Differential diagnosis of systemic inflammatory response syndrome versus sepsis based on a multiplex quantitative PCR assay

Introduction

Differential diagnosis of systemic inflammation versus sepsis is based primarily on clinical criteria and laboratory tests that lack the required sensitivity and specificity. This dilemma holds true for medical as well as surgical patients, for example after bypass surgery or autoimmune disease. Both populations are characterized by the presence of signs of systemic inflammation or by increased risk for nosocomial infection and, thus, reflect patient populations at risk for sepsis with significant diagnostic uncertainty. Novel and robust biomarkers are urgently needed to correctly and timely identify infection as the underlying cause of a systemic host response because each hour of delay of anti-infectious therapy leads to increased mortality.

Methods

We analyzed the clinical utility of a new class of transcriptomic biomarkers derived from circulating leukocytes. Prospectively collected whole blood samples from 460 patients admitted to the operative ICU were included in a microarray/quantitative PCR study to identify sensitive and specific biomarkers. The identification of a signature specific for the discrimination between systemic inflammatory response syndrome and sepsis in patients suffering from shock and organ dysfunction was performed in independent training and test phases. The training set of 96 patients was selected by an independent ICU committee.

Results

An algorithm was established combining and transforming the gene-expression signals into a continuous, nondimensional score indicating either infectious or noninfectious causes for organ dysfunction. The resulting classificator was validated in a test set comprising 1,784 ICU-days of 364 patients. For each marker, a robust quantitative PCR assay was established. The final microarray signature could be transferred into a multiplex quantitative PCR format retaining full sensitivity and specificity with time to result of approximately 5 hours. Moreover, it could be demonstrated that the combination of seven biomarkers possesses the same accuracy compared with the complete biomarker set. The AUC in the test group was determined as 0.79 (procalcitonin 0.65, C-reactive protein 0.67).

Conclusion

With its high predictive value for the differentiation between infectious and none infectious causes of shock and organ dysfunction, this new class of biomarkers may help to identify patients with life-threatening infections among patients at risk and to guide therapy, for example with anti-infective agents.