Universidad Peruana Cayetano Heredia
Discriminating active from latent tuberculosis in patients presenting to community clinics
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| dc.contributor.author | Sandhu, G. | |
| dc.contributor.author | Battaglia, F. | |
| dc.contributor.author | Ely, B.K. | |
| dc.contributor.author | Athanasakis, D. | |
| dc.contributor.author | Montoya, R. | |
| dc.contributor.author | Valencia, T. | |
| dc.contributor.author | Gilman, Robert Hugh | |
| dc.contributor.author | Evans, Carlton Anthony William | |
| dc.contributor.author | Friedland, J.S. | |
| dc.contributor.author | Fernandez-Reyes, D. | |
| dc.contributor.author | Agranoff, D.D. | |
| dc.date.accessioned | 2022-01-18T19:34:39Z | |
| dc.date.available | 2022-01-18T19:34:39Z | |
| dc.date.issued | 2012 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12866/11099 | |
| dc.description.abstract | Background: Because of the high global prevalence of latent TB infection (LTBI), a key challenge in endemic settings is distinguishing patients with active TB from patients with overlapping clinical symptoms without active TB but with co-existing LTBI. Current methods are insufficiently accurate. Plasma proteomic fingerprinting can resolve this difficulty by providing a molecular snapshot defining disease state that can be used to develop point-of-care diagnostics. Methods: Plasma and clinical data were obtained prospectively from patients attending community TB clinics in Peru and from household contacts. Plasma was subjected to high-throughput proteomic profiling by mass spectrometry. Statistical pattern recognition methods were used to define mass spectral patterns that distinguished patients with active TB from symptomatic controls with or without LTBI. Results: 156 patients with active TB and 110 symptomatic controls (patients with respiratory symptoms without active TB) were investigated. Active TB patients were distinguishable from undifferentiated symptomatic controls with accuracy of 87% (sensitivity 84%, specificity 90%), from symptomatic controls with LTBI (accuracy of 87%, sensitivity 89%, specificity 82%) and from symptomatic controls without LTBI (accuracy 90%, sensitivity 90%, specificity 92%). Conclusions: We show that active TB can be distinguished accurately from LTBI in symptomatic clinic attenders using a plasma proteomic fingerprint. Translation of biomarkers derived from this study into a robust and affordable point-of-care format will have significant implications for recognition and control of active TB in high prevalence settings. | en_US |
| dc.language.iso | eng | |
| dc.publisher | Public Library of Science | |
| dc.relation.ispartofseries | PLoS ONE | |
| dc.rights | info:eu-repo/semantics/restrictedAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es | |
| dc.subject | Humans | en_US |
| dc.subject | Peru | en_US |
| dc.subject | controlled study | en_US |
| dc.subject | major clinical study | en_US |
| dc.subject | metabolism | en_US |
| dc.subject | blood | en_US |
| dc.subject | community care | en_US |
| dc.subject | sensitivity and specificity | en_US |
| dc.subject | Diagnosis, Differential | en_US |
| dc.subject | clinical feature | en_US |
| dc.subject | Latent Tuberculosis | en_US |
| dc.subject | clinical assessment | en_US |
| dc.subject | diagnostic accuracy | en_US |
| dc.subject | mass spectrometry | en_US |
| dc.subject | tuberculosis control | en_US |
| dc.subject | high throughput screening | en_US |
| dc.subject | Proteomics | en_US |
| dc.subject | outpatient department | en_US |
| dc.subject | Ambulatory Care Facilities | en_US |
| dc.subject | disease marker | en_US |
| dc.subject | pattern recognition | en_US |
| dc.title | Discriminating active from latent tuberculosis in patients presenting to community clinics | en_US |
| dc.type | info:eu-repo/semantics/article | |
| dc.identifier.doi | https://doi.org/10.1371/journal.pone.0038080 | |
| dc.subject.ocde | https://purl.org/pe-repo/ocde/ford#3.01.00 | |
| dc.relation.issn | 1932-6203 |
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