ArticleFrontiers in medicine2026
Group-based trajectory modeling of platelet dynamics in sepsis: from phenotypic identification to the exploration of prognostic mediators.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Clinical Features and 1-Year Prognosis of Patients with Suspected Sepsis and Positive Blood Cultures in the Emergency Department: A Single-Center Retrospective Study.Medicina (Kaunas, Lithuania) · 2026Article
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Authors and funding
7 authors.
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Abstract
Sepsis continues to impose a substantial global burden, with persistently high rates of hospitalization and mortality. A major challenge in advancing sepsis therapeutics lies in its inherent clinical and biological heterogeneity. Recent research has increasingly shifted from static biomarker assessment toward the identification of dynamic trajectory subtypes to better capture this heterogeneity. Objective: This study aims to characterize the temporal patterns of platelet count changes during the first three days after admission among 280 sepsis patients using group-based trajectory modeling (GBTM), assess their association with clinical outcomes, and explore potential mediating pathways. Methods: We retrospectively analyzed data from 280 sepsis patients admitted to the Department of Critical Care Medicine at Jiangsu University Affiliated People's Hospital between September 2022 and December 2024. Baseline demographics, clinical characteristics, and serial platelet counts were collected. Missing data were addressed using multiple imputation techniques. GBTM was applied to identify distinct trajectory patterns of platelet counts. The association between each trajectory and 28-day all-cause mortality was evaluated using multivariable Cox proportional hazards regression models. Additionally, mediation analysis was performed to investigate potential mechanisms underlying these associations. Results: GBTM revealed three distinct platelet count trajectories: persistent low level (71.79%), high-level decline (20.00%), and rebound rise (8.21%). Patients in the persistent low-level group exhibited significantly higher 28-day all-cause mortality compared to the other two groups (71.63% vs. 55.36% vs. 13.04%, Conclusion: Dynamic modeling of platelet count trajectories enables effective identification of clinically meaningful subphenotypes in sepsis patients, offering a robust framework for prognosis prediction. This approach supports refined risk stratification and personalized management strategies, thereby providing novel insights into the pathophysiology and clinical care of sepsis.
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