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Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Presymptomatic Prediction of Sepsis in Intensive Care Unit Patients
Author(s): Lukaszewski, Roman A
Yates, Adam M
Jackson, Matthew C
Swingler, Kevin
Scherer, John M
Simpson, Andrew J H
Sadler, Paul
McQuillan, Peter
Titball, Richard W
Brooks, Timothy J G
Pearce, Michael J
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Keywords: Sepsis
Neural Network
Intensive care units
Patient monitoring
Issue Date: Jul-2008
Date Deposited: 21-Jun-2011
Citation: Lukaszewski RA, Yates AM, Jackson MC, Swingler K, Scherer JM, Simpson AJH, Sadler P, McQuillan P, Titball RW, Brooks TJG & Pearce MJ (2008) Presymptomatic Prediction of Sepsis in Intensive Care Unit Patients. Clinical and Vaccine Immunology, 15 (7), pp. 1089-1094.;
Abstract: 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-1 (IL-1 ), 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.
DOI Link: 10.1128/CVI.00486-07
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