Association of Mycobacterium tuberculosis infection test results with risk factors for tuberculosis transmission

Unrecognized Tuberculosis: Risk Factors for Smear-Positive/Cavitary Asymptomatic Cases

Background

Screening patients with asymptomatic active tuberculosis (TB) is crucial as they can transmit the disease. Identifying the risk factors for transmission is essential for targeted screening. Understanding how the infectiousness of asymptomatic patients with TB affects disease outcomes is crucial for developing strategies to control TB spread.

Methods

We analyzed the national Korean TB cohort data to determine the factors associated with transmission risk and clinical outcomes in patients with asymptomatic pulmonary TB. The primary outcome was the factors associated with a risk factor for transmission, while the secondary outcome was mortality in asymptomatic patients with pulmonary TB stratified by transmission risk.

Results

Among 20 455 patients with pulmonary TB, 7434 (36.4%) were asymptomatic, while 1520 (25.5%) had potential transmission risks, indicated by a positive sputum acid-fast bacillus smear test or cavitation on chest radiographs. The factors associated with a higher transmission risk included male sex (odds ratio [OR], 1.385; 95% CI, 1.172-1.636; P < .001), low body mass index (BMI; OR, 1.687; 95% CI, 1.420-2.004; P < .001), current smoking (OR, 1.443; 95% CI, 1.213-1.716; P < .001), diabetes (OR, 1.399; 95% CI, 1.201-1.629; P < .001), and autoimmune disease (OR, 2.233; 95% CI, 1.295-3.850; P = .004). The mortality rate was higher in patients with a risk factor for transmission risk than in those without (9.3 vs 7.1%; P = .008).

Conclusions

Lean, smoking men with asymptomatic TB who have DM and/or autoimmune diseases have higher transmission and mortality risk. Asymptomatic populations with these risk factors warrant targeted screening.

Candidate transmission survival genome of Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb), a leading cause of death from infection, completes its life cycle entirely in humans except for transmission through the air. To begin to understand how Mtb survives aerosolization, we mimicked liquid and atmospheric conditions experienced by Mtb before and after exhalation using a model aerosol fluid (MAF) based on the water-soluble, lipidic, and cellular constituents of necrotic tuberculosis lesions. MAF induced drug tolerance in Mtb, remodeled its transcriptome, and protected Mtb from dying in microdroplets desiccating in air. Yet survival was not passive: Mtb appeared to rely on hundreds of genes to survive conditions associated with transmission. Essential genes subserving proteostasis offered most protection. A large number of conventionally nonessential genes appeared to contribute as well, including genes encoding proteins that resemble antidesiccants. The candidate transmission survival genome of Mtb may offer opportunities to reduce transmission of tuberculosis.

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.