Application of Causal Discovery Algorithms in Studying the Nephrotoxicity of Remdesivir Using Longitudinal Data from the EHR

Abstract

Remdesivir has been widely used for the treatment of Coronavirus (COVID) in hospitalized patients, but its nephrotoxicity is still under investigation¹. Given the paucity of knowledge regarding the mechanism and optimal treatment of the development of acute kidney injury (AKI) in the setting of COVID, we analyzed the role of remdesivir and built multifactorial causal models of COVID-AKI by applying causal discovery machine learning techniques. Risk factors of COVID-AKI and renal function measures were represented in a temporal sequence using longitudinal data from EHR. Our models successfully recreated known causal pathways to changes in renal function and interactions with each other and examined the consistency of high-level causal relationships over a 4-day course of remdesivir. Results indicated a need for assessment of renal function on day 2 and 3 use of remdesivir, while uncovering that remdesivir may pose less risk to AKI than existing conditions of chronic kidney disease.