Saviauk Taavi, Kiiski Juha P, Nieminen Maarit K, Tamminen Nelly N, Roine Antti N, Kumpulainen Pekka S, Hokkinen Lauri J, Karjalainen Markus T, Vuento Risto E, Aittoniemi Janne J, Lehtimäki Terho J, Oksala Niku K
School of Medicine, University of Tampere, Tampere, Finland.
Department of Musculoskeletal Disease, Division of Plastic Surgery, Tampere University Hospital, Tampere, Finland.
Eur Surg Res. 2018;59(1-2):1-11. doi: 10.1159/000485461. Epub 2018 Jan 10.
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.
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.
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%.
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.
软组织感染，包括术后伤口感染，给现代社会带来了沉重负担。伤口感染的快速诊断基于细菌染色、培养和聚合酶链反应检测，结果最早在数小时后可得，但更多时候要等到数天后。因此，抗生素治疗往往是在没有明确诊断的情况下凭经验进行的。
我们在这项概念验证研究中使用了电子鼻（eNose）系统，旨在利用在相同血培养皿上的一组临床细菌培养物，并从气态顶空建立细菌系，来区分引起伤口感染的最相关细菌。
我们的电子鼻系统能够在数分钟内区分甲氧西林敏感金黄色葡萄球菌（MSSA）和甲氧西林耐药金黄色葡萄球菌（MRSA）、化脓性链球菌、大肠杆菌、铜绿假单胞菌和产气荚膜梭菌，准确率达78%，且无需事先进行样品制备。最重要的是，该系统能够以83%的灵敏度、100%的特异性和91%的总体准确率区分MRSA和MSSA。
我们的结果支持利用电子鼻通过气态顶空采样快速检测引起伤口感染的最相关细菌并最终区分MRSA和MSSA的概念。
