{"doi":"10.1101/2022.05.04.22274664","title":"Adherence trajectory as an on-treatment risk indicator among drug-resistant TB patients in the Philippines","abstract":"Abstract Introduction 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. Methods 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. Results 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). Conclusions Adherence patterns are strongly predictive of patient 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.","journal":"medRxiv","year":2022,"id":303359,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.953,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":924552,"name":"Donna Mae G. Gaviola","orcid":"0000-0003-2393-4343","position":1,"is_corresponding":false},{"id":557411,"name":"Rebecca Crowder","orcid":"0000-0001-6374-3088","position":2,"is_corresponding":false},{"id":995915,"name":"AR Lim","orcid":null,"position":3,"is_corresponding":false},{"id":925102,"name":"Evanisa Lopez","orcid":null,"position":4,"is_corresponding":false},{"id":995916,"name":"C.L. Valdez","orcid":null,"position":5,"is_corresponding":false},{"id":387482,"name":"Christopher A. Berger","orcid":"0000-0002-0034-7544","position":6,"is_corresponding":false},{"id":924554,"name":"Raul V. Destura","orcid":"0000-0002-2844-0142","position":7,"is_corresponding":false},{"id":231919,"name":"Midori Kato‐Maeda","orcid":"0000-0003-0539-2472","position":8,"is_corresponding":false},{"id":315466,"name":"Adithya Cattamanchi","orcid":"0000-0002-6553-2601","position":9,"is_corresponding":false},{"id":995917,"name":"AMC Garfin","orcid":null,"position":10,"is_corresponding":false},{"id":924551,"name":"Sophie Huddart","orcid":"0000-0001-6425-3371","position":0,"is_corresponding":true}],"reference_count":9,"raw_metadata":null,"created_at":"2026-07-19T00:32:24.307938Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}