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Postoperative or posttraumatic sepsis remains one of the leading causes of morbidity and mortality in hospital populations, especially in populations in intensive care units (ICUs). Central to the successful control of sepsis-associated infections is the ability to rapidly diagnose and treat disease. The ability to identify sepsis patients before they show any symptoms would have major benefits for the health care of ICU patients. For this study, 92 ICU patients who had undergone procedures that increased the risk of developing sepsis were recruited upon admission. Blood samples were taken daily until either a clinical diagnosis of sepsis was made or until the patient was discharged from the ICU. In addition to standard clinical and laboratory parameter testing, the levels of expression of interleukin-1beta (IL-1beta), IL-6, IL-8, and IL-10, tumor necrosis factor-alpha, FasL, and CCL2 mRNA were also measured by real-time reverse transcriptase PCR. The results of the analysis of the data using a nonlinear technique (neural network analysis) demonstrated discernible differences prior to the onset of overt sepsis. Neural networks using cytokine and chemokine data were able to correctly predict patient outcomes in an average of 83.09% of patient cases between 4 and 1 days before clinical diagnosis with high sensitivity and selectivity (91.43% and 80.20%, respectively). The neural network also had a predictive accuracy of 94.55% when data from 22 healthy volunteers was analyzed in conjunction with the ICU patient data. Our observations from this pilot study indicate that it may be possible to predict the onset of sepsis in a mixed patient population by using a panel of just seven biomarkers.

Original publication

DOI

10.1128/cvi.00486-07

Type

Journal article

Journal

Clinical and vaccine immunology : CVI

Publication Date

07/2008

Volume

15

Pages

1089 - 1094

Addresses

Dstl Porton Down, Salisbury, Wiltshire, United Kingdom. ralukaszewsk@dstl.gov.uk

Keywords

Humans, Bacteria, Sepsis, Cytokines, Pilot Projects, Predictive Value of Tests, Neural Networks (Computer), Adult, Aged, Aged, 80 and over, Middle Aged, Intensive Care Units, Female, Male