Optimization-enhanced Gaussian mixture modeling for data-driven subphenotyping of septic shock.

Area of Science:

• Critical Care Medicine
• Computational Biology
• Data Science

Background:

• Septic shock presents high mortality and clinical heterogeneity, complicating patient stratification and management.
• Existing subtyping methods often rely on empirical approaches, limiting their effectiveness.
• There is a need for robust, data-driven methods to identify distinct septic shock subphenotypes.

Purpose of the Study:

• To develop and validate an optimization-enhanced Gaussian Mixture Modeling (GMM) framework for data-driven subphenotyping of septic shock.
• To integrate the Black-Winged Kite Algorithm (BKA) with GMM for improved parameter optimization, clustering robustness, and subtype separability.
• To identify and characterize distinct clinical subphenotypes within a septic shock cohort.

Main Methods:

• A retrospective cohort of 780 septic shock patients was analyzed using admission clinical data.
• Data preprocessing and standardization were performed on vital signs and laboratory parameters.
• The Black-Winged Kite Algorithm (BKA) was used to optimize Gaussian Mixture Model (GMM) parameters for clustering.

Main Results:

• The BKA-GMM framework identified three clinically distinct septic shock subphenotypes.
• Subtype I (n=213) showed severe abnormalities, Subtype II (n=330) intermediate, and Subtype III (n=237) milder profiles.
• The framework achieved a high silhouette coefficient (0.8528 ± 0.0112), indicating stable and robust subtype separation.

Conclusions:

• The proposed BKA-GMM framework offers a data-driven approach for septic shock subphenotyping.
• This method enhances understanding of patient heterogeneity, potentially improving clinical stratification.
• The findings support further research into individualized management strategies for septic shock patients.