Updates in Pediatric Tuberculosis in International Settings

Assessing the risk of concurrent mycoplasma pneumoniae pneumonia in children with tracheobronchial tuberculosis: retrospective study

Objective

This study aimed to create a predictive model based on machine learning to identify the risk for tracheobronchial tuberculosis (TBTB) occurring alongside Mycoplasma pneumoniae pneumonia in pediatric patients.

Methods

Clinical data from 212 pediatric patients were examined in this retrospective analysis. This cohort included 42 individuals diagnosed with TBTB and Mycoplasma pneumoniae pneumonia (combined group) and 170 patients diagnosed with lobar pneumonia alone (pneumonia group). Three predictive models, namely XGBoost, decision tree, and logistic regression, were constructed, and their performances were assessed using the receiver's operating characteristic (ROC) curve, precision-recall curve (PR), and decision curve analysis (DCA). The dataset was divided into a 7:3 ratio to test the first and second groups, utilizing them to validate the XGBoost model and to construct the nomogram model.

Results

The XGBoost highlighted eight significant signatures, while the decision tree and logistic regression models identified six and five signatures, respectively. The ROC analysis revealed an area under the curve (AUC) of 0.996 for XGBoost, significantly outperforming the other models (p < 0.05). Similarly, the PR curve demonstrated the superior predictive capability of XGBoost. DCA further confirmed that XGBoost offered the highest AIC (43.226), the highest average net benefit (0.764), and the best model fit. Validation efforts confirmed the robustness of the findings, with the validation groups 1 and 2 showing ROC and PR curves with AUC of 0.997, indicating a high net benefit. The nomogram model was shown to possess significant clinical value.

Conclusion

Compared to machine learning approaches, the XGBoost model demonstrated superior predictive efficacy in identifying pediatric patients at risk of concurrent TBTB and Mycoplasma pneumoniae pneumonia. The model's identification of critical signatures provides valuable insights into the pathogenesis of these conditions.

Pulmonary Tuberculosis in Children: A Forgotten Disease?

Even today, tuberculosis in childhood is a disease that is often undiagnosed and undertreated. In the absence of therapy with antituberculosis drugs, children in the first years of life have a high degree of severe forms and mortality. In these children, symptoms are often not very specific and can easily be confused with other diseases of bacterial, viral or fungal etiology, making diagnosis more difficult. Nevertheless, the introduction of new diagnostic techniques has allowed a more rapid identification of the infection. Indeed, Interferon gamma release assay (IGRA) is preferred to the Mantoux, albeit with obvious limitations in children aged <2 years. While the Xpert Mtb/RIF Ultra test is recommended as an initial diagnostic investigation of the gastric aspirate and/or stools in children with signs and symptoms of pulmonary tuberculosis. The drugs used in the treatment of susceptible and resistant TB are the same as those used in adults but doses and combinations are different in the pediatric age. In children, brief therapy is preferable in both the latent infection and the active disease, as a significant reduction in side effects is obtained.