Acute kidney injury (AKI) is a complex systemic syndrome associated with high morbidity and mortality and risk for the subsequent development of renal and non-renal complications. Nearly 50% of patients in the ICU experience AKI. AKI severity is a key metric for evaluating patients risk of hospital mortality. Current AKI stratification is based on absolute changes in Serum Creatinine (SCr) and the maximal increase relative to the patients baseline value. However, such measurement does not consider either the progression or duration of AKI, both of which are associated with adverse outcomes post-AKI. In this article, by leveraging a large volume of SCr temporal variabilities, we present a novel model called Trajectory of Acute Kidney Injury (TAKI) for the identification of AKI trajectory subtypes. Experimental results demonstrate that TAKI is better than the existing trajectory subtyping methods on both the inpatient mortality stratification and the post-7-day AKI progression estimation. With TAKI, it is found that the trend of KDIGO trajectory appears to be more highly associated with inpatient mortality rates than the maximum KDIGO score.
Keywords: Acute Kidney Injury; Dynamic Trajectory Alignment; KDIGO Trajectory Subtyping.