The Library
Data for Identifying volatile metabolite signatures for the diagnosis of bacterial respiratory tract infection using electronic nose technology : a pilot study
Tools
Tools
Tools
Covington, James A., Lewis, Joseph M., Savage, Richard S., Beeching, Nicholas J., Beardsworth, Michael and Feasey, Nicholas (2017) Data for Identifying volatile metabolite signatures for the diagnosis of bacterial respiratory tract infection using electronic nose technology : a pilot study. [Dataset]
Research output not available from this repository.
Request-a-Copy directly from author or use local Library Get it For Me service.
Official URL: https://doi.org/10.5281/zenodo.1053769
Abstract
Objectives New point of care diagnostics are urgently needed to reduce the over-prescription of antimicrobials for bacterial respiratory tract infection (RTI). We performed a pilot cross sectional study to assess the feasibility of gas-capillary column ion mobility spectrometer (GC-IMS), for the analysis of volatile organic compounds (VOC) in exhaled breath to diagnose bacterial RTI in hospital inpatients. Methods 71 patients were prospectively recruited from the Acute Medical Unit of the Royal Liverpool University Hospital between March and May 2016 and classified as confirmed or probable bacterial or viral RTI on the basis of microbiologic, biochemical and radiologic testing. Breath samples were collected at the patient’s bedside directly into the electronic nose device, which recorded a VOC spectrum for each sample. Sparse principal component analysis and sparse logistic regression were used to develop a diagnostic model to classify VOC spectra as being caused by bacterial or non-bacterial RTI. Results Summary area under the receiver operator characteristic curve was 0.73 (95% CI 0.61–0.86), summary sensitivity and specificity were 62% (95% CI 41–80%) and 80% (95% CI 64–91%) respectively (p = 0.00147). Conclusions GC-IMS analysis of exhaled VOC for the diagnosis of bacterial RTI shows promise in this pilot study and further trials are warranted to assess this technique.
| Item Type: | Dataset | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Subjects: | R Medicine > RC Internal medicine | |||||||||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering Faculty of Science, Engineering and Medicine > Science > Statistics |
|||||||||||||||
| Type of Data: | Experimental data | |||||||||||||||
| Library of Congress Subject Headings (LCSH): | Respiratory infections -- Diagnosis, Respiratory infections -- Microbiology, Anti-infective agents | |||||||||||||||
| Publisher: | University of Warwick, Warwick Manufacturing Group | |||||||||||||||
| Official Date: | 28 November 2017 | |||||||||||||||
| Dates: |
|
|||||||||||||||
| Status: | Not Peer Reviewed | |||||||||||||||
| Publication Status: | Published | |||||||||||||||
| Media of Output (format): | .mea .xlsx | |||||||||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||
| Copyright Holders: | University of Warwick | |||||||||||||||
| Description: | Dara record consists of 7 data archives, containing subfolders and raw data files in .mea format, and one data file in .xlsx format. |
|||||||||||||||
| RIOXX Funder/Project Grant: |
|
|||||||||||||||
| Related URLs: | ||||||||||||||||
| Contributors: |
|
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
