Clustering based on renal and inflammatory admission parameters in critically ill patients admitted to the ICU

Introduction: The COVID-19 pandemic has been associated with significant variability in acute kidney injury (AKI) incidence, leading to concerns regarding patient heterogeneity. The study’s primary objective was a cluster analysis, to identify homogeneous subgroups of patients (clusters) using baseline characteristics, including inflammatory biomarkers. The secondary objectives were the comparisons of MAKE-90 and mortality between the different clusters at three months. Methods: This retrospective single-center study was conducted in the Medical Intensive Care Unit of the University Hospital of Clermont-Ferrand, France. Baseline data, clinical and biological characteristics on ICU admission, and outcomes at day 90 were recorded. The primary outcome was the risk of major adverse kidney events at 90 days (MAKE-90). Clusters were determined using hierarchical clustering on principal components approach based on admission characteristics, biomarkers and serum values of immune dysfunction and kidney function. Results: It included consecutive adult patients admitted between March 20, 2020 and February 28, 2021 for severe COVID-19. A total of 149 patients were included in the study. Three clusters were identified of which two were fully described (cluster 3 comprising 2 patients). Cluster 1 comprised 122 patients with fewer organ dysfunctions, moderate immune dysfunction, and was associated with reduced mortality and a lower incidence of MAKE-90. Cluster 2 comprised 25 patients with greater disease severity, immune dysfunction, higher levels of suPAR and L-FABP/U Creat, and greater organ support requirement, incidence of AKI, day-90 mortality and MAKE-90. Conclusions: This study identified two clusters of severe COVID-19 patients with distinct biological characteristics and renal event risks. Such clusters may help facilitate the identification of targeted populations for future clinical trials. Also, it may help to understand the significant variability in AKI incidence observed in COVID-19 patients.