Prevalence, risk factors, and early prediction of refractory pneumonia caused by Mycoplasma pneumoniae in children: a systematic review and meta-analysis.

Refractory Mycoplasma pneumoniae pneumonia (rMPP) poses significant challenges in pediatric care due to delayed recognition and limited systematic evidence. This meta-analysis evaluates the prevalence, risk factors, and predictive accuracy of models for rMPP. We systematically searched PubMed, Cochrane Library, and Web of Science until November 2024 for observational studies involving children aged 0-18 years with rMPP. Study quality was assessed using Newcastle-Ottawa and JBI scales. Data were analyzed via R4.4.2. Fifty-three studies (n = 35,275) revealed an overall rMPP prevalence of 37.8% (95% CI 30.5-45.5%), with significant temporal variation: 33.1% pre-COVID-19, 42.0% during, and 86.5% post-pandemic. Independent risk factors included elevated lactate dehydrogenase (OR = 1.018), C-reactive protein (OR = 1.106), procalcitonin (OR = 1.825), interleukin-6 (OR = 2.440), neutrophil count (OR = 2.955), pleural effusion (OR = 4.469), mucus plugs (OR = 5.456), and older age (OR = 1.188). Eleven prediction models demonstrated high accuracy, with ROC-AUCs of 0.913 (training) and 0.895 (validation).

conclusionThe prevalence of rMPP in children is significant and has increased markedly since the COVID-19 pandemic. Key biomarkers and clinical features facilitate early risk stratification, and validated predictive models improve clinical decision-making. These findings highlight the urgent need for targeted surveillance and customized interventions for high-risk populations. WHAT IS KNOWN: • Pneumonia caused by Mycoplasma pneumoniae in children can be effectively controlled by first-line macrolide therapy. • However, there is still a proportion of children who do not respond well to this treatment regimen and develop refractory Mycoplasma pneumoniae due to macrolide resistance. WHAT IS NEW: • This review and meta-analysis show the incidence of refractory Mycoplasma pneumonia and its potential risk factors. • Finding the feasibility of constructing a predictive model that facilitates early prediction, to provide evidence-based evidence for further in-depth clinical understanding of this disease.