Age, C-reactive protein, and hospital stay Are associated with switching from azithromycin to doxycycline in pediatric macrolide-resistant Mycoplasma pneumoniae pneumonia

Background This study aimed to evaluate whether Mycoplasma pneumoniae resistance gene detection can independently guide antibiotic therapy for Mycoplasma pneumoniae pneumonia in children and to identify key predictors for antibiotic adjustment. Methods We conducted a retrospective cohort study of children with Mycoplasma pneumoniae pneumonia who underwent resistance gene testing. Participants were recruited from two centers (Affiliated Hospital of North Sichuan Medical College and People's Hospital of Nanbu County) between January 2023 and October 2025. Resistance gene testing results served as the basis for assigning patients to either the positive or negative group. The expression of Mycoplasma pneumoniae resistance genes was analyzed. The positive group was further divided into a “switch group” (switched to doxycycline) and a “maintenance group” (continued azithromycin). We performed a comparative analysis of demographic, clinical, and laboratory parameters across the groups. Multivariate logistic regression identified factors associated with switching antibiotics, and ROC curves assessed predictive performance. Results A total of 150 children were enrolled, with 13 in the macrolide resistance gene-negative group and 137 in the positive group, resulting in a macrolide resistance gene positivity rate of 91.3%. Of these patients , 65 patients were in the switch group and 72 patients were in the maintenance group. Significant differences were observed in baseline characteristics (age), inflammatory markers (CRP, PCT, lymphocyte count), disease severity (severe MPP, pleural effusion, bronchoscopy), and clinical management (peak fever, hospital stay, antibiotic duration) ( P < 0.05). Logistic regression analysis identified increased age, elevated CRP, and prolonged hospital stay as independent predictors for switching to second-line antibiotics in macrolide-resistant Mycoplasma pneumoniae . Conclusion In the Nanchong area, macrolide resistance mediated by the 23S rRNA A2063G mutation predominates. Resistance gene detection alone indicates only potential resistance but cannot independently guide antibiotic switching. Among gene-positive children, elevated CRP and older age are early predictors of switching need, while prolonged hospital stay serves as a retrospective confirmatory marker. Clinical decisions should integrate these biomarkers rather than relying solely on genetic results.

Authors

Institutions

Publication Details

Journal
Frontiers in Pediatrics
Published
2026-04-30
DOI
https://doi.org/10.3389/fped.2026.1785723
Primary Topic
Pneumonia and Respiratory Infections
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Age, C-reactive protein, and hospital stay Are associated with switching from azithromycin to doxycycline in pediatric macrolide-resistant Mycoplasma pneumoniae pneumonia

M Zhang, Hao Wei, Yi-Xiang Wang, Qirui Liu et al.
Frontiers in Pediatrics
Pneumonia and Respiratory Infections
article

Age, C-reactive protein, and hospital stay Are associated with switching from azithromycin to doxycycline in pediatric macrolide-resistant Mycoplasma pneumoniae pneumonia

M Zhang, Hao Wei, Yi-Xiang Wang, Qirui Liu, Ailian Wang, Hai Li, Xueling Jing
article en

Abstract

Background This study aimed to evaluate whether Mycoplasma pneumoniae resistance gene detection can independently guide antibiotic therapy for Mycoplasma pneumoniae pneumonia in children and to identify key predictors for antibiotic adjustment. Methods We conducted a retrospective cohort study of children with Mycoplasma pneumoniae pneumonia who underwent resistance gene testing. Participants were recruited from two centers (Affiliated Hospital of North Sichuan Medical College and People's Hospital of Nanbu County) between January 2023 and October 2025. Resistance gene testing results served as the basis for assigning patients to either the positive or negative group. The expression of Mycoplasma pneumoniae resistance genes was analyzed. The positive group was further divided into a “switch group” (switched to doxycycline) and a “maintenance group” (continued azithromycin). We performed a comparative analysis of demographic, clinical, and laboratory parameters across the groups. Multivariate logistic regression identified factors associated with switching antibiotics, and ROC curves assessed predictive performance. Results A total of 150 children were enrolled, with 13 in the macrolide resistance gene-negative group and 137 in the positive group, resulting in a macrolide resistance gene positivity rate of 91.3%. Of these patients , 65 patients were in the switch group and 72 patients were in the maintenance group. Significant differences were observed in baseline characteristics (age), inflammatory markers (CRP, PCT, lymphocyte count), disease severity (severe MPP, pleural effusion, bronchoscopy), and clinical management (peak fever, hospital stay, antibiotic duration) ( P < 0.05). Logistic regression analysis identified increased age, elevated CRP, and prolonged hospital stay as independent predictors for switching to second-line antibiotics in macrolide-resistant Mycoplasma pneumoniae . Conclusion In the Nanchong area, macrolide resistance mediated by the 23S rRNA A2063G mutation predominates. Resistance gene detection alone indicates only potential resistance but cannot independently guide antibiotic switching. Among gene-positive children, elevated CRP and older age are early predictors of switching need, while prolonged hospital stay serves as a retrospective confirmatory marker. Clinical decisions should integrate these biomarkers rather than relying solely on genetic results.

Frontiers in PediatricsVol. 14
Affiliated Hospital of North Sichuan Medical College (CN), County Hospital (GB)
Good health and well-being
Openalex Percentile: Top 12%
Pneumonia and Respiratory Infections
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.