Longitudinal trajectory analysis of sepsis after laparoscopic surgery

作者：Boming Xia, Chengqiao Jiang, Jie Yang, Suibi Yang, Bo Zhang, Zhihao Wang, S. T. Wu, Yang Wang, Qian Gao, Yucai Hong, Huiqing Ge, Zhongheng Zhang · 发表于：Laparoscopic Endoscopic and Robotic Surgery · 年份：2025 · DOI：10.1016/j.lers.2025.11.004 · 被引用次数：3 · 研究领域：Sepsis Diagnosis and Treatment、Advanced Proteomics Techniques and Applications、Time Series Analysis and Forecasting

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 d...