2022
Predicting resistance to fluoroquinolones among patients with rifampicin-resistant tuberculosis using machine learning methods
You S, Chitwood MH, Gunasekera KS, Crudu V, Codreanu A, Ciobanu N, Furin J, Cohen T, Warren JL, Yaesoubi R. Predicting resistance to fluoroquinolones among patients with rifampicin-resistant tuberculosis using machine learning methods. PLOS Digital Health 2022, 1: e0000059. PMID: 36177394, PMCID: PMC9518704, DOI: 10.1371/journal.pdig.0000059.Peer-Reviewed Original ResearchDrug susceptibility testXpert MTB/RIFMachine learning-based modelsLearning-based modelsMachine learning methodsRifampicin-resistant tuberculosisTime of diagnosisRifampin-resistant tuberculosisMTB/RIFNeural network modelLearning methodsNetwork modelMulti-drug resistant tuberculosisNational TB surveillanceDrug-resistant tuberculosisOptimism-corrected areaSelection of antibioticsAnti-TB agentsDistrict-level prevalenceLow-resource settingsPatient characteristicsResistant tuberculosisTB surveillanceAppropriate treatmentDST results
2016
Second line drug susceptibility testing to inform the treatment of rifampin-resistant tuberculosis: a quantitative perspective
Kendall EA, Cohen T, Mitnick CD, Dowdy DW. Second line drug susceptibility testing to inform the treatment of rifampin-resistant tuberculosis: a quantitative perspective. International Journal Of Infectious Diseases 2016, 56: 185-189. PMID: 28007660, PMCID: PMC5576040, DOI: 10.1016/j.ijid.2016.12.010.Peer-Reviewed Original ResearchConceptsSecond-line drug susceptibility testingRifampin-resistant tuberculosisDrug susceptibility testingSecond-line drug resistanceDrug resistanceSusceptibility testingHigh-burden settingsSecond-line drugsDrug-resistant tuberculosisEffective regimensTreatment failureTreatment outcomesSmall incremental costEpidemiologic benefitsResistance amplificationPatientsTuberculosisIncremental costMost settingsWidespread implementationSettingRegimensPrevalence