Prediction Model for Severe Mycoplasma pneumoniae Pneumonia and Analysis of Macrolide-resistance in Children: A case-control Study

Prediction Model for Severe Mycoplasma pneumoniae Pneumonia and Analysis of Macrolide-resistance in Children: A case-control Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prediction Model for Severe Mycoplasma pneumoniae Pneumonia and Analysis of Macrolide-resistance in Children: A case-control Study Shaoying Liu, Lijun Zhang, Lei Dai, Jinhui Li, Deyuan Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6420625/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jun, 2025 Read the published version in Italian Journal of Pediatrics → Version 1 posted 5 You are reading this latest preprint version Abstract Background To analyze the clinical features, laboratory findings, and imaging characteristics of severe Mycoplasma pneumoniae pneumonia (SMPP) in children, identify early warning indicators, and characterize macrolide-resistant M. pneumoniae pneumonia (MRMPP). Additionally, we developed and validated a nomogram model for predicting the risk of SMPP. Methods This retrospective cohort study included children diagnosed with M. pneumoniae pneumonia (MPP) who were admitted to the West China Second Hospital of Sichuan University between September 2022 and February 2024. Data on demographics, clinical manifestations, laboratory results, and imaging findings were collected and analyzed. Results Compared to non-severe cases, children with SMPP had a significantly longer fever duration (8 days vs. 4 days, P < 0.001), higher peak body temperature (39.3°C vs. 38.5°C, P < 0.001), and a higher incidence of wheezing (13% vs. 0%, P 0.05). Radiological analysis revealed a higher frequency of pulmonary consolidation (69% vs. 0%, P < 0.001) and pleural effusion (22% vs. 7%, P = 0.031) in the SMPP cohort. LASSO regression identified eight key predictors: fever duration, peak body temperature, wheezing, extrapulmonary complications, hemoglobin levels, pulmonary consolidation, mosaic sign, and bronchial occlusion. The nomogram demonstrated excellent discriminative ability, with training and validation AUC values of 0.972 (95% CI 0.960–0.984) and 0.975 (95% CI 0.958–0.992), respectively. Conclusions We developed and validated a nomogram for quantitative risk assessment of SMPP. This model can aid clinicians in the early identification of severe cases and in optimizing treatment strategies. Drug resistance nomogram Prediction model Severe Mycoplasma pneumoniae Pneumonia Figures Figure 1 Figure 2 Figure 3 Background The pathogenesis of MPP caused by Mycoplasma pneumoniae (M. pneumoniae), a bacterium that lacks a cell wall, involves both pathogen characteristics and the host’s immune response [ 1 ]. In general, MPP presents with mild clinical symptoms, including fever, cough, headache, sore throat, and dyspnoea. However, a significant portion (22.0%) of patients may experience extra-respiratory manifestations, such as neurological[ 2 ], cutaneous, gastrointestinal, cardiovascular, musculoskeletal, haematological, and renal symptoms. In recent years, the rise of MRMPP has added complexity to the management of MPP. Studies, such as those by Jeffery Ho[ 3 ], have shown that macrolide resistance varies globally, with notably higher rates in China and lower resistance in North America and parts of Europe. The reduced susceptibility of MRMPP to macrolides can result in higher bacterial loads and exacerbated immune responses, contributing to more severe disease outcomes. SMPP is characterized by rapid disease progression, with high fever and sudden hypoxemia being prominent features. In contrast, refractory Mycoplasma pneumoniae pneumonia (RMPP) typically involves a prolonged disease course and poor response to first-line antibiotic treatment[ 4 ]. Therefore, early recognition of SMPP is crucial, especially in cases resistant to macrolides, as timely intervention can prevent further complications. Although several prediction models have been proposed for RMPP, there are few specific models designed for SMPP. For instance, Zhang et al.[ 5 ] identified CRP ≥ 16.5 mg/L, LDH ≥ 417 IU/L, and IL-6 ≥ 14.75 pg/ml as significant predictors of RMPP in children. Shen et al.[ 6 ] developed a model incorporating CRP, LDH, and D-dimer as predictive markers for RMPP. However, there is a noticeable lack of dedicated prediction tools for SMPP, which warrants further research and development. Given these challenges, the aim of this study is to develop and validate a sensitive and accurate predictive model for SMPP in children. This model will integrate various factors, including patient demographics, clinical characteristics, laboratory markers, imaging features, and macrolide resistance. By considering these multi-dimensional variables, the model aims to facilitate early diagnosis, improve prognostic accuracy, and provide more personalized treatment strategies for children with SMPP. Methods Study patients This study is a retrospective cohort analysis of children diagnosed with MPP who were hospitalized at West China Second Hospital of Sichuan University from September 2022 to February 2024. The study population was selected based on specific inclusion and exclusion criteria. The inclusion criteria were as follows: (1) hospitalized patients aged over 28 days and under 18 years; (2) diagnosis of MPP, defined by: (a) typical clinical manifestations, including fever, cough, tachypnea, and abnormal breath sounds; (b) characteristic chest imaging findings, such as interstitial infiltrates, segmental or lobar consolidation, and hilar lymphadenopathy; and (c) a positive result for M. pneumoniae RNA detected by nucleic acid amplification testing from throat swab specimens. Exclusion criteria included the presence of severe underlying conditions such as cardiac, hepatic, renal, or other critical illnesses, immunodeficiency (either congenital or acquired), or discharge against medical advice during hospitalization. All enrolled patients were subsequently classified into two groups based on disease severity in accordance with the "Guidelines for the Diagnosis and Treatment of Mycoplasma Pneumonia in Children, 2023 Edition." Patients who met any of the following criteria were included in the experimental group, diagnosed with SMPP: 1)Persistent high fever (≥ 39°C) for ≥ 5 days or fever lasting ≥ 7 days; 2)Presence of at least one of the following severe respiratory manifestations: wheezing, shortness of breath, respiratory distress, chest pain, or hemoptysis; 3)Oxygen saturation ≤ 93% measured by finger pulse oximetry while breathing room air at rest; 4) Radiographic findings of one of the following: (a) ≥ 2/3 involvement of a single lung lobe with homogeneous high-density consolidation or ≥ 2 lung lobes showing high-density consolidation regardless of the affected area size; (b) Diffuse involvement of a single lung or ≥ 4/5 lung lobes affected with fine bronchiolar changes; 5) Progressive clinical symptoms with radiographic evidence of lesion progression, defined as a > 50% increase in lesion size within 24–48 hours. Patients who did not meet these criteria were included in the control group. Data Collection In this study, patient data were collected, including demographic characteristics, clinical features, laboratory results, and imaging findings. Demographic information recorded for both groups of children included age, gender, duration of hospitalization, the total duration of fever, and maximum body temperature. Venous blood samples were collected within 24 hours of admission and sent to the laboratory for analysis. Laboratory tests included routine blood work, such as white blood cell count, neutrophil percentage, lymphocyte percentage, hemoglobin level, platelet count, and high-sensitivity C-reactive protein (hs-CRP). Liver function tests included aspartate aminotransferase (AST), alanine aminotransferase (ALT), and lactate dehydrogenase (LDH). Coagulation markers such as fibrinogen (Fg) and D-dimer were also assessed. Additionally, sputum culture, M. pneumoniae Macrolide resistance gene mutation testing (from pharyngeal swabs, including MP nucleic acid testing and A2063G/A2064G resistance mutation site testing), and respiratory virus multiplex nucleic acid tests (from pharyngeal swabs) were performed. Imaging studies primarily consisted of chest radiographs and/or CT scans to evaluate the extent of lung involvement and the type of lesions, including solid lung lesions, patchy lung shadows, mosaic patterns, exudative shadows, pleural thickening, pleural effusion, and bronchial obstruction. Statistical analysis In this study, all statistical analyses were performed using R language (version 4.3.2). A p-value of < 0.05 was considered statistically significant. Due to missing values in D-dimer, Fg, AST, and ALT, and the non-normal distribution of these data, missing values were imputed using the median filling method. The CBCgrps2.8 package was used to compare the baseline characteristics of the two groups, with categorical variables analyzed using the chi-square test. For continuous variables that were not normally distributed, the Mann-Whitney U test was applied. Subsequently, lasso regression and logistic regression were performed for multifactorial analysis. The final regression model was converted into nomograms using R software. The data were divided into train and test groups to validate the model's specificity and sensitivity. Receiver operating characteristic (ROC) curves were generated to evaluate the predictive performance of the regression model for SMPP, and the sensitivity and specificity of the predictive scales were calculated. Calibration curves were used to assess the uncertainty and stability of model predictions. Decision curve analysis was employed to evaluate the clinical utility of the predictive model across various risk thresholds. Results Patient Characteristics A total of 562 MPP patients were included, consisting of 519 children with SMPP and 43 with non-severe Mycoplasma pneumoniae pneumonia (non-SMPP). The median age in both groups was 6.5 years, consistent with the known prevalence of MPP in school-age children. No significant gender differences were observed between the groups.Hospitalization duration was shorter in the non-SMPP group, but this difference was not statistically significant (p = 0.09), possibly due to the small sample size of the non-SMPP group. Cough was the primary symptom in all patients. The median fever duration was significantly longer in the SMPP group (8 days) compared to the non-SMPP group (4 days), and the peak temperature was higher in the SMPP group (39.3°C vs. 38.5°C, p < 0.001). Wheezing was observed in 13% of SMPP patients but absent in the non-SMPP group. Laboratory findings showed no significant differences in white blood cell count, neutrophil and lymphocyte percentages, platelet count, or liver function markers. However, the SMPP group had significantly lower hemoglobin levels (p < 0.001) and higher hs-CRP (11.5 vs. 6.4, p = 0.003) and LDH levels (p = 0.014), indicating a stronger inflammatory response in severe cases. There were no significant differences between the groups regarding comorbidities with other infections or macrolide resistance. However, MRMPP was prevalent in both groups, with 455 cases (88%) in the SMPP group and 36 cases (84%) in the non-SMPP group. This high prevalence of macrolide resistance highlights the growing concern of resistance in M. pneumoniae infections, which should be considered when selecting treatment strategies for MPP. Imaging findings showed a significantly higher proportion of solid lung lesions in the SMPP group (69%) compared to the non-SMPP group (p < 0.001). Pleural effusion was also more common in the SMPP group (22%) than in the non-SMPP group (7%) (p = 0.031), suggesting more severe pulmonary involvement in the SMPP group. The non-SMPP group had a higher proportion of patchy lung lesions (p = 0.008), indicating more diffuse involvement in milder cases. In contrast, mosaic signs and bronchial occlusion were only seen in the SMPP group, with 41 and 19 cases, respectively, reflecting the more severe nature of SMPP. Predictive Model Based on Lasso-Logistics Regression Lasso regression was used in this study to identify key parameters related to SMPP. Using 20-fold cross-validation, the model showed strong performance with minimal variables when λ was set to 0.01163327. Key variables included age, fever duration, peak fever temperature, wheezing, extrapulmonary complications, hemoglobin levels, pulmonary solidity, mosaic sign, and bronchial occlusion. Patients were split into training and validation sets (7:3 ratio) for model construction and evaluation. Logistic regression was then applied to refine the model with the selected parameters. When comparing models with and without age, both showed excellent fit, with C-indices of 0.973 and 0.977 for the training and validation sets, respectively, for the model including age. The model without age had C-indices of 0.972 and 0.971. These results showed that age did not significantly improve predictive power, so the model excluding age, with eight variables, was selected as the final model. Establishment of a Nomogram for Predicting SMPP Based on the variables screened from lasso regression and validated by logistic regression, we developed a nomogram to predict SMPP (Fig. 1 ). Each level of each variable was assigned a score based on a scale. A total score was obtained by summing the scores of the selected variables. Predictions corresponding to this total score helped to estimate the incidence of SMPP. Validation of the Nomogram A nomogram was developed to predict the diagnostic probability of SMPP. The area under the curve (AUC) for the nomogram in the training cohort was 0.9722 (Fig. 2 a), while the AUC for the validation cohort was 0.975 (Fig. 2 b). These results demonstrate that the nomogram exhibits strong predictive performance in both cohorts. Notably, the AUC value in the validation set was slightly higher than in the training set, suggesting that the model is not overfitted and offers reliable predictions. Figure 3 displays the calibration curves for both the training (Fig. 3 a) and validation (Fig. 3 b) cohorts. The calibration curves indicate a low mean absolute error, reflecting high prediction accuracy in both datasets. This suggests that the nomogram fits the data well, with minimal over- or underestimation of risk. Finally, a decision curve analysis was performed to assess the clinical utility of the nomogram (Fig. 3 c). The results indicate that clinical decisions guided by our nomogram yield a high net benefit, highlighting its potential for practical application in clinical settings. Discussion In this paper, we performed a large-sample multirisk factor analysis and identified fever duration, peak of fever, wheezing, extrapulmonary complications, hemoglobin level, lung solidity, mosaic sign, and bronchial occlusion as independent risk factors for the development of SMPP in children. These results were used to construct a nomogram to estimate the risk of developing SMPP in hospitalized children. The validity of our nomogram model was determined using multiple metrics, including AUC, calibration curves, and decision curve analysis. In this study, we constructed a more comprehensive model based on a combination of risk factors to better identify SMPP at an early stage and contribute to the diagnosis, treatment, and prognosis of SMPP. M. pneumoniae predominantly infects the upper respiratory tract. In our study, we found that all the children exhibited coughs, with or without fever and wheezing. Other clinical manifestations such as muscle pain, chest pain, and hemoptysis were less common. From Table 1 , it is apparent that the SMPP group had a longer duration of fever and a higher fever peak than the non-SMPP group. The immune response prior to the inflammatory peak (with pro-inflammatory cytokines potentially playing a role during this stage) may be associated with lung cell injury, whereas the immune response following the peak is involved in tissue cell repair, potentially mediated by anti-inflammatory cytokines during the recovery phase[ 7 ]. A prolonged fever duration is often indicative of a stronger inflammatory response and may also suggest an excessive host immune response or be linked to macrolide resistance in M. pneumoniae[ 8 ]. Table 1 Comparison of clinical characteristics between SMPP and Non-SMPP groups Factors Non-SMPP group(n = 43) SMPP group(n = 519) p value age, Median 6.5 (3.04, 8.5) 6.5 (4.08, 8.33) 0.645 male, n(%) 24 (56) 255 (49) 0.494 Total hospital days, Median 8 (7, 10) 9 (7, 11) 0.09 Duration of fever, Median 4 (3, 6) 8 (6, 10) < 0.001 Peak of fever, Median 38.5 (38, 38.8) 39.3 (39, 39.9) < 0.001 cough, n (%) 43 (100) 519 (100) 1 Wheezing, n (%) 43 (100) 451 (87) 0.022 Extrapulmonary complications, n (%) 43 (100) 481 (93) 0.104 WBC(10^9/L), Median NEUT(%), Median 8.1 (6.65, 10.1) 56 (50.5, 68.45) 7.4 (5.9, 9.95) 61.4 (50.75, 69.9) 0.117 0.393 LYMPH(%), Median 32.6 (20.35, 39.65) 27.8 (19.95, 38.5) 0.285 HGB(g/L), Median 127 (121.5, 132.5) 121 (114, 128) < 0.001 PLT(10^9/L), Median 304 (249, 413.5) 282 (225, 380.5) 0.288 hs-CRP(mg/L), Median 6.4 (0.95, 13.95) 11.5 (3.05, 27.05) 0.003 ALT(U/L), Median 16 (13, 21.5) 18 (14, 24) 0.143 AST(U/L), Median 33 (27, 38) 33 (27, 40) 0.343 LDH(U/L), Median 300 (241.5, 332.5) 316 (272, 384) 0.014 Fg(mg/dl), Median 400 (379, 400) 400 (347, 456) 0.45 D-dimer(mg/l), Median 0.57 (0.38, 0.57) 0.57 (0.44, 0.85) 0.054 Co-infection, n (%) 0.154 Viral infection 5 (12) 61 (12) Bacterial infection 1 (2) 26 (5) Bacterial and viral infections 2 (5) 4 (1) Macrolide-resistance, n (%) 36 (84) 455 (88) 0.61 Consolidation of the lung, n (%) 11 (26) 360 (69) < 0.001 Lung exudation, n (%) 0 (0) 5 (1) 1 Pleural thickening, n (%) 7 (16) 101 (19) 0.759 Pleural effusion, n (%) 3 (7) 115 (22) 0.031 lung patch, n (%) 39 (91) 367 (71) 0.008 mosaic signs in the lungs, n (%) 0 (0) 41 (8) 0.063 bronchial occlusion., n (%) 0 (0) 19 (4) 0.386 Wheezing is another important clinical feature, often associated with severe lung lesions, plastic bronchitis, asthma attacks, pleural effusion, and pulmonary embolism. Many children with MPP experience recurrent wheezing and decreased small airway function even after clinical symptoms resolve, which may eventually lead to asthma[ 9 ]. The hemoglobin level in the SMPP group was lower than that in the non-SMPP group, potentially reflecting a more intense inflammatory response and compromised oxygen-carrying capacity. A similar reduction in hemoglobin has also been reported in the RMPP group (110.93 ± 11.26 g/L), which is significantly lower than in the typical MPP group (121.97 ± 9.73 g/L)[ 10 ]. This suggests that both SMPP and RMPP share the characteristic of reduced hemoglobin levels, which could be indicative of a more severe or prolonged inflammatory process in both conditions. Extrapulmonary complications in our study included impaired consciousness, convulsions, acute pancreatitis, pericardial effusion, and arrhythmias, as well as skin rashes. These are consistent with previous findings that extrapulmonary complications in MPP, such as hepatic and renal dysfunction, can indicate the severity of disease and influence both diagnostic and therapeutic decisions[ 11 ]. Furthermore, mucocutaneous reactions, including mucositis and target-shaped skin lesions, are not uncommon in MPP patients, especially in those with severe disease progression. Imaging is essential in the clinical diagnosis of diseases, with distinct imaging features reflecting disease severity. Lung solid lesions are a key indicator in SMPP, potentially resulting from immune responses that impair mucociliary clearance and cause mucosal obstruction. In our study, lung solid lesions were observed in 69% of the SMPP group and 26% of the Non-SMPP group (P < 0.001)[ 12 ]. The mosaic sign on CT imaging, associated with small airway lesions, often indicates inflammation or structural changes that restrict gas flow, resulting in regional gas trapping. These lesions typically affect airways 2 to 4 mm in diameter. A previous study[ 13 ] suggested a correlation between the mosaic sign and lung volume, though further research is needed. Bronchial occlusion, which can lead to occlusive bronchiolitis, is a chronic and irreversible condition[ 14 ]. Research[ 15 ] has identified pulmonary consolidation and pleural effusion as independent risk factors for embolism development in MPP, suggesting that severe pulmonary involvement, such as bronchial occlusion, may also be an important predictor for the progression of SMPP. A study by Kyunghoon Kim et al.[ 16 ] showed a significant rise in MRMPP infections globally, from 18.2% between 2000 and 2019 to 76.5%. In our study, 88% of children in the SMPP group and 84% in the non-SMPP group had MRMPP. However, macrolide resistance was not an independent risk factor for SMPP, consistent with findings from a meta-analysis[ 17 ]. Additionally, there was no significant difference in clinical severity between MRMPP and non-resistant MPP infections. Similarly, Kuan-Lin Lee et al.[ 18 ] found no difference in severity among children with MPP in the intensive care unit. The continuous increase in MRMPP infection rates may lead to higher hospitalization rates and increased treatment challenges. Alarmingly, our study shows that macrolide resistance is widespread in the MPP pediatric population, significantly reducing the clinical efficacy of first-line macrolide drugs. To effectively address this challenge, there is an urgent need to strengthen the exploration of alternative treatment options and monitor resistance trends to optimize infection management strategies. Relying solely on antibiotic treatment may have limited efficacy, particularly in severe and drug-resistant cases. Based on current clinical evidence and treatment experience, we recommend an early intervention, multimodal, and comprehensive treatment approach. In addition to the rational use of antibiotics, pulmonary rehabilitation should be emphasized to improve lung function, and corticosteroids should be used appropriately to control excessive inflammatory responses. Oxygen therapy should be promptly administered for patients with hypoxemia. Moreover, standardized airway management techniques, such as nebulization and postural drainage, along with targeted bronchoscopy lavage (especially for cases with mucus plug obstruction or plastic bronchitis), are important adjunctive treatments. This comprehensive treatment strategy not only effectively controls symptoms and shortens the disease course but also reduces complications and improves patient prognosis. Future high-quality clinical studies are needed to further optimize this multidisciplinary approach[ 19 ]. This study has some limitations. The high proportion of severe MPP cases among hospitalized patients (92%) is mainly due to mild cases being treated in outpatient settings or resolving spontaneously without seeking medical attention, which makes the results more reflective of severe case characteristics. Additionally, the single-center, retrospective design may introduce selection bias. However, this design allows for a deeper identification of specific indicators and risk factors for severe cases, providing direct guidance for inpatient diagnosis and treatment. Future research should focus on the following areas: First, multicenter prospective cohort studies should be conducted to validate the clinical applicability of the predictive model, with diverse patient populations from different regions and varying disease severities. Second, further exploration of other potential biomarkers, such as specific cytokine profiles (e.g., IL-6, IL-8), microbiome characteristics, or host genetic factors, and their association with disease progression is recommended. Conclusion In this study, we identified eight independent risk factors for children with SMPP and developed a risk prediction model based on these factors, which demonstrated high predictive accuracy and excellent calibration. Additionally, the study found a high prevalence of drug resistance (84–88%) among SMPP patients, highlighting the importance of early and appropriate medication use in clinical practice. This predictive model offers a valuable tool for the early identification and intervention of SMPP, aiding in the optimization of clinical decision-making and enabling clinicians to promptly identify and intervene in children at risk of SMPP. Abbreviations severe Mycoplasma pneumoniae Pneumonia SMPP macrolide-resistant Mycoplasma Pneumoniae Pneumonia MRMPP Mycoplasma pneumoniae Pneumonia MPP Mycoplasma pneumoniae M. pneumoniae Refractory Mycoplasma Pneumoniae Pneumonia RMPP High-sensitivity C-reactive protein Hs-CRP Aspartate aminotransferase AST Alanine aminotransferase ALT Lactate dehydrogenase LDH Fbrinogen Fg Receiver operating characteristic ROC General Mycoplasma pneumoniae Pneumoniae GMPP Non-severe Mycoplasma pneumoniae pneumonia Non-SMPP Area under the curve AUC Declarations Acknowledgements Not applicable. Author contributions SYL conceived the study, conducted data analysis and drafted the manuscript. LJZ. collected sample data. LD reviewed and discussed the results. DYL and JHL revised the paper. All authors approved the final manuscript as submitted and agreed to be accountable for all aspects of the work. Funding This study was supported by Grants from the Science and Technology Bureau of Sichuan Province(grant number 24NSFSC2401), the Key Research and Development Program of Sichuan Province (grant number 2023YFS0129), and the National Key Research and Development Program of China（grant number 2023YFC2706402）. Availability of data and materials The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval The study was conducted in accordance with the Declaration of Helsinki from the Medical Research Ethics Committee of West China Second Hospital of Sichuan University, which waived the need for informed consent (2024-384). Consent for publication Not applicable. Competing Interests The authors have no relevant financial or non-financial interests to disclose. References Jia Z, Sun Q, Zheng Y, Xu J, Wang Y. 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Kim K, Jung S, Kim M, Park S, Yang H-J, Lee E. Global Trends in the Proportion of Macrolide-Resistant Mycoplasma pneumoniae Infections: A Systematic Review and Meta-analysis. JAMA Netw Open. 2022;5:e2220949. Chen Y-C, Hsu W-Y, Chang T-H. Macrolide-Resistant Mycoplasma pneumoniae Infections in Pediatric Community-Acquired Pneumonia. Emerg Infect Dis. 2020;26:1382–91. Lee K-L, Lee C-M, Yang T-L, Yen T-Y, Chang L-Y, Chen J-M, et al. Severe Mycoplasma pneumoniae pneumonia requiring intensive care in children, 2010–2019. J Formos Med Assoc. 2021;120:281–91. Guo Q, Li L, Wang C, Huang Y, Ma F, Cong S, et al. Comprehensive virome analysis of the viral spectrum in paediatric patients diagnosed with Mycoplasma pneumoniae pneumonia. Virol J. 2022;19:181. Supplementary Files data1.xlsm Cite Share Download PDF Status: Published Journal Publication published 07 Jun, 2025 Read the published version in Italian Journal of Pediatrics → Version 1 posted Editorial decision: Accept 31 May, 2025 Reviewers agreed at journal 12 May, 2025 Reviewers invited by journal 05 May, 2025 Editor assigned by journal 15 Apr, 2025 First submitted to journal 10 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6420625","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452219373,"identity":"60663c28-86a6-46bf-a008-c9a7ace70b37","order_by":0,"name":"Shaoying Liu","email":"","orcid":"","institution":"West China Women's and Children's Hospital: Sichuan University West China Second University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shaoying","middleName":"","lastName":"Liu","suffix":""},{"id":452219374,"identity":"6fb2fa2e-ea2f-4b2a-8260-2bf8195598ca","order_by":1,"name":"Lijun Zhang","email":"","orcid":"","institution":"West China Women's and Children's Hospital: Sichuan University West China Second University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lijun","middleName":"","lastName":"Zhang","suffix":""},{"id":452219375,"identity":"558f3197-d2a2-415f-aa3a-ca9a125833bf","order_by":2,"name":"Lei Dai","email":"","orcid":"","institution":"Sichuan University State Key Laboratory of Biotherapy","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Dai","suffix":""},{"id":452219376,"identity":"8795f944-3a0f-4540-9679-f524a5c055c9","order_by":3,"name":"Jinhui Li","email":"","orcid":"","institution":"West China Women's and Children's Hospital: Sichuan University West China Second University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jinhui","middleName":"","lastName":"Li","suffix":""},{"id":452219377,"identity":"aac27c35-681a-4688-ab3a-2c81b79302ce","order_by":4,"name":"Deyuan Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYDCCAwwGzH//sMnxMzMffkC0FgbeBj5jyXa2NANStMglbjjPoyBBlA6+G8nbJCR3mCVuPswD1FxjE01Qi+SNtDIJwzNpxtsO8x54wHAsLbeBkBaDGzlmEglsx2S3HeZLMGBsOEyklgNs/xk3N/MYSBCtRbKxjU1xAzOxWiTPPCu2ZjjDZixxGBjICcT4he948sbbDBXAqOw/fPjBhxobwlpQQQJpykfBKBgFo2AU4AIAUSxABhrn3xAAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-3746-1110","institution":"West China Women's and Children's Hospital: Sichuan University West China Second University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Deyuan","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-04-10 13:35:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6420625/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6420625/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13052-025-02039-y","type":"published","date":"2025-06-07T15:56:59+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82559315,"identity":"687fa9a4-b001-49e8-805a-95247efad18f","added_by":"auto","created_at":"2025-05-13 01:25:33","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":362262,"visible":true,"origin":"","legend":"\u003cp\u003eNomograms for the prediction of Severe Mycoplasma pneumoniae Pneumonia.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWhen using a nomogram, a vertical line is drawn from each variable to the point scale, the corresponding scores are noted, and then the scores for all variables are added together to obtain a total. Finally, the probability of diagnosing Mycoplasma pneumoniae pneumonia is determined from the total score by referring to the bottom of the nomogram. For categorical variables, \"1\" indicates a positive result and \"0\" indicates a negative result.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6420625/v1/05251db8d4e94ca5a7aed6ec.jpg"},{"id":82559313,"identity":"de0d635f-470d-4bab-960c-7814d026a958","added_by":"auto","created_at":"2025-05-13 01:25:33","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":93163,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic (ROC) of training and validation queues.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003etrain queues\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e test queues\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6420625/v1/2c8bc88968acfd2a395b3739.jpg"},{"id":82559314,"identity":"586d42e4-b335-4724-8081-3de2e0db247f","added_by":"auto","created_at":"2025-05-13 01:25:33","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":258396,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves and decision curves of training and validation queues.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCalibration curves of the nomogram for both the training cohort (a) and validation cohort (b) are presented. These curves assess the accuracy of the nomogram in predicting the risk of SMPP in the queue. The x-axis represents the predicted risk of SMPP, while the y-axis shows the observed rate of SMPP. The diagonal dotted line indicates perfect prediction, representing an ideal model. The solid line reflects the performance of the nomogram, with a closer alignment to the diagonal dotted line indicating better predictive accuracy.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Calibration curves of train queues\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e Calibration curves of test queues\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec\u003c/strong\u003e Decision curves of training and validation queues.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6420625/v1/0e94b72f116581783632ee56.jpg"},{"id":84242502,"identity":"ee000ad5-cdca-4c67-8037-5e5616c3e33d","added_by":"auto","created_at":"2025-06-09 16:08:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1386854,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6420625/v1/905113f7-a265-4d34-82de-b45e6bbde656.pdf"},{"id":82559325,"identity":"17b7dc20-2ba3-48d0-9231-ac015ef51621","added_by":"auto","created_at":"2025-05-13 01:25:33","extension":"xlsm","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":84741,"visible":true,"origin":"","legend":"","description":"","filename":"data1.xlsm","url":"https://assets-eu.researchsquare.com/files/rs-6420625/v1/632757a4973f7a600479d49c.xlsm"}],"financialInterests":"","formattedTitle":"Prediction Model for Severe Mycoplasma pneumoniae Pneumonia and Analysis of Macrolide-resistance in Children: A case-control Study","fulltext":[{"header":"Background","content":"\u003cp\u003eThe pathogenesis of MPP caused by \u003cem\u003eMycoplasma pneumoniae\u003c/em\u003e (M. pneumoniae), a bacterium that lacks a cell wall, involves both pathogen characteristics and the host\u0026rsquo;s immune response [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In general, MPP presents with mild clinical symptoms, including fever, cough, headache, sore throat, and dyspnoea. However, a significant portion (22.0%) of patients may experience extra-respiratory manifestations, such as neurological[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], cutaneous, gastrointestinal, cardiovascular, musculoskeletal, haematological, and renal symptoms.\u003c/p\u003e \u003cp\u003eIn recent years, the rise of MRMPP has added complexity to the management of MPP. Studies, such as those by Jeffery Ho[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], have shown that macrolide resistance varies globally, with notably higher rates in China and lower resistance in North America and parts of Europe. The reduced susceptibility of MRMPP to macrolides can result in higher bacterial loads and exacerbated immune responses, contributing to more severe disease outcomes.\u003c/p\u003e \u003cp\u003eSMPP is characterized by rapid disease progression, with high fever and sudden hypoxemia being prominent features. In contrast, refractory Mycoplasma pneumoniae pneumonia (RMPP) typically involves a prolonged disease course and poor response to first-line antibiotic treatment[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, early recognition of SMPP is crucial, especially in cases resistant to macrolides, as timely intervention can prevent further complications. Although several prediction models have been proposed for RMPP, there are few specific models designed for SMPP. For instance, Zhang et al.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] identified CRP\u0026thinsp;\u0026ge;\u0026thinsp;16.5 mg/L, LDH\u0026thinsp;\u0026ge;\u0026thinsp;417 IU/L, and IL-6\u0026thinsp;\u0026ge;\u0026thinsp;14.75 pg/ml as significant predictors of RMPP in children. Shen et al.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] developed a model incorporating CRP, LDH, and D-dimer as predictive markers for RMPP. However, there is a noticeable lack of dedicated prediction tools for SMPP, which warrants further research and development.\u003c/p\u003e \u003cp\u003eGiven these challenges, the aim of this study is to develop and validate a sensitive and accurate predictive model for SMPP in children. This model will integrate various factors, including patient demographics, clinical characteristics, laboratory markers, imaging features, and macrolide resistance. By considering these multi-dimensional variables, the model aims to facilitate early diagnosis, improve prognostic accuracy, and provide more personalized treatment strategies for children with SMPP.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy patients\u003c/h2\u003e \u003cp\u003eThis study is a retrospective cohort analysis of children diagnosed with MPP who were hospitalized at West China Second Hospital of Sichuan University from September 2022 to February 2024. The study population was selected based on specific inclusion and exclusion criteria.\u003c/p\u003e \u003cp\u003eThe inclusion criteria were as follows: (1) hospitalized patients aged over 28 days and under 18 years; (2) diagnosis of MPP, defined by: (a) typical clinical manifestations, including fever, cough, tachypnea, and abnormal breath sounds; (b) characteristic chest imaging findings, such as interstitial infiltrates, segmental or lobar consolidation, and hilar lymphadenopathy; and (c) a positive result for M. pneumoniae RNA detected by nucleic acid amplification testing from throat swab specimens. Exclusion criteria included the presence of severe underlying conditions such as cardiac, hepatic, renal, or other critical illnesses, immunodeficiency (either congenital or acquired), or discharge against medical advice during hospitalization.\u003c/p\u003e \u003cp\u003e All enrolled patients were subsequently classified into two groups based on disease severity in accordance with the \"Guidelines for the Diagnosis and Treatment of Mycoplasma Pneumonia in Children, 2023 Edition.\" Patients who met any of the following criteria were included in the experimental group, diagnosed with SMPP: 1)Persistent high fever (\u0026ge;\u0026thinsp;39\u0026deg;C) for \u0026ge;\u0026thinsp;5 days or fever lasting\u0026thinsp;\u0026ge;\u0026thinsp;7 days; 2)Presence of at least one of the following severe respiratory manifestations: wheezing, shortness of breath, respiratory distress, chest pain, or hemoptysis; 3)Oxygen saturation\u0026thinsp;\u0026le;\u0026thinsp;93% measured by finger pulse oximetry while breathing room air at rest; 4) Radiographic findings of one of the following: (a)\u0026thinsp;\u0026ge;\u0026thinsp;2/3 involvement of a single lung lobe with homogeneous high-density consolidation or \u0026ge;\u0026thinsp;2 lung lobes showing high-density consolidation regardless of the affected area size; (b) Diffuse involvement of a single lung or \u0026ge;\u0026thinsp;4/5 lung lobes affected with fine bronchiolar changes; 5) Progressive clinical symptoms with radiographic evidence of lesion progression, defined as a\u0026thinsp;\u0026gt;\u0026thinsp;50% increase in lesion size within 24\u0026ndash;48 hours. Patients who did not meet these criteria were included in the control group.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eIn this study, patient data were collected, including demographic characteristics, clinical features, laboratory results, and imaging findings. Demographic information recorded for both groups of children included age, gender, duration of hospitalization, the total duration of fever, and maximum body temperature. Venous blood samples were collected within 24 hours of admission and sent to the laboratory for analysis. Laboratory tests included routine blood work, such as white blood cell count, neutrophil percentage, lymphocyte percentage, hemoglobin level, platelet count, and high-sensitivity C-reactive protein (hs-CRP). Liver function tests included aspartate aminotransferase (AST), alanine aminotransferase (ALT), and lactate dehydrogenase (LDH). Coagulation markers such as fibrinogen (Fg) and D-dimer were also assessed. Additionally, sputum culture, M. pneumoniae Macrolide resistance gene mutation testing (from pharyngeal swabs, including MP nucleic acid testing and A2063G/A2064G resistance mutation site testing), and respiratory virus multiplex nucleic acid tests (from pharyngeal swabs) were performed. Imaging studies primarily consisted of chest radiographs and/or CT scans to evaluate the extent of lung involvement and the type of lesions, including solid lung lesions, patchy lung shadows, mosaic patterns, exudative shadows, pleural thickening, pleural effusion, and bronchial obstruction.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eIn this study, all statistical analyses were performed using R language (version 4.3.2). A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant. Due to missing values in D-dimer, Fg, AST, and ALT, and the non-normal distribution of these data, missing values were imputed using the median filling method.\u003c/p\u003e \u003cp\u003eThe CBCgrps2.8 package was used to compare the baseline characteristics of the two groups, with categorical variables analyzed using the chi-square test. For continuous variables that were not normally distributed, the Mann-Whitney U test was applied. Subsequently, lasso regression and logistic regression were performed for multifactorial analysis. The final regression model was converted into nomograms using R software. The data were divided into train and test groups to validate the model's specificity and sensitivity. Receiver operating characteristic (ROC) curves were generated to evaluate the predictive performance of the regression model for SMPP, and the sensitivity and specificity of the predictive scales were calculated. Calibration curves were used to assess the uncertainty and stability of model predictions. Decision curve analysis was employed to evaluate the clinical utility of the predictive model across various risk thresholds.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePatient Characteristics\u003c/h2\u003e \u003cp\u003eA total of 562 MPP patients were included, consisting of 519 children with SMPP and 43 with non-severe Mycoplasma pneumoniae pneumonia (non-SMPP). The median age in both groups was 6.5 years, consistent with the known prevalence of MPP in school-age children. No significant gender differences were observed between the groups.Hospitalization duration was shorter in the non-SMPP group, but this difference was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.09), possibly due to the small sample size of the non-SMPP group.\u003c/p\u003e \u003cp\u003eCough was the primary symptom in all patients. The median fever duration was significantly longer in the SMPP group (8 days) compared to the non-SMPP group (4 days), and the peak temperature was higher in the SMPP group (39.3\u0026deg;C vs. 38.5\u0026deg;C, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Wheezing was observed in 13% of SMPP patients but absent in the non-SMPP group.\u003c/p\u003e \u003cp\u003eLaboratory findings showed no significant differences in white blood cell count, neutrophil and lymphocyte percentages, platelet count, or liver function markers. However, the SMPP group had significantly lower hemoglobin levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and higher hs-CRP (11.5 vs. 6.4, p\u0026thinsp;=\u0026thinsp;0.003) and LDH levels (p\u0026thinsp;=\u0026thinsp;0.014), indicating a stronger inflammatory response in severe cases.\u003c/p\u003e \u003cp\u003eThere were no significant differences between the groups regarding comorbidities with other infections or macrolide resistance. However, MRMPP was prevalent in both groups, with 455 cases (88%) in the SMPP group and 36 cases (84%) in the non-SMPP group. This high prevalence of macrolide resistance highlights the growing concern of resistance in M. pneumoniae infections, which should be considered when selecting treatment strategies for MPP.\u003c/p\u003e \u003cp\u003eImaging findings showed a significantly higher proportion of solid lung lesions in the SMPP group (69%) compared to the non-SMPP group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Pleural effusion was also more common in the SMPP group (22%) than in the non-SMPP group (7%) (p\u0026thinsp;=\u0026thinsp;0.031), suggesting more severe pulmonary involvement in the SMPP group. The non-SMPP group had a higher proportion of patchy lung lesions (p\u0026thinsp;=\u0026thinsp;0.008), indicating more diffuse involvement in milder cases. In contrast, mosaic signs and bronchial occlusion were only seen in the SMPP group, with 41 and 19 cases, respectively, reflecting the more severe nature of SMPP.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePredictive Model Based on Lasso-Logistics Regression\u003c/h2\u003e \u003cp\u003eLasso regression was used in this study to identify key parameters related to SMPP. Using 20-fold cross-validation, the model showed strong performance with minimal variables when λ was set to 0.01163327. Key variables included age, fever duration, peak fever temperature, wheezing, extrapulmonary complications, hemoglobin levels, pulmonary solidity, mosaic sign, and bronchial occlusion. Patients were split into training and validation sets (7:3 ratio) for model construction and evaluation. Logistic regression was then applied to refine the model with the selected parameters.\u003c/p\u003e \u003cp\u003eWhen comparing models with and without age, both showed excellent fit, with C-indices of 0.973 and 0.977 for the training and validation sets, respectively, for the model including age. The model without age had C-indices of 0.972 and 0.971. These results showed that age did not significantly improve predictive power, so the model excluding age, with eight variables, was selected as the final model.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEstablishment of a Nomogram for Predicting SMPP\u003c/h3\u003e\n\u003cp\u003eBased on the variables screened from lasso regression and validated by logistic regression, we developed a nomogram to predict SMPP (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Each level of each variable was assigned a score based on a scale. A total score was obtained by summing the scores of the selected variables. Predictions corresponding to this total score helped to estimate the incidence of SMPP.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eValidation of the Nomogram\u003c/h3\u003e\n\u003cp\u003eA nomogram was developed to predict the diagnostic probability of SMPP. The area under the curve (AUC) for the nomogram in the training cohort was 0.9722 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), while the AUC for the validation cohort was 0.975 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). These results demonstrate that the nomogram exhibits strong predictive performance in both cohorts. Notably, the AUC value in the validation set was slightly higher than in the training set, suggesting that the model is not overfitted and offers reliable predictions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the calibration curves for both the training (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) and validation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) cohorts. The calibration curves indicate a low mean absolute error, reflecting high prediction accuracy in both datasets. This suggests that the nomogram fits the data well, with minimal over- or underestimation of risk.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFinally, a decision curve analysis was performed to assess the clinical utility of the nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). The results indicate that clinical decisions guided by our nomogram yield a high net benefit, highlighting its potential for practical application in clinical settings.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this paper, we performed a large-sample multirisk factor analysis and identified fever duration, peak of fever, wheezing, extrapulmonary complications, hemoglobin level, lung solidity, mosaic sign, and bronchial occlusion as independent risk factors for the development of SMPP in children. These results were used to construct a nomogram to estimate the risk of developing SMPP in hospitalized children. The validity of our nomogram model was determined using multiple metrics, including AUC, calibration curves, and decision curve analysis. In this study, we constructed a more comprehensive model based on a combination of risk factors to better identify SMPP at an early stage and contribute to the diagnosis, treatment, and prognosis of SMPP.\u003c/p\u003e \u003cp\u003eM. pneumoniae predominantly infects the upper respiratory tract. In our study, we found that all the children exhibited coughs, with or without fever and wheezing. Other clinical manifestations such as muscle pain, chest pain, and hemoptysis were less common. From Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, it is apparent that the SMPP group had a longer duration of fever and a higher fever peak than the non-SMPP group. The immune response prior to the inflammatory peak (with pro-inflammatory cytokines potentially playing a role during this stage) may be associated with lung cell injury, whereas the immune response following the peak is involved in tissue cell repair, potentially mediated by anti-inflammatory cytokines during the recovery phase[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A prolonged fever duration is often indicative of a stronger inflammatory response and may also suggest an excessive host immune response or be linked to macrolide resistance in M. pneumoniae[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of clinical characteristics between SMPP and Non-SMPP groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-SMPP group(n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSMPP group(n\u0026thinsp;=\u0026thinsp;519)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage, Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.5 (3.04, 8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5 (4.08, 8.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e255 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal hospital days, Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (7, 10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (7, 11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of fever, Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (3, 6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (6, 10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak of fever, Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.5 (38, 38.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.3 (39, 39.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecough, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e519 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWheezing, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e451 (87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtrapulmonary complications, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e481 (93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC(10^9/L), Median\u003c/p\u003e \u003cp\u003eNEUT(%), Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.1 (6.65, 10.1)\u003c/p\u003e \u003cp\u003e56 (50.5, 68.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.4 (5.9, 9.95)\u003c/p\u003e \u003cp\u003e61.4 (50.75, 69.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLYMPH(%), Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.6 (20.35, 39.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.8 (19.95, 38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGB(g/L), Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127 (121.5, 132.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121 (114, 128)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT(10^9/L), Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e304 (249, 413.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e282 (225, 380.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP(mg/L), Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.4 (0.95, 13.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.5 (3.05, 27.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT(U/L), Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (13, 21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (14, 24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST(U/L), Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (27, 38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (27, 40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH(U/L), Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300 (241.5, 332.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e316 (272, 384)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFg(mg/dl), Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400 (379, 400)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e400 (347, 456)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-dimer(mg/l), Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57 (0.38, 0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57 (0.44, 0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo-infection, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eViral infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacterial infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacterial and viral infections\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMacrolide-resistance, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e455 (88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsolidation of the lung, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e360 (69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung exudation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePleural thickening, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePleural effusion, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elung patch, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e367 (71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emosaic signs in the lungs, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebronchial occlusion., n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWheezing is another important clinical feature, often associated with severe lung lesions, plastic bronchitis, asthma attacks, pleural effusion, and pulmonary embolism. Many children with MPP experience recurrent wheezing and decreased small airway function even after clinical symptoms resolve, which may eventually lead to asthma[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The hemoglobin level in the SMPP group was lower than that in the non-SMPP group, potentially reflecting a more intense inflammatory response and compromised oxygen-carrying capacity. A similar reduction in hemoglobin has also been reported in the RMPP group (110.93\u0026thinsp;\u0026plusmn;\u0026thinsp;11.26 g/L), which is significantly lower than in the typical MPP group (121.97\u0026thinsp;\u0026plusmn;\u0026thinsp;9.73 g/L)[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This suggests that both SMPP and RMPP share the characteristic of reduced hemoglobin levels, which could be indicative of a more severe or prolonged inflammatory process in both conditions.\u003c/p\u003e \u003cp\u003eExtrapulmonary complications in our study included impaired consciousness, convulsions, acute pancreatitis, pericardial effusion, and arrhythmias, as well as skin rashes. These are consistent with previous findings that extrapulmonary complications in MPP, such as hepatic and renal dysfunction, can indicate the severity of disease and influence both diagnostic and therapeutic decisions[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Furthermore, mucocutaneous reactions, including mucositis and target-shaped skin lesions, are not uncommon in MPP patients, especially in those with severe disease progression.\u003c/p\u003e \u003cp\u003eImaging is essential in the clinical diagnosis of diseases, with distinct imaging features reflecting disease severity. Lung solid lesions are a key indicator in SMPP, potentially resulting from immune responses that impair mucociliary clearance and cause mucosal obstruction. In our study, lung solid lesions were observed in 69% of the SMPP group and 26% of the Non-SMPP group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The mosaic sign on CT imaging, associated with small airway lesions, often indicates inflammation or structural changes that restrict gas flow, resulting in regional gas trapping. These lesions typically affect airways 2 to 4 mm in diameter. A previous study[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] suggested a correlation between the mosaic sign and lung volume, though further research is needed. Bronchial occlusion, which can lead to occlusive bronchiolitis, is a chronic and irreversible condition[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Research[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] has identified pulmonary consolidation and pleural effusion as independent risk factors for embolism development in MPP, suggesting that severe pulmonary involvement, such as bronchial occlusion, may also be an important predictor for the progression of SMPP.\u003c/p\u003e \u003cp\u003eA study by Kyunghoon Kim et al.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] showed a significant rise in MRMPP infections globally, from 18.2% between 2000 and 2019 to 76.5%. In our study, 88% of children in the SMPP group and 84% in the non-SMPP group had MRMPP. However, macrolide resistance was not an independent risk factor for SMPP, consistent with findings from a meta-analysis[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Additionally, there was no significant difference in clinical severity between MRMPP and non-resistant MPP infections. Similarly, Kuan-Lin Lee et al.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] found no difference in severity among children with MPP in the intensive care unit. The continuous increase in MRMPP infection rates may lead to higher hospitalization rates and increased treatment challenges. Alarmingly, our study shows that macrolide resistance is widespread in the MPP pediatric population, significantly reducing the clinical efficacy of first-line macrolide drugs. To effectively address this challenge, there is an urgent need to strengthen the exploration of alternative treatment options and monitor resistance trends to optimize infection management strategies. Relying solely on antibiotic treatment may have limited efficacy, particularly in severe and drug-resistant cases. Based on current clinical evidence and treatment experience, we recommend an early intervention, multimodal, and comprehensive treatment approach. In addition to the rational use of antibiotics, pulmonary rehabilitation should be emphasized to improve lung function, and corticosteroids should be used appropriately to control excessive inflammatory responses. Oxygen therapy should be promptly administered for patients with hypoxemia. Moreover, standardized airway management techniques, such as nebulization and postural drainage, along with targeted bronchoscopy lavage (especially for cases with mucus plug obstruction or plastic bronchitis), are important adjunctive treatments. This comprehensive treatment strategy not only effectively controls symptoms and shortens the disease course but also reduces complications and improves patient prognosis. Future high-quality clinical studies are needed to further optimize this multidisciplinary approach[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study has some limitations. The high proportion of severe MPP cases among hospitalized patients (92%) is mainly due to mild cases being treated in outpatient settings or resolving spontaneously without seeking medical attention, which makes the results more reflective of severe case characteristics. Additionally, the single-center, retrospective design may introduce selection bias. However, this design allows for a deeper identification of specific indicators and risk factors for severe cases, providing direct guidance for inpatient diagnosis and treatment. Future research should focus on the following areas: First, multicenter prospective cohort studies should be conducted to validate the clinical applicability of the predictive model, with diverse patient populations from different regions and varying disease severities. Second, further exploration of other potential biomarkers, such as specific cytokine profiles (e.g., IL-6, IL-8), microbiome characteristics, or host genetic factors, and their association with disease progression is recommended.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we identified eight independent risk factors for children with SMPP and developed a risk prediction model based on these factors, which demonstrated high predictive accuracy and excellent calibration. Additionally, the study found a high prevalence of drug resistance (84\u0026ndash;88%) among SMPP patients, highlighting the importance of early and appropriate medication use in clinical practice. This predictive model offers a valuable tool for the early identification and intervention of SMPP, aiding in the optimization of clinical decision-making and enabling clinicians to promptly identify and intervene in children at risk of SMPP.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003esevere Mycoplasma pneumoniae Pneumonia\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSMPP\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emacrolide-resistant Mycoplasma Pneumoniae Pneumonia\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMRMPP\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMycoplasma pneumoniae Pneumonia\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMPP\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMycoplasma pneumoniae\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eM. pneumoniae\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRefractory Mycoplasma Pneumoniae Pneumonia\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRMPP\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHigh-sensitivity C-reactive protein\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHs-CRP\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAspartate aminotransferase\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAlanine aminotransferase\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eALT\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLactate dehydrogenase\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLDH\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFbrinogen\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFg\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eReceiver operating characteristic\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eROC\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGeneral Mycoplasma pneumoniae Pneumoniae\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGMPP\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNon-severe Mycoplasma pneumoniae pneumonia\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNon-SMPP\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eArea under the curve\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSYL conceived the study, conducted data analysis and drafted the manuscript. LJZ. collected sample data. LD reviewed and discussed the results. DYL and JHL revised the paper. All authors approved the final manuscript as submitted and agreed to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Grants from the Science and Technology Bureau of Sichuan Province(grant number 24NSFSC2401), the Key Research and Development Program of Sichuan Province (grant number 2023YFS0129), and the National Key Research and Development Program of China（grant number 2023YFC2706402）.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki from the Medical Research Ethics Committee of West China Second Hospital of Sichuan University, which waived the need for informed consent (2024-384).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJia Z, Sun Q, Zheng Y, Xu J, Wang Y. The immunogenic involvement of miRNA-492 in mycoplasma pneumoniae infection in pediatric patients. J Pediatr (Rio J). 2023;99:187\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL DG RDEM, N DL. P, S E. Pathogenesis and Treatment of Neurologic Diseases Associated With Mycoplasma pneumoniae Infection. Frontiers in microbiology [Internet]. 2018 [cited 2025 Feb 9];9. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/30515139/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/30515139/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHo J, Ip M. Antibiotic-Resistant Community-Acquired Bacterial Pneumonia. Infect Dis Clin North Am. 2019;33:1087\u0026ndash;103.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang Q, Chen H-L, Wu N-S, Gao Y-M, Yu R, Zhu W-M. Prediction Model for Severe Mycoplasma pneumoniae Pneumonia in Pediatric Patients by Admission Laboratory Indicators. J Trop Pediatr. 2022;68:fmac059.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Zhou Y, Li S, Yang D, Wu X, Chen Z. The Clinical Characteristics and Predictors of Refractory Mycoplasma pneumoniae Pneumonia in Children. PLoS ONE. 2016;11:e0156465.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen F, Dong C, Zhang T, Yu C, Jiang K, Xu Y, et al. Development of a Nomogram for Predicting Refractory Mycoplasma pneumoniae Pneumonia in Children. Front Pediatr. 2022;10:813614.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRhim J-W, Kang J-H, Lee K-Y. Etiological and pathophysiological enigmas of severe coronavirus disease 2019, multisystem inflammatory syndrome in children, and Kawasaki disease. Clin Exp Pediatr. 2022;65:153\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi M, Wei X, Zhang S-S, Li S, Chen S-H, Shi S-J, et al. Recognition of refractory Mycoplasma pneumoniae pneumonia among Myocoplasma pneumoniae pneumonia in hospitalized children: development and validation of a predictive nomogram model. BMC Pulm Med. 2023;23:383.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang C, Mao Y, Jiang M, Yin W. Pediatric Critical Illness Score, Clinical Characteristics and Comprehensive Treatment of Children with Severe Mycoplasma Pneumoniae Pneumonia. Front Surg. 2022;9:897550.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng S, Lin J, Zheng X, Yan L, Zhang Y, Zeng Q, et al. Development and validation of a simple-to-use nomogram for predicting refractory Mycoplasma pneumoniae pneumonia in children. Pediatr Pulmonol. 2020;55:968\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang H, Yang J, Zhao W, Zhou J, He S, Shang Y, et al. Clinical features and risk factors of plastic bronchitis caused by refractory Mycoplasma pneumoniae pneumonia in children: a practical nomogram prediction model. Eur J Pediatr. 2023;182:1239\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan Y, Wei Y, Jiang W, Hao C. The clinical characteristics of corticosteroid-resistant refractory Mycoplasma Pneumoniae pneumonia in children. Sci Rep. 2016;6:39929.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang X, Gu H, Wu R, Chen L, Lv T, Jiang X, et al. Chest imaging classification in Mycoplasma pneumoniae pneumonia is associated with its clinical features and outcomes. Respir Med. 2024;221:107480.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKavaliunaite E, Aurora P. Diagnosing and managing bronchiolitis obliterans in children. Expert Rev Respir Med. 2019;13:481\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan C, Zhang T, Zheng J, Jin P, Zhang Q, Guo W, et al. Analysis of the risk factors and clinical features of Mycoplasma pneumoniae pneumonia with embolism in children: a retrospective study. Ital J Pediatr. 2022;48:153.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim K, Jung S, Kim M, Park S, Yang H-J, Lee E. Global Trends in the Proportion of Macrolide-Resistant Mycoplasma pneumoniae Infections: A Systematic Review and Meta-analysis. JAMA Netw Open. 2022;5:e2220949.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Y-C, Hsu W-Y, Chang T-H. Macrolide-Resistant Mycoplasma pneumoniae Infections in Pediatric Community-Acquired Pneumonia. Emerg Infect Dis. 2020;26:1382\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee K-L, Lee C-M, Yang T-L, Yen T-Y, Chang L-Y, Chen J-M, et al. Severe Mycoplasma pneumoniae pneumonia requiring intensive care in children, 2010\u0026ndash;2019. J Formos Med Assoc. 2021;120:281\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo Q, Li L, Wang C, Huang Y, Ma F, Cong S, et al. Comprehensive virome analysis of the viral spectrum in paediatric patients diagnosed with Mycoplasma pneumoniae pneumonia. Virol J. 2022;19:181.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"italian-journal-of-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"itjp","sideBox":"Learn more about [Italian Journal of Pediatrics](http://ijponline.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ITJP/default.aspx","title":"Italian Journal of Pediatrics","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Drug resistance, nomogram, Prediction model, Severe Mycoplasma pneumoniae Pneumonia","lastPublishedDoi":"10.21203/rs.3.rs-6420625/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6420625/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTo analyze the clinical features, laboratory findings, and imaging characteristics of severe Mycoplasma pneumoniae pneumonia (SMPP) in children, identify early warning indicators, and characterize macrolide-resistant M. pneumoniae pneumonia (MRMPP). Additionally, we developed and validated a nomogram model for predicting the risk of SMPP.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study included children diagnosed with M. pneumoniae pneumonia (MPP) who were admitted to the West China Second Hospital of Sichuan University between September 2022 and February 2024. Data on demographics, clinical manifestations, laboratory results, and imaging findings were collected and analyzed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCompared to non-severe cases, children with SMPP had a significantly longer fever duration (8 days vs. 4 days, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher peak body temperature (39.3\u0026deg;C vs. 38.5\u0026deg;C, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and a higher incidence of wheezing (13% vs. 0%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There was no significant difference in macrolide resistance rates between the groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Radiological analysis revealed a higher frequency of pulmonary consolidation (69% vs. 0%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and pleural effusion (22% vs. 7%, P\u0026thinsp;=\u0026thinsp;0.031) in the SMPP cohort. LASSO regression identified eight key predictors: fever duration, peak body temperature, wheezing, extrapulmonary complications, hemoglobin levels, pulmonary consolidation, mosaic sign, and bronchial occlusion. The nomogram demonstrated excellent discriminative ability, with training and validation AUC values of 0.972 (95% CI 0.960\u0026ndash;0.984) and 0.975 (95% CI 0.958\u0026ndash;0.992), respectively.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eWe developed and validated a nomogram for quantitative risk assessment of SMPP. This model can aid clinicians in the early identification of severe cases and in optimizing treatment strategies.\u003c/p\u003e","manuscriptTitle":"Prediction Model for Severe Mycoplasma pneumoniae Pneumonia and Analysis of Macrolide-resistance in Children: A case-control Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 01:25:28","doi":"10.21203/rs.3.rs-6420625/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accept","date":"2025-05-31T13:49:17+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-05-12T19:50:45+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-05T17:02:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-15T13:15:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Italian Journal of Pediatrics","date":"2025-04-10T09:30:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"italian-journal-of-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"itjp","sideBox":"Learn more about [Italian Journal of Pediatrics](http://ijponline.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ITJP/default.aspx","title":"Italian Journal of Pediatrics","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"df6d4835-0034-4653-bfc7-5b8ed1d56997","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-09T16:00:53+00:00","versionOfRecord":{"articleIdentity":"rs-6420625","link":"https://doi.org/10.1186/s13052-025-02039-y","journal":{"identity":"italian-journal-of-pediatrics","isVorOnly":false,"title":"Italian Journal of Pediatrics"},"publishedOn":"2025-06-07 15:56:59","publishedOnDateReadable":"June 7th, 2025"},"versionCreatedAt":"2025-05-13 01:25:28","video":"","vorDoi":"10.1186/s13052-025-02039-y","vorDoiUrl":"https://doi.org/10.1186/s13052-025-02039-y","workflowStages":[]},"version":"v1","identity":"rs-6420625","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6420625","identity":"rs-6420625","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}