The resistance trends in Mycobacterium kansasii pulmonary isolates and identification of risk factors for drug resistance in Taiwan
Bibliographic record
Abstract
OBJECTIVES: Mycobacterium kansasii (M. kansasii) pulmonary disease is an emerging global health concern, and the pathogen's resistance to antimicrobial agents is challenging. Understanding the epidemiology of drug resistance rates and the associated risk factors is useful for guiding antimicrobial agent selection for M. kansasii treatment. METHODS: This retrospective cohort study analysed 361 pathogenic M. kansasii isolates collected from patients with M. kansasii pulmonary disease in two tertiary medical centres in Taiwan between 2011 and 2022. Antimicrobial susceptibility testing was performed using Sensititre™ SLOMYCO1 or SLOMYCO2, and MIC breakpoints were according to CLSI 2018. We applied multivariate logistic regression and Cochran-Mantel-Haenszel test to assess the factors associated with drug resistance. RESULTS: Most M. kansasii isolates in Taiwan remained susceptible to the first-line agents, including rifampin (92.2%) and clarithromycin (98.6%). High resistance rates were observed in ciprofloxacin (42.7%), doxycycline (56.2%), and trimethoprim/sulfamethoxazole (87.8%). The drug resistance rates of tested antibiotics for M. kansasii increased notably in 2018-2022 compared to those between 2015-2017. M. kansasii isolates from central Taiwan exhibited significantly higher resistance rates in all drugs compared to those in southern Taiwan. Pulmonary fibrocavitary lesion was an independent risk factor for resistance to rifampin and ciprofloxacin. CONCLUSIONS: The study reveals increasing resistance to the first- and second-line agents in M. kansasii isolates across Taiwan. Resistance epidemiology differed between regions. Fibrocavitary lung lesion was significantly associated with drug resistance. These findings underscore the importance of region-specific surveillance to undergo individualized treatment strategies for M. kansasii pulmonary disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".