Blood biomarker trajectories in ICU-directed prediction models – A scoping review

Background: Despite advanced analytical methods and increasing data availability, most intensive care unit (ICU) prediction models rely on static measurements. However, longitudinal monitoring of biomarkers may better capture disease progression and support timely, individualized interventions within the framework of predictive, preventive, and personalized medicine (PPPM). Since the COVID-19 pandemic, interest in both static and dynamic modelling has expanded. Therefore, this review aimed to summarize current evidence on the use of longitudinal blood biomarker data in ICU prediction models, assess how the pandemic shaped this research, and report validation strategies. Methods: This scoping review followed the PRISMA-ScR guidelines. PubMed and Google Scholar were searched for studies on blood biomarker trajectory analysis in the ICU published between 2014 and 2025, covering five years before and after the onset of the COVID-19 pandemic. Results: = 5). Modelling approaches integrated longitudinal regression-based models (31.9%), latent class-based models (44.7%), and machine-learning/data-driven clustering (27.7%). Trajectory patterns varied depending on both biomarker type and modelling technique. Cox regression, Kaplan-Meier, and logistic regression were commonly applied to assess associations with outcomes. Notably, only 21% of studies reported any form of validation, highlighting a major limitation for clinical applicability. Conclusion: Blood biomarker trajectories have potential to improve dynamic risk prediction and stratification, supporting targeted prevention through early identification of high-risk patterns, and enable more personalized treatment via adaptive, patient-specific approaches. Nevertheless, substantial methodological heterogeneity and the low proportion of validated models limit clinical applicability. Greater standardization and robust validation are essential to facilitate translation into PPPM-oriented intensive care. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-026-00456-5.