Optimization strategy for the early timing of bronchoalveolar lavage treatment for children with severe mycoplasma pneumoniae pneumonia.

Early evaluation of severe mycoplasma pneumoniae pneumonia (SMPP) and the prompt utilization of fiberoptic bronchoscopic manipulation can effectively alleviate complications and restrict the progression of sequelae. This study aim to establish a nomogram forecasting model for SMPP in children and explore an optimal early therapeutic bronchoalveolar lavage (TBAL) treatment strategy. This retrospective study included children with mycoplasma pneumoniae pneumonia (MPP) from January 2019 to December 2021. Multivariate logistic regression analysis was used to screen independent risk factors for SMPP and establish a nomogram model. The bootstrap method was employed and a receiver operator characteristic (ROC) curve was drawn to evaluate the accuracy and robustness of the model. Kaplan-Meier analysis was used to assess the effect of lavage and hospitalization times. A total of 244 cases were enrolled in the study, among whom 68 with SMPP and 176 with non-SMPP (NSMPP). A prediction model with five independent risk factors: left upper lobe computed tomography (CT) score, sequential organ failure assessment (SOFA) score, acute physiology and chronic health assessment (APACHE) II score, bronchitis score (BS), and c-reactive protein (CRP) was established based on the multivariate logistic regression analysis. The ROC curve of the prediction model showed the area under ROC curve (AUC) was 0.985 (95% confidence interval (CI) 0.972-0.997). The Hosmer-Lemeshow goodness-of-fit test results showed that the nomogram model predicted the risk of SMPP well (χ2 = 2.127, P = 0.977). The log-rank result suggested that an early BAL treatment could shorten MPP hospitalization time (P = 0.0057). This nomogram model, based on the left upper lobe CT score, SOFA score, APACHE II score, BS, and CRP level, represents a valuable tool to predict the risk of SMPP in children and optimize the timing of TBAL.

Wu X ，Lu W ，Wang T ，Xiao A ，Guo X ，Xu Y ，Li S ，Liu X ，Zeng H ，He S ，Zhang X ... -  《BMC INFECTIOUS DISEASES》

Predicting the severity of mycoplasma pneumoniae pneumonia in pediatric and adult patients: a multicenter study.

The purpose of this study is to develop a nomogram model for early prediction of the severe mycoplasma pneumoniae pneumonia (SMPP) in Pediatric and Adult Patients. A retrospective analysis was conducted on patients with MPP, classifying them into SMPP and non-severe MPP (NSMPP) groups. A total of 550 patients (NSMPP 374 and SMPP 176) were enrolled in the study and allocated to training, validation cohorts. 278 patients (NSMPP 224 and SMPP 54) were retrospectively collected from two institutions and allocated to testing cohort. The risk factors for SMPP were identified using univariate analysis. For radiomic feature selection, Spearman's correlation and the least absolute shrinkage and selection operator (LASSO) were utilized. Logistic regression was used to build different models, including clinical, imaging, radiomics, and integrated models (combining clinical, imaging, and radiomics features selected). The model's discrimination was evaluated using a receiver operating characteristic curve, its calibration with a calibration curve, and the results were visualized using the Hosmer-Lemeshow goodness-of-fit test. Thirteen clinical features and fourteen imaging features were selected for constructing the clinical and imaging models. Simultaneously, a set of twenty-five radiomics features were utilized to build the radiomics model. The integrated model demonstrated good calibration and discrimination in the training cohorts (AUC, 0.922; 95% CI: 0.900, 0.942), validation cohorts (AUC, 0.879; 95% CI: 0.806, 0.920), and testing cohorts (AUC, 0.877; 95% CI: 0.836, 0.916). The discriminatory and predictive efficacy of the clinical model in testing cohorts increased further after clinical and radiological features were incorporated (AUC, 0.849 vs. 0.922, P = 0.002). The model demonstrated exemplary predictive efficacy for SMPP by leveraging a comprehensive set of inputs, encompassing clinical data, quantitative and qualitative radiological features, along with radiomics features. The integration of these three aspects in the predictive model further enhanced the performance of the clinical model, indicating the potential for extensive clinical applications.

Zhuo LY ，Hao JW ，Song ZJ ，Meng H ，Wang TD ，Yang LL ，Yang ZM ，Ma JM ，Shen D ，Cui JJ ，Chen WJ ，Yang W ，Zang LL ，Wang JN ，Yin XP ... -  《Scientific Reports》

Prediction Model for Severe Mycoplasma pneumoniae Pneumonia in Pediatric Patients by Admission Laboratory Indicators.

The purpose of this study was to develop a model for predicting severe Mycoplasma pneumoniae pneumonia (SMPP) in pediatric patients with Mycoplasma pneumoniae pneumonia (MPP) on admission by laboratory indicators. Pediatric patients with MPP from January 2019 to December 2020 in our hospital were enrolled in this study. SMPP was diagnosed according to guideline for diagnosis and treatment of community-acquired pneumonia in children (2019 version). Prediction model was developed according to the admission laboratory indicators. Receiver operating characteristic curve and Goodness-of-fit test were analyzed for the predictive value. A total of 233 MPP patients were included in the study, with 121 males and 112 females, aged 4.541 (1-14) years. Among them, 84 (36.1%, 95% CI 29.9-42.6%) pediatric patients were diagnosed as SMPP. Some admission laboratory indicators (immunoglobulins M (IgM), eosinophil proportion, eosinophil count, hemoglobin, erythrocyte sedimentation rate (ESR), total protein, albumin and prealbumin) were found statistically different (p < 0.05) between non-SMPP group and SMPP group. Logistic regress analysis showed IgM, eosinophil proportion, eosinophil count, ESR and prealbumin were independent risk factors for SMPP. According to these five admission laboratory indicators, the prediction model for SMPP in pediatric patients was developed. The area under curve of the prediction model was 0.777, and the goodness-of-fit test showed that the predicted SMPP incidence by the model was consistent with the actual incidence (χ2 = 244.51, p = 0.203). We developed a model for predicting SMPP in pediatric patients by admission laboratory indicators. This model has good discrimination and calibration, which provides a basis for the early identification SMPP on admission. However, this model should be validated by multicenter studies with large sample.

Chang Q ，Chen HL ，Wu NS ，Gao YM ，Yu R ，Zhu WM ... -  《-》

[Construction of a predictive model for performing bronchoalveolar lavage in children with Mycoplasma pneumoniae pneumonia and pulmonary consolidation].

Wang SY ，Zhang WB ，Wan Y 《-》

Recognition of refractory Mycoplasma pneumoniae pneumonia among Myocoplasma pneumoniae pneumonia in hospitalized children: development and validation of a predictive nomogram model.

The current diagnostic criteria for refractory Mycoplasma pneumoniae pneumonia (RMPP) among Mycoplasma pneumoniae Pneumonia (MPP) are insufficient for early identification, and potentially delayed appropriate treatment. This study aimed to develop an effective individualized diagnostic prediction nomogram for pediatric RMPP. A total of 517 hospitalized children with MPP, including 131 with RMPP and 386 without RMPP (non-RMPP), treated at Lianyungang Maternal and Child Health Care Hospital from January 2018 to December 2021 were retrospectively enrolled as a development (modeling) cohort to construct an RMPP prediction nomogram. Additionally, 322 pediatric patients with MPP (64 with RMPP and 258 with non-RMPP, who were treated at the Affiliated Hospital of Xuzhou Medical University from June 2020 to May 2022 were retrospectively enrolled as a validation cohort to assess the prediction accuracy of model. Univariable and multivariable logistic regression analyses were used to identify RMPP risk factors among patients with MPP. Nomogram were generated based on these risk factors using the rms package of R, and the predictive performance was evaluated based on receiver operating characteristic (ROC) curves and using decision curve analysis (DCA). Multivariate analysis revealed five significant independent predictors of RMPP among patients with MPP: age (hazard ratio [HR] 1.16, 95% confidence interval [CI] 1.08-1.33, P = 0.038), fever duration (HR 1.34, 95%CI 1.20-1.50, P < 0.001), lymphocyte count (HR 0.45, 95%CI 0.23-0.89, P = 0.021), serum D-dimer (D-d) level (HR 1.70, 95%CI 1.16-2.49, P = 0.006), and pulmonary imaging score (HR 5.16, 95%CI 2.38-11.21, P < 0.001). The area under the ROC curve was 90.7% for the development cohort and 96.36% for the validation cohort. The internal and external verification calibration curves were almost linear with slopes of 1, and the DCA curve revealed a net benefit with the final predictive nomogram. This study proposes a predictive nomogram only based on five variables. The nomogram can be used for early identification of RMPP among pediatric patients with MPP, thereby facilitating more timely and effective intervention.

Li M ，Wei X ，Zhang SS ，Li S ，Chen SH ，Shi SJ ，Zhou SH ，Sun DQ ，Zhao QY ，Xu Y ... -  《BMC Pulmonary Medicine》