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.
BackgroundMajor short-notice or sudden impact incidents, which result in a large number of casualties, are rare events. However health services must be prepared to respond to such events appropriately. In the United Kingdom (UK), a mass casualties incident is when the normal response of several National Health Service organizations to a major incident, has to be supported with extraordinary measures. Having the right type and quantity of clinical equipment is essential, but planning for such emergencies is challenging. To date, the equipment stored for such events has been selected on the basis of local clinical judgment and has evolved without an explicit evidence-base. This has resulted in considerable variations in the types and quantities of clinical equipment being stored in different locations. This study aimed to develop an expert consensus opinion of the essential items and minimum quantities of clinical equipment that is required to treat 100 people at the scene of a big bang mass casualties event.MethodsA three round modified Delphi study was conducted with 32 experts using a specifically developed web-based platform. Individuals were invited to participate if they had personal clinical experience of providing a pre-hospital emergency medical response to a mass casualties incident, or had responsibility in health emergency planning for mass casualties incidents and were in a position of authority within the sphere of emergency health planning. Each item’s importance was measured on a 5-point Likert scale. The quantity of items required was measured numerically. Data were analyzed using nonparametric statistics.ResultsExperts achieved consensus on a total of 134 items (54%) on completion of the study. Experts did not reach consensus on 114 (46%) items. Median quantities and interquartile ranges of the items, and their recommended quantities were identified and are presented.ConclusionsThis study is the first to produce an expert consensus on the items and quantities of clinical equipment that are required to treat 100 people at the scene of a big bang mass casualties event. The findings can be used, both in the UK and internationally, to support decision makers in the planning of equipment for such incidents.
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