Sepsis is a life-threatening condition with complex and dynamic progression, often requiring timely and personalized treatment strategies. In this paper, we propose a multivariate longitudinal clustering, an advanced data analysis technique, as a powerful approach to understanding the diverse trajectories of sepsis by grouping patients based on multiple clinical variables measured over time. Dynamic Time Warping (DTW) is integrated into the longitudinal clustering as a distance measure to identify subgroups of patients with similar temporal patterns in multivariate data. By leveraging sepsis-related electronic health records (EHRs), which provide rich time-series data on laboratory results along with patient demographics and underlying health conditions, the proposed method reveals distinct sepsis phenotypes that reflect variations in disease progression. Our results confirm the critical role of the Thrombin-Antigen complex and the International Normalized Ratio as predictors of poor outcomes for septic patients. Despite challenges like missing data and interpretability, multivariate longitudinal clustering in sepsis offers significant potential to enhance clinical decision-making and improve patient outcomes.