Evidence for a novel blood RNA diagnostic for pediatric appendicitis: The riboleukogram

Jared T. Muenzer, David M. Jaffe, Steve J. Schwulst, David J. Dixon, Will S. Schierding, Qing Li, Sandra K. MacMillan, Doug Oppedal, Brad W. Warner, Patrick A. Dillon, Nan Lin, Paul A. Checchia, J. Perren Cobb

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Objective: To test the hypothesis that gene expression analysis of circulating white blood cells and/or plasma cytokines could be used to improve diagnostic accuracy in children being evaluated for appendicitis. Methods : We recruited 28 children being evaluated for abdominal pain from a tertiary pediatric emergency department. Twenty patients were used as a training cohort and 8 patients as a validation cohort. After consent was obtained, blood was processed for plasma cytokine analysis and RNA gene expression. Alvarado and pediatric appendicitis scores were obtained. Principal components analysis was used to explore global differences in gene expression. The random forest method was used to classify patients into those with and without appendicitis in the prospective cohort. Comparisons were made evaluating clinical scoring systems, cytokine analysis, and gene expression analysis to accurately predict appendicitis. Results: The random forest method accurately predicted appendicitis in 4 of 5 patients in the prospective cohort. Cytokine analysis was not as accurate as gene expression analysis; however, it did accurately rule out all 3 patients in the prospective cohort. Pediatric appendicitis scores and Alvarado scores were not useful for predicting appendicitis. Conclusions: Our findings provide proof of technical feasibility and support the diagnostic potential of plasma cytokines to rule out and riboleukograms to rule in the diagnosis of appendicitis.

Original languageEnglish
Pages (from-to)333-338
Number of pages6
JournalPediatric emergency care
Issue number5
StatePublished - May 2010


  • GeneChip
  • Genomics
  • Microarray
  • Proteomics


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