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Background <p>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.</p> Methods <p>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.</p> Results <p>Forty-seven studies were included, mainly from North America (47%), Europe (45%), and Asia (34%). ICU and hospital mortality were the predominant outcomes. Although 53% of studies used pre-pandemic data, 94% were published afterwards. The most frequent biomarker categories were immune response (74.5%) and metabolic/organ function (66.0%). Common biomarkers included platelets and lactate (<i>n</i> = 9), lymphocytes and mHLA-DR (<i>n</i> = 6), and creatinine and interleukin-6 (<i>n</i> = 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.</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月13日

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  • Cristiana P. Von Rekowski,
  • Tiago A. H. Fonseca,
  • Rúben Araújo,
  • Cecília R. C. Calado,
  • Luís Bento,
  • Iola Pinto

摘要

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

Forty-seven studies were included, mainly from North America (47%), Europe (45%), and Asia (34%). ICU and hospital mortality were the predominant outcomes. Although 53% of studies used pre-pandemic data, 94% were published afterwards. The most frequent biomarker categories were immune response (74.5%) and metabolic/organ function (66.0%). Common biomarkers included platelets and lactate (n = 9), lymphocytes and mHLA-DR (n = 6), and creatinine and interleukin-6 (n = 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.