#3434 A FREE OPEN-SOURCE FRAMEWORK TO INFER PATIENT-LEVEL EGFR SLOPES FOR ASSOCIATION WITH MOLECULAR DATA
نویسندگان
چکیده
Abstract Background and Aims Unfolding over years or decades, patient kidney disease trajectories can be characterised using routinely collected longitudinal data. One frequently measured marker of function is serum creatinine, used in clinical settings to estimate the glomerular filtration rate (eGFR). Change eGFR time has been put forward as a proxy endpoint for trials, most work on inferring slopes done this context. However, 'omics-based studies molecular mechanisms have so far largely neglected make use data linked tissue samples. Here, we build existing approaches [1, 2] provide an open-source, R framework infer from available biobank Method In our framework, calculate creatinine measurements CKD-EPI 2021 formula. To aid adjudication, implement criteria outlined by Hapca et al. [1] automatic flagging acute injury (AKI) events, extend detect potential in-patient stays. Since biopsy procedure may arise result loss and/or coincide with change treatment regime, two-spline linear mixed effects model similar that proposed Vonesh [2] differentiate between immediate-post-biopsy (acute) chronic slopes. Additionally, account non-random dropout due onset dialysis occurrence transplant, jointly processes. Our made free, open-source software R. Results From data, generate patient-specific values characterising post-biopsy trajectory. These then incorporated into analysis 'omics find patterns associated response prognosis. Conclusion We developed patient-level Leveraging could improve disease, allowing us incorporate information about trajectory thus ask questions which real relevance.
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ژورنال
عنوان ژورنال: Nephrology Dialysis Transplantation
سال: 2023
ISSN: ['1460-2385', '0931-0509']
DOI: https://doi.org/10.1093/ndt/gfad063c_3434