ArticlePloS one2025
Application value of systemic immune-inflammation index in predicting severe Mycoplasma pneumoniae pneumonia.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning-based prediction models for severeFrontiers in public health · 2026Pooled it
- Complete Blood Count-Derived Inflammatory Markers in Pediatric Lower Respiratory Tract Infection: A Narrative Review.Pathogens (Basel, Switzerland) · 2026Review
- High-Dimensional Immune Profiling Reveals Innate-Adaptive Rebalancing and Subpopulation Trajectories in Pediatric SevereDiagnostics (Basel, Switzerland) · 2026Article
- Novel inflammatory indices compared with CRP for diagnosing pelvic inflammatory disease: a case-control study.BMC women's health · 2026Article
- The diagnostic and prognostic value of miR-200b on severe pneumonia.BMC immunology · 2026Article
- Explainable machine learning for the early differentiation of pediatric bronchopneumonia using routine laboratory parameters.PloS one · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mycoplasma pneumoniae (MP) is the primary causative agent of community-acquired pneumonia. Severe mycoplasma pneumoniae pneumonia (SMPP) can result in multiorgan damage and even respiratory failure or death. This study aimed to evaluate the predictive value of the Systemic Immune-Inflammation Index (SII) for SMPP. This retrospective study included 254 hospitalized children with MP infections (SMPP group, n = 103; non-SMPP group, n = 151). Patient data, including complete blood count parameters (white blood cell, absolute neutrophil, absolute lymphocyte, absolute monocyte, and platelet counts), C-reactive protein (CRP), serum amyloid A (SAA), and other markers, were collected. Furthermore, the SII, Systemic Inflammation Response Index (SIRI), neutrophil/lymphocyte ratio (NLR), monocyte/lymphocyte ratio (MLR), and platelet/lymphocyte ratio (PLR) were calculated. T-tests and the Mann-Whitney U test were used to analyze differences between the groups. Logistic regression was applied to analyze the risk factors. Receiver operating characteristic (ROC) curves were plotted to evaluate the predictive performance of the SII and CRP for SMPP. The SMPP group exhibited significantly higher CRP and SAA levels, SII, NLR, MLR, PLR, and SIRI than the non-SMPP group (all P < 0.001). Logistic regression revealed that the SII (odds ratio [OR] = 1.006, 95% confidence interval [CI]: 1.001-1.010) and CRP (OR = 1.080, 95% CI: 1.041-1.120) were independent risk factors. ROC curve of the SII (area under the ROC curve = 0.883, sensitivity = 0.699, and specificity = 0.881) outperformed that of CRP. Thus, SII can serve as an effective biomarker for SMPP prediction. It can be a rapid and cost-effective method when combined with routine blood tests, thereby demonstrating considerable potential for clinical application.
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