# Antimicrobial resistance dynamics in Mycobacterium tuberculosis coinfection systems: A spatiotemporal strain analysis

## TL;DR

This study examines antimicrobial resistance patterns in secondary bacterial infections among tuberculosis patients in China, highlighting regional and temporal trends.

## Contribution

The paper introduces a detailed spatiotemporal analysis of resistance dynamics in tuberculosis co-infections, with a focus on regional and urban-rural disparities.

## Key findings

- Klebsiella pneumoniae was the most common Gram-negative bacterium, showing significant resistance to multiple antibiotics.
- Resistance rates of Pseudomonas aeruginosa to cefazolin and ciprofloxacin were statistically significant.
- ESBL-producing K. pneumoniae resistant strains showed significant annual increases, with higher detection rates in rural areas.

## Abstract

Secondary pulmonary infections are crucial and pivotal contributing factors to clinical deterioration in patients with pulmonary tuberculosis. Research remains limited regarding the specific population and regional epidemiology of these secondary infections, despite its clinical importance. This study provides an in-depth analysis of the primary bacterial pathogens and their antimicrobial resistance rates among hospitalized pulmonary tuberculosis patients with concurrent pulmonary infections in Yangzhou, China, from 2021 to 2024. Suitable sputum samples and leftover bronchoalveolar lavage fluid samples were collected for bacterial culture identification and drug sensitivity testing. The annual and regional distributions of strains and the evolution of resistance rates were analyzed using the chi-square test and the Cochran-Armitage trend test. A total of 514 strains of pathogenic bacteria were cultured from 410 samples, with 499 strains (97.08 %) being Gram-negative bacteria. Klebsiella pneumoniae was the most common Gram-negative bacterium (40.27 %), followed by Pseudomonas aeruginosa (15.18 %), while Staphylococcus aureus was the primary Gram-positive bacterium (1.75 %). The drug sensitivity tests results over different years showed that K. pneumoniae exhibited significant resistance rates against doxycycline, ceftazidime, cefepime, cefuroxime, cefazolin, cefotaxime, levofloxacin, ticarcillin/clavulanate, amikacin, and norfloxacin (P < 0.05). The resistance rates of P. aeruginosa to cefazolin and ciprofloxacin were also statistically significant (P < 0.05). The drug sensitivity test results across different regions showed that the resistance rate of K. pneumoniae to minocycline in urban patients was statistically significant (P = 0.012), while the resistance rates of P. aeruginosa to tobramycin and ciprofloxacin in rural patients were statistically significant (P < 0.05). Annual statistics of extended-spectrum beta-lactamase ESBL-producing K. pneumoniae resistant strains showed significant annual increases (P < 0.05). Moreover, data on resistant strains in urban and rural areas indicated that the detection rate of ESBL-producing Escherichia coli in rural areas was higher than that in urban areas (P = 0.052), and the detection rate of ESBL-producing K. pneumoniae resistant strains in rural areas was significantly higher than in urban areas (P = 0.005). The rise of new carbapenem-resistant K. pneumoniae strains in 2023 should be monitored. This study provides strong data support to formulate antibiotic treatment plans for patients with tuberculosis and respiratory infections, reduce the risk of resistant strain transmission, and optimize clinical treatment strategies.

•Novel Focus on Tuberculosis-Pulmonary Co-infection Resistome Dynamics.•Longitudinal, high-resolution resistance trend analysis.•Regional-national and urban-rural stratification analysis of antimicrobial resistance.•Mechanistic insights into ESBL and carbapenem resistance.•Clinically Actionable Precision Medicine Implications

Novel Focus on Tuberculosis-Pulmonary Co-infection Resistome Dynamics.

Longitudinal, high-resolution resistance trend analysis.

Regional-national and urban-rural stratification analysis of antimicrobial resistance.

Mechanistic insights into ESBL and carbapenem resistance.

Clinically Actionable Precision Medicine Implications