Taavi Saviauk1, Juha P Kiiski1,2, Maarit K Nieminen1, Nelly N Tamminen1, Antti N Roine1, Pekka S Kumpulainen3, Lauri J Hokkinen1, Markus T Karjalainen3, Risto E Vuento4, Janne J Aittoniemi4, Terho J Lehtimäki1,5, Niku K Oksala6,7. 1. School of Medicine, University of Tampere, Tampere, Finland. 2. Department of Musculoskeletal Disease, Division of Plastic Surgery, Tampere University Hospital, Tampere, Finland. 3. Department of Automation Science and Engineering, Tampere University of Technology, Tampere, Finland. 4. Department of Clinical Microbiology, Fimlab Laboratories, Tampere, Finland. 5. Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland. 6. Department of Surgery, School of Medicine, University of Tampere, Tampere, Finland. 7. Department of Vascular Surgery, Tampere University Hospital, Tampere, Finland.
Abstract
BACKGROUND: Soft tissue infections, including postoperative wound infections, result in a significant burden for modern society. Rapid diagnosis of wound infections is based on bacterial stains, cultures, and polymerase chain reaction assays, and the results are available earliest after several hours, but more often not until days after. Therefore, antibiotic treatment is often administered empirically without a specific diagnosis. METHODS: We employed our electronic nose (eNose) system for this proof-of-concept study, aiming to differentiate the most relevant bacteria causing wound infections utilizing a set of clinical bacterial cultures on identical blood culture dishes, and established bacterial lines from the gaseous headspace. RESULTS: Our eNose system was capable of differentiating both methicillin-sensitive Staphylococcus aureus (MSSA) and methicillin-resistant Staphylococcus aureus (MRSA), Streptococcus pyogenes, Escherichia coli, Pseudomonas aeruginosa, and Clostridium perfringens with an accuracy of 78% within minutes without prior sample preparation. Most importantly, the system was capable of differentiating MRSA from MSSA with a sensitivity of 83%, a specificity of 100%, and an overall accuracy of 91%. CONCLUSIONS: Our results support the concept of rapid detection of the most relevant bacteria causing wound infections and ultimately differentiating MRSA from MSSA utilizing gaseous headspace sampling with an eNose.
BACKGROUND: Soft tissue infections, including postoperative wound infections, result in a significant burden for modern society. Rapid diagnosis of wound infections is based on bacterial stains, cultures, and polymerase chain reaction assays, and the results are available earliest after several hours, but more often not until days after. Therefore, antibiotic treatment is often administered empirically without a specific diagnosis. METHODS: We employed our electronic nose (eNose) system for this proof-of-concept study, aiming to differentiate the most relevant bacteria causing wound infections utilizing a set of clinical bacterial cultures on identical blood culture dishes, and established bacterial lines from the gaseous headspace. RESULTS: Our eNose system was capable of differentiating both methicillin-sensitive Staphylococcus aureus (MSSA) and methicillin-resistant Staphylococcus aureus (MRSA), Streptococcus pyogenes, Escherichia coli, Pseudomonas aeruginosa, and Clostridium perfringens with an accuracy of 78% within minutes without prior sample preparation. Most importantly, the system was capable of differentiating MRSA from MSSA with a sensitivity of 83%, a specificity of 100%, and an overall accuracy of 91%. CONCLUSIONS: Our results support the concept of rapid detection of the most relevant bacteria causing wound infections and ultimately differentiating MRSA from MSSA utilizing gaseous headspace sampling with an eNose.
Authors: Jörg Haupenthal; Yannik Kautz; Walid A M Elgaher; Linda Pätzold; Teresa Röhrig; Matthias W Laschke; Thomas Tschernig; Anna K H Hirsch; Vadim Molodtsov; Katsuhiko S Murakami; Rolf W Hartmann; Markus Bischoff Journal: ACS Infect Dis Date: 2020-09-21 Impact factor: 5.084
Authors: Jussi Virtanen; Lauri Hokkinen; Markus Karjalainen; Anton Kontunen; Risto Vuento; Jura Numminen; Markus Rautiainen; Niku Oksala; Antti Roine; Ilkka Kivekäs Journal: Eur Arch Otorhinolaryngol Date: 2018-07-24 Impact factor: 2.503
Authors: David W Bates; David Levine; Ania Syrowatka; Masha Kuznetsova; Kelly Jean Thomas Craig; Angela Rui; Gretchen Purcell Jackson; Kyu Rhee Journal: NPJ Digit Med Date: 2021-03-19