A key aim of this proposal is to equip the candidate, Dr.Ozrazgat Baslanti, with the necessary protected time and additional training and resources to develop her skillset on quantitative methods and understanding of underlying mechanism of progression of kidney disease and facilitate her transition to an independent translational researcher in health care. The long-term career goal is to become an independent data scientist, with a focus on hospital care for acute disease and complications arising from that care. The overall objective of this application is to build the foundation of the analytical approach for identifying patients? health trajectories during episode of acute hospitalization and quantifying the transitions in health states that can be applied to any acute illness. Our central hypothesis is that using kidney health as a paradigm for this approach we can determine individual states of change in kidney health during hospitalization using longitudinal, highly granular temporal data in electronic health records, determine transition probabilities to more severe stages of acute and chronic kidney disease, and improve understanding of the underlying processes influencing these transitions. Current diagnosis and risk evaluation for acute kidney injury (AKI) are focused on determination of severity of AKI episode and an integrated framework for assessing renal recovery does not exist. There is a clear lack of research on estimating transition probabilities among different states of kidney health through nonlinear and non-normal time- dependent domains using longitudinal electronic health records data. The complexity of underlying processes influencing the transition probabilities from renal risk to more severe stages of acute and chronic kidney disease requires application of advanced computational models in sufficiently large and granular datasets.
The specific aims of the proposal are:
Aim 1 - Expand and validate computable phenotypes of kidney health in large-scale medical data.
Aim 2 - Determine the epidemiology and clinical outcomes of changes in kidney health.
Aim 3 - Develop and validate probabilistic graphical models to predict transition through the states of kidney health and identify risk factors for progression. The proposed research is significant as we will have phenotyping algorithms of kidney health, validated in multi-center study, that can enhance their inter-institutional sharing and that enable to study epidemiology and outcomes of changes in kidney health. The approach is innovative because it implements technological advances in data science and statistics in innovative steps to develop and validate a phenotyping algorithm that determines computable phenotypes of changes in kidney health and graphical models to predict transition through the states of kidney health through nonlinear and non-normal time- dependent domains using highly granular electronic health records. This will provide foundation for changes in the care of patients with AKI, through identification of those patients at risk of developing AKI and progressing to acute and chronic kidney disease. On completion of the proposed investigations the deliverables will be new knowledge and a diagnosis and prognostication tool for kidney health.
Acute kidney injury (AKI) is one of the most common complication among hospitalized patients and is central to the subsequent development of chronic kidney disease and increased mortality. Besides the severity of an initial episode of acute kidney injury, the timing and duration of renal recovery are required to characterize the natural history of this complex condition and its effect on overall kidney health. Thus, the proposed research is relevant to the part of NIH?s mission that pertains to developing fundamental knowledge that will help to enhance health, since our research may provide diagnosis and prediction tools for changes in kidney health using electronic records data that contain wealth of clinical data that could be used to identify all dimensions of AKI episode as well as key determinants of undesired outcomes.
