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Related Concept Videos

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Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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Related Experiment Video

Updated: Jan 9, 2026

Evaluation of a Reliable Biomarker in a Cecal Ligation and Puncture-Induced Mouse Model of Sepsis
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Temporal robustness of biomarker-based classification algorithms for sepsis.

Emma Rademaker1,2, Rombout B E van Amstel3, Said El Bouhaddani4,5

  • 1Julius Centre for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands. e.rademaker-2@umcutrecht.nl.

Intensive Care Medicine
|December 1, 2025
PubMed
Summary

Sepsis patient immune profiles are unstable over time, with frequent shifts between subgroups. This immune endotype instability challenges their clinical use for guiding sepsis treatment.

Keywords:
BiomarkerImmune profileSepsisSubphenotypesTemporal stability

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Area of Science:

  • Critical care medicine
  • Immunology
  • Computational biology

Background:

  • Sepsis treatment is hindered by host response heterogeneity.
  • Data-driven analyses identified distinct sepsis subphenotypes (endotypes) at single timepoints.
  • The temporal stability of these endotypes is largely unknown.

Purpose of the Study:

  • To assess the temporal stability of immune profiles in sepsis patients.
  • To determine if identified sepsis endotypes remain consistent over time.
  • To evaluate the clinical utility of dynamic biomarker-derived endotypes.

Main Methods:

  • Analysis of immune biomarker data from 345 sepsis patients across two ICU cohorts.
  • Measurement of 30 immune biomarkers every 8 hours for up to 7 days.
  • Application of latent profile analysis for initial classification and re-classification, assessing temporal robustness via transition rates and Rand Index (RI).

Main Results:

  • Three distinct immune profiles identified at ICU admission: adaptive immune activation (A), hyperinflammation (B), and attenuated inflammation (C).
  • Significant shifts in profile prevalence within 48 hours, with profile C increasing from 39% to 56%.
  • Poor intra-class cohesion (median RI 65%) indicated patients did not consistently remain in their admission profile, with frequent inter-class transitions.

Conclusions:

  • Sepsis patient immune profiles are dynamic and unstable over short intervals.
  • Approximately one-third of patients shifted profiles at each timepoint, challenging endotype consistency.
  • The observed instability questions the clinical utility of current biomarker-derived endotyping strategies for sepsis management.