Construction of a Nomogram for Identifying Refractory Mycoplasma pneumoniae Pneumonia Among Macrolide-Unresponsive Mycoplasma pneumoniae Pneumonia in Children

Azithromycin Effectiveness in Children with Mutated Mycoplasma Pneumoniae Pneumonia

Objective

Mycoplasma pneumoniae (MP) is highly resistant to macrolides in China. However, macrolides still exhibit clinical effectiveness in some macrolide-resistant patients. We tend to explore azithromycin effectiveness in Mycoplasma pneumoniae pneumonia (MPP) children with A2063/2064G mutation.

Methods

This retrospective observational cohort study was conducted at the Children's Hospital of the Chongqing Medical University. Children with macrolide-resistant mutations (A2063/2064G) diagnosed as MPP were retrospectively enrolled. Receiver operating characteristic (ROC) curves and logistic regression analysis were used to evaluate and identify independent risk factors for treatment failure (progress to refractory Mycoplasma pneumoniae pneumonia [RMPP]) in macrolide-unresponsive Mycoplasma pneumoniae pneumonia (MUMPP) children with the A2063/2064G mutation.

Results

One hundred fifty-five children were retrospectively enrolled. More than 20% (36/155, 23.23%) of patients experienced defervescence within 3 days of azithromycin treatment. RMPP was diagnosed in 54 patients (54/155, 34.84%) and the incidence of RMPP during hospitalization was 22.72 per 1000 person-days. Logistic regression analysis showed that lactate dehydrogenase (LDH) ≥ 399 (U/L) was an independent risk factor for RMPP (odds ratio [OR] 4.66, 95% confidence interval [CI] 1.31-17.10, P=0.017). During the year followed, RMPP patients had a significantly higher incidence of bronchiolitis obliterans and bronchiectasis than non-RMPP patients (16.67% vs 1.98%, P=0.001; 9.26% vs 0.00%, P=0.005, respectively).

Conclusion

Azithromycin was effective in children with MPP with the A2063/2064G mutation. For MUMPP children with A2063/2064G mutation, children with LDH ≥ 399 (U/L) had significant higher risk for progression to RMPP, and should consider to be treated with alternative antibiotics (eg tetracyclines, and fluoroquinolones).

Development and validation of an online dynamic nomogram system for pulmonary consolidation in children with Mycoplasma pneumoniae pneumonia

INTRODUCTION

The occurrence of pulmonary consolidation in children with Mycoplasma pneumoniae pneumonia (MPP) can lead to exacerbation of the disease. Therefore, early identification of children with MPP in combination with pulmonary consolidation is critical. The purpose of this study was to develop a straightforward, easy-to-use online dynamic nomogram for the identification of children with MPP who are at high risk of developing pulmonary consolidation.

METHODS

491 MPP patients were chosen and divided randomly into a training cohort and an internal validation cohort at a 4:1 ratio. Multi-factor logistic regression was used to identify the risk variables for mixed pulmonary consolidation in children with Mycoplasma pneumoniae (MP). The selected variables were utilized to build the nomograms and validated using the C-index, decision curve analysis, calibration curves, and receiver operating characteristic (ROC) curves.

RESULTS

Seven variables were included in the Nomogram model: age, fever duration, lymphocyte count, C-reactive protein (CRP), ferritin, T8 lymphocyte percentage, and T4 lymphocyte percentage. We created a dynamic nomogram that is accessible online ( https://ertong.shinyapps.io/DynNomapp/ ). The C-index was 0.90. The nomogram calibration curves in the training and validation cohorts were highly comparable to the standard curves. The area under the curve (AUC) of the prediction model was, respectively, 0.902 and 0.883 in the training cohort and validation cohort. The decision curve analysis (DCA) curve shows that the model has a significant clinical benefit.

CONCLUSIONS

We developed a dynamic online nomogram for predicting combined pulmonary consolidation in children with MP based on 7 variables for the first time. The predictive value and clinical benefit of the nomogram model were acceptable.

Neutrophil-to-lymphocyte ratio as a predictor of poor outcomes of Mycoplasma pneumoniae pneumonia

Introduction

Mycoplasma pneumoniae pneumonia (MPP) may lead to various significant outcomes, such as necrotizing pneumonia(NP) and refractory MPP (RMPP). We investigated the potential of the peripheral blood neutrophil-to-lymphocyte ratio (NLR) to predict outcomes in patients with MPP.

Methods and materials

This was a prospective study of patients with MPP who were admitted to our hospital from 2019 to 2021. Demographic and clinical data were collected from patient records and associated with the development of NP and RMPP and other outcome measures.

Results

Of the 1,401 patients with MPP included in the study, 30 (2.1%) developed NP. The NLR was an independent predictor of NP (odds ratio 1.153, 95% confidence interval 1.022-1.300, P=0.021). The probability of NP was greater in patients with a high NLR (≥1.9) than in those with a low NLR (<1.9) (P<0.001). The NLR was also an independent predictor of RMPP (odds ratio 1.246, 95% confidence interval 1.102-1.408, P<0.005). Patients with a high NLR were more likely to develop NP and RMPP and require intensive care, and had longer total fever duration, longer hospital stays, and higher hospitalization expenses than those with a low NLR (all P<0.005).

Discussion

The NLR can serve as a predictor of poor prognosis in patients with MPP. It can predict the occurrence of NP, RMPP, and other poor outcomes. The use of this indicator would allow the simple and rapid prediction of prognosis in the early stages of MPP, enabling the implementation of appropriate treatment strategies.

Global research trends of Mycoplasma pneumoniae pneumonia in children: a bibliometric analysis

BACKGROUND

Mycoplasma pneumoniae pneumonia (MPP), attributable to Mycoplasma pneumoniae (MP), represents a predominant form of community-acquired pneumonia in pediatric populations, thereby posing a significant threat to pediatric health. Given the burgeoning volume of research literature associated with pediatric MPP in recent years, it becomes imperative to undertake a bibliometric analysis aimed at delineating the current research landscape and emerging trends, thereby furnishing a framework for subsequent investigations.

METHODS

A comprehensive literature search targeting pediatric MPP was conducted in the Web of Science Core Collection. After the removal of duplicate entries through Endnote software, the remaining articles were subject to scientometric analysis via Citespace software, VOSviewer software and R language, focusing on variables such as publication volume, contributing nations, institutions and authors, references and keywords.

RESULTS

A total of 1,729 articles pertinent to pediatric MPP were included in the analysis. China and the United States emerged as the nations with the highest publication output. Italian scholar Susanna Esposito and Japanese scholar Kazunobu Ouchi were the most influential authors in the domain of pediatric MPP. Highly-cited articles primarily focused on the epidemiological investigation of pediatric MPP, the clinical characteristics and treatment of macrolide-resistant MPP, and biomarkers for refractory Mycoplasma pneumoniae pneumonia (RMPP). From the corpus of 1,729 articles, 636 keywords were extracted and categorized into ten clusters: Cluster #0 centered on molecular-level typing of macrolide-resistant strains; Cluster #1 focused on lower respiratory tract co-infections; Clusters #2 and #6 emphasized other respiratory ailments caused by MP; Cluster #3 involved biomarkers and treatment of RMPP; Clusters #4 and #9 pertained to extrapulmonary complications of MPP, Clusters #5 and #7 addressed etiological diagnosis of MPP, and Cluster #8 explored pathogenic mechanisms.

CONCLUSIONS

The past few years have witnessed extensive attention directed towards pediatric MPP. Research in pediatric MPP principally revolves around diagnostic techniques for MP, macrolide resistance, complications of MPP, treatment and diagnosis of RMPP, and elucidation of pathogenic mechanisms. The present study provides pediatric clinicians and researchers with the research status and focal points in this field, thereby guiding the orientation of future research endeavors.

Risk Prediction Model for Necrotizing Pneumonia in Children with Mycoplasma pneumoniae Pneumonia

Objective

To analyze the predictive factors for necrotizing pneumonia (NP) in children with Mycoplasma pneumoniae pneumonia (MPP) and construct a prediction model.

Methods

The clinical data with MPP at the Children's Hospital of Kunming Medical University from January 2014 to November 2022 were retrospectively analyzed. Eighty-four children with MPP who developed NP were divided into the necrotizing group, and 168 children who did not develop NP were divided into the non-necrotizing group by propensity-score matching. LASSO regression was used to select the optimal factors, and multivariate logistic regression analysis was used to establish a clinical prediction model. The receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the discrimination and calibration of the nomogram. Clinical decision curve analysis was used to evaluate the clinical predictive value.

Results

LASSO regression analysis showed that bacterial co-infection, chest pain, LDH, CRP, duration of fever, and D-dimer were the influencing factors for NP in children with MPP (P < 0.05). The results of ROC analysis showed that the AUC of the prediction model established in this study for predicting necrotizing MPP was 0.870 (95% CI: 0.813-0.927, P < 0.001) in the training set and 0.843 (95% CI: 0.757-0.930, P < 0.001) in the validation set. The Bootstrap repeated sampling for 1000 times was used for internal validation, and the calibration curve showed that the model had good consistency. The Hosmer-Lemeshow test showed that the predicted probability of the model had a good fit with the actual probability in the training set and the validation set (P values of 0.366 and 0.667, respectively). The clinical decision curve showed that the model had good clinical application value.

Conclusion

The prediction model based on bacterial co-infection, chest pain, LDH, CRP, fever duration, and D-dimer has a good predictive value for necrotizing MPP.

Development of a Nomogram for Predicting Massive Necrotizing Pneumonia in Children

Objective

This study aimed to develop a nomogram model for predicting massive necrotizing pneumonia (NP) in children.

Methods

A total of 282 children with NP admitted to Kunming Children's Hospital from January 2014 to November 2022 were enrolled. The children with NP were divided into massive necrotizing pneumonia (MNP) group and non-MNP group according to the severity of the lung necrosis. The clinical data of the children were collected, and least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression models were used to analyze the influencing factors of MNP. A nomogram model was constructed, and its predictive efficacy was evaluated.

Results

The predictors selected by LASSO regression analysis were: haematogenous spread, white blood cell (WBC), hemoglobin (Hb), C-reactive protein (CRP), lactate dehydrogenase (LDH), and activated partial thromboplastin time (APTT) (P < 0.05). Based on the above independent influencing factors, a nomogram model for MNP was constructed. The bootstrap method was used to repeat sampling 1000 times. The results showed that the consistency index of the nomogram model in predicting MNP was 0.833 in the training set and 0.810 in the validation set. The results of ROC curve analysis showed that the area under the receiver-operating-characteristic curve (AUC) of the nomogram model for predicting MNP was 0.889 [95% CI (0.818, 0.959)] in the training set and 0.814 [95% CI (0.754, 0.874)] in the validation set. The calibration curve of the nomogram predicting MNP was basically close to the actual curve. The decision curve showed that the nomogram had good clinical utility.

Conclusion

We developed a nomogram for predicting MNP, which can help clinicians identify the severity of lung necrosis early.