Longitudinal trajectory analysis of sepsis after laparoscopic surgery

Résumé fourni par la source

Objective Sepsis exhibits remarkable heterogeneity in disease progression trajectories, and accurate identification of distinct trajectory-based phenotypes is critical for implementing personalized therapeutic strategies and prognostic assessment. However, trajectory clustering analysis of time-series clinical data poses substantial methodological challenges for researchers. This study provides a comprehensive tutorial framework demonstrating six trajectory modeling approaches integrated with proteomic analysis to guide researchers in identifying sepsis subtypes after laparoscopic surgery. Methods This study employs simulated longitudinal data from 300 septic patients after laparoscopic surgery to demonstrate six trajectory modeling methods (group-based trajectory modeling, latent growth mixture modeling, latent transition analysis, time-varying effect modeling, K-means for longitudinal data, agglomerative hierarchical clustering) for identifying associations between predefined sequential organ failure assessment trajectories and 25 proteomic biomarkers. Clustering performance was evaluated via multiple metrics, and a biomarker discovery pipeline integrating principal component analysis, random forests, feature selection, and receiver operating characteristic analysis was developed. Results The six methods demonstrated varying performance in identifying trajectory structures, with each approach exhibiting distinct analytical characteristics. The performance metrics revealed differences across methods, which may inform context-specific method selection and interpretation strategies. Conclusion This study illustrates practical implementations of trajectory modeling approaches under controlled conditions, facilitating informed method selection for clinical researchers. The inclusion of complete R code and integrated proteomics workflows offers a reproducible analytical framework connecting temporal pattern recognition to biomarker discovery. Beyond sepsis, this pipeline-oriented approach may be adapted to diverse clinical scenarios requiring longitudinal disease characterization and precision medicine applications. The comparative analysis reveals that each method has distinct strengths, providing a practical guide for clinical researchers in selecting appropriate methods based on their specific study goals and data characteristics.

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