Disease activity classification based on routinely collected blood tests in a prospective paediatric inflammatory

Rona Lujan1, Oren Ledder1, Dotan Yogev1

  • 1Juliet Keidan Institute of Pediatric Gastroenterology Hepatology and Nutrition, The Eisenberg R&D Authority, Shaare Zedek Medical Center, The Hebrew University of Jerusalem, Jerusalem, Israel.

Insights

Routine blood tests can identify disease activity clusters in pediatric Crohn's disease (CD). These clusters predict disease outcomes and course, aiding in personalized treatment strategies for children with CD.

Area of Science:

  • Pediatric Gastroenterology
  • Clinical Data Analysis
  • Biomarker Discovery

Background:

  • Disease activity clusters from administrative data previously predicted Crohn's disease (CD) outcomes.
  • Validation in a prospective inception cohort was needed to confirm these findings.

Purpose of the Study:

  • To validate disease-activity clusters derived from routine blood tests in a prospective pediatric CD cohort.
  • To assess the correlation of these clusters with established disease activity measures.
  • To determine the predictive value of these clusters for disease course.

Main Methods:

  • Hierarchical clustering was used to group newly diagnosed pediatric CD patients based on blood tests and fecal calprotectin.
  • Clusters were ordered by median laboratory values and validated against pediatric Crohn's disease activity index, physician global assessment, and SES-CD.
  • The ability of clusters to predict disease course was analyzed.

Main Results:

  • Three distinct clusters (mild, moderate, severe) were identified, showing progressively worsening laboratory values.
  • A significant stepwise increase in disease activity constructs (SES-CD) was observed across clusters (p < 0.001).
  • Higher cluster severity was associated with an increased risk of complicated disease course (HR 2.68).

Conclusions:

  • Clustering routinely collected blood tests effectively reflects disease activity in pediatric Crohn's disease.
  • These clusters serve as a valuable tool for predicting disease course and outcomes.
  • This approach supports personalized management strategies for pediatric CD patients.

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