Factors Associated With Extrapulmonary Tuberculosis in Spain and Its Distribution in Immigrant Population

Extrapulmonary and Drug-Resistant Childhood Tuberculosis: Unveiling the Disease to Adopt the Optimal Treatment Strategy

Paediatric tuberculosis (TB) is a substantial threat among infectious diseases, particularly considering the high risk of extrapulmonary tuberculosis (EPTB), severe forms of the disease, and the spreading of drug-resistant strains. Describing the characteristics of children with EPTB and those with drug-resistant tuberculosis (DR-TB) and analysing the role of second-line drugs could facilitate the management of these cases. This retrospective study was conducted on 271 children diagnosed with active TB disease (44 EPTB cases, 9 DR-TB cases), originating from diverse geographic areas, who were referred to the infectious disease unit at Meyer Children's Hospital, Florence, Italy, from 2006 to 2022. In most patients, the management of therapies was complicated by the impossibility to obtain drug susceptibility testing (DST) results, which improved over the years: 17/154 (11.04%) children had DST results between 2006 and 2013, and 50/117 (42.73%, p < 0.001) between 2014 and 2022. Second-line drugs were not exclusively administered to DR-TB cases, but also to EPTB cases (20/44, 45.45%). Drugs were generally well tolerated; adverse events occurred in 13 children (13/271, 4.80%) and were generally mild and reversable. Therapies were successful in 267 children (98.52%) considered cured, while 4 (1.48%) presented sequelae. Both univariate and multivariate logistic regression analyses were conducted to investigate factors associated with EPTB, DR-TB, and second-line drugs administration. Originating from Asia emerged as a risk factor associated with both EPTB and DR-TB (p = 0.013 and p = 0.045, respectively). The introduction of GeneXpert tests has significantly improved TB diagnosis and the obtaining of DST results. The administration of second-line therapies should be limited primarily to DR-TB cases, but it is possible that these drugs may also be beneficial in selected EPTB cases.

Epidemiology and factors associated with Extra-pulmonary tuberculosis in a Low-prevalence area

Background

Tuberculosis is a global public health problem. Extra-pulmonary tuberculosis accounts for an increasing proportion of cases worldwide, although information about epidemiological, clinical, or microbiological factors is lacking.

Methods

We conducted a retrospective observational study of tuberculosis cases diagnosed between 2016 and 2021, classified into Pulmonary and Extra-pulmonary tuberculosis. Univariable and multivariable logistic regression models were used to investigate risk factors of Extra-pulmonary tuberculosis.

Results

20.9% of overall cases were classified as Extra-pulmonary tuberculosis, with a rising trend from 22.6% in 2016 to 27.9% in 2021. Lymphatic tuberculosis accounted for 50.6% of cases, followed by pleural tuberculosis (24.1%). 55.4% of cases belonged to foreign-born patients. Microbiological culture tested positive in 92.8% of Extra-pulmonary cases. Logistic regression analysis showed that women were more predisposed to develop Extra-pulmonary tuberculosis (aOR 2.46, 95% CI 1.45-4.20) as well as elderly patients (aged ≥ 65 years) (aOR 2.47, 95% CI 1.19-5.13) and persons with previous history of tuberculosis (4.99, 95% CI 1.40-17.82).

Conclusions

Extra-pulmonary Tuberculosis have increased within our study period. A profound decline occurred in 2021 tuberculosis cases, probably due to COVID-19. Women, elderly population, and persons with previous history of tuberculosis are at higher risk of developing Extra-pulmonary tuberculosis in our setting.

Greedy Autoaugment for classification of mycobacterium tuberculosis image via generalized deep CNN using mixed pooling based on minimum square rough entropy

Although tuberculosis (TB) is a disease whose cause, epidemiology and treatment are well known, some infected patients in many parts of the world are still not diagnosed by current methods, leading to further transmission in society. Creating an accurate image-based processing system for screening patients can help in the early diagnosis of this disease. We provided a dataset containing1078 confirmed negative and 469 positive Mycobacterium tuberculosis instances. An effective method using an improved and generalized convolutional neural network (CNN) was proposed for classifying TB bacteria in microscopic images. In the preprocessing phase, the insignificant parts of microscopic images are excluded with an efficient algorithm based on the square rough entropy (SRE) thresholding. Top 10 policies of data augmentation were selected with the proposed model based on the Greedy AutoAugment algorithm to resolve the overfitting problem. In order to improve the generalization of CNN, mixed pooling was used instead of baseline one. The results showed that employing generalized pooling, batch normalization, Dropout, and PReLU have improved the classification of Mycobacterium tuberculosis images. The output of classifiers such as Naïve Bayes-LBP, KNN-LBP, GBT-LBP, Naïve Bayes-HOG, KNN-HOG, SVM-HOG, GBT-HOG indicated that proposed CNN has the best results with an accuracy of 93.4%. The improvements of CNN based on the proposed model can yield promising results for diagnosing TB.