The Unyvero Hospital-Acquired pneumonia panel for diagnosis of secondary bacterial pneumonia in COVID-19 patients.

Chaitanya Tellapragada, Christian G Giske
Author Information
  1. Chaitanya Tellapragada: Divison of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institute, Alfred Nobels Allé 8, 14183, Stockholm, Sweden. Chaitanya.tellapragada@ki.se.
  2. Christian G Giske: Divison of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institute, Alfred Nobels Allé 8, 14183, Stockholm, Sweden.

Abstract

The study was undertaken to evaluate the performance of Unyvero Hospitalized Pneumonia (HPN) panel application, a multiplex PCR-based method for the detection of bacterial pathogens from lower respiratory tract (LRT) samples, obtained from COVID-19 patients with suspected secondary hospital-acquired pneumonia. Residual LRT samples obtained from critically ill COVID-19 patients with predetermined microbiological culture results were tested using the Unyvero HPN Application. Performance evaluation of the HPN Application was carried out using the standard-of-care (SoC) microbiological culture findings as the reference method. Eighty-three LRT samples were used in the evaluation. The HPN Application had a full concordance with SoC findings in 59/83 (71%) samples. The new method detected additional bacterial species in 21 (25%) and failed at detecting a bacterial species present in lower respiratory culture in 3 (3.6%) samples. Overall the sensitivity, specificity, positive, and negative predictive values of the HPN Application were 95.1% (95%CI 96.5-98.3%), 98.3% (95% CI 97.5-98.9%), 71.6% (95% CI 61.0-80.3%), and 99.8% (95% CI 99.3-99.9%), respectively. In conclusion, the HPN Application demonstrated higher diagnostic yield in comparison with the culture and generated results within 5 h.

Keywords

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MeSH Term

Adult
Aged
Bacteria
COVID-19
Cross Infection
Female
Hospitals
Humans
Lung
Male
Middle Aged
Multiplex Polymerase Chain Reaction
Pneumonia, Bacterial
SARS-CoV-2
Sweden

Word Cloud

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