ArticleBiomarker insights2022
Joint Modeling of Repeated Measurements of Different Biomarkers Predicts Mortality in COVID-19 Patients in the Intensive Care Unit.
Article in Biomarker insights, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- Blood biomarker trajectories in ICU-directed prediction models - A scoping review.The EPMA journal · 2026Review
- Dynamic inflammatory trajectories and 28-day mortality in patients in the intensive care unit: An exploratory observational study.Journal of intensive medicine · 2026Article
- Personalized prediction of SARS-CoV-2 vaccine-induced immunity after boost: a longitudinal analysis using joint modeling.Frontiers in immunology · 2025Article
- Joint modeling of longitudinal and competing risks for assessing blood oxygen saturation and its association with survival outcomes in COVID-19 patients.Journal of education and health promotion · 2024Article
- Diagnostic performance and longitudinal analysis of fungal biomarkers in COVID-19 associated pulmonary aspergillosis.Heliyon · 2023Article
- Cytokine levels as predictors of mortality in critically ill patients with severe COVID-19 pneumonia: Case-control study nested within a cohort in Colombia.Frontiers in medicine · 2022Article
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Predicting disease severity is important for treatment decisions in patients with COVID-19 in the intensive care unit (ICU). Different biomarkers have been investigated in COVID-19 as predictor of mortality, including C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), and soluble urokinase-type plasminogen activator receptor (suPAR). Using repeated measurements in a prediction model may result in a more accurate risk prediction than the use of single point measurements. The goal of this study is to investigate the predictive value of trends in repeated measurements of CRP, PCT, IL-6, and suPAR on mortality in patients admitted to the ICU with COVID-19. Methods: This was a retrospective single center cohort study. Patients were included if they tested positive for SARS-CoV-2 by PCR test and if IL-6, PCT, suPAR was measured during any of the ICU admission days. There were no exclusion criteria for this study. We used joint models to predict ICU-mortality. This analysis was done using the framework of joint models for longitudinal and survival data. The reported hazard ratios express the relative change in the risk of death resulting from a doubling or 20% increase of the biomarker's value in a day compared to no change in the same period. Results: A total of 107 patients were included, of which 26 died during ICU admission. Adjusted for sex and age, a doubling in the next day in either levels of PCT, IL-6, and suPAR were significantly predictive of in-hospital mortality with HRs of 1.523 (1.012-6.540), 75.25 (1.116-6247), and 24.45 (1.696-1057) respectively. With a 20% increase in biomarker value in a subsequent day, the HR of PCT, IL-6, and suPAR were 1.117 (1.03-1.639), 3.116 (1.029-9.963), and 2.319 (1.149-6.243) respectively. Conclusion: Joint models for the analysis of repeated measurements of PCT, suPAR, and IL-6 are a useful method for predicting mortality in COVID-19 patients in the ICU. Patients with an increasing trend of biomarker levels in consecutive days are at increased risk for mortality.
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