Temporal phenotyping and prognostic stratification of patients with sepsis through longitudinal clustering

• Published in: BioData mining Vol. 18; no. 1; pp. 64 - 27
• Main Authors: Ribino, Patrizia, Mannone, Maria, Di Napoli, Claudia, Paragliola, Giovanni, Chicco, Davide, Gasparini, Francesca
• Format: Journal Article
• Language: English
• Published: London BioMed Central 26.09.2025; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Advances in Data Mining for Biomedical Informatics and Healthcare; Algorithms; Analysis; Bioinformatics; Biomedical and Life Sciences; Care and treatment; Chronic illnesses; Cluster analysis; Clustering; Complexity; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Data Mining and Knowledge Discovery; Datasets; Demographics; Development and progression; Disease; Electronic health records; Electronic medical records; Heart; Heterogeneity; Infection; Infections; Kidneys; Learning strategies; Life Sciences; Longitudinal clustering; Longitudinal studies; Machine learning; Mathematical analysis; Medical prognosis; Medical records; Mortality; Patient trajectories; Patients; Phenotypes; Phenotypic variations; Phenotyping; Physiology; Planning; Prothrombin; Sepsis; Subgroups; Thrombin; Unsupervised machine learning; Urogenital system; Variables; Patient trajectories; Unsupervised machine learning; Longitudinal clustering; Sepsis; Clustering; Electronic health records; Intensive care unit
• ISSN: 1756-0381; 1756-0381
• DOI: 10.1186/s13040-025-00480-7

Sepsis is a critical medical condition characterized by a highly variable and rapidly evolving clinical course, often necessitating early intervention and tailored treatment plans to improve patient outcomes. Due to its complexity and heterogeneity, understanding the progression of sepsis across different patient populations remains a significant challenge. In this study, we exploit a sophisticated analytical framework based on k-means multivariate longitudinal clustering to capture the diverse trajectories of sepsis. We do so by analyzing multiple clinical parameters tracked over time, providing a nuanced view of disease progression. By incorporating Dynamic Time Warping (DTW) as the distance metric, the proposed method effectively accounts for temporal misalignments and variability in the rate of disease progression, an essential capability given the unpredictable and heterogeneous nature of sepsis. This integration enhances the model’s ability to detect distinct temporal patterns and phenotypic subgroups that may remain undetected using conventional analytical approaches. 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. We perform several experiments varying the number of clusters and clinical variable combinations, evaluating the clustering performances using Silhouette score, Caliski-Harabasz Index, and Davies-Bouldin Index, as reference quality metrics. Our results confirm the prognostic role of the Thrombin-Antigen complex and the Prothrombin Time–International Normalized Ratio for septic patients. Furthermore, to evaluate the relevance of subjects’ stratification, the Adjusted Rand Index metric is used to quantify the survival prediction capability of our longitudinal clustering method, considering the 28-day death feature as the target variable. The same metric demonstrates that our proposal outperforms other longitudinal clustering algorithms available in the literature.