The pre-symptomatic prediction of sepsis in intensive care unit patients: a pilot study

Clin. Vaccine Immunol.
doi:10.1128/CVI.00486-07
Copyright (c) 2008,
American Society for Microbiology and/or the Listed Authors/Institutions.
All Rights Reserved.
R A Lukaszewski*, A M Yates, M C Jackson, K Swingler, J M Scherer, A J Simpson, P Sadler, P McQuillan, R W Titball, T JG Brooks, and M J Pearce

Dstl Porton Down, Salisbury, Wiltshire, UK. SP4 0JQ; INCITE Group, University of Stirling, Scotland; Department of Critical Care, Queen Alexandra Hospital, Cosham, Portsmouth, Hampshire, UK. PO6 3LY; HPA Centre for emergency preparedness and response, Porton Down, Salisbury, Wiltshire, UK; School of Biosciences, Geoffrey Pope Building, University of Exeter, Exeter, UK
* To whom correspondence should be addressed. Email: ralukaszewsk@dstl.gov.uk

Abstract:
Post-operative or post-traumatic 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 healthcare of ICU patients. In this study ninety-two 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 ICU. In addition to standard clinical and laboratory parameter testing, the expression of Interleukins (IL) -1, -6, -8, -10, Tumour necrosis factor (TNF)-, FasL and CCL2 mRNA was also measured by real-time RT-PCR. Analysis of the data using a non-linear 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 outcome in an average 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 analysed 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 using a panel of just 7 biomarkers.