The predictive accuracy of systemic immune-inflammation index and lymphocyte-to-monocyte ratio for extrapulmonary complications in pediatric Mycoplasma pneumoniae pneumonia: a retrospective cohort study

Introduction

Mycoplasma pneumoniae pneumonia (MPP) is one of the leading causes of community-acquired pneumonia in children, accounting for a significant proportion of pediatric respiratory infections globally (1,2). Although MPP is often self-limiting and responsive to macrolide treatment, an increasing number of cases present with severe disease progression and extrapulmonary complications (EP complications), posing challenges in clinical management (3,4). These complications can involve multiple organ systems, including the skin, digestive, circulatory, hematologic, urinary and central nervous system, leading to significant morbidity and prolonged hospitalization in affected children (4,5). The exact pathophysiology of extrapulmonary involvement in MPP remains unclear, but it is believed to be primarily mediated by immune dysregulation rather than direct Mycoplasma pneumoniae invasion (6-8). Several mechanisms have been proposed, including molecular mimicry, cytokine storm, immune complex deposition, and autoimmune responses, all of which can contribute to systemic inflammatory damage and organ dysfunction (6-8). Given these immunopathological features, inflammatory biomarkers may play a crucial role in identifying cases at risk for severe or extrapulmonary involvement, thereby facilitating early intervention and improved clinical outcomes.

Inflammatory indicators, including white blood cell (WBC) count, C-reactive protein (CRP), and other related biomarkers, have traditionally served as reference parameters to evaluate the extent of disease progression and severity in pediatric MPP cases (9,10). However, their predictive value for extrapulmonary involvement remains limited and inconsistent, as they do not fully capture the complex immune-inflammatory interactions associated with MPP pathogenesis. Therefore, novel biomarkers capable of reflecting systemic immune and inflammatory responses more comprehensively are urgently needed for early risk stratification and improved prognostic assessment. In recent years, two emerging immune-inflammatory indices—the systemic immune-inflammation index (SIRI) and the lymphocyte-to-monocyte ratio (LMR)—have gained attention as potential markers of systemic inflammation and immune response dysregulation in various infectious and inflammatory diseases (11,12). SIRI, determined by the formula below (neutrophil count × monocyte count)/lymphocyte count, functions as a marker of systemic immune response and inflammatory activity, representing the interplay among neutrophil-associated inflammatory processes, monocyte-influenced immune modulation, and lymphocyte-driven regulatory mechanisms (13,14). In contrast, LMR, defined as lymphocyte count/monocyte count, represents the host immune status, with lower LMR values often associated with immune suppression and uncontrolled inflammation (15,16). These indices have been investigated in several conditions, including sepsis, malignancies, and autoimmune diseases, and have demonstrated promising prognostic value in predicting disease severity and systemic inflammatory burden.

EP complications in pediatric MPP remain challenging to predict, and current biomarkers have limitations. Biomarkers such as CRP, procalcitonin (PCT), and cytokines [e.g., interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α)] have poor specificity, limited predictive accuracy, and high costs. CRP, though commonly used, is nonspecific, while PCT lacks sufficient sensitivity for predicting extrapulmonary involvement. Given the immunopathology of MPP, which involves complex immune dysregulation, we hypothesize that the SIRI and LMR may offer superior predictive value. SIRI integrates inflammatory markers like neutrophils, monocytes, and lymphocytes, providing a broader view of systemic inflammation, while LMR, reflecting the balance between lymphocytes and monocytes, may help identify patients at risk for EP complications. These biomarkers could improve early detection and risk stratification, addressing the limitations of existing markers and enhancing clinical outcomes. However, their role in predicting EP complications in pediatric MPP remains largely unexplored. Therefore, we performed this study to investigate the predictive value of SIRI and LMR in the early onset of EP complications among children diagnosed with MPP. We present this article in accordance with the TRIPOD reporting checklist (available at https://tp.amegroups.com/article/view/10.21037/tp-2025-488/rc).

Methods

Participants enrollment and study design

A total of 160 pediatric MPP cases who visited Capital Center for Children’s Health, Capital Medical University between January 2025 and February 2025 were enrolled in the study and retrospectively analyzed. These individuals were recruited using a consecutive sampling method, where all eligible cases meeting the inclusion criteria during the study period were included. These individuals were classified into two distinct cohorts according to whether EP complications were present or not, forming the MPP group (n=112) and the MPP + EP group (n=48). EPs were defined as non-respiratory manifestations during the course of MPP, supported by clinical or laboratory evidence and not attributable to drug reactions. In this study, EP included: (I) skin involvement (e.g., rash, urticaria); (II) gastrointestinal symptoms (vomiting, abdominal pain, diarrhea); (III) circulatory complications (e.g., abnormal heart rate or perfusion); (IV) hematologic abnormalities (anemia, thrombocytopenia); (V) urinary manifestations (e.g., hematuria, proteinuria); and (VI) arthritis (joint pain or swelling). All EP cases were identified before immunomodulatory treatment, and suspected drug-induced events were excluded.

Eligibility for study participation was determined based on specific inclusion criteria, which required that cases (I) had a diagnosis of MPP established through a combination of clinical manifestations, imaging assessments, and confirmation via MP serology or polymerase chain reaction (PCR) testing; (II) aged 3–14 years; (III) hospitalized and received standard treatment for MPP. Exclusion criteria included: (I) co-infections with other respiratory pathogens, identified by routine pathogen screening at admission, including PCR testing for common respiratory viruses and Mycoplasma pneumoniae, bacterial cultures (sputum or blood, when indicated), and serologic testing for atypical pathogens. Patients with positive results for additional pathogens were excluded. (II) Presence of congenital or chronic diseases affecting immune function. (III) Pneumonia recovery after admission, evidenced by stable temperature and improved chest X-ray findings. (IV) Use of immunosuppressive therapy prior to admission. (V) Incomplete medical records or missing laboratory data relevant to the study.

Demographic details, clinical characteristics, and laboratory findings were extracted from electronic medical records maintained by the hospital. Cases were assigned to the MPP + EP group if they presented with EP complications affecting various organ systems, including the skin, gastrointestinal tract, circulatory system, hematologic profile, urinary system, joints, or central nervous system. Key laboratory parameters, such as WBC count, CRP, SIRI, LMR, were assessed at the time of hospital admission. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Capital Institute of Pediatrics (No. SHERLL2024072). Prior to data collection, informed consent was secured from the legal guardians or parents of all enrolled participants.

Diagnosis of MPP

The diagnosis of MPP in cases was established through a combination of clinical manifestations, including fever, cough, dyspnea, and auscultatory findings such as crackles, along with radiological assessments and serological antibody analysis. Anti-mycoplasma immunoglobulin M (IgM) titers were quantified using a microparticle agglutination method (Serodia-Myco II, Fujirebio, Tokyo, Japan), where a titer of ≥1:160 upon admission or a fourfold elevation in immunoglobulin G (IgG) levels between the acute and recovery phases was considered indicative of infection (17,18). Additionally, PCR analysis of respiratory secretions was performed to detect the presence of other viral pathogens, including influenza, parainfluenza, adenovirus, and respiratory syncytial virus. Individuals who tested positive for any of these respiratory viruses were excluded from the study.

Calculation of SIRI and LMR

The values of SIRI and LMR were derived from routine hematological indices measured in peripheral blood samples obtained at the time of hospital admission. A complete blood count was conducted utilizing an automated hematology analyzer, adhering to established laboratory procedures. SIRI was computed using the formula: SIRI = (neutrophil count × monocyte count)/lymphocyte count, with absolute cell counts reported in cells/µL. Similarly, LMR was determined by dividing the lymphocyte count by the monocyte count.

Statistical analysis

SPSS software (IBM Corp., Armonk, NY, USA) was used for data analysis. The Shapiro-Wilk test was used to evaluate distribution of continuous variables. To compare groups, the independent t-test was applied to normally distributed data, while the Mann-Whitney U test was used for non-normally distributed data. Categorical variables were examined through the Chi-squared test or Fisher’s exact test. To identify potential risk factors associated with EP complications, both univariate and multivariate logistic regression models were utilized. Significant variables indicated by univariate analysis were incorporated into the multivariate regression models. Odds ratios (ORs) along with 95% confidence intervals (CIs) were calculated. The ability of SIRI, LMR, and CRP to predict EP complications was evaluated through receiver operating characteristic (ROC) curve analysis, with area under the curve (AUC) values compared via the DeLong test. To assess the combined predictive performance of SIRI, LMR, and CRP, we first included these biomarkers in a multivariate logistic regression model to develop a regression function. The predicted probabilities from this model were then used as a composite score and evaluated through ROC curve analysis to determine the overall predictive accuracy (AUC), sensitivity, and specificity of the combined biomarkers in predicting EP complications. Youden’s index was used for determination of optimal cutoff values. A P value of <0.05 was considered statistically significant, and all reported P values are two-sided.

Results

Baseline features of the MPP pediatric cases with or without EP complications

The baseline features of pediatric MPP cases, with and without EP complications, are outlined in Table 1. Among the total of 160 children included in the study, 112 were categorized in the MPP group, while 48 were classified in the MPP + EP group. No significant differences were found in terms of age (5.86±2.43 vs. 5.54±2.34 years; P=0.45) or gender distribution (P=0.63) between the two groups. However, the duration of hospitalization was notably prolonged in cases with EP complications (13.67±3.49 vs. 8.80±3.07 days; P<0.001). Regarding pneumonia type, bronchopneumonia was the most commonly identified form in both groups (69.64% vs. 62.50%), while lobar pneumonia was present in 30.36% and 37.50%, respectively (P=0.48). No significant variation in the incidence of tachypnea (12.50% vs. 20.83%; P=0.27) and cyanosis (0.89% vs. 4.17%, P=0.45) was found between the groups. Laboratory markers revealed distinct differences. WBC count was significantly increased in the MPP + EP group [(8,098.92±97.49) vs. (7,204.04±85.42) ×10⁶/L; P<0.001], as were CRP levels (10.39±2.76 vs. 8.49±2.51 mg/L; P<0.001). Additionally, SIRI was markedly elevated [(9.25±1.57) vs. (7.23±1.41) ×10⁹/L; P<0.001], whereas LMR was significantly lower in the MPP + EP group (2.61±0.51 vs. 2.91±0.48; P=0.001), suggesting a possible link between reduced LMR values and the presence of EP complications. Among the observed extrapulmonary manifestations, dermatological involvement was the most frequently reported (37.50%), followed by gastrointestinal (22.92%), circulatory (20.83%), and hematologic (6.25%) complications. Additionally, urinary system abnormalities (6.25%), arthritis-related symptoms (4.17%), and central nervous system involvement (2.08%) were documented.

Uni- and multi-variate logistic regression analysis identifying independent factors of EP complications in MPP pediatric cases

To explore potential determinants linked to EP complications in pediatric cases diagnosed with MPP, both uni- and multi-variate regression analyses were performed (Tables 2,3). Laboratory indices that exhibited significant differences between groups, including WBC count, CRP levels, SIRI, and LMR, were subjected to univariate logistic regression analysis (Table 2). The results showed that increased SIRI values (OR =2.231, 95% CI: 1.709–2.913; P<0.001) and elevated CRP concentrations (OR =1.311, 95% CI: 1.142–1.505; P<0.001) were independently associated with a higher likelihood of EP complications. Conversely, LMR exhibited an inverse relationship with EP complications (OR =0.289, 95% CI: 0.139–0.604; P=0.001), indicating that a higher LMR was associated with a lower probability of extrapulmonary manifestations. To further delineate independent predictors, significant variables indicated by univariate analysis were incorporated into a multivariate logistic regression model (Table 3). After multivariable adjustment, SIRI remained an independent predictor of EP complications (OR 2.08; 95% CI, 1.62–2.68; P<0.001). Similarly, higher CRP levels were independently linked to a greater probability of EP complications (OR =1.199, 95% CI: 1.014–1.418; P=0.03). In contrast, LMR was identified as a statistically significant protective factor (OR =0.143, 95% CI: 0.059–0.350; P<0.001), highlighting its potential role in reflecting the inflammatory response dynamics associated with MPP progression.

The ROC analysis evaluating the prediction efficacy of SIRI and LMR for the EP complications of MPP pediatric cases

The ability of SIRI, LMR, and CRP to predict EP complications in pediatric MPP cases was evaluated through ROC curve analysis (Table 4 and Figure 1). Among these markers, SIRI exhibited the strongest predictive performance, yielding an AUC of 0.824 (95% CI: 0.749–0.899; P<0.001). The optimal threshold of 8.59×10⁹/L corresponded to a sensitivity of 75.0% and a specificity of 89.3%, suggesting robust discriminative capacity. CRP also demonstrated notable predictive value, with an AUC of 0.717 (95% CI: 0.627–0.806, P<0.001). The optimal cutoff of 8.7 mg/L provided 77.1% sensitivity and 62.5% specificity, indicating its role as a moderate predictor of EP complications. Compared to SIRI and CRP, LMR exhibited relatively lower predictive efficiency, with an AUC of 0.673 (95% CI: 0.582–0.764; P<0.001). The most effective threshold of 2.835 resulted in 58.9% sensitivity and 70.8% specificity, suggesting a more limited, yet still relevant, contribution to risk assessment. When SIRI, CRP, and LMR were evaluated in combination, predictive performance improved significantly, with the AUC increasing to 0.858 (95% CI: 0.779–0.937; P<0.001). This combination resulted in enhanced specificity (92.9%) while maintaining a sensitivity of 75.0%, highlighting its potential for more accurate identification of cases at risk for EP complications.

Figure 1 ROC curve evaluating the prediction effects of (A) SIRI, (B) CRP, (C) LMR, and (D) combined analysis on the extrapulmonary complications of MPP pediatric cases. CRP, C-reactive protein; LMR, lymphocyte-to-monocyte ratio; MPP, Mycoplasma pneumoniae pneumonia; ROC, receiver operating characteristic; SIRI, systemic immune-inflammation index.

Discussion

In this study, we investigated the role of the SIRI and LMR in predicting EP complications in pediatric MPP cases. Our findings demonstrate that SIRI and LMR are significantly associated with extrapulmonary involvement, with higher SIRI and lower LMR serving as independent risk factors for EP complications. Furthermore, SIRI exhibited the highest predictive accuracy among the studied biomarkers, and its combination with CRP and LMR further improved diagnostic performance. The findings above provide new insights into the systemic inflammatory response in pediatric MPP and highlight the potential utility of SIRI and LMR as early predictive markers for extrapulmonary involvement.

This study revealed that cases in the MPP + EP group had significantly longer hospitalization durations, suggesting a greater disease burden and more severe systemic involvement. This finding aligns with previous reports indicating that EP complications prolong recovery and increase morbidity in pediatric MPP cases (19-21). The observed significant elevation in WBC count, CRP, and SIRI in the MPP + EP group reflects a more pronounced systemic inflammatory response, reinforcing the idea that Mycoplasma pneumoniae infection can trigger an exaggerated immune response, leading to immune-mediated tissue damage beyond the lungs. While the precise pathophysiology of extrapulmonary involvement in MPP is not fully understood, it is believed to result from immune dysregulation rather than direct bacterial invasion (6-8). Multiple underlying mechanisms could potentially account for this phenomenon, with cytokine storm and excessive inflammatory responses being key contributors. The overactivation of pro-inflammatory mediators, including TNF-α, IL-6, and IL-8, may play a crucial role in driving widespread inflammation and impairing multiple organ systems (22,23). Additionally, molecular mimicry and autoimmunity may play a role, as Mycoplasma pneumoniae shares antigenic structures with host tissues, potentially leading to cross-reactive immune responses and autoantibody production, which could contribute to neurological, dermatological, and hematologic complications (6,24,25). Another potential mechanism is immune complex deposition, where circulating immune complexes accumulate in various organs, leading to vascular inflammation and tissue damage, as seen in extrapulmonary MPP manifestations such as encephalitis, myocarditis, and hemolytic anemia (26,27). Given these underlying mechanisms, the marked increase in SIRI in cases with EP complications suggests that neutrophil-driven inflammation and monocyte-mediated immune responses are critical factors in the development of systemic manifestations of MPP.

This study revealed that SIRI is a strong independent predicting factor for EP complications in pediatric MPP cases. Logistic regression analysis confirmed that higher SIRI values were significantly associated with an increased risk of EP complications (OR =1.007; P<0.001). Additionally, ROC curve analysis showed that SIRI had the highest predictive accuracy (AUC =0.824, sensitivity =75.0%, specificity =89.3%), suggesting that it may be a valuable biomarker for the early identification of high-risk cases.

SIRI, which integrates neutrophil, monocyte, and lymphocyte counts, reflects the balance between pro-inflammatory and regulatory immune responses (28,29). Elevated SIRI values indicate increased neutrophil and monocyte activity, coupled with lymphopenia, a pattern commonly observed in systemic inflammatory diseases (30-32). In pediatric MPP, neutrophils and monocytes contribute to the excessive inflammatory response, exacerbating systemic involvement (33). The ability of SIRI to capture this immune imbalance may explain its superior predictive performance compared to traditional markers such as CRP and WBC count.

In contrast, LMR was significantly lower in cases with EP complications (OR =0.143, P<0.001). LMR is considered an indicator of host immune homeostasis, with higher values suggesting effective immune regulation and lower values reflecting immune suppression and increased inflammatory burden (34-36). Previous studies have demonstrated that low LMR is associated with adverse outcomes in inflammatory and infectious diseases (37-39), reinforcing our findings that decreased LMR correlates with a higher risk of systemic involvement in MPP. However, the predictive ability of LMR alone was moderate (AUC =0.673, sensitivity =58.9%, specificity =70.8%), suggesting that while it plays a role in risk assessment, its diagnostic utility is enhanced when combined with other inflammatory indices. In our study, a higher LMR was associated with a reduced risk of EP complications in children with MPP. This finding, while differing from patterns observed in sepsis and other severe infections, may reflect differences in the immune response specific to MP infection. A relatively higher LMR may indicate a more balanced immune state, characterized by preserved lymphocyte-mediated adaptive immunity and a less pronounced monocyte-driven inflammatory response. Monocytes are known to contribute to systemic inflammation and tissue injury through the release of proinflammatory cytokines; thus, a lower monocyte count relative to lymphocytes may mitigate the risk of immune-mediated damage beyond the lungs. Nevertheless, the precise immunopathological mechanisms underlying this association remain to be clarified. Future studies involving longitudinal immune profiling, cytokine analysis, and mechanistic animal models will be essential to determine whether LMR plays a direct regulatory role or serves as a surrogate marker of immune status in pediatric MPP.

CRP, an acute-phase inflammatory marker, was also significantly elevated in the MPP + EP group and was independently associated with EP complications (OR =1.199, P=0.03). However, its predictive value was lower than that of SIRI, as indicated by a lower AUC [0.717]. While CRP is widely used to assess systemic inflammation, its lack of specificity limits its ability to distinguish localized pulmonary inflammation from extrapulmonary involvement. Our findings suggest that CRP alone may not be sufficient for predicting EP complications, but can be a useful adjunct when combined with SIRI and LMR.

Importantly, our study revealed that the combination of SIRI, CRP, and LMR resulted in the highest predictive accuracy (AUC =0.858, sensitivity =75.0%, specificity =92.9%), significantly outperforming the individual biomarkers. This suggests that integrating multiple inflammatory markers provides a more comprehensive assessment of the immune-inflammatory response in MPP, enabling early identification of high-risk cases with greater specificity and sensitivity. Given that EP complications can be life-threatening and require timely intervention, this combined biomarker approach may enhance early risk stratification and clinical decision-making, leading to improved patient management and better outcomes.

While this study provides valuable insights, it has several limitations. First, the retrospective design, single-center setting, and potential selection bias may affect the findings. Future research should focus on larger, multi-center prospective studies to improve reliability and refine cutoff values for SIRI and LMR. Second, although SIRI and LMR showed predictive value, incorporating additional inflammatory markers (e.g., IL-6, TNF-α, IL-8) would help clarify the immunological mechanisms behind EP complications in MPP. Third, the sample size of 160 cases was based on available data, not a formal a priori calculation. While sufficient for robust analyses, the lack of a sample size calculation may limit power and generalizability, highlighting the need for larger, prospectively powered studies. We also acknowledge that the high specificity of the combined model could be driven by less severe manifestations, emphasizing the need for future studies with more precise outcomes. External validation (e.g., testing the model on an independent cohort) will be conducted in future research before drawing definitive conclusions about the clinical utility of these biomarkers. Lastly, this study did not explore the temporal variations of SIRI and LMR throughout the disease course, and evaluating their fluctuations over time could provide deeper insights into disease progression and treatment responses.

Conclusions

In conclusion, our study highlights SIRI and LMR as valuable biomarkers for predicting EP complications in pediatric MPP cases. Elevated SIRI and CRP levels were associated with higher systemic inflammatory responses, while higher LMR was associated with reduced risk of EP complications in pediatric MPP. The combined use of SIRI, CRP, and LMR showed promising predictive value, although external validation (e.g., testing the model on an independent cohort) will be conducted in future research before drawing definitive conclusions about their clinical utility. Our findings remain to be further verified by well-designed studies in the future.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-488/rc

Data Sharing Statement: Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-488/dss

Peer Review File: Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-488/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tp.amegroups.com/article/view/10.21037/tp-2025-488/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Capital Institute of Pediatrics (No. SHERLL2024072). Prior to data collection, informed consent was secured from the legal guardians or parents of all enrolled participants.

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

References

1. Wang Y, Huang L, Qian J, et al. Clinical profile and risk factors for respiratory failure in children with Mycoplasma pneumoniae infection. Biomol Biomed 2025;25:1972-81. [Crossref] [PubMed]
2. Lv W, Guo C, Lv G, et al. Epidemiological characteristics and related risk factors of mixed infection in children with mycoplasma pneumoniae pneumonia. New Microbiol 2024;47:251-7.
3. Yang S, Lu S, Guo Y, et al. A comparative study of general and severe mycoplasma pneumoniae pneumonia in children. BMC Infect Dis 2024;24:449. [Crossref] [PubMed]
4. Wang Z, Peng Y, Yang S, et al. Risk factors for complications of Mycoplasma pneumoniae pneumonia in hospitalized children in China: a systematic review and meta-analysis. BMC Pediatr 2024;24:810. [Crossref] [PubMed]
5. Zhou L, Li Y, Xu Z, et al. Increased Total Serum Immunoglobulin E Is Likely to Cause Complications of Mycoplasma pneumoniae Pneumonia in Children. Front Cell Infect Microbiol 2021;11:783635. [Crossref] [PubMed]
6. Jiang Z, Li S, Zhu C, et al. Mycoplasma pneumoniae Infections: Pathogenesis and Vaccine Development. Pathogens 2021;10:119. [Crossref] [PubMed]
7. Fan L, Xu N, Guo Y, et al. Enhanced insights into the neutrophil-driven immune mechanisms during Mycoplasma pneumoniae infection. Heliyon 2024;10:e38950. [Crossref] [PubMed]
8. Shi S, Zhang X, Zhou Y, et al. Immunosuppression Reduces Lung Injury Caused by Mycoplasma pneumoniae Infection. Sci Rep 2019;9:7147. [Crossref] [PubMed]
9. Ling Y, Zhang T, Guo W, et al. Identify clinical factors related to Mycoplasma pneumoniae pneumonia with hypoxia in children. BMC Infect Dis 2020;20:534. [Crossref] [PubMed]
10. Huang X, Li D, Liu F, et al. Clinical significance of D-dimer levels in refractory Mycoplasma pneumoniae pneumonia. BMC Infect Dis 2021;21:14. [Crossref] [PubMed]
11. Yun S, Yi HJ, Lee DH, et al. Systemic Inflammation Response Index and Systemic Immune-inflammation Index for Predicting the Prognosis of Patients with Aneurysmal Subarachnoid Hemorrhage. J Stroke Cerebrovasc Dis 2021;30:105861. [Crossref] [PubMed]
12. Söğütlü Y, Altaş U. Predictive Value of Neutrophil-Lymphocyte Ratio and Other Inflammation Indices in Febrile Seizures in Children. J Clin Med 2024;13:5330. [Crossref] [PubMed]
13. Cao C, Li C, Li X, et al. Association of systemic immune-inflammation index (SII) and aggregate index of systemic inflammation (AISI) with thyroid nodules in patients with type 2 diabetes mellitus: a retrospective study. BMC Endocr Disord 2023;23:251. [Crossref] [PubMed]
14. Okutan İ, Aci R, Keskin Â, et al. New inflammatory markers associated with disease activity in rheumatoid arthritis: pan-immune-inflammation value, systemic immune-inflammation index, and systemic inflammation response index. Reumatologia 2024;62:439-46. [Crossref] [PubMed]
15. Gawiński C, Mróz A, Roszkowska-Purska K, et al. A Prospective Study on the Roles of the Lymphocyte-to-Monocyte Ratio (LMR), Neutrophil-to-Lymphocyte Ratio (NLR), and Platelet-to-Lymphocyte Ratio (PLR) in Patients with Locally Advanced Rectal Cancer. Biomedicines 2023;11:3048. [Crossref] [PubMed]
16. Wang HK, Wei Q, Yang YL, et al. Clinical usefulness of the lymphocyte-to-monocyte ratio and aggregate index of systemic inflammation in patients with esophageal cancer: a retrospective cohort study. Cancer Cell Int 2023;23:13. [Crossref] [PubMed]
17. Talkington DF, Shott S, Fallon MT, et al. Analysis of eight commercial enzyme immunoassay tests for detection of antibodies to Mycoplasma pneumoniae in human serum. Clin Diagn Lab Immunol 2004;11:862-7. [Crossref] [PubMed]
18. Subspecialty Group of Respiratory Diseases, The Society of Pediatrics, Chinese Medical Association; Editorial Board, Chinese Journal of Pediatrics. Guidelines for management of community acquired pneumonia in children (the revised edition of 2013) (I). Zhonghua Er Ke Za Zhi 2013;51:745-52.
19. Wang L, Xie Q, Xu S, et al. The role of flexible bronchoscopy in children with Mycoplasma pneumoniae pneumonia. Pediatr Res 2023;93:198-206. [Crossref] [PubMed]
20. Bian C, Li S, Huo S, et al. Association of atopy with disease severity in children with Mycoplasma pneumoniae pneumonia. Front Pediatr 2023;11:1281479. [Crossref] [PubMed]
21. Zhang X, Sun R, Hou J, et al. Clinical characteristics and risk factors of pulmonary embolism with Mycoplasma pneumoniae pneumonia in children. Sci Rep 2024;14:24043. [Crossref] [PubMed]
22. Zou Y, Huang F, Sun J, et al. The role of IFN-γ/CXCL10 axis in Mycoplasma pneumonia infection. Sci Rep 2025;15:2671. [Crossref] [PubMed]
23. Lin X, Xu E, Zhang T, et al. Cytokine-based nomogram for discriminating viral pneumonia from Mycoplasma pneumoniae pneumonia in children. Diagn Microbiol Infect Dis 2025;111:116611. [Crossref] [PubMed]
24. Wang J, Liang K, Chen L, et al. Unveiling the stealthy tactics: mycoplasma’s immune evasion strategies. Front Cell Infect Microbiol 2023;13:1247182. [Crossref] [PubMed]
25. Berg CP, Kannan TR, Klein R, et al. Mycoplasma antigens as a possible trigger for the induction of antimitochondrial antibodies in primary biliary cirrhosis. Liver Int 2009;29:797-809. [Crossref] [PubMed]
26. Narita M. Classification of Extrapulmonary Manifestations Due to Mycoplasma pneumoniae Infection on the Basis of Possible Pathogenesis. Front Microbiol 2016;7:23. [Crossref] [PubMed]
27. He J, Liu M, Ye Z, et al. Insights into the pathogenesis of Mycoplasma pneumoniae Mol Med Rep 2016;14:4030-6. (Review). [Crossref] [PubMed]
28. Wang S, Wan Y, Zhang W. The Clinical Value of Systemic Immune Inflammation Index (SII) in Predicting the Severity of Hospitalized Children with Mycoplasma Pneumoniae Pneumonia: A Retrospective Study. Int J Gen Med 2024;17:935-42. [Crossref] [PubMed]
29. Tang C, Zhang M, Jia H, et al. The systemic inflammation response index (SIRI) predicts survival in advanced non-small cell lung cancer patients undergoing immunotherapy and the construction of a nomogram model. Front Immunol 2024;15:1516737. [Crossref] [PubMed]
30. Ma R, Cui L, Cai J, et al. Association between systemic immune inflammation index, systemic inflammation response index and adult psoriasis: evidence from NHANES. Front Immunol 2024;15:1323174. [Crossref] [PubMed]
31. Gu L, Xia Z, Qing B, et al. Systemic Inflammatory Response Index (SIRI) is associated with all-cause mortality and cardiovascular mortality in population with chronic kidney disease: evidence from NHANES (2001-2018). Front Immunol 2024;15:1338025. [Crossref] [PubMed]
32. Luo S, Liu Z, Jiao R, et al. The associations of two novel inflammation indexes, systemic immune-inflammation index (SII) and system inflammation response index (SIRI), with periodontitis: evidence from NHANES 2009-2014. Clin Oral Investig 2024;28:129. [Crossref] [PubMed]
33. Zhu J, Liu X, Zhan X, et al. Predictive value of chemokines (CCL 2) in bronchoalveolar lavage fluid for refractory mycoplasma pneumonia in children. Ital J Pediatr 2023;49:125. [Crossref] [PubMed]
34. Ren H, Liu X, Wang L, et al. Lymphocyte-to-Monocyte Ratio: A Novel Predictor of the Prognosis of Acute Ischemic Stroke. J Stroke Cerebrovasc Dis 2017;26:2595-602. [Crossref] [PubMed]
35. Bumbasirevic U, Bojanic N, Simic T, et al. Interplay between Comprehensive Inflammation Indices and Redox Biomarkers in Testicular Germ-Cell Tumors. J Pers Med 2022;12:833. [Crossref] [PubMed]
36. Hsu YC, Yang YY, Tsai IT. Lymphocyte-to-monocyte ratio predicts mortality in cirrhotic patients with septic shock. Am J Emerg Med 2021;40:70-6. [Crossref] [PubMed]
37. Hong YF, Chen ZH, Wei L, et al. Identification of the prognostic value of lymphocyte-to-monocyte ratio in patients with HBV-associated advanced hepatocellular carcinoma. Oncol Lett 2017;14:2089-96. [Crossref] [PubMed]
38. Zhang H, Li Y, Liu G, et al. Expression analysis of lymphocyte subsets and lymphocyte-to-monocyte ratio: reveling immunosuppression and chronic inflammation in breast cancer. J Cancer Res Clin Oncol 2024;150:28. [Crossref] [PubMed]
39. Piotrowski D, Sączewska-Piotrowska A, Jaroszewicz J, et al. Lymphocyte-To-Monocyte Ratio as the Best Simple Predictor of Bacterial Infection in Patients with Liver Cirrhosis. Int J Environ Res Public Health 2020;17:1727. [Crossref] [PubMed]