ABSTRACT
BackgroundAcute Kidney Injury (AKI) is a very frequent condition, occurring in about one in three patients admitted to an intensive care unit (ICU). AKI is a syndrome defined as a sudden decrease in glomerular filtration rate. However, this unified definition does not reflect the various mechanisms involved in AKI pathophysiology, each with its own characteristics and sensitivity to therapy. In this study, we aimed at developing an innovative machine learning based method able to subphenotype AKI according to its pattern of risk factors. MethodsWe adopted a three-step pipeline of analyses. Firstly, we looked for factors associated with AKI using a generalized additive model. Secondly, we calculated the importance of each identified AKI related factor in the estimated AKI risk to find the main risk factor for AKI, at the single patient level. Lastly, we clusterized AKI patients according to their profile of risk factors and compared the clinical characteristics and outcome of every cluster. We applied this method to a cohort of severe Covid19 patients hospitalized in the ICU of Geneva University Hospitals. ResultsAmong the 250 patients analyzed, we found ten factors associated with AKI development. Using the individual expression of these factors, we identified three groups of AKI patients, based on the use of Lopinavir/Ritonavir, a prior history of diabetes mellitus and baseline eGFR and ventilation. The three clusters expressed distinct characteristic in terms of AKI severity and recovery, metabolic patterns and ICU mortality. ConclusionWe propose here a new method to phenotype AKI patients according to their most important individual risk factors for AKI development. When applied to an ICU cohort of Covid19 patients, we were able to differentiate three groups of patients. Each expressed specific AKI characteristics and outcomes, which probably reflects a distinct pathophysiology.
