decline. This series expands and complements the seminars. In Part 1, we provide an overview of the key components essential for successful clinical kidney research. First, formulating a robust research question is crucial, grounded in clinical experience and informed by up-to-date evidence. Common outcomes or exposures in clinical kidney studies include eGFR slope (as a marker of chronic kidney disease progression), AKI incidence, and initiation of kidney replacement therapy. Second, identifying appropriate data sources is necessary. In addition to primary data collection, routinely collected electronic health records and real-world databases (including disease registries) have become more accessible. Here, we summarize real-world databases in Japan, particularly those that include serum creatinine and urine test results. Finally, researchers require proper data handling and analytical skills. We highlight kidney research-specific techniques, such as AKI detection and eGFR slope calculation from longitudinal creatinine data. Subsequent articles in this series (Part 2 and beyond) will detail each specific method and include practical R commands.