Associations of Mycoplasma pneumoniae load, co-infections, and macrolide resistance with clinical-laboratory profiles in hospitalized pediatric pneumonia: A targeted next-generation sequencing(tNGS) study of bronchoalveolar lavage fluid (BALF)

Associations of Mycoplasma pneumoniae load, co-infections, and macrolide resistance with clinical-laboratory profiles in hospitalized pediatric pneumonia: A targeted next-generation sequencing(tNGS) study of bronchoalveolar lavage fluid (BALF) | 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 Associations of Mycoplasma pneumoniae load, co-infections, and macrolide resistance with clinical-laboratory profiles in hospitalized pediatric pneumonia: A targeted next-generation sequencing(tNGS) study of bronchoalveolar lavage fluid (BALF) Xingzhen Liang, Rong Wei, Dongna liang, Wenxiu Huang, Ruizhen Huang, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7950608/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose This study aimed to investigate the associations of Mycoplasma pneumoniae (MP) DNA load, co-infection, and macrolide resistance with clinical phenotypes in pediatric pneumonia, using targeted next-generation sequencing (tNGS) of bronchoalveolar lavage fluid (BALF) Methods We conducted a retrospective cohort study of 791 hospitalized children with MP pneumonia. All patients underwent bronchoscopy, and bronchoalveolar lavage fluid (BALF) was analyzed using targeted next-generation sequencing (tNGS). This allowed for the simultaneous quantification of MP DNA load, comprehensive co-infection profiling, and detection of macrolide resistance mutations (A2063G/A2064G in 23S rRNA). Clinical data were correlated with these multidimensional tNGS parameters. Results Our analysis revealed distinct clinical phenotypes linked to tNGS findings. A high MP DNA load was associated with a "classic" phenotype of persistent fever and lung consolidation, typically in monoinfection. Conversely, a low MP DNA load served as a key marker for a co-infection-driven syndrome in younger children, characterized by more severe respiratory symptoms (wheezing, dyspnea) and systemic inflammation. Furthermore, macrolide-resistant MP (MRMP) was the strongest independent predictor of treatment failure, tripling the odds of requiring second-line antibiotics, without influencing initial disease severity. Conclusion This study demonstrates that the integration of MP DNA load, co-infection status, and resistance profiling from BALF-based tNGS provides a powerful framework for stratifying pediatric MP pneumonia. These multidimensional data offer critical insights for anticipating disease course and tailoring therapeutic strategies, paving the way for more personalized and effective patient management. Mycoplasma pneumoniae Targeted next-generation sequencing DNA load Co-infection Macrolide resistance Pediatrics Figures Figure 1 What is Known M. pneumoniae causes heterogeneous pediatric pneumonia with rising macrolide resistance and co-infections, which are not captured by conventional diagnostics. What is New BALF-tNGS defines novel clinical phenotypes by integrating MP load, co-infection, and resistance, where low load identifies a co-infection-driven severe respiratory syndrome, enabling early personalized management. Introduction Mycoplasma pneumoniae (MP), a leading cause of community-acquired pneumonia (CAP) in children, accounting for 30–50% of pediatric pneumonia cases globally[1, 2]. While MP infections typically manifest as mild respiratory illness, emerging evidence indicates substantial heterogeneity in disease severity, ranging from self-limiting bronchitis to life-threatening necrotizing pneumonia or extrapulmonary complications[3]. This clinical variability is exacerbated by the global surge in macrolide-resistant M. pneumoniae (MRMP)[4] and MP co-infection[5]. Critically, current diagnostic methods fail to explain this heterogeneity, Serological assays suffer from delayed seroconversion and cross-reactivity[6], while qPCR-based methods, though sensitive and quantitative[7], can’t simultaneously identify co-infecting pathogens or detect macrolide resistance-associated genetic mutations. These constraints perpetuate therapeutic delays, pediatric MP pneumonia cases progressing to severe disease due to undetected mixed infections or unrecognized macrolide resistance[8]. The emergence of targeted next-generation sequencing (tNGS) provides comprehensive diagnostic potential by concurrently quantifying pathogen burden, identifying co-infections, and detecting macrolide resistance mutations within 12 hours [9]. Some studies have applied tNGS to the diagnosis of MP[10–13]. However, current clinical practice predominantly reduces tNGS to a pathogen identification tool, little clinicians utilizing its quantitative load data or resistance gene metrics for therapeutic decisions[14, 15]. This implementation gap likely reflects insufficient evidence linking multidimensional tNGS parameters to clinically actionable thresholds. To systematically explore these associations, we analyzed a retrospective cohort of pediatric MP pneumonia cases with paired bronchoalveolar lavage fluid (BALF) tNGS results and clinical records. The multidimensional tNGS data, including MP DNA load (copies/mL), 23S rRNA mutation abundance, and co-pathogen profiles, were analyzed with clinical data (laboratory results, respiratory dynamics, and treatment responses). This study aims to establish clinically relevant correlations between tNGS result and clinical data while evaluating the diagnostic performance of tNGS in MP pneumonia. Materials and methods Study population This retrospective cohort study was conducted at the Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region and approved by the Medical Ethics Committee of Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region. This study included pediatric patients aged 1 month to 16 years diagnosed with lower respiratory infection from August 2023 to July 2024. MP pneumonia diagnosis was confirmed by a combination of serological criteria (particle agglutination titer ≥ 1:160) and/or MP-DNA positivity in BALF/throat swab with corresponding clinical symptoms and imaging evidence[16]. The inclusion criteria were (1) clinical diagnosed with MP pneumonia; (2) underwent bronchoscopy with standardized BALF collection within 72 hours of admission; (3) BALF-tNGS confirmed MP infection (MP reads ≥ 50 and estimated concentration ≥ 1×10 3 copies/mL); (4) Complete clinical data of the children. The exclusion criteria were (1) HIV, congenital immunodeficiency, glucocorticoids use and other immunocompromised status; (2) Asthma, chronic heart and lung diseases, rheumatic diseases and organic disease; (3) Incomplete clinical records or missing tNGS data; (4) Lack of diagnosis for MP pneumonia or medication for MP pneumoniae. The study was approved by the Medical Ethics Committee of Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region. Informed consent was obtained from the parents of the child patients. Pathogen-targeted next-generation sequencing The BALF samples were collected according to the standard clinical procedure and guideline[17]. Nucleic acid from BALF samples was extracted and purified using MagPure Pathogen DNA/RNA Kit (R6672-01B, Magen, Guangzhou, China) according to the manufacturer’s instructions. A multiplex PCR library system (Respiration100TM, KingCreate, Guangzhou, China) and the next-generation sequencing technology were applied to detect 198 respiratory pathogens (80 bacteria, 79 viruses, 32 Fungi, 7 Other) and more than 300 drug-resistant gene sites, including MP and its antibiotic resistance gene sites in the macrolides. The methods and workflows of tNGS have been described in detail previously[18]. Mono-infection was defined as MP DNA concentration ≥ 10 3 copies/mL in BALF without any co-detected pathogens ≥ 10³ copies/mL, whereas co-infection required the presence of ≥ 1 additional pathogen with DNA concentration ≥ 10³ copies/mL. Pathogen DNA estimated concentration was calculated by normalizing pathogen-derived sequencing reads to a known-concentration spike-in control. According to the level of MP estimated concentration, it was categorized into low-load (10 3 copies/mL 10 6 copies /mL)[19] Macrolide resistance was defined by the detection of 23S rRNA mutations A2063G or A2064G via tNGS[20]. Strains lacking these mutations were classified as macrolide-susceptible. According to the resistance profile, cases were further stratified into macrolide-resistant M. pneumoniae (MRMP) and macrolide-susceptible M. pneumoniae (MSMP) cohorts for clinical correlation analysis. Data Collection Demographic characteristics, clinical symptoms, imaging findings, laboratory data were extracted from electronic medical records. The clinical data of the patients were comprehensively evaluated independently by two experienced clinicians to determine the relevance of lower respiratory infection and clinical potential pathogens. When two clinicians have a different opinion, further consult the senior doctor to reach a consensus. Statistical Analysis IBM SPSS statistics 26.0 was used for statistical analyses. Categorical variables are expressed as numbers (percentages) and were analyzed by the Chi-square test or Fisher’s exact test, as appropriate. Continuous variables are expressed as median (interquartile range, IQR) and were compared using the Mann–Whitney U test owing to non-normally distributed data. Statistical significance was defined as * P < 0.05, ** P < 0.01, and *** P < 0.001. Result Study Population and Group Stratification A total of 1,646 pediatric patients with suspected M. pneumoniae pneumonia were initially enrolled based on tNGS testing. Among them, 978 patients provided BALF samples and were thus included for further analysis. The application of our predefined clinical case criteria (MP DNA load ≥ 1×10³ copies/mL and reads ≥ 50) identified 860 eligible cases. After excluding 69 patients who met the exclusion criteria (e.g., immunocompromised status, incomplete records), a final cohort of 791 patients was constituted for all subsequent analyses. As pre-specified in the Methods, the entire cohort was independently stratified along three distinct dimensions for all subsequent comparative analyses: by co-infection status (monoinfection, n = 309; co-infection, n = 482), by bacterial DNA load (low load, n = 139; high load, n = 652), and by macrolide resistance (MSMP, n = 330; MRMP, n = 461) (Fig. 1 ). Comparison of clinical characteristics between Monoinfection versus Co-infection The comparative analysis of clinical characteristics between children with MP monoinfection and co-infection is summarized in Table 1 . Patients in the co-infection group were significantly younger than those in the monoinfection group (median 48 vs. 72 months, P < 0.001) and had a longer median duration of hospitalization (7 days vs. 6 days, P < 0.001). Notably, the prevalence of a high MP DNA load was substantially lower in the co-infection group compared to the monoinfection group (71.8% vs. 99.0%, P < 0.001). In terms of clinical manifestations, children with co-infection presented with a higher incidence of wheezing (13.3% vs. 7.8%, P = 0.016) but a lower incidence of fever (87.8% vs. 92.6%, P = 0.031) and persistent fever (41.5% vs. 51.3%, P = 0.007). Laboratory findings further differentiated the two groups. The co-infection group exhibited significantly higher levels of systemic inflammatory markers, including white blood cell count (WBC), platelet count, and CK-MB ( P < 0.001 for all), alongside lower neutrophil percentages ( P = 0.003). However, no significant differences were observed in the rates of severe pneumonia or the requirement for switching to second-line non-macrolide antibiotics Comparison of clinical characteristics between high versus low MP DNA Load Stratification by MP DNA load revealed a distinct and more severe clinical profile in the low load group (Table 2 ). Patients in this group were significantly younger (median 36.0 vs. 69.0 months, P < 0.001) yet presented with more pronounced respiratory symptoms, including a higher incidence of dyspnea (14.4% vs. 5.4%, P < 0.001), wheezing (23.7% vs. 8.4%, P < 0.001), and severe pneumonia (21.6% vs. 13.3%, P = 0.011). Consistently, the low load group also had a longer duration of hospitalization ( P = 0.018). The overall distribution of radiographic findings differed significantly between the two groups ( P = 0.002). Lung consolidation was the most common finding across the entire cohort but was notably more frequent in the high load group (83.1% vs. 69.8%). In contrast, the low load group exhibited a distinct pattern, with a numerically higher proportion of cases presenting with pleural effusion, prominent lung markings, and inflammation/exudation. The etiology underlying these differences was further clarified. The low load group was overwhelmingly composed of co-infections (97.8%), in stark contrast to the high load group (53.1%, P < 0.001). Conversely, macrolide resistance was significantly more prevalent in the high load group (64.4% vs. 29.5%, P < 0.001). Despite the lower MP DNA load, these patients experienced a lower incidence of both fever (82.0% vs. 91.3%, p < 0.001) and persistent fever (31.1% vs. 48.3%, P < 0.001) Laboratory parameters reinforced this profile of heightened inflammation in the low load group, which exhibited significantly elevated WBC, platelet count, ALT, and CM-MB ( P < 0.001 for all) Comparison of clinical characteristics between MSMP and MRMP The comparative analysis between macrolide-susceptible (MSMP) and macrolide-resistant (MRMP) infections revealed that resistance primarily impacted treatment course rather than the initial disease severity (Table 3 ). The most pronounced effects were on clinical management: patients infected with MRMP strains required a significantly longer duration of macrolide therapy (median 7 days vs. 5 days, P < 0.001) and experienced a nearly three-fold higher rate of treatment failure, necessitating a switch to second-line non-macrolide antibiotics (16.5% vs. 5.8%, P < 0.001) Despite these marked differences in treatment response, the two groups presented with largely similar clinical profiles. Notably, the severity of cough was greater in the MRMP group ( P = 0.003), and pharyngeal congestion was more prevalent (83.5% vs. 63.6%, P < 0.001). However, critical indicators of disease severity—including the incidence of dyspnea, wheezing, severe pneumonia, and the duration of hospitalization—were not significantly different. Laboratory parameters also showed minimal differences between the groups. Multivariable Regression Identifies Independent Predictors for Diverse Clinical Outcomes To delineate the independent effects of co-infection, MP DNA load, and macrolide resistance, we performed multivariable regression analyses adjusting for potential confounders (Table 4 ). The results solidified and refined several key associations observed in the univariate analyses. MP DNA load emerged as a pivotal independent predictor for several outcomes. A high load was significantly associated with an increased risk of persistent fever (OR 2.13, P < 0.001) but, paradoxically, with a decreased risk of severe pneumonia (OR 0.56, P < 0.05) and a shorter duration of hospitalization (β -1.29 days, P < 0.001). Macrolide resistance confirmed its primary role as a determinant of treatment failure. Infection with an MRMP strain was the strongest independent predictor for switching to second-line antibiotics, conferring a more than three-fold increase in odds (OR 3.13, P < 0.001). It was also independently associated with a longer duration of macrolide therapy (β 0.56 days, P < 0.01) Finally, the model identified younger age as a strong independent predictor for co-infection status (β -11.2 months, P < 0.001). However, co-infection itself showed a more limited independent profile for clinical outcomes, being significantly associated only with a longer hospital stay (β 0.62 days, P < 0.05) and an increased duration of macrolide therapy (β 0.45 days, P < 0.05) Discussion MP is a prevalent cause of community-acquired pneumonia in children, notorious for its extensive clinical heterogeneity—ranging from mild, self-limiting disease to severe, refractory pneumonia with extrapulmonary complications. This variability poses a significant challenge for clinicians, who have traditionally relied on diagnostic tools that offer a fragmented view of the infection. The advent of tNGS presents an opportunity for a more holistic pathogen assessment. Our study uniquely leverages the comprehensive and quantitative data from tNGS performed on BALF to dissect the roles of MP DNA load, co-infection, and macrolide resistance in shaping clinical disease. The use of BALF, collected directly from the site of infection, is a critical methodological strength, as it provides a more accurate reflection of the causative pathogen burden and minimizes the risk of contamination from upper respiratory tract colonizers, a common confounding factor in studies using sputum[21] or throat swabs[22]. Our findings indicated that MP co-infection as a key determinant of a specific clinical phenotype. The high co-infection rate (60.9%) aligns with recent tNGS-based studies[12, 23] and defines a distinct patient profile: younger children presenting with heightened airway reactivity, as evidenced by increased wheezing, and elevated systemic inflammatory markers such as WBC and platelet counts. This suggests that the clinical severity in this group is driven by the combined pathophysiological effect of multiple pathogens. The analysis of MP DNA load revealed a critical and nuanced relationship. While a high load was independently associated with persistent fever—consistent with a high bacterial burden driving a systemic inflammatory response[19]—it paradoxically predicted a lower risk of severe pneumonia. This apparent contradiction is resolved when viewed alongside co-infection status. The low MP load group was overwhelmingly characterized by co-infections (97.8%). This indicates that a low MP load in a sick child often serves as a marker of a complex, co-infection-driven disease, where the overall clinical severity and elevated inflammatory indices are attributable to the collective effect of all pathogens present, not solely MP. Conversely, high MP loads were typically seen in monoinfection, presenting a more "classic" MP phenotype dominated by fever but with less frequent severe respiratory compromise. The significantly higher incidence of lung consolidation in the high-load group further supports this phenotype, aligning with the traditional imaging hallmark of primary MP infection[16]. Regarding macrolide resistance, our data define its primary impact on treatment efficacy rather than initial disease severity. Patients with MRMP presented with similar initial severity as those with MSMP. However, MRMP was the strongest independent predictor of macrolide treatment failure, conferring a three-fold higher odds of requiring second-line antibiotics. This underscores the clinical value of tNGS in detecting resistance mutations early to guide appropriate therapy and avoid prolonged ineffective treatment[15]. Multivariable regression consolidates the independent roles of these three dimensions and hints at their clinical interplay. The analysis confirmed that each factor exerts a significant and independent influence on distinct outcomes after adjusting for confounders. High MP load independently predicted persistent fever but protected against severe pneumonia; macrolide resistance was the paramount factor for treatment failure; and younger age strongly predicted co-infection status. While this study primarily analyzed these dimensions separately, their independent significance in the regression model strongly suggests that their combination in individual patients likely creates unique risk profiles. For example, the clinical course of a child with high-load, macrolide-resistant monoinfection would be expected to differ markedly from one with low-load, macrolide-susceptible co-infection. The formal exploration of these interactive effects represents a critical next step. Our study has limitations. First, its retrospective and single-center design may affect the generalizability of our findings, and future multi-center validation is necessary. Second, the inherent quantitative range of tNGS, while clinically useful, may not precisely quantify extremely high or low pathogen loads, potentially influencing the dichotomization of load groups. Furthermore, the clinical synergies between load, co-infection, and resistance remain to be fully elucidated. Future prospective studies with larger cohorts are expressly warranted to develop integrated predictive models that combine these three tNGS parameters. Such models could stratify patients into distinct risk groups at diagnosis, enabling truly personalized management strategies. In conclusion, this BALF-based tNGS study offers a refined perspective on pediatric MP pneumonia by evaluating three key dimensions. Our data suggest that MP DNA load may be a pivotal factor in defining disease phenotype, providing crucial context for interpreting co-infection status. The findings indicate that a high MP load, often associated with monoinfection, appears to correlate with a classic presentation of fever and lung consolidation. Conversely, a low MP load may serve as a key indicator of a co-infection-driven syndrome in younger children, which tends to present with more pronounced respiratory symptoms. Additionally, the presence of macrolide resistance was strongly associated with an increased risk of treatment failure. Collectively, these insights highlight the potential clinical value of integrating multidimensional tNGS data—encompassing pathogen load, co-infections, and resistance markers—to better stratify patients at diagnosis, thereby informing more tailored therapeutic approaches and paving the way for more personalized management strategies. Abbreviations ALT: Alanine Aminotransferase; BALF: bronchoalveolar lavage fluid; CAP: community-acquired pneumonia, CK: Creatine kinase; CK-MB: Creatine kinase-MB; CI: confidence interval; DNA: deoxyribonucleic acid. LDH: Lactic acid dehydrogenase; OR: odds ratio; PCT: Procalcitonin; MP : Mycoplasma pneumoniae , MRMP, macrolide-resistant M. pneumoniae ; MSMP, macrolide-susceptible M. pneumoniae ; tNGS: targeted next-generation sequencing, WBC: White blood cell; SCr: Serum creatinine. Declarations Funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript Competing interests: The authors declare no competing interests. Author contributions: Xingzhen Liang: Conceptualization, Methodology, Formal Analysis; Rong Wei: Data Curation, Methodology, Resources, Supervision, Investigation; Dongna liang, Wenxiu Huang, Ruizhen Huang, Siyu Lu, Yining Lu, Huifei Ma, Qi Shi, Dongyun Li, Donglu Chen, Bing Qin, Xiheng Qi: Resources, Data Curation, Investigation, Yupeng Tang, Wugui Mo: Resources, Data Curation, Supervision; Zihan Wei: Conceptualization, Formal Analysis, Writing Original Draft, Supervision, Writing -Review & Editing. Ethics approval: This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Medical Ethics Committee of Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region Consent to participate: Informed consent was obtained from all individual participants included in the study Consent to publish: The authors affirm that human research participants provided informed consent for publication of the images in Figure 1 References Yan C, Xue GH, Zhao HQ, Feng YL, Cui JH, Yuan J. Current status of Mycoplasma pneumoniae infection in China. World J Pediatr. 2024;20(1):1-4. Epub 2024/01/08. doi: 10.1007/s12519-023-00783-x. PubMed PMID: 38185707; PubMed Central PMCID: PMCPMC10827902 interest. Nordholm AC, Soborg B, Jokelainen P, Lauenborg Moller K, Flink Sorensen L, Grove Krause T, et al. Mycoplasma pneumoniae epidemic in Denmark, October to December, 2023. Euro Surveill. 2024;29(2). Epub 2024/01/12. doi: 10.2807/1560-7917.ES.2024.29.2.2300707. PubMed PMID: 38214084; PubMed Central PMCID: PMCPMC10785206. 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Tables Table1 Comparison of general information and clinical characteristics between children with Mycoplasma pneumoniae monoinfection and co-infection Clinical characteristics Monoinfection of MP(n=309) Co-infection of MP (n=482) P value Categorical data Male 154(50.2%) 271(56.2%) 0.095 High load 306(99.0%) 346(71.8%) ***＜0.001 MRSR 199(64.4%) 262(54.4%) **0.005 Dyspnea 16(5.2%) 39(8.1%) 0.116 Wheezing 24(7.8%) 64(13.3%) *0.016 Chills 18(5.8%) 13(2.7%) *0.027 Cyanosis 1(0.3%) 6(1.2%) 0.178 Pharyngeal congestion 238(77.0%) 357(74.1%) 0.347 Fever 286(92.6%) 423(87.8%) *0.031 Persistent fever (≥5 days) 156(51.3%) 198(41.5%) **0.007 Productive cough 262(84.8%) 415(86.6%) 0.466 Defervescence within 72 hours of macrolide therapy 260(84.1%) 383(80.5%) 0.195 Switch to second-line non-macrolide antibiotics 41(13.3%) 53(11.0%) 0.383 Severe pneumonia 41(13.3%) 75(15.6%) 0.374 Maximum fever temperature 37.5-38 18(6.3%) 33(7.8%) 38.1-39 101(35.3%) 148(34.9%) 39.1-40 145(50.7%) 194(45.8%) ≥40.1 22(7.7%) 49(11.6%) 0.266 Severity of cough Mild 200(64.7%) 306(63.5%) Moderate 97(31.4%) 149(30.9%) Severe 12(3.9%) 27(5.6%) 0.553 Radiographic findings Prominent lung markings 3(1.0%) 17(3.5%) Inflammation/Exudation 35(11.3%) 71(14.8%) lung consolidation 260(84.1%) 376(78.5%) Pleural effusion 11(3.6%) 12(2.5%) *0.040 Quantitative data Age(months) 72(48,94) 48(26,78) ***＜0.001 Length of hospitalization (days) 6(5,8) 7(6,9) ***＜0.001 Duration of fever (days) 6(5,7) 5(3,7) 0.255 Duration of macrolide therapy, (days) 6(5,7) 6(5,7) 0.140 WBC, ×10⁹/L 7.4(6.1,9.3) 8.4(6.8,10.6) ***＜0.001 Neutrophil percentage, % 60.4(52.4,66.8) 55.9(45.3,65.9) **0.003 Lymphocyte percentage, % 29.4(22.7,34.4) 32.9(23.5,43.1) **0.002 Platelet count, ×10⁹/L 278(234,352) 322(249,417) ***＜0.001 C-reactive protein, mg/L 12.1(6.7,24.3) 11.4(5.9,23.8) 0.121 PCT, ng/mL 0.10(0.07,0.20) 0.10(0.07,0.22) 0.658 ALT, U/L 11.0(9.0,14.0) 12.0(9.5,16.0) **0.002 CK, U/L 95(68,135) 82(63,121) 0.398 CKMB, U/L 19(16,23) 21(17,25) ***＜0.001 LDH, U/L 298.0(268.0,349.8) 308.0(274.0,349.5) 0.186 SCr, μmol/L 32.0(26.7,37.0) 28.0(22.0,34.7) ***＜0.001 Fibrinogen, g/L 4.00(3.67,4.30) 3.85(3.40,4.26) ***＜0.001 MP pneumoniae antibody titer 160(80,320) 160(40,320) 0.554 MP: Mycoplasma pneumoniae , WBC: White blood cell; PCT: Procalcitonin; LDH: Lactic acid dehydrogenase; CK: Creatine kinase; CK-MB: Creatine kinase-MB; ALT: Alanine Aminotransferase; SCr: Serum creatinine. * P < 0.05, ** P < 0.01 and *** P < 0.001 represent statistically significant differences. Table 2 Comparison of general information and clinical characteristics between children with low and high MP DNA load Clinical characteristics Low load group (n=139) High load group (n=652) P value Categorical data Male 79(57.8%) 347(53.2%) 0.438 Co-infection 136(97.8%) 346(53.1%) ***＜0.001 MRSR 41(29.5%) 420(64.4%) ***＜0.001 Dyspnea 20(14.4%) 35(5.4%) ***＜0.001 Wheezing 33(23.7%) 55(8.4%) ***＜0.001 Chills 2(1.4%) 29(4.4%) 0.097 Cyanosis 3(2.2%) 4(0.6%) 0.078 Pharyngeal congestion 103(74.1%) 492(75.5%) 0.736 Fever 114(82.0%) 595(91.3%) ***＜0.001 Persistent fever (≥5 days) 42(31.1%) 312(48.3%) ***＜0.001 Productive cough 124(89.9%) 553(85.1%) 0.143 Defervescence within 72 hours of macrolide therapy 109(80.1%) 535(82.3%) 0.551 Switch to second-line non-macrolide antibiotics 11(7.9%) 84(12.9%) 0.102 Severe pneumonia 30(21.6%) 86(13.25%) **0.011 Maximum fever temperature 37.5-38 7(6.1%) 44(7.4%) 38.1-39 42(37.2%) 207(34.7%) 39.1-40 50(43.9%) 289(48.5%) ≥40.1 15(13.2%) 56(9.4%) 0.550 Severity of cough Mild 89(64.0%) 417(64.0%) Moderate 40(28.8%) 206(31.6%) Severe 10(7.2%) 29(4.4%) 0.360 Radiographic findings Prominent lung markings 6(4.3%) 14(2.2%) Inflammation/Exudation 27(19.4%) 79(12.2%) lung consolidation 97(69.8%) 539(83.1%) Pleural effusion 7(5.0%) 16(2.5%) **0.002 Quantitative data Age(months) 36.0(16.8,49.8) 69.0(43.0,85.0) ***＜0.001 Length of hospitalization (days) 7(6,9) 7(5,8) **0.018 Duration of fever (days) 5(3,8) 6(4,7) 0.086 Duration of macrolide therapy, (days) 6(4,7) 6(5,7) *0.024 WBC, ×10⁹/L 9.6(7.1,15.5) 7.8(6.3,9.8) ***＜0.001 Neutrophil percentage, % 52.7(39.7,71.3) 58.8(49.7,65.6) *0.025 Lymphocyte percentage, % 35.2(19.7,47.8) 30.5(23.6,37.6) 0.130 Platelet count, ×10⁹/L 371.5(290.5,450.0) 292.0(240.0,378.0) ***＜0.001 C-reactive protein, mg/L 9.9 (5.2,28.3) 11.9(6.5,23.3) 0.099 PCT, ng/mL 0.15(0.06,0.43) 0.10(0.07,0.20) 0.257 ALT, U/L 14.0(9.8,19.0) 11.0(9.0,15.0) ***＜0.001 CK, U/L 81.0(61.0,111.3) 89.0(66.0,130.0) 0.341 CKMB, U/L 22.5(17.8,28.0) 19.0(16.0,2.03) ***＜0.001 LDH, U/L 313(273,363) 302(272,348) 0.346 SCr, μmol/L 25.2(19.0,31.3) 30.4(25.0,37.0) ***＜0.001 Fibrinogen, g/L 3.7(3.1,4.1) 3.9(3.6,4.3) ***＜0.001 MP antibody titer 160(40,320) 160(40,320) 0.542 MP: Mycoplasma pneumoniae , WBC: White blood cell; PCT: Procalcitonin; LDH: Lactic acid dehydrogenase; CK: Creatine kinase; CK-MB: Creatine kinase-MB; ALT: Alanine Aminotransferase; SCr: Serum creatinine Table 3 Comparison of demographics and clinical characteristics between children with macrolide-susceptible and macrolide-resistant Mycoplasma pneumonia infection Clinical characteristics MSMP (n=330) MRMP (n=461) P value Categorical data Male 173(52.4%) 253(54.9%) 0.494 Co-infection 220(66.7%) 262(56.8%) **0.005 High load 232(70.3%) 420(91.1%) ***＜0.001 Dyspnea 23(7.0%) 32(6.9%) 0.988 Wheezing 39(11.8%) 49(10.6%) 0.600 Chills 10(3.0%) 21(4.6%0 0.276 Cyanosis 3(0.9%) 4(0.9%) 0.953 Pharyngeal congestion 210(63.6%) 385(83.5%) ***＜0.001 Fever 295(89.4%) 414(89.8%) 0.852 Persistent fever (≥5 days) 156(48.1%) 198(43.3%) 0.182 Productive cough 280(85.4%) 397(86.3%) 0.709 Defervescence within 72 hours of macrolide therapy 273(83.0%) 370(81.2%) 0.509 Switch to second-line non-macrolide antibiotics 19(5.8%) 76(16.5%) ***＜0.001 Severe pneumonia 53(16.1%) 63(13.7%) 0.348 Maximum fever temperature 37.5-38 24(8.1%) 27(6.5%) 38.1-39 109(36.9%) 140(33.7%) 39.1-40 128(43.4%) 211(50.8%) ≥40.1 34(11.5%) 37(8.9%) 0.228 Severity of cough Mild 229(69.4%) 277(60.1%) Moderate 81(24.5%) 165(35.8%) Severe 20(6.1%) 19(4.1%) **0.003 Radiographic findings Prominent lung markings 7(2.1%) 13(2.8%) Inflammation/Exudation 54(16.4%) 52(11.4%) lung consolidation 256(77.6%) 380(83.0%) Pleural effusion 12(3.6%) 11(2.4%) 0.219 Quantitative data Age(months) 51.0(36.0,82.0) 65.0(36.8,84.3) 0.220 Length of hospitalization (days) 6(5,8) 7(6,9) 0.138 Duration of fever (days) 5(4,7) 6(4,7) 0.896 Duration of macrolide therapy (days) 5(5,7) 7(5,7) ***＜0.001 WBC, ×10⁹/L 7.9(6.3,10.4) 8.0(6.5,10.0) *0.036 Neutrophil percentage, % 57.3(45.3,66.9) 58.6(50.1,65.3) 0.405 Lymphocyte percentage, % 31.1(22.6,41.0) 31.2(24.0,38.1) 0.947 Platelet count, ×10⁹/L 302.0(236.0,422.0) 299.5(245.0,380.8) 0.205 C-reactive protein, mg/L 10.6(5.5,24.5) 12.3(6.7,23.7) 0.510 PCT, ng/mL 0.104(0.060,0.200) 0.100(0.080,0.200) 0.811 ALT, U/L 12(9,16) 12(9,15) 0.154 CK, U/L 84.0(62.0,118.0) 91.0(65.0,135.3) 0.073 CKMB, U/L 21.0(17.0,25.0) 19.0(16.0,23.0) ***＜0.001 LDH, U/L 299.0(264.0,351.0) 307.5(275.8,348.0) 0.137 SCr, μmol/L 29.0(23.0,34.0) 30.0(24.0,36.3) 0.392 Fibrinogen, g/L 3.9(3.4,4.2) 3.9 (3.6,4.3) 0.568 MP pneumoniae antibody titer 160(40,320) 160(80,320) 0.259 MSMP, macrolide-susceptible Mycoplasma pneumoniae ; MRMP, macrolide-resistant Mycoplasma pneumoniae . MP: Mycoplasma pneumoniae , WBC: White blood cell; PCT: Procalcitonin; LDH: Lactic acid dehydrogenase; CK: Creatine kinase; CK-MB: Creatine kinase-MB; ALT: Alanine Aminotransferase; SCr: Serum creatinine. Table 4 Associations of co-infection, MP DNA load, and macrolide resistance with clinical outcomes in MP pneumonia: A multivariable regression analysis Predictors Co-infection vs Monoinfection High load vs Low load MRMP vs MSMP Fever 0.72(0.42,1.25) *2.13(1.19,3.81) 0.83(0.51,1.36) Persistent fever 0.79(0.58,1.07) ***2.13(1.38,3.29) *0.69(0.51,0.93) Switch to second-line non-macrolide antibiotics 0.93(0.59,1.48) 1.14(0.56,2.34) ***3.13(1.82,5.37) Severe pneumonia 1.00(0.64,1.57) *0.56(0.33,0.95) 0.95(0.62,1.43) Age ***-11.2(-16.1, -6.3) ***17.7 (11.2, 24.3) 0.50 (-4.30, 5.30) Length of hospitalization *0.62(0.13, 1.10) ***-1.29(-1.92, -0.65) *0.47(0.01, 0.94) Duration of macrolide therapy *0.45(0.07, 0.84) 0.36(-0.15, 0.88) **0.56(0.19, 0.93) WBC **0.87(0.27, 1.47) ***-1.94 (-2.74, -1.15) -0.27(-0.85, 0.31) Platelet count **27.10 (8.65, 45.62) **-34.23(-58.81, -9.66) -2.07 (-19.90, 15.75) 1.Data are presented as OR (95% CI) for categorical data (Fever, Persistent fever, Switch to second-line non-macrolide antibiotics, Severe pneumonia) and β (95% CI) for quantitative data (Age, Length of hospitalization, Duration of macrolide therapy, WBC, PLT). *P < 0.05, **P < 0.01, ***P < 0.001. 2. WBC: white blood cell count; PLT: platelet count; MRMP: macrolide-resistant M. pneumoniae ; MSMP: macrolide-susceptible M. pneumoniae ; CI: confidence interval; OR: odds ratio. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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1","display":"","copyAsset":false,"role":"figure","size":163161,"visible":true,"origin":"","legend":"\u003cp\u003eDemographic and clinical characteristics of the hospitalized pediatric patients with suspected MP pneumonia using targeted next-generation sequencing\u003c/p\u003e\n\u003cp\u003eMP: \u003cem\u003eMycoplasma pneumoniae\u003c/em\u003e; tNGS: targeted next-generation sequencing; DNA: deoxyribonucleic acid\u003c/p\u003e\n\u003cp\u003e¹ MP DNA load was categorized as high or low using a cut-off value of 10^6 copies/mL.\u003c/p\u003e\n\u003cp\u003e²MSMP, macrolide-susceptible \u003cem\u003eMycoplasma pneumoniae\u003c/em\u003e; MRMP, macrolide-resistant \u003cem\u003eMycoplasma pneumoniae\u003c/em\u003e. Resistance was defined by the presence of a mutation in the 23S rRNA gene.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7950608/v1/23b87b6087280fc53867e6e6.png"},{"id":96604674,"identity":"f94ded1e-951f-4e05-b06d-f69444ff0453","added_by":"auto","created_at":"2025-11-24 09:14:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1246554,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7950608/v1/c00c362b-72ce-419c-9058-0d20d646692c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations of Mycoplasma pneumoniae load, co-infections, and macrolide resistance with clinical-laboratory profiles in hospitalized pediatric pneumonia: A targeted next-generation sequencing(tNGS) study of bronchoalveolar lavage fluid (BALF)","fulltext":[{"header":"What is Known","content":"\u003cp\u003e\u003cem\u003eM. pneumoniae\u003c/em\u003e causes heterogeneous pediatric pneumonia with rising macrolide resistance and co-infections, which are not captured by conventional diagnostics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is New\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBALF-tNGS defines novel clinical phenotypes by integrating MP load, co-infection, and resistance, where low load identifies a co-infection-driven severe respiratory syndrome, enabling early personalized management.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003e\u003cem\u003eMycoplasma pneumoniae\u003c/em\u003e (MP), a leading cause of community-acquired pneumonia (CAP) in children, accounting for 30\u0026ndash;50% of pediatric pneumonia cases globally[1, 2]. While MP infections typically manifest as mild respiratory illness, emerging evidence indicates substantial heterogeneity in disease severity, ranging from self-limiting bronchitis to life-threatening necrotizing pneumonia or extrapulmonary complications[3]. This clinical variability is exacerbated by the global surge in macrolide-resistant \u003cem\u003eM. pneumoniae\u003c/em\u003e (MRMP)[4] and MP co-infection[5]. Critically, current diagnostic methods fail to explain this heterogeneity, Serological assays suffer from delayed seroconversion and cross-reactivity[6], while qPCR-based methods, though sensitive and quantitative[7], can\u0026rsquo;t simultaneously identify co-infecting pathogens or detect macrolide resistance-associated genetic mutations. These constraints perpetuate therapeutic delays, pediatric MP pneumonia cases progressing to severe disease due to undetected mixed infections or unrecognized macrolide resistance[8].\u003c/p\u003e\u003cp\u003eThe emergence of targeted next-generation sequencing (tNGS) provides comprehensive diagnostic potential by concurrently quantifying pathogen burden, identifying co-infections, and detecting macrolide resistance mutations within 12 hours [9]. Some studies have applied tNGS to the diagnosis of MP[10\u0026ndash;13]. However, current clinical practice predominantly reduces tNGS to a pathogen identification tool, little clinicians utilizing its quantitative load data or resistance gene metrics for therapeutic decisions[14, 15]. This implementation gap likely reflects insufficient evidence linking multidimensional tNGS parameters to clinically actionable thresholds.\u003c/p\u003e\u003cp\u003eTo systematically explore these associations, we analyzed a retrospective cohort of pediatric MP pneumonia cases with paired bronchoalveolar lavage fluid (BALF) tNGS results and clinical records. The multidimensional tNGS data, including MP DNA load (copies/mL), 23S rRNA mutation abundance, and co-pathogen profiles, were analyzed with clinical data (laboratory results, respiratory dynamics, and treatment responses). This study aims to establish clinically relevant correlations between tNGS result and clinical data while evaluating the diagnostic performance of tNGS in MP pneumonia.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy population\u003c/h2\u003e\u003cp\u003e This retrospective cohort study was conducted at the Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region and approved by the Medical Ethics Committee of Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region. This study included pediatric patients aged 1 month to 16 years diagnosed with lower respiratory infection from August 2023 to July 2024. MP pneumonia diagnosis was confirmed by a combination of serological criteria (particle agglutination titer\u0026thinsp;\u0026ge;\u0026thinsp;1:160) and/or MP-DNA positivity in BALF/throat swab with corresponding clinical symptoms and imaging evidence[16].\u003c/p\u003e\u003cp\u003eThe inclusion criteria were (1) clinical diagnosed with MP pneumonia; (2) underwent bronchoscopy with standardized BALF collection within 72 hours of admission; (3) BALF-tNGS confirmed MP infection (MP reads\u0026thinsp;\u0026ge;\u0026thinsp;50 and estimated concentration\u0026thinsp;\u0026ge;\u0026thinsp;1\u0026times;10\u003csup\u003e3\u003c/sup\u003e copies/mL); (4) Complete clinical data of the children.\u003c/p\u003e\u003cp\u003eThe exclusion criteria were (1) HIV, congenital immunodeficiency, glucocorticoids use and other immunocompromised status; (2) Asthma, chronic heart and lung diseases, rheumatic diseases and organic disease; (3) Incomplete clinical records or missing tNGS data; (4) Lack of diagnosis for MP pneumonia or medication for MP pneumoniae.\u003c/p\u003e\u003cp\u003e The study was approved by the Medical Ethics Committee of Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region. Informed consent was obtained from the parents of the child patients.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePathogen-targeted next-generation sequencing\u003c/h3\u003e\n\u003cp\u003e The BALF samples were collected according to the standard clinical procedure and guideline[17]. Nucleic acid from BALF samples was extracted and purified using MagPure Pathogen DNA/RNA Kit (R6672-01B, Magen, Guangzhou, China) according to the manufacturer\u0026rsquo;s instructions. A multiplex PCR library system (Respiration100TM, KingCreate, Guangzhou, China) and the next-generation sequencing technology were applied to detect 198 respiratory pathogens (80 bacteria, 79 viruses, 32 Fungi, 7 Other) and more than 300 drug-resistant gene sites, including MP and its antibiotic resistance gene sites in the macrolides. The methods and workflows of tNGS have been described in detail previously[18].\u003c/p\u003e\u003cp\u003eMono-infection was defined as MP DNA concentration\u0026thinsp;\u0026ge;\u0026thinsp;10\u003csup\u003e3\u003c/sup\u003e copies/mL in BALF without any co-detected pathogens\u0026thinsp;\u0026ge;\u0026thinsp;10\u0026sup3; copies/mL, whereas co-infection required the presence of \u0026ge;\u0026thinsp;1 additional pathogen with DNA concentration\u0026thinsp;\u0026ge;\u0026thinsp;10\u0026sup3; copies/mL.\u003c/p\u003e\u003cp\u003ePathogen DNA estimated concentration was calculated by normalizing pathogen-derived sequencing reads to a known-concentration spike-in control. According to the level of MP estimated concentration, it was categorized into low-load (10\u003csup\u003e3\u003c/sup\u003e copies/mL\u0026thinsp;\u0026lt;\u0026thinsp;MP DNA\u0026thinsp;\u0026le;\u0026thinsp;10\u003csup\u003e6\u003c/sup\u003e copies /mL) and high-load groups (MP DNA\u0026thinsp;\u0026gt;\u0026thinsp;10\u003csup\u003e6\u003c/sup\u003e copies /mL)[19]\u003c/p\u003e\u003cp\u003eMacrolide resistance was defined by the detection of 23S rRNA mutations A2063G or A2064G via tNGS[20]. Strains lacking these mutations were classified as macrolide-susceptible. According to the resistance profile, cases were further stratified into macrolide-resistant \u003cem\u003eM. pneumoniae\u003c/em\u003e (MRMP) and macrolide-susceptible \u003cem\u003eM. pneumoniae\u003c/em\u003e (MSMP) cohorts for clinical correlation analysis.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eDemographic characteristics, clinical symptoms, imaging findings, laboratory data were extracted from electronic medical records. The clinical data of the patients were comprehensively evaluated independently by two experienced clinicians to determine the relevance of lower respiratory infection and clinical potential pathogens. When two clinicians have a different opinion, further consult the senior doctor to reach a consensus.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eIBM SPSS statistics 26.0 was used for statistical analyses. Categorical variables are expressed as numbers (percentages) and were analyzed by the Chi-square test or Fisher\u0026rsquo;s exact test, as appropriate. Continuous variables are expressed as median (interquartile range, IQR) and were compared using the Mann\u0026ndash;Whitney U test owing to non-normally distributed data. Statistical significance was defined as * \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and *** \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStudy Population and Group Stratification\u003c/h2\u003e\u003cp\u003eA total of 1,646 pediatric patients with suspected M. pneumoniae pneumonia were initially enrolled based on tNGS testing. Among them, 978 patients provided BALF samples and were thus included for further analysis. The application of our predefined clinical case criteria (MP DNA load\u0026thinsp;\u0026ge;\u0026thinsp;1\u0026times;10\u0026sup3; copies/mL and reads\u0026thinsp;\u0026ge;\u0026thinsp;50) identified 860 eligible cases. After excluding 69 patients who met the exclusion criteria (e.g., immunocompromised status, incomplete records), a final cohort of 791 patients was constituted for all subsequent analyses. As pre-specified in the Methods, the entire cohort was independently stratified along three distinct dimensions for all subsequent comparative analyses: by co-infection status (monoinfection, n\u0026thinsp;=\u0026thinsp;309; co-infection, n\u0026thinsp;=\u0026thinsp;482), by bacterial DNA load (low load, n\u0026thinsp;=\u0026thinsp;139; high load, n\u0026thinsp;=\u0026thinsp;652), and by macrolide resistance (MSMP, n\u0026thinsp;=\u0026thinsp;330; MRMP, n\u0026thinsp;=\u0026thinsp;461) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eComparison of clinical characteristics between Monoinfection versus Co-infection\u003c/h3\u003e\n\u003cp\u003eThe comparative analysis of clinical characteristics between children with MP monoinfection and co-infection is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Patients in the co-infection group were significantly younger than those in the monoinfection group (median 48 vs. 72 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and had a longer median duration of hospitalization (7 days vs. 6 days, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eNotably, the prevalence of a high MP DNA load was substantially lower in the co-infection group compared to the monoinfection group (71.8% vs. 99.0%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In terms of clinical manifestations, children with co-infection presented with a higher incidence of wheezing (13.3% vs. 7.8%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016) but a lower incidence of fever (87.8% vs. 92.6%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031) and persistent fever (41.5% vs. 51.3%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007).\u003c/p\u003e\u003cp\u003eLaboratory findings further differentiated the two groups. The co-infection group exhibited significantly higher levels of systemic inflammatory markers, including white blood cell count (WBC), platelet count, and CK-MB (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all), alongside lower neutrophil percentages (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). However, no significant differences were observed in the rates of severe pneumonia or the requirement for switching to second-line non-macrolide antibiotics\u003c/p\u003e\n\u003ch3\u003eComparison of clinical characteristics between high versus low MP DNA Load\u003c/h3\u003e\n\u003cp\u003eStratification by MP DNA load revealed a distinct and more severe clinical profile in the low load group (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Patients in this group were significantly younger (median 36.0 vs. 69.0 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) yet presented with more pronounced respiratory symptoms, including a higher incidence of dyspnea (14.4% vs. 5.4%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), wheezing (23.7% vs. 8.4%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and severe pneumonia (21.6% vs. 13.3%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011). Consistently, the low load group also had a longer duration of hospitalization (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018).\u003c/p\u003e\u003cp\u003eThe overall distribution of radiographic findings differed significantly between the two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). Lung consolidation was the most common finding across the entire cohort but was notably more frequent in the high load group (83.1% vs. 69.8%). In contrast, the low load group exhibited a distinct pattern, with a numerically higher proportion of cases presenting with pleural effusion, prominent lung markings, and inflammation/exudation.\u003c/p\u003e\u003cp\u003eThe etiology underlying these differences was further clarified. The low load group was overwhelmingly composed of co-infections (97.8%), in stark contrast to the high load group (53.1%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, macrolide resistance was significantly more prevalent in the high load group (64.4% vs. 29.5%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Despite the lower MP DNA load, these patients experienced a lower incidence of both fever (82.0% vs. 91.3%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and persistent fever (31.1% vs. 48.3%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\u003cp\u003eLaboratory parameters reinforced this profile of heightened inflammation in the low load group, which exhibited significantly elevated WBC, platelet count, ALT, and CM-MB (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all)\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eComparison of clinical characteristics between MSMP and MRMP\u003c/h2\u003e\u003cp\u003eThe comparative analysis between macrolide-susceptible (MSMP) and macrolide-resistant (MRMP) infections revealed that resistance primarily impacted treatment course rather than the initial disease severity (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The most pronounced effects were on clinical management: patients infected with MRMP strains required a significantly longer duration of macrolide therapy (median 7 days vs. 5 days, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and experienced a nearly three-fold higher rate of treatment failure, necessitating a switch to second-line non-macrolide antibiotics (16.5% vs. 5.8%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\u003cp\u003eDespite these marked differences in treatment response, the two groups presented with largely similar clinical profiles. Notably, the severity of cough was greater in the MRMP group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), and pharyngeal congestion was more prevalent (83.5% vs. 63.6%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, critical indicators of disease severity\u0026mdash;including the incidence of dyspnea, wheezing, severe pneumonia, and the duration of hospitalization\u0026mdash;were not significantly different. Laboratory parameters also showed minimal differences between the groups.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eMultivariable Regression Identifies Independent Predictors for Diverse Clinical Outcomes\u003c/h2\u003e\u003cp\u003eTo delineate the independent effects of co-infection, MP DNA load, and macrolide resistance, we performed multivariable regression analyses adjusting for potential confounders (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The results solidified and refined several key associations observed in the univariate analyses.\u003c/p\u003e\u003cp\u003eMP DNA load emerged as a pivotal independent predictor for several outcomes. A high load was significantly associated with an increased risk of persistent fever (OR 2.13, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but, paradoxically, with a decreased risk of severe pneumonia (OR 0.56, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and a shorter duration of hospitalization (β -1.29 days, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eMacrolide resistance confirmed its primary role as a determinant of treatment failure. Infection with an MRMP strain was the strongest independent predictor for switching to second-line antibiotics, conferring a more than three-fold increase in odds (OR 3.13, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). It was also independently associated with a longer duration of macrolide therapy (β 0.56 days, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003cp\u003eFinally, the model identified younger age as a strong independent predictor for co-infection status (β -11.2 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, co-infection itself showed a more limited independent profile for clinical outcomes, being significantly associated only with a longer hospital stay (β 0.62 days, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and an increased duration of macrolide therapy (β 0.45 days, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMP is a prevalent cause of community-acquired pneumonia in children, notorious for its extensive clinical heterogeneity\u0026mdash;ranging from mild, self-limiting disease to severe, refractory pneumonia with extrapulmonary complications. This variability poses a significant challenge for clinicians, who have traditionally relied on diagnostic tools that offer a fragmented view of the infection. The advent of tNGS presents an opportunity for a more holistic pathogen assessment. Our study uniquely leverages the comprehensive and quantitative data from tNGS performed on BALF to dissect the roles of MP DNA load, co-infection, and macrolide resistance in shaping clinical disease. The use of BALF, collected directly from the site of infection, is a critical methodological strength, as it provides a more accurate reflection of the causative pathogen burden and minimizes the risk of contamination from upper respiratory tract colonizers, a common confounding factor in studies using sputum[21] or throat swabs[22].\u003c/p\u003e\u003cp\u003eOur findings indicated that MP co-infection as a key determinant of a specific clinical phenotype. The high co-infection rate (60.9%) aligns with recent tNGS-based studies[12, 23] and defines a distinct patient profile: younger children presenting with heightened airway reactivity, as evidenced by increased wheezing, and elevated systemic inflammatory markers such as WBC and platelet counts. This suggests that the clinical severity in this group is driven by the combined pathophysiological effect of multiple pathogens.\u003c/p\u003e\u003cp\u003eThe analysis of MP DNA load revealed a critical and nuanced relationship. While a high load was independently associated with persistent fever\u0026mdash;consistent with a high bacterial burden driving a systemic inflammatory response[19]\u0026mdash;it paradoxically predicted a lower risk of severe pneumonia. This apparent contradiction is resolved when viewed alongside co-infection status. The low MP load group was overwhelmingly characterized by co-infections (97.8%). This indicates that a low MP load in a sick child often serves as a marker of a complex, co-infection-driven disease, where the overall clinical severity and elevated inflammatory indices are attributable to the collective effect of all pathogens present, not solely MP. Conversely, high MP loads were typically seen in monoinfection, presenting a more \"classic\" MP phenotype dominated by fever but with less frequent severe respiratory compromise. The significantly higher incidence of lung consolidation in the high-load group further supports this phenotype, aligning with the traditional imaging hallmark of primary MP infection[16].\u003c/p\u003e\u003cp\u003eRegarding macrolide resistance, our data define its primary impact on treatment efficacy rather than initial disease severity. Patients with MRMP presented with similar initial severity as those with MSMP. However, MRMP was the strongest independent predictor of macrolide treatment failure, conferring a three-fold higher odds of requiring second-line antibiotics. This underscores the clinical value of tNGS in detecting resistance mutations early to guide appropriate therapy and avoid prolonged ineffective treatment[15].\u003c/p\u003e\u003cp\u003eMultivariable regression consolidates the independent roles of these three dimensions and hints at their clinical interplay. The analysis confirmed that each factor exerts a significant and independent influence on distinct outcomes after adjusting for confounders. High MP load independently predicted persistent fever but protected against severe pneumonia; macrolide resistance was the paramount factor for treatment failure; and younger age strongly predicted co-infection status. While this study primarily analyzed these dimensions separately, their independent significance in the regression model strongly suggests that their combination in individual patients likely creates unique risk profiles. For example, the clinical course of a child with high-load, macrolide-resistant monoinfection would be expected to differ markedly from one with low-load, macrolide-susceptible co-infection. The formal exploration of these interactive effects represents a critical next step.\u003c/p\u003e\u003cp\u003eOur study has limitations. First, its retrospective and single-center design may affect the generalizability of our findings, and future multi-center validation is necessary. Second, the inherent quantitative range of tNGS, while clinically useful, may not precisely quantify extremely high or low pathogen loads, potentially influencing the dichotomization of load groups. Furthermore, the clinical synergies between load, co-infection, and resistance remain to be fully elucidated. Future prospective studies with larger cohorts are expressly warranted to develop integrated predictive models that combine these three tNGS parameters. Such models could stratify patients into distinct risk groups at diagnosis, enabling truly personalized management strategies.\u003c/p\u003e\u003cp\u003eIn conclusion, this BALF-based tNGS study offers a refined perspective on pediatric MP pneumonia by evaluating three key dimensions. Our data suggest that MP DNA load may be a pivotal factor in defining disease phenotype, providing crucial context for interpreting co-infection status. The findings indicate that a high MP load, often associated with monoinfection, appears to correlate with a classic presentation of fever and lung consolidation. Conversely, a low MP load may serve as a key indicator of a co-infection-driven syndrome in younger children, which tends to present with more pronounced respiratory symptoms. Additionally, the presence of macrolide resistance was strongly associated with an increased risk of treatment failure. Collectively, these insights highlight the potential clinical value of integrating multidimensional tNGS data\u0026mdash;encompassing pathogen load, co-infections, and resistance markers\u0026mdash;to better stratify patients at diagnosis, thereby informing more tailored therapeutic approaches and paving the way for more personalized management strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eALT: Alanine Aminotransferase; BALF: bronchoalveolar lavage fluid; CAP: community-acquired pneumonia, CK: Creatine kinase; CK-MB: Creatine kinase-MB; CI: confidence interval; DNA: deoxyribonucleic acid. LDH: Lactic acid dehydrogenase; OR: odds ratio; PCT: Procalcitonin; MP\u003cem\u003e: Mycoplasma pneumoniae\u003c/em\u003e, MRMP, macrolide-resistant \u003cem\u003eM. pneumoniae\u003c/em\u003e; MSMP, macrolide-susceptible \u003cem\u003eM. pneumoniae\u003c/em\u003e; tNGS: targeted next-generation sequencing, WBC: White blood cell; SCr: Serum creatinine.\u0026nbsp;\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXingzhen Liang:\u0026nbsp;Conceptualization, Methodology, Formal Analysis; Rong Wei:\u0026nbsp;Data Curation, Methodology, Resources, Supervision,\u0026nbsp;Investigation; Dongna liang, Wenxiu Huang, Ruizhen Huang, Siyu Lu, Yining Lu, Huifei Ma, Qi Shi, Dongyun Li, Donglu Chen, Bing Qin, Xiheng Qi:\u0026nbsp;Resources, Data Curation, Investigation, Yupeng Tang, Wugui Mo:\u0026nbsp;Resources, Data Curation, Supervision; Zihan Wei: Conceptualization, Formal Analysis, Writing Original Draft, Supervision, Writing -Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Medical Ethics Committee of Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish:\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors affirm that human research participants provided informed consent for publication of the images in Figure 1\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYan C, Xue GH, Zhao HQ, Feng YL, Cui JH, Yuan J. Current status of Mycoplasma pneumoniae infection in China. World J Pediatr. 2024;20(1):1-4. Epub 2024/01/08. doi: 10.1007/s12519-023-00783-x. PubMed PMID: 38185707; PubMed Central PMCID: PMCPMC10827902 interest.\u003c/li\u003e\n\u003cli\u003eNordholm AC, Soborg B, Jokelainen P, Lauenborg Moller K, Flink Sorensen L, Grove Krause T, et al. Mycoplasma pneumoniae epidemic in Denmark, October to December, 2023. Euro Surveill. 2024;29(2). Epub 2024/01/12. doi: 10.2807/1560-7917.ES.2024.29.2.2300707. PubMed PMID: 38214084; PubMed Central PMCID: PMCPMC10785206.\u003c/li\u003e\n\u003cli\u003eDing G, Zhang X, Vinturache A, van Rossum AMC, Yin Y, Zhang Y. Challenges in the treatment of pediatric Mycoplasma pneumoniae pneumonia. Eur J Pediatr. 2024;183(7):3001-11. Epub 2024/04/18. doi: 10.1007/s00431-024-05519-1. PubMed PMID: 38634891.\u003c/li\u003e\n\u003cli\u003eKim K, Jung S, Kim M, Park S, Yang HJ, Lee E. Global Trends in the Proportion of Macrolide-Resistant Mycoplasma pneumoniae Infections: A Systematic Review and Meta-analysis. JAMA Netw Open. 2022;5(7):e2220949. Epub 2022/07/12. doi: 10.1001/jamanetworkopen.2022.20949. 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Epub 2012/10/09. doi: 10.1016/j.diagmicrobio.2012.08.027. PubMed PMID: 23040512.\u003c/li\u003e\n\u003cli\u003eTan J, Chen Y, Lu J, Lu J, Liu G, Mo L, et al. Pathogen distribution and infection patterns in pediatric severe pneumonia: A targeted next-generation sequencing study. Clin Chim Acta. 2025;565:119985. Epub 2024/10/04. doi: 10.1016/j.cca.2024.119985. PubMed PMID: 39362455.\u003c/li\u003e\n\u003cli\u003eChen Q, Yi J, Liu Y, Yang C, Sun Y, Du J, et al. Clinical diagnostic value of targeted next‑generation sequencing for infectious diseases (Review). Mol Med Rep. 2024;30(3). Epub 2024/07/04. doi: 10.3892/mmr.2024.13277. PubMed PMID: 38963022.\u003c/li\u003e\n\u003cli\u003eXiao Y, Dekyi, Wang X, Feng S, Yang Y, Zheng J, et al. Interpretation of pathogenicity and clinical features of multiple pathogens in pediatric lower respiratory tract infections by tNGS RPTM analysis. Eur J Clin Microbiol Infect Dis. 2025. Epub 2025/03/14. doi: 10.1007/s10096-025-05094-9. PubMed PMID: 40085381.\u003c/li\u003e\n\u003cli\u003eZhao J, Xu M, Tian Z, Wang Y. Clinical characteristics of pathogens in children with community-acquired pneumonia were analyzed via targeted next-generation sequencing detection. PeerJ. 2025;13:e18810. Epub 2025/01/13. doi: 10.7717/peerj.18810. PubMed PMID: 39802179; PubMed Central PMCID: PMCPMC11724655.\u003c/li\u003e\n\u003cli\u003eFu C, Mo L, Feng Y, Zhu N, Huang H, Huang Z, et al. Detection of Mycoplasma pneumoniae in hospitalized pediatric patients presenting with acute lower respiratory tract infections utilizing targeted next-generation sequencing. Infection. 2025;53(4):1437-47. Epub 2025/01/31. doi: 10.1007/s15010-024-02467-8. PubMed PMID: 39888587.\u003c/li\u003e\n\u003cli\u003eLin R, Xing Z, Liu X, Chai Q, Xin Z, Huang M, et al. Performance of targeted next-generation sequencing in the detection of respiratory pathogens and antimicrobial resistance genes for children. J Med Microbiol. 2023;72(11). Epub 2023/11/01. doi: 10.1099/jmm.0.001771. PubMed PMID: 37910007.\u003c/li\u003e\n\u003cli\u003eWei M, Mao S, Li S, Gu K, Gu D, Bai S, et al. Comparing the diagnostic value of targeted with metagenomic next-generation sequencing in immunocompromised patients with lower respiratory tract infection. Ann Clin Microbiol Antimicrob. 2024;23(1):88. Epub 2024/10/01. doi: 10.1186/s12941-024-00749-5. PubMed PMID: 39350160; PubMed Central PMCID: PMCPMC11443791.\u003c/li\u003e\n\u003cli\u003eHe M, Xie J, Rui P, Li X, Lai M, Xue H, et al. Clinical efficacy of macrolide antibiotics in mycoplasma pneumoniae pneumonia carrying a macrolide-resistant mutation in the 23 S rRNA gene in pediatric patients. BMC Infect Dis. 2024;24(1):758. Epub 2024/08/01. doi: 10.1186/s12879-024-09612-6. PubMed PMID: 39085799; PubMed Central PMCID: PMCPMC11292884.\u003c/li\u003e\n\u003cli\u003eExpert Committee on Rational Use of Medicines for Children Pharmaceutical Group NH, Family Planning C. [Expert consensus on laboratory diagnostics and clinical practice of Mycoplasma pneumoniae infection in children in China (2019)]. Zhonghua Er Ke Za Zhi. 2020;58(5):366-73. Epub 2020/05/13. doi: 10.3760/cma.j.cn112140-20200304-00176. PubMed PMID: 32392951.\u003c/li\u003e\n\u003cli\u003eBranch of Pediatric Critical Care Physicians CMA, Neonatologists Branch of Chinese Medical A, Gansu Provincial M, Child Health Hospital/Gansu Provincial Central Hospital/Gansu Pediatric Clinical Medical Research C, Center for Evidence-Based Medicine SoBMLUWHOGfP, Knowledge Transformation Cooperation Center/Gansu Province Medical Guideline Technology C. [Clinical practice guidelines for bronchoalveolar lavage in Chinese children (2024)]. Zhongguo Dang Dai Er Ke Za Zhi. 2024;26(1):1-13. Epub 2024/01/25. doi: 10.7499/j.issn.1008-8830.2308072. PubMed PMID: 38269452; PubMed Central PMCID: PMCPMC10817737.\u003c/li\u003e\n\u003cli\u003eDai Y, Sheng K, Hu L. Diagnostic efficacy of targeted high-throughput sequencing for lower respiratory infection in preterm infants. Am J Transl Res. 2022;14(11):8204-14. Epub 2022/12/13. PubMed PMID: 36505277; PubMed Central PMCID: PMCPMC9730095.\u003c/li\u003e\n\u003cli\u003eWang W, Wang L, Yin Z, Zeng S, Yao G, Liu Y, et al. Correlation of DNA load, genotyping, and clinical phenotype of Mycoplasma pneumoniae infection in children. Front Pediatr. 2024;12:1369431. Epub 2024/04/24. doi: 10.3389/fped.2024.1369431. PubMed PMID: 38655275; PubMed Central PMCID: PMCPMC11035820.\u003c/li\u003e\n\u003cli\u003eZhan XW, Deng LP, Wang ZY, Zhang J, Wang MZ, Li SJ. Correlation between Mycoplasma pneumoniae drug resistance and clinical characteristics in bronchoalveolar lavage fluid of children with refractory Mycoplasma pneumoniae pneumonia. Ital J Pediatr. 2022;48(1):190. Epub 2022/11/27. doi: 10.1186/s13052-022-01376-6. PubMed PMID: 36435821; PubMed Central PMCID: PMCPMC9701416.\u003c/li\u003e\n\u003cli\u003eZhang C, Zhang Q, Du JL, Deng D, Gao YL, Wang CL, et al. Correlation Between the Clinical Severity, Bacterial Load, and Inflammatory Reaction in Children with Mycoplasma Pneumoniae Pneumonia. Curr Med Sci. 2020;40(5):822-8. Epub 2020/10/31. doi: 10.1007/s11596-020-2261-6. PubMed PMID: 33123897; PubMed Central PMCID: PMCPMC7595045.\u003c/li\u003e\n\u003cli\u003eMedjo B, Atanaskovic-Markovic M, Radic S, Nikolic D, Lukac M, Djukic S. Mycoplasma pneumoniae as a causative agent of community-acquired pneumonia in children: clinical features and laboratory diagnosis. Ital J Pediatr. 2014;40:104. Epub 2014/12/19. doi: 10.1186/s13052-014-0104-4. PubMed PMID: 25518734; PubMed Central PMCID: PMCPMC4279889.\u003c/li\u003e\n\u003cli\u003eDong X, Li R, Zou Y, Chen L, Zhang H, Lyu F, et al. Co-detection of respiratory pathogens in children with Mycoplasma pneumoniae pneumonia: a multicenter study. Front Pediatr. 2025;13:1482880. Epub 2025/06/09. doi: 10.3389/fped.2025.1482880. PubMed PMID: 40487016; PubMed Central PMCID: PMCPMC12141229.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable1\u003c/strong\u003e Comparison of general information and clinical characteristics between children with \u003cem\u003eMycoplasma pneumoniae\u003c/em\u003e monoinfection and co-infection\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"571\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eClinical characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eMonoinfection of MP(n=309)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eCo-infection of MP\u003c/p\u003e\n \u003cp\u003e(n=482)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 571px;\"\u003e\n \u003cp\u003eCategorical data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e154(50.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e271(56.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.095\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eHigh load\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e306(99.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e346(71.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e***＜0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eMRSR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e199(64.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e262(54.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e**0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eDyspnea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e16(5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e39(8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.116\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eWheezing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e24(7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e64(13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e*0.016\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eChills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e18(5.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e13(2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e*0.027\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eCyanosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e1(0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e6(1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePharyngeal congestion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e238(77.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e357(74.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eFever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e286(92.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e423(87.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e*0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePersistent fever (\u0026ge;5 days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e156(51.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e198(41.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e**0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eProductive cough\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e262(84.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e415(86.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eDefervescence within 72 hours of macrolide therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e260(84.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e383(80.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eSwitch to second-line non-macrolide antibiotics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e41(13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e53(11.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eSevere pneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e41(13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e75(15.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 350px;\"\u003e\n \u003cp\u003eMaximum fever temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e37.5-38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e18(6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e33(7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e38.1-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e101(35.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e148(34.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e39.1-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e145(50.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e194(45.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026ge;40.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e22(7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e49(11.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eSeverity of cough\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e200(64.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e306(63.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e97(31.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e149(30.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e12(3.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e27(5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.553\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eRadiographic findings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eProminent lung markings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e3(1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e17(3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eInflammation/Exudation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e35(11.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e71(14.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003elung consolidation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e260(84.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e376(78.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePleural effusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e11(3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e12(2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e*0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eQuantitative data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eAge(months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e72(48,94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e48(26,78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e***＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eLength of hospitalization (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e6(5,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e7(6,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e***＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eDuration of fever (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e6(5,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e5(3,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eDuration of macrolide therapy, (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e6(5,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e6(5,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eWBC, \u0026times;10⁹/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e7.4(6.1,9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e8.4(6.8,10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e***＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eNeutrophil percentage, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e60.4(52.4,66.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e55.9(45.3,65.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e**0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eLymphocyte percentage, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e29.4(22.7,34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e32.9(23.5,43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e**0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePlatelet count, \u0026times;10⁹/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e278(234,352)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e322(249,417)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e***＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eC-reactive protein, mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e12.1(6.7,24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e11.4(5.9,23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePCT, ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.10(0.07,0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.10(0.07,0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.658\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eALT, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e11.0(9.0,14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e12.0(9.5,16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e**0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eCK, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e95(68,135)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e82(63,121)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eCKMB, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e19(16,23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e21(17,25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e***＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eLDH, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e298.0(268.0,349.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e308.0(274.0,349.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eSCr, \u0026mu;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e32.0(26.7,37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e28.0(22.0,34.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e***＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eFibrinogen,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e4.00(3.67,4.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e3.85(3.40,4.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e***＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eMP pneumoniae antibody titer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e160(80,320)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e160(40,320)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.554\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMP: \u003cem\u003eMycoplasma pneumoniae\u003c/em\u003e, WBC: White blood cell; PCT: Procalcitonin; LDH: Lactic acid\u003c/p\u003e\n\u003cp\u003edehydrogenase; CK: Creatine kinase; CK-MB: Creatine kinase-MB; ALT: Alanine Aminotransferase; SCr: Serum creatinine. *\u003cem\u003e\u0026nbsp;P\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003e\u0026nbsp;P\u003c/em\u003e \u0026lt; 0.01 and ***\u003cem\u003e\u0026nbsp;P\u003c/em\u003e \u0026lt; 0.001 represent statistically significant differences.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Comparison of general information and clinical characteristics between children with low and high MP DNA load\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"571\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eClinical characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eLow load group\u003c/p\u003e\n \u003cp\u003e(n=139)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eHigh load group\u003c/p\u003e\n \u003cp\u003e(n=652)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 571px;\"\u003e\n \u003cp\u003eCategorical data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e79(57.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e347(53.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eCo-infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e136(97.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e346(53.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e***＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eMRSR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e41(29.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e420(64.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e***＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eDyspnea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e20(14.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e35(5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e***＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eWheezing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e33(23.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e55(8.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e***＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eChills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e2(1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e29(4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eCyanosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e3(2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e4(0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePharyngeal congestion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e103(74.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e492(75.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.736\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eFever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e114(82.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e595(91.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e***＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePersistent fever (\u0026ge;5 days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e42(31.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e312(48.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e***＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eProductive cough\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e124(89.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e553(85.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eDefervescence within 72 hours of macrolide therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e109(80.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e535(82.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eSwitch to second-line non-macrolide antibiotics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e11(7.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e84(12.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eSevere pneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e30(21.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e86(13.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e**0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 325px;\"\u003e\n \u003cp\u003eMaximum fever temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e37.5-38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e7(6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e44(7.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e38.1-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e42(37.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e207(34.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e39.1-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e50(43.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e289(48.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026ge;40.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e15(13.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e56(9.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.550\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eSeverity of cough\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e89(64.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e417(64.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e40(28.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e206(31.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e10(7.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e29(4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eRadiographic findings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eProminent lung markings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e6(4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e14(2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eInflammation/Exudation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e27(19.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e79(12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003elung consolidation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e97(69.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e539(83.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePleural effusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e7(5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e16(2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e**0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eQuantitative data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eAge(months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e36.0(16.8,49.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e69.0(43.0,85.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e***＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eLength of hospitalization (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e7(6,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e7(5,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e**0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eDuration of fever (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e5(3,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e6(4,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eDuration of macrolide therapy, (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e6(4,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e6(5,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e*0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eWBC, \u0026times;10⁹/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e9.6(7.1,15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e7.8(6.3,9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e***＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eNeutrophil percentage, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e52.7(39.7,71.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e58.8(49.7,65.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e*0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eLymphocyte percentage, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e35.2(19.7,47.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e30.5(23.6,37.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePlatelet count, \u0026times;10⁹/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e371.5(290.5,450.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e292.0(240.0,378.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e***＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eC-reactive protein, mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e9.9 (5.2,28.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e11.9(6.5,23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePCT, ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0.15(0.06,0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.10(0.07,0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eALT, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e14.0(9.8,19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e11.0(9.0,15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e***＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eCK, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e81.0(61.0,111.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e89.0(66.0,130.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eCKMB, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e22.5(17.8,28.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e19.0(16.0,2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e***＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eLDH, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e313(273,363)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e302(272,348)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eSCr, \u0026mu;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e25.2(19.0,31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e30.4(25.0,37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e***＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eFibrinogen,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e3.7(3.1,4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e3.9(3.6,4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e***＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eMP antibody titer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e160(40,320)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e160(40,320)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMP: \u003cem\u003eMycoplasma pneumoniae\u003c/em\u003e, WBC: White blood cell; PCT: Procalcitonin; LDH: Lactic acid\u003c/p\u003e\n\u003cp\u003edehydrogenase; CK: Creatine kinase; CK-MB: Creatine kinase-MB; ALT: Alanine Aminotransferase; SCr: Serum creatinine\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 3\u003c/strong\u003e Comparison of demographics and clinical characteristics between children with macrolide-susceptible and macrolide-resistant\u003cem\u003e\u0026nbsp;Mycoplasma pneumonia\u003c/em\u003e infection\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"567\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eClinical characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eMSMP\u003c/p\u003e\n \u003cp\u003e(n=330)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eMRMP\u003c/p\u003e\n \u003cp\u003e(n=461)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 567px;\"\u003e\n \u003cp\u003eCategorical data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e173(52.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e253(54.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.494\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eCo-infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e220(66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e262(56.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e**0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eHigh load\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e232(70.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e420(91.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e***＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eDyspnea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e23(7.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e32(6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eWheezing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e39(11.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e49(10.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.600\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eChills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e10(3.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e21(4.6%0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eCyanosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e3(0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e4(0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003ePharyngeal congestion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e210(63.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e385(83.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e***＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eFever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e295(89.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e414(89.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003ePersistent fever (\u0026ge;5 days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e156(48.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e198(43.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eProductive cough\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e280(85.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e397(86.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eDefervescence within 72 hours of macrolide therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e273(83.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e370(81.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eSwitch to second-line non-macrolide antibiotics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e19(5.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e76(16.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e***＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eSevere pneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e53(16.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e63(13.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 348px;\"\u003e\n \u003cp\u003eMaximum fever temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003e37.5-38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e24(8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e27(6.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003e38.1-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e109(36.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e140(33.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003e39.1-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e128(43.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e211(50.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003e\u0026ge;40.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e34(11.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e37(8.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eSeverity of cough\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e229(69.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e277(60.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e81(24.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e165(35.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e20(6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e19(4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e**0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eRadiographic findings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eProminent lung markings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e7(2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e13(2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eInflammation/Exudation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e54(16.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e52(11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003elung consolidation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e256(77.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e380(83.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003ePleural effusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e12(3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e11(2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eQuantitative data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eAge(months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e51.0(36.0,82.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e65.0(36.8,84.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eLength of hospitalization (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e6(5,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e7(6,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eDuration of fever (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e5(4,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e6(4,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eDuration of macrolide therapy (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e5(5,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e7(5,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e***＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eWBC, \u0026times;10⁹/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e7.9(6.3,10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e8.0(6.5,10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e*0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eNeutrophil percentage, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e57.3(45.3,66.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e58.6(50.1,65.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eLymphocyte percentage, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e31.1(22.6,41.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e31.2(24.0,38.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003ePlatelet count, \u0026times;10⁹/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e302.0(236.0,422.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e299.5(245.0,380.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eC-reactive protein, mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e10.6(5.5,24.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e12.3(6.7,23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.510\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003ePCT, ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0.104(0.060,0.200)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e0.100(0.080,0.200)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eALT, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e12(9,16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e12(9,15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eCK, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e84.0(62.0,118.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e91.0(65.0,135.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eCKMB, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e21.0(17.0,25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e19.0(16.0,23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e***＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eLDH, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e299.0(264.0,351.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e307.5(275.8,348.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eSCr, \u0026mu;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e29.0(23.0,34.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e30.0(24.0,36.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eFibrinogen,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e3.9(3.4,4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e3.9 (3.6,4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.568\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eMP pneumoniae antibody titer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e160(40,320)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e160(80,320)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMSMP, macrolide-susceptible \u003cem\u003eMycoplasma pneumoniae\u003c/em\u003e; MRMP, macrolide-resistant \u003cem\u003eMycoplasma pneumoniae\u003c/em\u003e. MP: \u003cem\u003eMycoplasma pneumoniae\u003c/em\u003e, WBC: White blood cell; PCT: Procalcitonin; LDH: Lactic acid dehydrogenase; CK: Creatine kinase; CK-MB: Creatine kinase-MB; ALT: Alanine Aminotransferase; SCr: Serum creatinine.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 4\u003c/strong\u003e Associations of co-infection, MP DNA load, and macrolide resistance with clinical outcomes in MP pneumonia: A multivariable regression analysis\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"633\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003ePredictors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eCo-infection vs Monoinfection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eHigh load vs Low load\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eMRMP vs MSMP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eFever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.72(0.42,1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e*2.13(1.19,3.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.83(0.51,1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003ePersistent fever\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.79(0.58,1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e***2.13(1.38,3.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e*0.69(0.51,0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eSwitch to second-line non-macrolide antibiotics\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.93(0.59,1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1.14(0.56,2.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e***3.13(1.82,5.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eSevere pneumonia\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1.00(0.64,1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e*0.56(0.33,0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.95(0.62,1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e***-11.2(-16.1, -6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e***17.7 (11.2, 24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.50 (-4.30, 5.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eLength of hospitalization\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e*0.62(0.13, 1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e***-1.29(-1.92, -0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e*0.47(0.01, 0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eDuration of macrolide therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e*0.45(0.07, 0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.36(-0.15, 0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e**0.56(0.19, 0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eWBC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e**0.87(0.27, 1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e***-1.94 (-2.74, -1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e-0.27(-0.85, 0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003ePlatelet count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e**27.10 (8.65, 45.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e**-34.23(-58.81, -9.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e-2.07 (-19.90, 15.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e1.Data are presented as OR (95% CI) for categorical data (Fever, Persistent fever, Switch to second-line non-macrolide antibiotics, Severe pneumonia) and \u0026beta; (95% CI) for quantitative data (Age, Length of hospitalization, Duration of macrolide therapy, WBC, PLT). *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003e2. WBC: white blood cell count; PLT: platelet count; MRMP: macrolide-resistant \u003cem\u003eM. pneumoniae\u003c/em\u003e; MSMP: macrolide-susceptible \u003cem\u003eM. pneumoniae\u003c/em\u003e; CI: confidence interval; OR: odds ratio.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mycoplasma pneumoniae, Targeted next-generation sequencing, DNA load, Co-infection, Macrolide resistance, Pediatrics","lastPublishedDoi":"10.21203/rs.3.rs-7950608/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7950608/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eThis study aimed to investigate the associations of \u003cem\u003eMycoplasma pneumoniae\u003c/em\u003e (MP) DNA load, co-infection, and macrolide resistance with clinical phenotypes in pediatric pneumonia, using targeted next-generation sequencing (tNGS) of bronchoalveolar lavage fluid (BALF)\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective cohort study of 791 hospitalized children with MP pneumonia. All patients underwent bronchoscopy, and bronchoalveolar lavage fluid (BALF) was analyzed using targeted next-generation sequencing (tNGS). This allowed for the simultaneous quantification of MP DNA load, comprehensive co-infection profiling, and detection of macrolide resistance mutations (A2063G/A2064G in 23S rRNA). Clinical data were correlated with these multidimensional tNGS parameters.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOur analysis revealed distinct clinical phenotypes linked to tNGS findings. A high MP DNA load was associated with a \"classic\" phenotype of persistent fever and lung consolidation, typically in monoinfection. Conversely, a low MP DNA load served as a key marker for a co-infection-driven syndrome in younger children, characterized by more severe respiratory symptoms (wheezing, dyspnea) and systemic inflammation. Furthermore, macrolide-resistant MP (MRMP) was the strongest independent predictor of treatment failure, tripling the odds of requiring second-line antibiotics, without influencing initial disease severity.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study demonstrates that the integration of MP DNA load, co-infection status, and resistance profiling from BALF-based tNGS provides a powerful framework for stratifying pediatric MP pneumonia. These multidimensional data offer critical insights for anticipating disease course and tailoring therapeutic strategies, paving the way for more personalized and effective patient management.\u003c/p\u003e","manuscriptTitle":"Associations of Mycoplasma pneumoniae load, co-infections, and macrolide resistance with clinical-laboratory profiles in hospitalized pediatric pneumonia: A targeted next-generation sequencing(tNGS) study of bronchoalveolar lavage fluid (BALF)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-10 10:19:57","doi":"10.21203/rs.3.rs-7950608/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2cae9934-26a6-4ce1-9e99-b1990b326da8","owner":[],"postedDate":"November 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-22T16:08:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-10 10:19:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7950608","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7950608","identity":"rs-7950608","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}