Advancing acute kidney injury phenotyping using biological and clinical criteria

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PROJECT ABSTRACT This is a new K24 application for Kathleen D. Liu, MD, PhD, MAS, who is an Associate Professor of Medicine at the University of California, San Francisco where she is a nephrologist and critical care medicine specialist with a strong record of mentoring medical students, residents and fellows who want to train for a career in academic medicine. In the 10 years since completing her fellowship, she has established a well- funded independent research program focused primarily on acute kidney injury (AKI), a common disease of hospitalized patients for which no therapies apart from supportive care exist. One of Dr. Liu's long term goals is to conduct randomized clinical trials that will improve the care of critically ill patients with AKI. Sepsis is the leading cause of AKI in the Intensive Care Unit (ICU). A major criticism of failed sepsis clinical trials has been that the patient population is likely too heterogeneous to benefit. Thus, the overall theme of the research proposed in this K24 application is to refine phenotyping of sepsis-associated AKI. For these studies, she will extend her research by leveraging the Early Assessment of Renal and Lung Injury (EARLI) cohort, a NIH- supported cohort of ICU patients admitted from the Emergency Department at 2 UCSF-affiliated hospitals. In Aim 1, Dr. Liu will test the impact of fluid overload on AKI ascertainment in patients with sepsis. Serum creatinine (sCr), which is used to define AKI, is affected by volume of distribution (e.g., sCr is lower in patients with fluid overload). Among patients with the acute respiratory distress syndrome (ARDS), Dr. Liu has shown that fluid overload impacts AKI ascertainment. Further research is now needed to better understand the impact of fluid overload on AKI ascertainment in patients with sepsis, and on the relationship of biomarkers with the development of AKI. In Aim 2, she will use “clinically agnostic”, or unbiased methods, that may allow for identification of AKI sub-phenotypes. Latent class analysis has been applied to ARDS to identify sub- phenotypes using clinical and biological data. When sub-phenotypes of AKI are identified, these can be used to (1) further define the biology of these sub-phenotypes and (2) test potential therapies in a sub-phenotype that is more likely to benefit. Thus Aim 2 will use latent class analysis methods to incorporate biological and clinical criteria to identify more homogenous patient groups with AKI. This proposal will support additional biomarker measurements using banked samples and further clinical data collection in the EARLI cohort to provide a platform for mentoring new investigators in patient-oriented translational research. Additionally, as detailed in the Specific Aims, through this proposal Dr. Liu will acquire new skills in latent class analysis which will enable her to test this approach in other patient cohorts and will enhance her role as mentor to junior investigators in patient-oriented translational research. Finally, this award will provide Dr. Liu with critical protected time to further develop her mentorship skills and to devote to mentoring trainees committed to careers in patient-oriented research.