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电子鼻分析支气管肺泡灌洗液。

Electronic nose analysis of bronchoalveolar lavage fluid.

机构信息

Biophotonics Research Unit, Gloucestershire Royal Hospital, Gloucester, UK.

出版信息

Eur J Clin Invest. 2011 Jan;41(1):52-8. doi: 10.1111/j.1365-2362.2010.02376.x. Epub 2010 Sep 14.

Abstract

BACKGROUND

Electronic nose (E-nose) technology has been successfully used to diagnose a number of microbial infections. We have investigated the potential use of an E-nose for the diagnosis of ventilator-associated pneumonia (VAP) by detecting micro-organisms in bronchoalveolar lavage (BAL) fluid in a prospective comparative study of E-nose analysis and microbiology.

MATERIALS AND METHODS

BAL samples were collected using a blind technique from 44 patients following a minimum of 72 h mechanical ventilation. Control samples were collected from six patients mechanically ventilated on the intensive care unit (ICU) immediately following elective surgery. Quantitative microbiological culture and E-nose headspace analysis of the BAL samples were undertaken. Multivariate analysis was applied to correlate E-nose response with microbiological growth.

RESULTS

E-nose fingerprints correctly classified 77% of the BAL samples, with and without microbiological growth from patients not on antibiotics. Inclusion of patients on antibiotics resulted in 68% correct classification. Seventy per cent of isolates, cultured in the laboratory from the clinical samples, were accurately discriminated into four clinically significant groups.

CONCLUSIONS

E-nose technology can accurately discriminate between different microbial species in BAL samples from ventilated patients on ICU at risk of developing VAP with accuracy comparable with accepted microbiological techniques.

摘要

背景

电子鼻(E-nose)技术已成功用于诊断多种微生物感染。我们通过检测支气管肺泡灌洗液(BAL)中的微生物,在一项电子鼻分析与微生物学的前瞻性对比研究中,调查了电子鼻用于诊断呼吸机相关性肺炎(VAP)的潜力。

材料和方法

对至少接受 72 小时机械通气的 44 名患者,采用盲法技术采集 BAL 样本。对照组采集 6 名在 ICU 接受择期手术后即刻行机械通气的患者的 BAL 样本。对 BAL 样本进行定量微生物培养和 E-nose 顶空分析。应用多元分析将 E-nose 反应与微生物生长相关联。

结果

E-nose 指纹图谱正确分类了 77%的 BAL 样本,包括未接受抗生素治疗的患者和有微生物生长的患者。纳入接受抗生素治疗的患者后,正确分类率为 68%。从临床样本中实验室培养的 70%的分离株准确地分为四个有临床意义的组。

结论

E-nose 技术可准确区分 ICU 机械通气患者 BAL 样本中的不同微生物种类,与公认的微生物学技术相比,其准确性相当。

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