Superiority of transcriptional profiling over procalcitonin for distinguishing bacterial from viral lower respiratory tract infections in hospitalized adults

NM Suarez, E Bunsow, AR Falsey… - The Journal of …, 2015 - academic.oup.com
NM Suarez, E Bunsow, AR Falsey, EE Walsh, A Mejias, O Ramilo
The Journal of infectious diseases, 2015academic.oup.com
Background. Distinguishing between bacterial and viral lower respiratory tract infection
(LRTI) remains challenging. Transcriptional profiling is a promising tool for improving
diagnosis in LRTI. Methods. We performed whole blood transcriptional analysis in 118
patients (median age [interquartile range], 61 [50–76] years) hospitalized with LRTI and 40
age-matched healthy controls (median age, 60 [46–70] years). We applied class
comparisons, modular analysis, and class prediction algorithms to identify and validate …
Abstract
Background.  Distinguishing between bacterial and viral lower respiratory tract infection (LRTI) remains challenging. Transcriptional profiling is a promising tool for improving diagnosis in LRTI.
Methods.  We performed whole blood transcriptional analysis in 118 patients (median age [interquartile range], 61 [50–76] years) hospitalized with LRTI and 40 age-matched healthy controls (median age, 60 [46–70] years). We applied class comparisons, modular analysis, and class prediction algorithms to identify and validate diagnostic biosignatures for bacterial and viral LRTI.
Results.  Patients were classified as having bacterial (n = 22), viral (n = 71), or bacterial-viral LRTI (n = 25) based on comprehensive microbiologic testing. Compared with healthy controls, statistical group comparisons (P < .01; multiple-test corrections) identified 3376 differentially expressed genes in patients with bacterial LRTI, 2391 in viral LRTI, and 2628 in bacterial-viral LRTI. Patients with bacterial LRTI showed significant overexpression of inflammation and neutrophil genes (bacterial > bacterial-viral > viral), and those with viral LRTI displayed significantly greater overexpression of interferon genes (viral > bacterial-viral > bacterial). The K–nearest neighbors algorithm identified 10 classifier genes that discriminated between bacterial and viral LRTI with a 95% sensitivity (95% confidence interval, 77%–100%) and 92% specificity (77%–98%), compared with a sensitivity of 38% (18%–62%) and a specificity of 91% (76%–98%) for procalcitonin.
Conclusions.  Transcriptional profiling is a helpful tool for diagnosis of LRTI.
Oxford University Press