Critical insights from recent outbreaks of Mycoplasma pneumoniae: decoding the challenges and effective interventions strategies

• Published in: International journal of infectious diseases 2024-10, Vol.147, p.107200, Article 107200
• Main Authors: Kant, Ravi, Kumar, Naveen, Malik, Yashpal Singh, Everett, Dean, Saluja, Daman, Launey, Thomas, Kaushik, Rahul
• Format: Article
• Language: English
• Subjects: Anti-Bacterial Agents - pharmacology; Anti-Bacterial Agents - therapeutic use; Antimicrobial resistance; Antimicrobial Stewardship; Artificial Intelligence; Community acquired pneumonia; Diagnostics and therapeutics; Disease Outbreaks; Disease surveillance; Drug Resistance, Bacterial; Humans; Mycoplasma pneumoniae; Mycoplasma pneumoniae - drug effects; Mycoplasma pneumoniae - genetics; Pneumonia, Mycoplasma - diagnosis; Pneumonia, Mycoplasma - drug therapy; Pneumonia, Mycoplasma - epidemiology; Pneumonia, Mycoplasma - microbiology; Respiratory diseases
• ISSN: 1201-9712; 1878-3511; 1878-3511
• DOI: 10.1016/j.ijid.2024.107200

•Recent M. pneumoniae outbreaks show evolving dynamics with public health implications.•Advocating integration of advanced technologies, genomics, and clinical strategies.•Antibiotic resistance in M. pneumoniae underscores the need for antimicrobial stewardship.•AI provides important tools to assist in the diagnosis and treatment of M. pneumoniae.•Assessing current treatments amid evolving resistance highlights the need for novel strategies. Mycoplasma pneumoniae (M. pneumoniae) continues to pose a significant disease burden on global public health as a respiratory pathogen. The antimicrobial resistance among M. pneumoniae strains has complicated the outbreak control efforts, emphasizing the need for robust surveillance systems and effective antimicrobial stewardship programs. This review comprehensively investigates studies stemming from previous outbreaks to emphasize the multifaceted nature of M. pneumoniae infections, encompassing epidemiological dynamics, diagnostic innovations, antibiotic resistance, and therapeutic challenges. We explored the spectrum of clinical manifestations associated with M. pneumoniae infections, emphasizing the continuum of disease severity and the challenges in gradating it accurately. Artificial intelligence and machine learning have emerged as promising tools in M. pneumoniae diagnostics, offering enhanced accuracy and efficiency in identifying infections. However, their integration into clinical practice presents hurdles that need to be addressed. Further, we elucidate the pivotal role of pharmacological interventions in controlling and treating M. pneumoniae infections as the efficacy of existing therapies is jeopardized by evolving resistance mechanisms. Lessons learned from previous outbreaks underscore the importance of adaptive treatment strategies and proactive management approaches. Addressing these complexities demands a holistic approach integrating advanced technologies, genomic surveillance, and adaptive clinical strategies to effectively combat this pathogen.