ArticleTranslational pediatrics2024
Exploring high-risk factors for the prediction of severe mycoplasma pneumonia in children.
Article in Translational pediatrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning-based prediction models for severeFrontiers in public health · 2026Pooled it
- Risk factors associated with pleural effusion in pediatric patients with severe Mycoplasma pneumonia.Medicine · 2026Observational
- East Asia as a sentinel: risk-stratified corticosteroid therapy for paediatric macrolide-resistantEuropean respiratory review : an official journal of the European Respiratory Society · 2026Review
- Association between normalizedTranslational pediatrics · 2026Article
- Blood-based inflammatory parameters and machine learning models for mortality prediction in critically ill pediatric pneumonia: a retrospective cohort study.Translational pediatrics · 2026Article
- [Development of a predictive model for severeZhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2026Article
- Clinical analysis of Mycoplasma pneumoniae necrotizing pneumonia in children.BMC infectious diseases · 2026Article
- Comparing differences in onset and clinical manifestations between children and adults withFrontiers in public health · 2026Article
- Clinical characteristics and cytokine profiles for early prediction of severeFrontiers in pediatrics · 2026Article
- The Expression Levels and Clinical Significance of CARDS TX, MyD88, and IL-8 of Children With Severe Mycoplasma pneumoniae Pneumonia.Mediators of inflammation · 2026Article
- Influence of Mycoplasma Resistance Genes on Pediatric Mycoplasma Pneumonia Treatment and Determinants for Second-line Antimicrobial Adjustment.Combinatorial chemistry & high throughput screening · 2026Article
- The Predictive Value of Nomogram Model Based on Neutrophil-to-Lymphocyte Ratio (NLR) and Prothrombin Time-International Normalized Ratio (PT-INR)-to-Albumin Ratio (PTAR) for Early Bacterial Intra-Abdominal Infection After Orthotopic Liver Transplantation.Journal of inflammation research · 2026Article
- The development and validation of a privacy-preserving model based on federated learning for diagnosing severe pediatric pneumonia.Translational pediatrics · 2025Article
- Impact of body mass index on clinical outcomes in pediatric patients with mycoplasma pneumoniae pneumonia: a retrospective cohort study.Translational pediatrics · 2025Article
- Case Report: A neonatal case of severe congenitalFrontiers in pediatrics · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: As one of the most common causes of community-acquired pneumonia in children, the prevalence of mycoplasma pneumonia (MPP) has long been underestimated. This study aimed to analyze children's severe MPP (SMPP) by examining laboratory characteristics and identifying high-risk factors. Methods: Clinical data from 447 hospitalized children with MPP were retrospectively analyzed. Patients were categorized into ordinary MPP and SMPP groups. Initial laboratory results on admission were compared between groups, and risk factors for SMPP were assessed using receiver operating characteristic (ROC) and logistic regression analyses. Results: Children with SMPP exhibited significantly higher levels of neutrophils, neutrophil/lymphocyte ratio (NLR), C-reactive protein (CRP), interleukin-6 (IL-6), erythrocyte sedimentation rate (ESR), and lactate dehydrogenase (LDH), along with lower lymphocyte, albumin (ALB), and prealbumin (PAB) levels compared to the ordinary MPP group (all P<0.05). SMPP children also had a higher incidence of multiple pathogens (P<0.05). ROC analysis identified cutoff values for ESR, LDH, IL-6, CRP, neutrophil percentage, and NLR, with corresponding areas under the curve (AUCs) indicating their predictive values for SMPP. A combined analysis of these factors yielded an AUC of 0.732 (P<0.05). Multivariate logistic regression confirmed ESR >35.50 mm/h, LDH >360.50 U/L, IL-6 >20.28 pg/mL, and CRP >9.74 mg/L as independent high-risk factors for SMPP (P<0.05). Conclusions: ESR, LDH, IL-6, CRP, neutrophil percentage, and NLR are valuable predictors for early identification of SMPP in children. These findings provide essential insights for clinical management aimed at assessing and intervening in prognosis.
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