Prevalence and clinical characteristics of septicemia in children with <i>Mycoplasma pneumoniae</i> pneumonia

Liu, Li; Maharjan, Shobha; Sun, Jialiang; Li, Yanchun; Cheng, Huanji

doi:10.1177/03000605211021733

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…Despite treatment with macrolide antibiotics for 7 days or more, some patients may exhibit ongoing clinical and imaging progression, which is defined as RMPP 4 . The incidence of RMPP is on the rise due to the emergence of drug-resistant MP strains 5 . Consequently, early recognition and management of RMPP are crucial for optimizing patient outcomes.…”

Section: Introductionmentioning

confidence: 99%

Model based on the automated AI-driven CT quantification is effective for the diagnosis of refractory Mycoplasma pneumoniae pneumonia

Qian,

Tao,

et al. 2024

Sci Rep

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The prediction of refractory Mycoplasma pneumoniae pneumonia (RMPP) remains a clinically significant challenge. This study aimed to develop an early predictive model utilizing artificial intelligence (AI)-derived quantitative assessment of lung lesion extent on initial computed tomography (CT) scans and clinical indicators for RMPP in pediatric inpatients. A retrospective cohort study was conducted on patients with M. pneumoniae pneumonia (MP) admitted to the Children’s Hospital of Nanjing Medical University, China from January 2019 to December 2020. An early prediction model was developed by stratifying the patients with Mycoplasma pneumoniae pneumonia (MPP) into two cohorts according to the presence or absence of refractory pneumonia. A retrospective cohort of 126 children diagnosed with Mycoplasma pneumoniae pneumonia (MPP) was utilized as a training set, with 85 cases classified as RMPP. Subsequently, a prospective cohort comprising 54 MPP cases, including 37 instances of RMPP, was assembled as a validation set to assess the performance of the predictive model for RMPP from January to December 2021. We defined a constant Φ which can combine the volume and CT value of pulmonary lesions and be further used to calculate the logarithm of Φ to the base of 2 (Log2Φ). A clinical-imaging prediction model was then constructed utilizing Log2Φ and clinical characteristics. Performance was evaluated by the area under the receiver operating characteristic curve (AUC). The clinical model demonstrated AUC values of 0.810 and 0.782, while the imaging model showed AUC values of 0.764 and 0.769 in the training and test sets, respectively. The clinical-imaging model, incorporating Log2Φ, temperature(T), aspartate aminotransferase (AST), preadmission fever duration (PFD), and preadmission macrolides therapy duration (PMTD), achieved the highest AUC values of 0.897 and 0.895 in the training and test sets, respectively. A prognostic model developed through automated quantification of lung disease on CT scans, in conjunction with clinical data in MPP may be utilized for the early identification of RMPP.

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Section: Introductionmentioning

confidence: 99%

Model based on the automated AI-driven CT quantification is effective for the diagnosis of refractory Mycoplasma pneumoniae pneumonia

Qian,

Tao,

et al. 2024

Sci Rep

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show abstract

“…Despite treatment with macrolide antibiotics for 7 days or more, some patients still have clinical and imaging progress, which is de ned as refractory Mycoplasma pneumoniae pneumonia (RMPP) 4 . However, in recent years, with the increase of drug-resistant Mycoplasma pneumoniae strains, the incidence rate of RMPP is rising 5 . Therefore, it is very important to improve doctors' understanding of RMPP at an early stage.…”

Section: Introductionmentioning

confidence: 99%

Model based on the automated AI-Driven CT quantification is effective for the diagnosis of refractory Mycoplasma pneumoniae pneumonia

Qian,

Tao,

et al. 2023

Preprint

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Refractory Mycoplasma pneumoniae pneumonia（RMPP）prediction is a challenging but clinically significant challenge. A model based on AI-derived quantitative determination of lung lesions extent on initial computed tomography (CT) scan and clinical indicators has the potential to facilitate early RMPP prediction in hospitalized children. In this study, we conducted a retrospective cohort as a training set including 126 children with M. pneumoniae pneumonia (MP) admitted to Children’s Hospital of Nanjing Medical University, China from January 2019 to December 2020. We defined a constant Φ which can combine the volume and CT value of pulmonary lesions and be further used to calculate the logarithm of Φ to the base of 2 (Log2Φ). Finally, a clinical-imaging prediction model was developed using Log2Φ and clinical characteristics. The area under the receiver operating characteristic curve (ROC-AUC) was applied to performance evaluation. Then we conducted a prospective study including 54 children with MP as a test set to validate the predictive model for RMPP from January to December 2021. The clinical model yielded the AUC of 0.810 and 0.782, and the imaging model yielded the AUC of 0.764 and 0.769 in the training set and the test set, respectively. The clinical-imaging model combining Log2Φ, temperature(T), aspartate aminotransferase (AST), preadmission fever duration (PFD), and preadmission macrolides therapy duration (PMTD) produced the highest AUC values of 0.897 and 0.895 in the training set and the test set, respectively. Our work demonstrated that using automated quantification of lung disease at CT combined with clinical data in MPP is useful to predict RMPP.

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“…Classical typical pneumonia is caused by bacteria, while atypical pneumonia is caused by atypical pathogens, such as Mycoplasma pneumoniae (MP), Legionella pneumoniae, and Chlamydia pneumoniae. These three pathogens combined are responsible for 21% to 28% of adult CAP worldwide[ 6 ]. MP is one of the most common pathogens causing respiratory illness in adolescents and children, accounting for up to 40% of CAP in children above five years of age[ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Clinical characteristics of community-acquired pneumonia in children caused by mycoplasma pneumoniae with or without myocardial damage: A single-center retrospective study

Yusuf¹,

Chen²

2023

World J Clin Pediatr

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BACKGROUND Mycoplasma pneumoniae (MP) is a prevalent pathogen that causes respiratory infections in children and adolescents. AIM To assess the differences in the clinical features of MP-associated community-acquired pneumonia (CAP) in children who presented with mild or severe mycoplasma pneumoniae pneumonia (MPP); to identify the incidence of myocardial damage between the two groups. METHODS This work is a retrospective study. We identified children between 2 mo and 16 years of age with clinical and radiological findings consistent with CAP. We admitted patients to the inpatient department of the Second Hospital of Jilin University, Changchun, China, from January 2019 to December 2019. RESULTS A total of 409 hospitalized patients were diagnosed with MPP. Among them were 214 (52.3%) males and 195 (47.7%) females. The duration of fever and cough was the longest in severe MPP cases. Similarly, plasma levels of highly sensitive C-reactive protein ( t = -2.834, P < 0.05), alanine transaminase ( t = -2.511, P < 0.05), aspartate aminotransferase ( t = -2.939, P < 0.05), and lactate dehydrogenase (LDH) ( t = -2.939, P < 0.05) were all elevated in severe MPP cases compared with mild MPP cases, and these elevations were statistically significant ( P < 0.05). Conversely, the neutrophil percentage was significantly lower in severe MPP cases than in mild MPP cases. The incidence of myocardial damage was significantly higher in severe MPP cases than in mild MPP cases ( χ 2 = 157.078, P < 0.05). CONCLUSION Mycoplasma pneumoniae is the main cause of CAP. The incidence of myocardial damage was higher and statistically significant in severe MPP cases than in mild MPP cases.

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Prevalence and clinical characteristics of septicemia in children with Mycoplasma pneumoniae pneumonia

Cited by 4 publications

References 24 publications

Model based on the automated AI-driven CT quantification is effective for the diagnosis of refractory Mycoplasma pneumoniae pneumonia

Model based on the automated AI-driven CT quantification is effective for the diagnosis of refractory Mycoplasma pneumoniae pneumonia

Model based on the automated AI-Driven CT quantification is effective for the diagnosis of refractory Mycoplasma pneumoniae pneumonia

Clinical characteristics of community-acquired pneumonia in children caused by mycoplasma pneumoniae with or without myocardial damage: A single-center retrospective study

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