Improving risk classification of critical illness with biomarkers: A simulation study

Christopher W. Seymour, Colin R. Cooke, Zheyu Wang, Kathleen F. Kerr, Donald M. Yealy, Derek C. Angus, Thomas D. Rea, Jeremy M. Kahn, Margaret S. Pepe

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Purpose: Optimal triage of patients at risk for critical illness requires accurate risk prediction, yet few data on the performance criteria required of a potential biomarker to be clinically useful exists. Materials and Methods: We studied an adult cohort of nonarrest, nontrauma emergency medical services encounters transported to a hospital from 2002 to 2006. We simulated hypothetical biomarkers increasingly associated with critical illness during hospitalization and determined the biomarker strength and sample size necessary to improve risk classification beyond a best clinical model. Results: Of 57647 encounters, 3121 (5.4%) were hospitalized with critical illness and 54526 (94.6%) without critical illness. The addition of a moderate-strength biomarker (odds ratio, 3.0, for critical illness) to a clinical model improved discrimination (c statistic, 0.85 vs 0.8; P < .01) and reclassification (net reclassification improvement, 0.15; 95% confidence interval, 0.13-0.18) and increased the proportion of cases in the highest-risk category by +. 8.6% (95% confidence interval, 7.5%-10.8%). Introducing correlation between the biomarker and physiological variables in the clinical risk score did not modify the results. Statistically significant changes in net reclassification required a sample size of at least 1000 subjects. Conclusions: Clinical models for triage of critical illness could be significantly improved by incorporating biomarkers, yet substantial sample sizes and biomarker strength may be required.

Original languageEnglish (US)
Pages (from-to)541-548
Number of pages8
JournalJournal of Critical Care
Issue number5
StatePublished - Oct 2013
Externally publishedYes


  • Biomarker
  • Reclassification
  • Sample size
  • Simulation

ASJC Scopus subject areas

  • Critical Care and Intensive Care Medicine


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