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Clinical and Vaccine Immunology, July 2008, p. 1089-1094, Vol. 15, No. 7
1071-412X/08/$08.00+0 doi:10.1128/CVI.00486-07
Copyright © 2008, American Society for Microbiology. All Rights Reserved.
Presymptomatic Prediction of Sepsis in Intensive Care Unit Patients
R. A. Lukaszewski,1*
A. M. Yates,1
M. C. Jackson,1
K. Swingler,2
J. M. Scherer,1
A. J. Simpson,1
P. Sadler,3
P. McQuillan,3
R. W. Titball,5
T. J. G. Brooks,4 and
M. J. Pearce1
Dstl Porton Down, Salisbury, Wiltshire, United Kingdom SP4 0JQ,1 INCITE Group, University of Stirling, Stirling, Scotland,2 Department of Critical Care, Queen Alexandra Hospital, Cosham, Portsmouth, Hampshire, United Kingdom PO6 3LY,3 HPA Centre for Emergency Preparedness and Response, Porton Down, Salisbury, Wiltshire, United Kingdom,4 School of Biosciences, Geoffrey Pope Building, University of Exeter, Exeter, United Kingdom5
Received 22 November 2007/ Returned for modification 30 January 2008/ Accepted 29 April 2008
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-1β (IL-1β), IL-6, IL-8, and IL-10, tumor necrosis factor-
, 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.
* Corresponding author. Mailing address: Dstl Porton Down, Salisbury, Wiltshire, United Kingdom SP4 0JQ. Phone: (44) 1980 614377. Fax: (44) 1980 613284. E-mail: ralukaszewsk{at}dstl.gov.uk
Published ahead of print on 14 May 2008.
Clinical and Vaccine Immunology, July 2008, p. 1089-1094, Vol. 15, No. 7
1071-412X/08/$08.00+0 doi:10.1128/CVI.00486-07
Copyright © 2008, American Society for Microbiology. All Rights Reserved.
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