Background: Acute 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. Methods: We 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. Results: Among 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. Conclusion: We 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.
Background Sepsis and septic shock are common causes of ICU admission with devastating outcomes. Adjunctive therapies are urgently needed, and the use of high dose of vitamin B1 and C have recently gained interest. However, on the basis of a perceived possible synergic effect, most trials have never tested the combination of thiamine and ascorbic acid, with a separate assessment of the effect of each individual component. In this context, while the association of thiamine and ascorbic acid was not found to improve survival rates, potentially harmful effects were found when administering ascorbic acid alone. We have conducted a retrospective cohort study, comparing ICU mortality of septic shock patients receiving standard treatment, thiamine alone or a combination of thiamine and ascorbic acid. Results A total of 1800 patients were included, 1260 receiving standard care, 436 receiving only thiamine and 104 patients receiving a thiamine / ascorbic acid combination. Using doubly robust estimation of the treatment effect, combining propensity score weighting and variables adjustment, we found thiamine alone to be associated with a decrease in ICU mortality compared to the use of a thiamine / ascorbic acid combination (Hazar Ratio equal to 0.60, 95% Confidence Interval [0.36;0.99], p=0.048). Conclusions In septic shock patients, administration of thiamine is associated with improved ICU mortality when used alone rather than when associated with ascorbic acid. This result strengthens the evidence showing a lack of effectiveness of the ascorbic acid / thiamine combination reported in recent randomized controlled trials. Furthermore, it argues in favor of the need for further trials investigating the effect of thiamine in septic ICU patients as an adjunctive therapy.
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