Di Lian

Macrolide-resistant Mycoplasma pneumoniae (MRMP) complicates the treatment of pediatric M. pneumoniae pneumonia (MPP). We evaluated whether treatment with

Mycoplasma pneumoniae pneumonia (MPP) exhibits significant clinical heterogeneity in children. This study investigated the clinical utility of serum interleukin-6 (IL-6), interleukin-8 (IL-8), and the T helper/T suppressor (Th/Ts) cell ratio for evaluating disease severity and 1-month short-term clinical outcomes in pediatric MPP. We retrospectively enrolled 194 hospitalized children with MPP without co-infection (102 mild, 92 severe; 171 favorable short-term clinical outcome, 23 poor short-term clinical outcome). Core biomarker levels were compared between groups. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curves. Bootstrap mediation analysis was used to explore the statistical association pathway between systemic inflammation and cellular immune status. For short-term outcome risk stratification, LASSO logistic regression was performed as a sensitivity analysis, and candidate prognostic models were compared. Patients with severe MPP and poor short-term clinical outcomes exhibited significantly elevated IL-6 and IL-8 levels and a reduced Th/Ts ratio (all P < 0.001). A combined diagnostic model incorporating these three biomarkers achieved an AUC of 0.943 for predicting severe MPP. Mediation analysis suggested that the Th/Ts ratio partially mediated the associations of IL-6 (30.3%) and IL-8 (19.2%) with disease severity. For short-term outcome risk stratification, a parsimonious exploratory nomogram incorporating lactate dehydrogenase (LDH), IL-6, and the Th/Ts ratio showed favorable internal performance (C-index = 0.89), calibration, and clinical net benefit. Conclusions: Pediatric MPP is characterized by an inflammation-immune imbalance pattern involving elevated IL-6 and IL-8 levels and a reduced Th/Ts ratio. The LDH, IL-6, and Th/Ts ratio-based nomogram may serve as an exploratory risk-stratification tool for 1-month short-term clinical outcomes, but external validation in multicenter prospective cohorts is required before routine clinical application. What is Known? • Pediatric Mycoplasma pneumoniae pneumonia (MPP) shows marked clinical heterogeneity, and early identification of severe disease and poor short-term outcomes remains clinically challenging. • Serum inflammatory markers and cellular immune indices, including IL-6, IL-8, and the T helper/T suppressor(Th/Ts) cell ratio, have been associated with disease severity in pediatric MPP. What is New? • The combined assessment of serum IL-6, IL-8, and the Th/Ts ratio showed high diagnostic performance for identifying severe pediatric MPP. • An exploratory nomogram incorporating lactate dehydrogenase, IL-6, and the Th/Ts ratio may help stratify 1-monthshort-term clinical outcome risk, supporting the clinical relevance of inflammation–immune imbalance in pediatric MPP.

Background & Objective: The clinical impact of testing timing for targeted next-generation sequencing (tNGS) in pediatric community-acquired pneumonia (CAP) is poorly understood. We hypothesized that early tNGS implementation accelerates clinical recovery and evaluated its comprehensive value. Methodology: In a retrospective study at Fujian Children's Hospital (2021-2023), 199 hospitalized children with CAP (BALF tested) were analyzed. Patients were grouped by tNGS vs. conventional testing (CT), with the tNGS group stratified into early (≤48h) and late (>48h) testing. We compared diagnostic yield, clinical outcomes, therapeutic adjustments, and costs. Results: tNGS demonstrated significantly higher sensitivity than CT for both bacteria (86.0% vs. 19.0%, P<0.001) and viruses (88.1% vs. 26.2%, P<0.001), and excelled at identifying mixed infections (55.8%). Critically, the early-testing group showed significantly shorter times to cough improvement (median 6.0 vs. 9.0 days, P=0.002) and rale resolution (median 7.0 vs. 8.0 days, P=0.027) compared to the late-testing group. This was associated with more timely therapeutic adjustments (P<0.05). A non-significant trend toward lower costs and shorter hospital stays was also observed. Conclusion: Early tNGS implementation in pediatric CAP not only enhances diagnostic yield but, more importantly, accelerates clinical recovery by facilitating timely, targeted therapeutic interventions. These findings provide strong evidence for integrating early tNGS into clinical workflows, marking a shift from simple pathogen identification to an outcome-driven diagnostic strategy.

Background: To evaluate the diagnostic value of cytokine levels in paediatric patients with Mycoplasma pneumoniae pneumonia (MPP) complicated by bacterial or viral infections, to enhance early detection and treatment strategies. Methods: A retrospective analysis was conducted on 145 paediatric patients with MPP at Fujian Children’s Hospital between October 2023 and April 2024. The patients were categorised into three groups: MPP only, MPP with bacterial co-infection, and MPP with viral co-infection. Clinical and laboratory data were analysed. Diagnostic accuracy was assessed using receiver operating characteristic (ROC) curves. Machine learning models with Boruta feature selection and 5-fold cross-validation were applied for co-infection classification. Statistical significance was set at P < 0.05. Results: Significant differences were observed between groups. The bacterial co-infection group had higher white blood cell and platelet counts and elevated levels of interferon-alpha, interleukin-1 beta, interleukin-6, and tumour necrosis factor-alpha (all P < 0.05). The viral co-infection group had lower interleukin-12P70 levels, longer fever and cough durations, and higher lactate dehydrogenase levels (all P < 0.05). Correlation analysis showed strong associations between white blood cells, platelets, and proinflammatory cytokines. ROC analysis of single parameters showed modest discrimination: for bacterial co-infections the best single marker was interferon-alpha with an AUC of 0.774 (95% CI 0.649–0.899), whereas for viral co-infections the best was lactate dehydrogenase with an AUC of 0.684 (95% CI 0.563–0.806). In contrast, machine learning models achieved higher performance, reaching an AUC of 0.868 (95% CI 0.772–0.964; SVM) for bacterial co-infections and 0.753 (95% CI 0.636–0.870; LightGBM) for viral co-infections. Conclusion: The combination of clinical data, laboratory parameters and cytokine profiles may improve the early diagnostic accuracy of MPP with bacterial or viral co-infections in some patients, and potentially guide optimised treatment strategies.

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Objectives: Respiratory syncytial virus (RSV) is a leading cause of hospitalization for acute lower respiratory tract infections (ALRIs) in children, with bacterial coinfection complicating diagnosis and often driving antibiotic overuse. This study aimed to develop and validate a clinical prediction model using common laboratory biomarkers to enable early, accurate identification of clinically significant bacterial coinfection in children with RSV infection. Methods: A single-center, retrospective cohort study was conducted at Fujian Children’s Hospital, enrolling 518 hospitalized children with RSV infection, which was confirmed via targeted next-generation sequencing (tNGS). Patients were randomly divided into a training set (n = 363) and a test set (n = 155) at a 7:3 ratio. The primary outcome, bacterial coinfection, was defined by a composite reference standard integrating etiological evidence from tNGS with clinical, inflammatory, and imaging data, and adjudicated by a blinded expert panel. LASSO regression identified independent predictors, followed by multivariable logistic regression modeling. Model performance was assessed via discrimination (AUC), calibration (Hosmer–Lemeshow test), and clinical utility (Decision Curve Analysis) in both sets. Results: Neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), and serum amyloid A (SAA) were selected as predictors. The model achieved an AUC of 0.832 (95% CI: 0.788–0.875) in the training set and 0.811 (95% CI: 0.737–0.885) in the test set, with well-calibrated predictions (p > 0.05). Decision curve analysis demonstrated net clinical benefit across 10–80% threshold probabilities. A nomogram was developed for practical application. Conclusions: This study established a model integrating NLR, CRP, and SAA. It offers a reliable tool for the early detection of bacterial coinfection in RSV-infected children, enabling targeted antibiotic stewardship and improving clinical outcomes.