期刊
JOURNAL OF BREATH RESEARCH
卷 15, 期 4, 页码 -出版社
IOP Publishing Ltd
DOI: 10.1088/1752-7163/ac135e
关键词
VOC analysis; disease diagnosis; ion mobility; machine learning; FAIMS; point-of-care testing
资金
- University of New South Wales (UNSW)
Non-invasive medical diagnosis through the analysis of volatile organic compounds (VOCs) is becoming feasible at the point-of-care, with promising results using micro-chip high-field asymmetric waveform ion mobility spectrometry (FAIMS). The diagnostic accuracy is particularly high for coeliac and inflammatory bowel disease, and consistent across breath, urine, and faecal matrices. Further improvements in technique should enhance the diagnostic accuracy of future VOC and point-of-care studies.
Non-invasive medical diagnosis by analysing volatile organic compounds (VOCs) at the point-of-care is becoming feasible owing to recent advances in portable instrumentation. A number of studies have assessed the performance of a state-of-the-art VOC analyser (micro-chip high-field asymmetric waveform ion mobility spectrometry, FAIMS) for medical diagnosis. However, a comprehensive meta-analysis is needed to investigate the overall diagnostic performance of these novel methods across different medical conditions. An electronic search was performed using the CAplus and MEDLINE database through the SciFinder platform. The review identified a total of 23 studies and 2312 individuals. Eighteen studies were used for meta-analysis. A pooled analysis found an overall sensitivity of 80% (95% CI, 74%-85%, I (2) = 62%), and specificity of 78% (95% CI, 70%-84%, I (2) = 80%), which corresponds to the overall diagnostic performance of micro-chip FAIMS across many different medical conditions. The diagnostic accuracy was particularly high for coeliac and inflammatory bowel disease (sensitivity and specificity from 74% to 97%). The overall diagnostic performance was similar across breath, urine, and faecal matrices with sparse logistic regression and random forests algorithms resulting in higher diagnostic accuracy. Sources of variability likely arise from differences in sample storage, sampling protocol, method of data analysis, type of disease, sample matrix, and potentially to clinical and disease factors. The results of this meta-analysis indicate that micro-chip FAIMS is a promising candidate for disease screening at the point-of-care, particularly for gastroenterology diseases. This review provides recommendations that should improve the techniques relevant to diagnostic accuracy of future VOC and point-of-care studies.
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