Skip navigation
Skip navigation

Development of a risk score for prediction of poor treatment outcomes among patients with multidrug-resistant tuberculosis

Alene, Kefyalew Addis; Viney, Kerri; Gray, Darren; McBryde, Emma; Xu, Zuhui; Clements, Archie

Description

Background Treatment outcomes among patients treated for multidrug-resistant tuberculosis (MDR-TB) are often sub-optimal. Therefore, the early prediction of poor treatment outcomes may be useful in patient care, especially for clinicians when they have the ability to make treatment decisions or offer counselling or additional support to patients. The aim of this study was to develop a simple clinical risk score to predict poor treatment outcomes in patients with MDR-TB, using routinely...[Show more]

dc.contributor.authorAlene, Kefyalew Addis
dc.contributor.authorViney, Kerri
dc.contributor.authorGray, Darren
dc.contributor.authorMcBryde, Emma
dc.contributor.authorXu, Zuhui
dc.contributor.authorClements, Archie
dc.date.accessioned2020-12-08T23:27:08Z
dc.date.available2020-12-08T23:27:08Z
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/1885/216769
dc.description.abstractBackground Treatment outcomes among patients treated for multidrug-resistant tuberculosis (MDR-TB) are often sub-optimal. Therefore, the early prediction of poor treatment outcomes may be useful in patient care, especially for clinicians when they have the ability to make treatment decisions or offer counselling or additional support to patients. The aim of this study was to develop a simple clinical risk score to predict poor treatment outcomes in patients with MDR-TB, using routinely collected data from two large countries in geographically distinct regions. Methods We used MDR-TB data collected from Hunan Chest Hospital, China and Gondar University Hospital, Ethiopia. The data were divided into derivation (n = 343; 60%) and validation groups (n = 227; 40%). A poor treatment outcome was defined as treatment failure, lost to follow up or death. A risk score for poor treatment outcomes was derived using a Cox proportional hazard model in the derivation group. The model was then validated in the validation group. Results The overall rate of poor treatment outcome was 39.5% (n = 225); 37.9% (n = 86) in the derivation group and 40.5% (n = 139) in the validation group. Three variables were identified as predictors of poor treatment outcomes, and each was assigned a number of points proportional to its regression coefficient. These predictors and their points were: 1) history of taking second-line TB treatment (2 points), 2) resistance to any fluoroquinolones (3 points), and 3) smear did not convert from positive to negative at two months (4 points). We summed these points to calculate the risk score for each patient; three risk groups were defined: low risk (0 to 2 points), medium risk (3 to 5 points), and high risk (6 to 9 points). In the derivation group, poor treatment outcomes were reported for these three groups as 14%, 27%, and 71%, respectively. The area under the receiver operating characteristic curve for the point system in the derivation group was 0.69 (95% CI 0.60 to 0.77) and was similar to that in the validation group (0.67; 95% CI 0.56 to 0.78; p = 0.82). Conclusion History of second-line TB treatment, resistance to any fluoroquinolones, and smear non-conversion at two months can be used to estimate the risk of poor treatment outcome in patients with MDR-TB with a moderate degree of accuracy (AUROC = 0.69).
dc.format.mimetypeapplication/pdf
dc.language.isoen_AU
dc.publisherPublic Library of Science
dc.rights© 2020 Alene et al.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourcePLOS ONE (Public Library of Science)
dc.titleDevelopment of a risk score for prediction of poor treatment outcomes among patients with multidrug-resistant tuberculosis
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume15
dc.date.issued2020
local.identifier.absfor111706 - Epidemiology
local.identifier.ariespublicationu6269649xPUB873
local.publisher.urlhttp://www.plosone.org/
local.type.statusPublished Version
local.contributor.affiliationAlene, Kefyalew, College of Health and Medicine, ANU
local.contributor.affiliationViney, Kerri, College of Health and Medicine, ANU
local.contributor.affiliationGray, Darren, College of Health and Medicine, ANU
local.contributor.affiliationMcBryde, Emma, James Cook University
local.contributor.affiliationXu, Zuhui, Tuberculosis Control Institute of Hunan Province
local.contributor.affiliationClements , Archie , Curtin University
local.bibliographicCitation.issue1
local.bibliographicCitation.startpage1
local.bibliographicCitation.lastpage14
local.identifier.doi10.1371/journal.pone.0227100
local.identifier.absseo920404 - Disease Distribution and Transmission (incl. Surveillance and Response)
dc.date.updated2020-07-19T08:32:45Z
local.identifier.scopusID2-s2.0-85077375951
dcterms.accessRightsOpen Access
dc.provenance© 2020 Alene et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.rights.licenseCreative Commons Attribution License
CollectionsANU Research Publications

Download

File Description SizeFormat Image
01_Alene_Development_of_a_risk_score_2020.pdf1.12 MBAdobe PDFThumbnail


This item is licensed under a Creative Commons License Creative Commons

Updated:  19 May 2020/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator