Lymphocyte Trajectory Modeling for Mortality Risk Prediction in Pneumonia-Associated ARDS

Pneumonia-associated acute respiratory distress syndrome (ARDS) is characterized by high mortality, yet current prognostic models often rely on static biomarkers that fail to capture dynamic immune responses. This protocol introduces a reproducible computational framework that employs Group-Based Trajectory Modeling (GBTM) to identify distinct lymphocyte count trajectories and assess their prognostic value for mortality risk in ARDS patients with pneumonia. Using data extracted from the MIMIC-IV v2.2 database, the protocol details each step from data curation and preprocessing to trajectory construction and model validation. The approach includes subgroup identification through GBTM, followed by multivariable logistic and Cox regression analyses to quantify associations between trajectory patterns and 28-day mortality, adjusting for key clinical covariates such as APS III score, ICU stay, and heart rate. Model performance is comprehensively evaluated using ROC curves, calibration plots, and decision curve analysis, ensuring both statistical robustness and clinical interpretability. By leveraging longitudinal immune data rather than single-timepoint measurements, this workflow provides clinicians with a methodologically transparent, data-driven strategy to improve risk stratification and explore immune heterogeneity in critical illness. The protocol is fully reproducible, adaptable to other longitudinal biomarkers, and designed for visualization and instructional demonstration, making it an accessible tool for researchers seeking to integrate temporal biomarker modeling into critical care prognostics.