%0 Journal Article
%T Multi-Trajectory Modeling to Predict Acute Kidney Injury in Chronic Kidney Disease Patients
%A Daniel Nagin
%A Philipp Burckhardt
%A Rema Padman
%A Vijaya Priya Rama Vijayasarathy
%J Archive of "AMIA Annual Symposium Proceedings".
%D 2018
%X Risk-stratifying chronic disease patients in real time has the potential to facilitate targeted interventions and improve disease management and outcomes. We apply group-based multi-trajectory modeling to risk stratify patients with chronic kidney disease (CKD) and its major complications into distinct trajectories of disease development and predict acute kidney injury (AKI), a serious, under-diagnosed outcome of CKD that is both preventable and treatable with early detection. Utilizing Electronic Health Record data of 1,947 patients, we identify eight risk groups with distinct trajectories and profiles. We observe that a higher estimated probability of AKI generally coincides with a higher risk group. Overall, at least 75% of patients stabilize into their final groups within less than two years from diagnosis of CKD Stage 3. Model calibration confirms that the estimated outcome probabilities are highly correlated with AKI incidence, providing group-specific and individual level predictions to improve clinical management of AKI in CKD patients
%U https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6371306/