Detection of Aspergillus DNA by a nested PCR assay is able to improve the diagnosis of invasive aspergillosis in paediatric patients.
Hummel, Margit 1; Spiess, Birgit 1; Roder, Julia 1; von Komorowski, Gregor 2; Durken, Matthias 2; Kentouche, Karim 3; Laws, Hans J. 4; Morz, Handan 1; Hehlmann, Ruediger 1; Buchheidt, Dieter 1
Journal of Medical Microbiology.
58(10):1291-1297, October 2009.
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Fungal infections are a leading cause of morbidity and mortality in severely immunocompromised patients and have been increasing in incidence in recent years. Invasive aspergillosis (IA) is the most common filamentous fungal infection and is, in adults as well as in children, difficult to diagnose. Several PCR assays to detect Aspergillus DNA have been established, but so far, studies on molecular tools for the diagnosis of IA in children are few. We evaluated the results of a nested PCR assay to detect Aspergillus DNA in clinical samples from paediatric and adolescent patients with suspected IA. Blood and non-blood samples from immunocompromised paediatric and adolescent patients with suspected invasive fungal infection were sent for processing Aspergillus PCR to our laboratory. PCR results from consecutive patients from three university children's hospitals investigated between November 2000 and January 2007 were evaluated. Fungal infections were classified according to the EORTC classification on the grounds of clinical findings, microbiology and radio-imaging results. Two hundred and ninety-one samples from 71 patients were investigated for the presence of Aspergillus DNA by our previously described nested PCR assay. Two, 3 and 34 patients had proven, probable and possible IA, respectively. Sensitivity (calculated from proven and probable patients, n=5) and specificity (calculated from patients without IA, n=32) rates of the PCR assay were 80 and 81 %, respectively. Our nested PCR assay was able to detect Aspergillus DNA in blood, cerebrospinal fluid and bronchoalveolar lavage samples from paediatric and adolescent patients with IA with high sensitivity and specificity rates.
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