Prevalence of Nontuberculous Mycobacterial Pulmonary Disease, Germany, 2009 – 2014

Ringshausen, FC; Wagner, Dirk; Roux, A de; Diel, Roland; Hohmann, David; Hickstein, Lennart; Welte, Tobias; Rademacher, Jessica

doi:10.1055/s-0036-1592245

Cited by 7 publications

(10 citation statements)

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“…These bacteria exist wildly in water and soiland a total of 156 types of NTM have been found in former study (3,(14)(15), of which only a small number of bacteria can cause disease. The incidence and prevalence of NTM pulmonary disease are increasing in some countries and regions (16)(17)(18)(19). In some areas, they are even higher than those of PTB (3).…”

Section: Discussionmentioning

confidence: 99%

The Value of CT Radiomics Analysis of Cavity Characteristics In Differentiating Nontuberculous Mycobacteriumpulmonary Disease From Pulmonary Tuberculosis

Yan¹,

Cui²,

Zhao³

et al. 2021

Preprint

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Background: The CT features and clinical manifestations of the NTM pulmonary disease are similar to those of pulmonary tuberculosis (PTB), so it is very difficult to identify these two diseases simply via CT or clinical features.Methods: From February 2013 to March 2018, 73 patients of NTM pulmonary diseases and 69 patients of PTB with the cavity in Shandong Province Chest Hospital and Qilu Hospital of Shandong University were retrospctively analyzed. A double-blind assessment and manual delineation of 300 cavities as the region of interesting (ROI) from chest CT images were performed by 2 experienced radiologists. 80% of cavities were allocated to the training set and 20% to the validation set using a random number generated by a computer. 1409 radiomics features extracted from the Huiying Radcloud platform were used to analyze the two kinds of diseases' CT cavity characteristics. Feature selection was performed using analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) methods, and six supervised learning classifiers (KNN, SVM, XGBoost，RF，LR , and DT models) were used to analyze the features, The Receiver Operating Characteristic (ROC), sensitivity and specificity were used to evaluate the model's performance.Results: 29 optimal features were selected by the variance threshold method, K best method, and Lasso algorithm.and the ROC curve values are obtained. In the training set, the AUC values of the six models were all greater than 0.97, 95% CI were 0.95-1.00, the sensitivity was greater than 0.92, and the specificity was greater than 0.92. In the validation set, the AUC values of the six models were all greater than 0.84, 95% CI were 0.76-1.00, the sensitivity was greater than 0.79, and the specificity was greater than 0.79.Conclusion: The radiomics features extracted from cavity on CT images can provide effective proof in distinguishing the NTM pulmonary disease from PTB, and the radiomics analysis shows a more accurate diagnosis than the radiologists .

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Section: Discussionmentioning

confidence: 99%

The Value of CT Radiomics Analysis of Cavity Characteristics In Differentiating Nontuberculous Mycobacteriumpulmonary Disease From Pulmonary Tuberculosis

Yan¹,

Cui²,

Zhao³

et al. 2021

Preprint

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“…A higher risk of pulmonary NTM‐related infections has been identified among subjects suffering from pre‐existing respiratory distress associated with ciliary activity or other defence mechanisms. Direct associations between NTM‐based pulmonary infections have been identified for cystic fibrosis, chronic pulmonary obstructive disease, diabetes, Lady Windermere syndrome, and others 24‐26 …”

Section: Epidemiology Of Ntmmentioning

confidence: 99%

Non‐tuberculous mycobacteria immunopathogenesis: Closer than they appear. a prime of innate immunity trade‐off and NTM ways into virulence

et al. 2021

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“…Actually, NTM‐related lung diseases (NTM‐LDs) are not mandatory to treat, as there may be circumstances where patients meet diagnostic criteria for NTM‐LD but do not have progressive disease or sufficiently severe disease to warrant therapy 4 . As the symptoms are atypical, NTM‐LDs are always misdiagnosed as other chronic lung diseases, such as tuberculosis or lung cancer 5,6 . The difficulties in diagnosis may lead to inappropriate treatment for other diseases 7 .…”

Section: Introductionmentioning

confidence: 99%

Establishment and validation of a predictive model for nontuberculous mycobacterial infections in acid‐fast bacilli smear‐positive patients

Chen

Zhang

2021

Clinical Respiratory J

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Background: To identify non-tuberculous mycobacteria (NTM) infections in initially acid-fast bacilli (AFB) smear-positive patients more quickly and effectively, we attempted to establish a predictive model and validate it in clinical practice.Methods: A total of 125 AFB smear-positive patients with mycobacteriosis diagnosed in the Respiratory Department of Shanghai Pulmonary Hospital were retrospectively analyzed including 64 cases of NTM and 61 cases of pulmonary tuberculosis (PTB), to identify the clinical features distinguishing NTM from PTB patients. A bivariate regression was then set up to determine the independent predictive risk factors to NTM infections. A receiver operating characteristic (ROC) curve was used to determine the model's predictive discrimination. Finally, the model was veri ed both internally and externally for its predictive ability.Results: Compared with PTB, female patient, the symptom of hemoptysis, the exists of bronchiectasis, and negative test for QuantiFERON tuberculosis (QFT) were more common in NTM patients (78.1% vs.34.4%, 37.5% vs. 11.5%, 76.6% vs. 19.7%, 82.8% vs. 8.2%, respectively, P≤0.001). The lesions on chest high-resolution computed tomography (HRCT) in NTM patients were more commonly involved in the right middle lobe (87.5% vs. 52.5%, P<0.001) and left lingular lobe (71.9% vs. 50.8%, P=0.016), and cystic change was more commonly morphological alteration (32.8% vs. 8.2%, P=0.001). Binary regression analysis showed that female patient, the exists of bronchiectasis, negative test for QFT and right middle lobe lesion were independent risk factors for NTM in AFB smear-positive patients (P<0.05). A ROC curve combining the four risk factors showed a sensitivity and speci city of 85.9% and 93.4%, respectively, and the area under the curve (AUC) was 0.963. Moreover, internal validation and external validation of the later clinical data both con rmed the effectiveness of the model. Conclusions:The predictive model would be useful for early differential diagnosis of NTM in initially AFB smear-positive patients.(AFB) smear, he/she is likely to be diagnosed with pulmonary tuberculosis (PTB) and undergo antituberculosis treatment. When species identi cation tests are unavailable or not conducted, patients are not suspected to have NTM infections until they are not susceptible to anti-tuberculosis drugs, often resulting in undesirable side effects and heavy nancial burdens [7,8]. Therefore, early differential diagnosis of NTM is particularly important in the clinical practice, which may have a signi cant impact on the prognosis of the disease.Although there have been many studies on the clinical characteristics of NTM, but few predictive models have been established, and most of them have to rely on the conventional culture results for diagnosis, the time required is about 4-8 weeks. In the present study, we retrospectively analyzed the clinical characteristics of AFB smear-positive patients with mycobacteriosis admitted to the Respiratory Department of Shanghai Pulmonary Hospital, in...

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Prevalence of Nontuberculous Mycobacterial Pulmonary Disease, Germany, 2009 – 2014

Cited by 7 publications

References 0 publications

The Value of CT Radiomics Analysis of Cavity Characteristics In Differentiating Nontuberculous Mycobacteriumpulmonary Disease From Pulmonary Tuberculosis

The Value of CT Radiomics Analysis of Cavity Characteristics In Differentiating Nontuberculous Mycobacteriumpulmonary Disease From Pulmonary Tuberculosis

Non‐tuberculous mycobacteria immunopathogenesis: Closer than they appear. a prime of innate immunity trade‐off and NTM ways into virulence

Establishment and validation of a predictive model for nontuberculous mycobacterial infections in acid‐fast bacilli smear‐positive patients

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