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Published on: May 24, 2020
Early Prediction of Bloodstream Infection with Complete Blood Count Parameters: an Ex-Vivo Human Whole Blood Model
Background:
Despite the advanced laboratory technologies available today, blood culture is the gold standard method in the diagnosis of bloodstream infections. Automated blood culture devices give blood culture results for laboratories approximately in 2 - 3 days up to 7 days. Moreover, some microorganisms like nonreproducible bacteria, fungi or viruses cannot be produced in culture. Among all samples taken for blood culture on suspicion of infection approximately 10% are determined as positive whereas the false positive rate due to contamination is 5%. Especially in life-threatening severe conditions such as sepsis early diagnosis and prompt treatment are crucial. Based on this the aim of this study is to investigate complete blood count parameters as potential early markers in Escherichia coli, Staphylococcus aureus and Candida albicans bloodstream infections using an ex vivo whole blood model.
Methods:
Blood samples collected from healthy donors (n = 10) were treated with suspensions containing a certain concentration of microorganisms (107 CFU/mL for both E. coli ATCC 25922 and S. aureus ATCC 29213, 106 CFU/mL for C. albicans ATCC 14053). After bacteremia and candidemia were induced, complete blood count parameters were analyzed hourly in the samples until the end of the 4th hour with a Mindray BC-6800 hematology analyzer. Statistical analysis was performed by Tukey-Kramer post-hoc multiple comparison test and statistical significance was accepted as p < 0.05.
Results:
When platelet derived parameter baseline values were compared to hourly values in E. coli and S. aureus induced whole blood samples, it was found that the decrease in PLT, P-LCC and the increase in IPF% was significant from the first hour whereas the increase in IMG% was found to be significant only from the 3rd hour onward. In the experiments with C. albicans, it was observed that the increase in IPF% and IMG% was significant from the 2nd and 3rd hour onward, respectively. There was no relationship between MPV, P-LCR, and NLR baseline and hourly results in any microorganism induced model.
Conclusions:
IPF% can guide clinicians in the early diagnosis and management of treatment of infections caused by S. aureus, E. coli, and C. albicans.
