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

Abstract

PurposeOptimal 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 MethodsWe 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. ResultsOf 57 647 encounters, 3121 (5.4%) were hospitalized with critical illness and 54 526 (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. ConclusionsClinical models for triage of critical illness could be significantly improved by incorporating biomarkers, yet substantial sample sizes and biomarker strength may be required.