Association of lung ultrasound findings with disease severity in Mycoplasma pneumoniae pneumonia among the pediatric population

Introduction

Mycoplasma pneumoniae (MP) is a common etiological agent of community-acquired pneumonia (CAP) in the pediatric population, particularly among school-aged children and adolescents. Mycoplasma pneumoniae pneumonia (MPP) accounts for approximately 10–40% of pediatric CAP cases (1). Co-infection of MP with other respiratory pathogens has been associated with more severe clinical manifestations, prolonged disease duration, and delayed recovery.

In recent years, the incidence of MP infection has shown an upward trend, resulting in an increasing number of MPP cases and a rising proportion of children diagnosed with severe Mycoplasma pneumoniae pneumonia (SMPP) (2). SMPP is characterized by rapid disease progression, complex clinical manifestations, and the occurrence of multiple pulmonary and extrapulmonary complications, which may lead to long-term sequelae. Early recognition of mixed infections and SMPP, followed by timely and targeted therapeutic intervention, is therefore critical for improving clinical outcomes in affected children.

Lung ultrasound (LUS) is an imaging modality that employs ultrasound artifacts for the differential diagnosis of pulmonary disorders. It offers several advantages, including the absence of ionizing radiation, high reproducibility, and operational simplicity (3). Evidence from both domestic and international studies has indicated that, under specific clinical conditions, LUS demonstrates higher sensitivity and specificity than chest radiography and may serve as an alternative diagnostic tool for pneumonia (4-6). Although chest computed tomography (CT) remains the diagnostic reference standard for pneumonia, its relatively high radiation exposure limits its acceptability among caregivers of pediatric patients, thereby contributing to the increasing clinical utilization of LUS. While LUS has been extensively investigated in pediatric CAP, studies specifically examining its diagnostic role in MPP remain limited. To further clarify the clinical applicability of LUS in this context, the present study retrospectively evaluated its diagnostic value in hospitalized pediatric patients with MPP. We present this article in accordance with the STARD reporting checklist (available at https://tp.amegroups.com/article/view/10.21037/tp-2025-aw-799/rc).

Methods

Study participants

The study protocol was registered at the Chinese Clinical Trial Registry. Available at: https://www.medicalresearch.org.cn/clinicalResearch/researchInfo?id=c1c95381-70aa-4e50-970f-73c09f8e7fa6. A retrospective analysis was conducted involving 392 hospitalized pediatric patients diagnosed with MPP between April 2023 and January 2024. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Ningbo Hangzhou Bay Hospital (No. LY2024-09). Written informed consent was obtained from all patients’ guardians. This study employed a continuous enrollment approach. During the study period from April 1, 2023 to January 31, 2024, all hospitalized patients with MPP who visited the Pediatrics Department of Ningbo Hangzhou Bay Hospital and met all inclusion and exclusion criteria were invited to participate. Ultimately, 392 patients were enrolled. The inclusion criteria were as follows: (I) diagnosis of CAP in accordance with the Guidelines for Diagnosis and Treatment of Community-Acquired Pneumonia in Children (2019 edition) (7); (II) confirmation of MP infection through detection of positive MP-DNA in sputum or pharyngeal swab samples; (III) age below 14 years, excluding neonates; and (IV) completion of LUS within 48 hours of hospital admission. According to the medical records, the time from symptom onset to undergoing this LUS examination was concentrated within 2–5 days for the majority of patients. Exclusion criteria included patients with immunodeficiency or immune dysfunction, as well as those with congenital or chronic comorbidities.

Based on pathogen detection findings, patients were categorized into a single-infection group (n=263) and a mixed-infection group (n=129). Mixed infection was defined as the detection of one or more additional respiratory pathogens concurrent with MP. Identification of respiratory pathogens was conducted using nucleic acid amplification testing and microbiological culture. MP nucleic acid detection was performed using real-time quantitative polymerase chain reaction (real-time PCR). Reagents were purchased from Xi’an Tianlong Technology Co., Ltd. (Xi’an, China). Procedures were strictly followed the kit instructions, with cycle threshold (Ct) values <38 interpreted as positive. According to relevant expert consensus, a Ct value below this threshold in respiratory tract samples is indicative of active MP infection rather than colonization, supporting its use as a diagnostic criterion for MPP in this study. Within the single-infection group, patients were further stratified by disease severity into a mild group (n=156) and a severe group (n=107). SMPP was defined as MPP meeting the diagnostic criteria for severe CAP, as outlined in the Guidelines for the Diagnosis and Treatment of Community-Acquired Pneumonia in Children (2019 edition) (8).

Data collection

Clinical data were extracted from the electronic medical record system and included demographic information, clinical symptoms, laboratory test results, and findings from ancillary examinations.

Ultrasound examination data and evaluation criteria: LUS examinations were conducted using a portable color Doppler ultrasound system (EPIQ5, Philips, Netherlands) with a high-frequency linear array probe operating at 5–12 MHz. Patients were examined in both supine and prone positions. Each lung was stratified into six regions—anterosuperior, anteroinferior, upper axillary, lower axillary, posterosuperior, and posteroinferior—using anatomical landmarks including the parasternal line, anterior axillary line, posterior axillary line, midspinal line, and the line connecting the bilateral nipples, resulting in a total of 12 regions assessed.

According to established LUS scoring criteria (9), each region was scored based on the most severe finding: smooth A-lines or fewer than three isolated B-lines received 4 points; scattered discrete B-lines received 3 points; numerous partially confluent B-lines received 2 points; numerous fully confluent B-lines forming a waterfall sign received 1 point; and pulmonary consolidation received 0 points. The total LUS score was calculated as the sum of all regional scores, with a possible range of 0–48. The lower the score, the more severe the lung infection.

LUS examinations were conducted by trained ultrasound physicians, and results were finalized through consensus between two independent physicians. All ultrasound images were independently analyzed and scored by two senior sonographers without knowledge of each other’s results or clinical data. The final score was the consensus reached between the two. The independent scores from both sonographers were used to calculate inter-rater reliability. Since the scores used in this study were continuous variables, we employed a two-way random-effects model with the intraclass correlation coefficient (ICC) for absolute agreement to quantify inter-rater consistency. Both sonographers independently scored pulmonary ultrasounds for all 392 patients. Inter-rater reliability analysis revealed an ICC of 0.92 [95% confidence interval (CI): 0.88–0.95], indicating high agreement between the two evaluators. Smooth A-lines or fewer than three isolated B-lines were classified as normal findings. LUS abnormality was defined as the presence of at least one of the following: numerous partially confluent B-lines, numerous confluent B-lines (waterfall sign), pulmonary consolidation, or pleural effusion.

Diagnostic criteria for specific findings were as follows: (I) multiple confluent B-lines were defined as confluent B-lines present in two or more regions. (II) Pulmonary consolidation was defined as a subpleural, tissue-like hypoechoic area. In this study, we assessed pulmonary consolidation from archived ultrasound videos. Following scan completion, video files were anonymized and stored. Subsequently, two operators independently analyzed and measured these archived videos. The size of the consolidation was recorded as the largest measurement among the longitudinal, transverse, or vertical diameters. The number of consolidations referred to the total count of consolidated regions across both lungs. Bilateral consolidation was defined as the presence of consolidation in both the left and right lungs. (III) Pleural effusion was defined as an anechoic fluid collection within the pleural space.

Statistical analysis

Data management and statistical analyses were performed using SPSS version 23.0. Quantitative variables with a normal distribution were expressed as mean ± standard deviation, while non-normally distributed variables were expressed as median [interquartile range (IQR)]. Categorical variables were presented as frequencies and percentages. Intergroup comparisons for categorical variables between groups were conducted using the chi-square test or Fisher’s exact test, as appropriate. For continuous variables, normally distributed data were compared using the t-test, and non-normally distributed data were assessed using non-parametric tests.

Multivariate logistic regression analysis was applied to identify LUS-related factors associated with mixed MPP infection and SMPP. Receiver operating characteristic (ROC) curves were generated to evaluate diagnostic performance, and the area under the curve (AUC), sensitivity, and specificity were calculated. Optimal cut-off values were determined using Youden’s index. A P value <0.05 was considered statistically significant.

Additionally, to control for Type I errors arising from multiple comparisons across 12 lung subregions, we applied the Bonferroni correction as suggested by reviewers, setting the significance threshold for each subregion to P<0.004 (0.05/12).

Results

Univariate analysis of mixed MPP infection

A total of 392 pediatric patients with MPP were included, comprising 200 males and 192 females, with a median age of 5.49 years (IQR, 3–7 years). Of these, 263 cases (67.1%) were classified as single infections, while 129 cases (32.9%) were classified as mixed infections. Among patients with mixed infections, viral pathogens identified included respiratory syncytial virus in 50 cases (12.8%), rhinovirus in 46 cases (11.7%), influenza A virus in 17 cases (4.3%), adenovirus in 12 cases (3.1%), influenza B virus in 1 case (0.3%), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 1 case (0.3%), and Epstein-Barr virus in 1 case (0.3%). Bacterial co-pathogens included Streptococcus pneumoniae in 5 cases (1.3%), Haemophilus influenzae in 3 cases (0.7%), Haemophilus parainfluenzae in 2 cases (0.5%), and Staphylococcus aureus in 1 case (0.3%). Additionally, 3 cases (0.7%) of Chlamydia pneumoniae and 3 cases (0.7%) of Legionella were detected.

No statistically significant differences were observed between the single-infection and mixed-infection groups in terms of sex, proportion of severe cases, or consolidation size (P>0.05). Independent comparisons across 12 lung regions underwent Bonferroni correction, with the significance threshold set at P<0.004 (0.05/12). Significant differences were observed in patient age and the number of consolidations (P<0.05). No significant differences were observed in the presence of multiple confluent B-lines, pleural effusion, or LUS score (P>0.05). Detailed results are presented in Table 1.

Multivariate logistic regression model analysis of predictive factors for mixed MPP infection

Mixed infection was designated as the dependent variable (0 = single infection; 1 = mixed infection), and variables that demonstrated statistical significance in the univariate analysis were included as independent variables. A multivariate logistic regression model was constructed to assess factors associated with mixed infection. The Hosmer-Lemeshow goodness-of-fit test yielded a P value of 0.881, indicating satisfactory model calibration. The model demonstrated an overall predictive accuracy of 66.3%. The results indicate that for each additional real variable, the probability of mixed infection decreases by 22.6%, and for each additional year of age, the probability of mixed infection decreases by 10.4%, as summarized in Table 2.

Evaluation of the predictive value of the number of consolidations for mixed MPP infection

ROC curve results showed that the AUC for the actual variable was 0.590 (95% CI: 0.531–0.650, P=0.004), with an optimal cutoff value of 1.5, sensitivity of 80.6%, and specificity of 29.3%. The AUC for age was 0.562 (95% CI: 0.503–0.620, P=0.047), with an optimal cutoff value of 4.5, sensitivity of 49.6%, and specificity of 65.8%. Age and number of solid lesions can serve as effective preliminary screening tools, but should be combined with other indicators for definitive diagnosis (see Figure 1).

Figure 1 ROC curve for the number of consolidations in predicting mixed infection MPP. MPP, Mycoplasma pneumoniae pneumonia; ROC, receiver operating characteristic.

Univariate analysis of LUS characteristics in SMPP

Among the 263 cases of single MPP infection, 156 were classified as mild and 107 as severe. No statistically significant differences were observed between the mild and severe groups in terms of sex (P=0.40) or age (P=0.06). Pleural effusion was detected in 15 cases, all of which met the diagnostic criteria for severe pneumonia as defined in the Guidelines for the Diagnosis and Treatment of Community-Acquired Pneumonia in Children (2019 edition) (7) (Table 3).

The severe group demonstrated significantly higher proportions of abnormal LUS findings, presence of multiple confluent B-lines, pulmonary consolidation, number of consolidations, and consolidation diameter compared to the mild group (P<0.05). The LUS score was significantly higher in the severe group (P<0.05). The incidence of right lung consolidation and multiple confluent B-lines in the right lung was also significantly greater in the severe group (P<0.05). Furthermore, the severe group demonstrated a higher number of consolidations in five specific lung regions—left anteroinferior, left posteroinferior, right superoaxillary, right inferoaxillary, and right posteroinferior—compared with the mild group (P<0.05). After Bonferroni correction, the right apical lung region remained significantly different (P=0.003), while the left anterior-lower, left posterior-lower, right apical, and right posterior-lower lung regions failed to pass correction. Detailed findings are presented in Table 3.

Multivariate logistic regression model analysis of predictive factors for SMPP

A multivariate logistic regression model was established using SMPP as the dependent variable (0 = mild; 1 = severe). LUS parameters that demonstrated statistically significant differences in Table 3 were included as independent variables. Consolidation in the right lung was excluded due to high collinearity. The Hosmer-Lemeshow goodness-of-fit test yielded a P value of 0.956, indicating adequate model calibration, with a predictive accuracy of 68.1%. Larger consolidation diameter and the presence of consolidation in the left anteroinferior region were identified as significant risk factors for SMPP (see Table 4).

Given that only the right lower lobe maintained robust significance after multiple comparison correction, we included the right lower lobe in the multivariate logistic regression model while excluding other lung segments. Multivariate logistic regression analysis was performed with SMPP as the dependent variable (0 = mild; 1 = severe), excluding the right lower lobe due to high multicollinearity. The Hosmer-Leimark test yielded a significant P value of 0.196, indicating good model fit with a predictive accuracy of 65.5%. Results demonstrated that solid lesion diameter is a risk factor for SMPP infection (see Table 5).

Evaluation of the predictive value of consolidation diameter for SMPP

ROC curve analysis demonstrated that a consolidation diameter cut-off value of 2.25 cm yielded an AUC of 0.693 (95% CI: 0.627–0.759) for predicting SMPP. The corresponding specificity and sensitivity were 77.6% and 53.3%, respectively. These findings indicate that a consolidation diameter exceeding 2.25 cm is significantly associated with an increased risk of being classified as SMPP in pediatric patients (see Figure 2). However, the clinical relevance of this cutoff cannot be fully evaluated due to the absence of key patient outcomes data [e.g., pediatric intensive care unit (PICU) admission, mechanical ventilation, treatment response] in the present study.

Figure 2 ROC curve for consolidation diameter in predicting SMPP. ROC, receiver operating characteristic; SMPP, severe Mycoplasma pneumoniae pneumonia.

Discussion

LUS has distinct advantages, including non-invasiveness, absence of ionizing radiation, and feasibility for bedside application, enabling real-time visualization of pulmonary lesions. It has been extensively applied in the diagnosis and management of pneumonia (10). Previous studies have demonstrated that LUS is effective for diagnosing pediatric pneumonia and is valuable for assessing disease severity as well as monitoring therapeutic response (11,12).

Typical sonographic findings of pneumonia include pulmonary consolidation with poorly defined margins, often accompanied by air bronchograms or confluent B-lines (13). In MPP, particularly in SMPP, imaging findings frequently reveal consolidation involving one or more pulmonary lobes, sometimes associated with pleural effusion. Compared with chest radiography, LUS demonstrates superior sensitivity in detecting small pulmonary consolidations and minimal pleural effusions (14,15).

Due to the limitations of chest CT, including radiation exposure, higher cost, and the challenge of maintaining pediatric patient cooperation, its utility for dynamic disease monitoring is restricted. Consequently, LUS represents a practical and reliable alternative for serial evaluation of pneumonia progression and treatment response in pediatric populations (16).

Among pediatric patients with single MPP, those in the severe group demonstrated significantly higher frequencies of abnormal LUS findings, multiple confluent B-lines, pulmonary consolidations, and a greater total number of consolidations compared to the mild group (P<0.05). The LUS score was significantly higher in the severe group (P<0.05). Multiple confluent B-lines in the right lung and right-sided pulmonary consolidation were also significantly more prevalent in the severe group (P<0.05).

In addition, consolidations located in the left anteroinferior, left posteroinferior, right superoaxillary, right inferoaxillary, and right posteroinferior lung regions occurred with significantly greater frequency in the severe group (P<0.05). However, after applying Bonferroni correction, no single pulmonary lobe involvement was found to independently predict severe mycoplasma pneumonia. This finding suggests that for the assessment of severe mycoplasma pneumonia examined in this study, radiological prediction may need to shift from identifying specific sites of involvement to focusing on quantifying lesion severity. The valid conclusions of this study further support this shift: quantitative measurements of the extent of pulmonary consolidation demonstrated statistical significance. A prior study on complicated MPP reported a higher prevalence of pulmonary consolidations exceeding 5 cm on LUS among pediatric patients with severe disease (17), a finding consistent with the present results. In this study, the mean consolidation diameter was significantly greater in the severe group, with a significantly higher proportion of cases exhibiting consolidations larger than 5 cm (P<0.05). This study confirms that actual variable diameter is a risk factor for SMPP. Additionally, each 1 cm increase in consolidation diameter was associated with a 38.3% increase in the risk of SMPP.

It should be noted that during the study period, none of the enrolled pediatric patients (including those in the severe group) experienced outcomes such as transfer to the PICU, requirement for invasive mechanical ventilation support, or death. This further underscores the early warning value of LUS as explored in this study. Early identification of children exhibiting radiographic features consistent with severe pneumonia enabled clinicians to provide closer monitoring and timely intervention, potentially preventing disease progression toward organ support requirements. When this model was applied to predict SMPP, the AUC was 0.693 (95% CI: 0.627–0.759), with a specificity of 77.6% and a sensitivity of 53.3%. The optimal cut-off value for consolidation diameter was determined to be 2.25 cm. In a study conducted by Miao et al. on pediatric CAP, the AUC values for LUS-based prediction of severe pneumonia were 0.857, 0.806, and 0.826 for the transverse, longitudinal, and sagittal axis diameters, respectively, with corresponding cut-off values of 2.95, 2.35, and 2.55 cm (18). Although specific numerical values differ, this study, together with that of Miao et al., confirms that ultrasound measurements of pulmonary consolidation size (at the 2–3 cm scale) correlate with disease severity, providing a basis for future standardized measurement protocols. The measurement variability observed in this study presents a potential quantitative indicator for future research. Whether and how this variability correlates with specific treatment decisions or clinical outcomes requires further validation in prospective studies. In the present analysis, mixed infections were detected in 32.9% of pediatric cases with MPP, which is consistent with recent multicenter and single-center studies reporting a global co-infection rate of 34.0–49.6% in pediatric MPP (19-21). Similar to our findings, these updated studies identify rhinovirus, adenovirus, and human metapneumovirus as the most prevalent viral co-pathogens, while Haemophilus influenzae and Streptococcus pneumoniae are the primary bacterial co-pathogens (19,20). Notably, a 2025 multicenter study further confirmed that multi-pathogen co-detection is more frequent in SMPP cases (54.05%) than in mild cases (39.51%), highlighting the association between co-infections and disease severity (19).

Clinical manifestations of MPP with co-infections are characterized by more intense inflammatory responses, as evidenced by higher levels of inflammatory biomarkers [e.g., interleukin (IL)-6, IL-10, procalcitonin (PCT), lactate dehydrogenase (LDH)] and increased rates of clinical signs such as wet rales, pleural effusion, and persistent high fever (20,21). These co-infected children also tend to have longer fever duration and hospital stays, and may even present with liver function impairment (elevated alanine aminotransferase and glutamyltransferase), underscoring the adverse impact of co-infections on disease progression (20). Risk factors for co-infection include winter and spring seasons, a history of respiratory tract infection within 3 months, and household exposure to respiratory symptoms, which may be attributed to increased pathogen transmission in confined environments and compromised immune defense in previously infected children (20).

Additionally, recent studies have identified potential predictive markers for co-infections: the combination of IL-6 and IL-10 demonstrates high sensitivity (93.8%) for detecting multi-pathogen co-detection in SMPP, while PCT and LDH show promising predictive value for adenovirus-specific co-infection (AUC = 0.791–0.819) (19,21). Targeted next-generation sequencing (tNGS) has also emerged as a valuable tool for comprehensive pathogen detection, covering over 95% of common respiratory pathogens and facilitating early identification of co-infections (19).

Previous domestic studies have reported detection rates ranging from 13.6% to 51.2% (22,23), with variability likely reflecting differences in sample size, pathogen detection methodologies (e.g., tNGS vs. conventional PCR), and regional epidemiological characteristics (e.g., predominant pathogens varying by region) (19,20). No statistically significant difference in the proportion of severe cases was observed between the single- and mixed-infection groups (P>0.05) in our study, which may be related to the absence of critical outcomes (e.g., PICU admission) and relatively mild disease severity in our cohort.

Multivariate logistic regression analysis indicated that each additional pulmonary consolidation was associated with a 21.3% reduction in the probability of mixed infection. The number of consolidations, using an optimal cut-off value of 1.5, demonstrated relatively high sensitivity but low specificity for predicting mixed infection, suggesting limited diagnostic applicability.

This study was a single-center retrospective analysis with a relatively limited sample size, which may restrict the generalizability of the findings. One of the main limitations of this study is the absence of a precise statistical analysis regarding the time interval from symptom onset to LUS examination. Although this interval was mostly within 2–5 days, it varied substantially across individuals. This may lead to inconsistent pulmonary pathological stages among the included patients (e.g., early exudation and late consolidation), which in turn affects the unified interpretation of the sensitivity and specificity of LUS. Future studies should be designed prospectively to analyze the exact relationship between this temporal parameter and LUS manifestations. None of the pediatric patients experienced outcomes such as admission to the PICU or death, which limits the accuracy of the model constructed in this study for predicting such extreme outcomes. Notably, we did not assess macrolide-resistant M. pneumoniae (MRMP) status (a major issue in Asia affecting outcomes) due to retrospective constraints. And due to the retrospective study, for LUS scoring, the most updated guidance was not adopted. Future multicenter, prospective investigations with larger cohorts are warranted to validate and refine these findings.

Conclusions

A higher number of pulmonary consolidations was associated with a lower probability of mixed infection in MPP. Conversely, the presence of left anteroinferior consolidation and a consolidation diameter greater than 2.25 cm on LUS was significantly associated with an increased risk of being classified as SMPP. These findings suggest that these LUS features may serve as valuable references for identifying high-risk pediatric MPP patients, prompting closer clinical monitoring. Due to the absence of critical outcome data (e.g., PICU admission, treatment response) in the present study, the clinical utility of the 2.25 cm cut-off for guiding therapeutic decisions requires further validation in prospective cohorts with comprehensive outcome assessment.

Acknowledgments

We would like to acknowledge the hard and dedicated work of all the staff who implemented the intervention and evaluation components of the study.

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-aw-799/rc

Data Sharing Statement: Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-aw-799/dss

Peer Review File: Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-aw-799/prf

Funding: This study was supported by Ningbo Hangzhou Bay Hospital Qihang Talent Incubation Plan in 2024 (No. QHKY2024-05).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tp.amegroups.com/article/view/10.21037/tp-2025-aw-799/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Ningbo Hangzhou Bay Hospital (No. LY2024-09). Written informed consent was obtained from all patient’s guardians.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Song M, Zhang Y, Li S, et al. A sensitive and rapid immunoassay for Mycoplasma pneumoniae in children with pneumonia based on single-walled carbon nanotubes. Sci Rep 2017;7:16442. [Crossref] [PubMed]
2. Yan C, Sun H, Zhao H. Latest Surveillance Data on Mycoplasma pneumoniae Infections in Children, Suggesting a New Epidemic Occurring in Beijing. J Clin Microbiol 2016;54:1400-1. [Crossref] [PubMed]
3. Porter P, Brisbane J, Abeyratne U, et al. Diagnosing community-acquired pneumonia via a smartphone-based algorithm: a prospective cohort study in primary and acute-care consultations. Br J Gen Pract 2021;71:e258-65. [Crossref] [PubMed]
4. Ciuca IM, Dediu M, Marc MS, et al. Lung Ultrasound Is More Sensitive for Hospitalized Consolidated Pneumonia Diagnosis Compared to CXR in Children. Children (Basel) 2021;8:659. [Crossref] [PubMed]
5. Bloise S, La Regina DP, Pepino D, et al. Lung ultrasound compared to chest X-ray for the diagnosis of CAP in children. Pediatr Int 2021;63:448-53. [Crossref] [PubMed]
6. Mearelli F, Casarsa C, Trapani A, et al. Lung ultrasound may support internal medicine physicians in predicting the diagnosis, bacterial etiology and favorable outcome of community-acquired pneumonia. Sci Rep 2021;11:17016. [Crossref] [PubMed]
7. Ni X. Diagnosis and Treatment Guidelines for Community-Acquired Pneumonia in Children (2019 Edition). General Medicine and Clinical Education 2019;17:771-7.
8. Guidelines for the Diagnosis and Treatment of Community-Acquired Pneumonia in Children. (2019 edition). Chinese Journal of Pediatrics 2019;57:83-90.
9. Bouhemad B, Brisson H, Le-Guen M, et al. Bedside ultrasound assessment of positive end-expiratory pressure-induced lung recruitment. Am J Respir Crit Care Med 2011;183:341-7. [Crossref] [PubMed]
10. Liu J, Hu CB, Ye RZ, et al. Expert suggestions on ultrasound diagnosis of infectious pneumonia. Chinese Journal of Medical Ultrasound 2020;17:244-50. (Electronic Edition).
11. Hong BZ. Research on the Value of Lung Ultrasound in the Diagnosis and Clinical Treatment of Pneumonia in Children. Chengde: Chengde Medical University; 2022.
12. Su J, Zhou HY, Li CF. The predictive significance of lung ultrasound score and CRP for the severity of pneumonia in children. Chinese Journal of Lung Diseases 2021;14:605-7. (Electronic Edition).
13. Volpicelli G, Elbarbary M, Blaivas M, et al. International evidence-based recommendations for point-of-care lung ultrasound. Intensive Care Med 2012;38:577-91. [Crossref] [PubMed]
14. Balk DS, Lee C, Schafer J, et al. Lung ultrasound compared to chest X-ray for diagnosis of pediatric pneumonia: A meta-analysis. Pediatr Pulmonol 2018;53:1130-9. [Crossref] [PubMed]
15. Iorio G, Capasso M, Prisco S, et al. Lung Ultrasound Findings Undetectable by Chest Radiography in Children with Community-Acquired Pneumonia. Ultrasound Med Biol 2018;44:1687-93. [Crossref] [PubMed]
16. Cellina M, Martinenghi C, Marino P, et al. COVID-19 pneumonia-ultrasound, radiographic, and computed tomography findings: a comprehensive pictorial essay. Emerg Radiol 2021;28:519-26. [Crossref] [PubMed]
17. Song Q, Xu BP, Shen KL. Effects of bacterial and viral co-infections of mycoplasma pneumoniae pneumonia in children: analysis report from Beijing Children's Hospital between 2010 and 2014. Int J Clin Exp Med 2015;8:15666-74.
18. Musolino AM, Tomà P, Supino MC, et al. Lung ultrasound features of children with complicated and noncomplicated community acquired pneumonia: A prospective study. Pediatr Pulmonol 2019;54:1479-86. [Crossref] [PubMed]
19. Dong X, Li R, Zou Y, et al. Co-detection of respiratory pathogens in children with Mycoplasma pneumoniae pneumonia: a multicenter study. Front Pediatr 2025;13:1482880. [Crossref] [PubMed]
20. Ge Q, Wang W, Fang X. Analysis of combined viral infections in hospitalized children with Mycoplasma pneumoniae. Adv Clin Med 2025;15:2019-29.
21. Bi R, Yuan W, Ren Y, et al. Investigation of clinical features and risk factors of children with Mycoplasma pneumoniae pneumonia complicated with adenovirus infection. J Pathogen Biol 2025;20:903-6.
22. Song Q, Xu BP, Shen KL. Effects of bacterial and viral co-infections of mycoplasma pneumoniae pneumonia in children: analysis report from Beijing Children's Hospital between 2010 and 2014. Int J Clin Exp Med 2015;8:15666-74.
23. Chen LL, Cheng YG, Chen ZM, et al. Research on Mixed Infections in Children with Mycoplasma pneumoniae Pneumonia. Chinese Journal of Pediatrics 2012;50:163-6.