<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Huddart S</submitter><funding>NIAID NIH HHS</funding><funding>NHLBI Division of Intramural Research</funding><funding>Division of Intramural Research, National Institute of Allergy and Infectious Diseases</funding><funding>CIHR</funding><pagination>e0277078</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9642894</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>17(11)</volume><pubmed_abstract>&lt;h4>Introduction&lt;/h4>High levels of treatment adherence are critical for achieving optimal treatment outcomes among patients with tuberculosis (TB), especially for drug-resistant TB (DR TB). Current tools for identifying high-risk non-adherence are insufficient. Here, we apply trajectory analysis to characterize adherence behavior early in DR TB treatment and assess whether these patterns predict treatment outcomes.&lt;h4>Methods&lt;/h4>We conducted a retrospective analysis of Philippines DR TB patients treated between 2013 and 2016. To identify unique patterns of adherence, we performed group-based trajectory modelling on adherence to the first 12 weeks of treatment. We estimated the association of adherence trajectory group with six-month and final treatment outcomes using univariable and multivariable logistic regression. We also estimated and compared the predictive accuracy of adherence trajectory group and a binary adherence threshold for treatment outcomes.&lt;h4>Results&lt;/h4>Of 596 patients, 302 (50.7%) had multidrug resistant TB, 11 (1.8%) extremely drug-resistant (XDR) TB, and 283 (47.5%) pre-XDR TB. We identified three distinct adherence trajectories during the first 12 weeks of treatment: a high adherence group (n = 483), a moderate adherence group (n = 93) and a low adherence group (n = 20). Similar patterns were identified at 4 and 8 weeks. Being in the 12-week moderate or low adherence group was associated with unfavorable six-month (adjusted OR [aOR] 3.42, 95% CI 1.90-6.12) and final (aOR 2.71, 95% 1.73-4.30) treatment outcomes. Adherence trajectory group performed similarly to a binary threshold classification for the prediction of final treatment outcomes (65.9% vs. 65.4% correctly classified), but was more accurate for prediction of six-month treatment outcomes (79.4% vs. 60.0% correctly classified).&lt;h4>Conclusions&lt;/h4>Adherence patterns are strongly predictive of DR TB treatment outcomes. Trajectory-based analyses represent an exciting avenue of research into TB patient adherence behavior seeking to inform interventions which rapidly identify and support patients with high-risk adherence patterns.</pubmed_abstract><journal>PloS one</journal><pubmed_title>Adherence trajectory as an on-treatment risk indicator among drug-resistant TB patients in the Philippines.</pubmed_title><pmcid>PMC9642894</pmcid><funding_grant_id>R25AI147375</funding_grant_id><funding_grant_id>5K12HL138046-04</funding_grant_id><funding_grant_id>R01AI121144</funding_grant_id><funding_grant_id>R01 AI121144</funding_grant_id><funding_grant_id>R25 AI147375</funding_grant_id><pubmed_authors>Kato-Maeda M</pubmed_authors><pubmed_authors>Huddart S</pubmed_authors><pubmed_authors>Berger CA</pubmed_authors><pubmed_authors>Geocaniga-Gaviola DM</pubmed_authors><pubmed_authors>Garfin AMC</pubmed_authors><pubmed_authors>Crowder R</pubmed_authors><pubmed_authors>Lim AR</pubmed_authors><pubmed_authors>Cattamanchi A</pubmed_authors><pubmed_authors>Lopez E</pubmed_authors><pubmed_authors>Destura R</pubmed_authors><pubmed_authors>Valdez CL</pubmed_authors></additional><is_claimable>false</is_claimable><name>Adherence trajectory as an on-treatment risk indicator among drug-resistant TB patients in the Philippines.</name><description>&lt;h4>Introduction&lt;/h4>High levels of treatment adherence are critical for achieving optimal treatment outcomes among patients with tuberculosis (TB), especially for drug-resistant TB (DR TB). Current tools for identifying high-risk non-adherence are insufficient. Here, we apply trajectory analysis to characterize adherence behavior early in DR TB treatment and assess whether these patterns predict treatment outcomes.&lt;h4>Methods&lt;/h4>We conducted a retrospective analysis of Philippines DR TB patients treated between 2013 and 2016. To identify unique patterns of adherence, we performed group-based trajectory modelling on adherence to the first 12 weeks of treatment. We estimated the association of adherence trajectory group with six-month and final treatment outcomes using univariable and multivariable logistic regression. We also estimated and compared the predictive accuracy of adherence trajectory group and a binary adherence threshold for treatment outcomes.&lt;h4>Results&lt;/h4>Of 596 patients, 302 (50.7%) had multidrug resistant TB, 11 (1.8%) extremely drug-resistant (XDR) TB, and 283 (47.5%) pre-XDR TB. We identified three distinct adherence trajectories during the first 12 weeks of treatment: a high adherence group (n = 483), a moderate adherence group (n = 93) and a low adherence group (n = 20). Similar patterns were identified at 4 and 8 weeks. Being in the 12-week moderate or low adherence group was associated with unfavorable six-month (adjusted OR [aOR] 3.42, 95% CI 1.90-6.12) and final (aOR 2.71, 95% 1.73-4.30) treatment outcomes. Adherence trajectory group performed similarly to a binary threshold classification for the prediction of final treatment outcomes (65.9% vs. 65.4% correctly classified), but was more accurate for prediction of six-month treatment outcomes (79.4% vs. 60.0% correctly classified).&lt;h4>Conclusions&lt;/h4>Adherence patterns are strongly predictive of DR TB treatment outcomes. Trajectory-based analyses represent an exciting avenue of research into TB patient adherence behavior seeking to inform interventions which rapidly identify and support patients with high-risk adherence patterns.</description><dates><release>2022-01-01T00:00:00Z</release><publication>2022</publication><modification>2024-11-08T20:06:18.874Z</modification><creation>2024-11-08T20:06:18.874Z</creation></dates><accession>S-EPMC9642894</accession><cross_references><pubmed>36346814</pubmed><doi>10.1371/journal.pone.0277078</doi></cross_references></HashMap>