Chronic Pneumonia Due to Klebsiella oxytoca Mimicking Pulmonary Tuberculosis

Gera, Kamal; Roshan, Rahul; Varma-Basil, Mandira; Shah, Ashok

doi:10.5603/piap.2015.0061

Cited by 12 publications

(8 citation statements)

References 12 publications

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“…We conducted an extensive PubMed search using the following keywords: 1) Legionella or aspiration; 2) abscess, cavity, or cavitary; and 3) immunocompetent, non-immunocompromised, or non-immunosuppressed. There are reports of immunocompetent patients developing aspiration pneumonia, but they were all non-Legionella species [7,8,9,10]. However, upon finding none with LLA, we report the first LLA case in an immunocompetent host.…”

Section: Discussionmentioning

confidence: 62%

<i>Legionella </i>Causing Lung Abscess in an Immunocompetent Patient

Prasad¹,

Raziq²,

Kemnic³

et al. 2020

AJMCR

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Legionella lung abscess (LLA) is known to develop from pneumonia and Legionnaires' disease in immunocompromised patients and aspiration. Literature review showed reports of aspiration pneumonia in immunocompetent patients; however, no such was found between LLA and immunocompetency. A 53-year-old male with history of depression and paraumbilical hernia presented for chest pain, which was right sided, started acutely, constant, radiated to the back, and exaggerated with taking deep breaths. The patient denied all other symptoms. Social history was only pertinent for being an active 35 pack-year smoker. The patient was recently admitted one month ago for viral meningitis from Echovirus. On physical exam, the patient was vitally stable, had absent breath sounds in the right middle lobe, and was significantly tender at the right anterior chest. Labs revealed elevated sedimentation rate and C-reactive protein. Imaging demonstrated a right middle lobe lung abscess. Throughout the hospital course, Legionella pneumophila serogroup 1 (LPS1) was found to be positive on urine antigen, sputum polymerase chain reaction, and sputum cultures. The patient was switched to intravenous levofloxacin and ampicillin-sulbactam. Upon symptomatic resolution, the patient was discharged home with recommendations for a follow-up chest computed tomography and diagnostic bronchoscopy. We hypothesize that smoking causes neutrophilic stasis within the lung and the development of the LLA. Treatment includes intravenous long term antibiotics and possibly drainage.

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Section: Discussionmentioning

confidence: 62%

<i>Legionella </i>Causing Lung Abscess in an Immunocompetent Patient

Prasad¹,

Raziq²,

Kemnic³

et al. 2020

AJMCR

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“…Most of the study used relatively healthy patients as their control group against the TB patients for screening [ 11 , 12 ]. In real-world clinical practice, however, physicians need to distinguish TB and NTM-LD from multiple mimicking diseases such as structural lung diseases with secondary bacterial infection, cavitating lung cancer, or chronic pneumonia [ 27 – 29 ]. Therefore, we decided to include the Imitator group and made a more challenging but commonly faced test setting, for both physicians and our DNN.…”

Section: Discussionmentioning

confidence: 99%

A deep learning model using chest X-ray for identifying TB and NTM-LD patients: a cross-sectional study

et al. 2023

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Background Timely differentiating between pulmonary tuberculosis (TB) and nontuberculous mycobacterial lung disease (NTM-LD), which are radiographically similar, is important because infectiousness and treatment differ. This study aimed to evaluate whether artificial intelligence could distinguish between TB or NTM-LD patients by chest X-rays (CXRs) from suspects of mycobacterial lung disease. Methods A total of 1500 CXRs, including 500 each from patients with pulmonary TB, NTM-LD, and patients with clinical suspicion but negative mycobacterial culture (Imitator) from two hospitals, were retrospectively collected and evaluated in this study. We developed a deep neural network (DNN) and evaluated model performance using the area under the receiver operating characteristic curves (AUC) in both internal and external test sets. Furthermore, we conducted a reader study and tested our model under three scenarios of different mycobacteria prevalence. Results Among the internal and external test sets, the AUCs of our DNN model were 0.83 ± 0.005 and 0.76 ± 0.006 for pulmonary TB, 0.86 ± 0.006 and 0.64 ± 0.017 for NTM-LD, and 0.77 ± 0.007 and 0.74 ± 0.005 for Imitator. The DNN model showed higher performance on the internal test set in classification accuracy (66.5 ± 2.5%) than senior (50.8 ± 3.0%, p < 0.001) and junior pulmonologists (47.5 ± 2.8%, p < 0.001). Among different prevalence scenarios, the DNN model has stable performance in terms of AUC to detect TB and mycobacterial lung disease. Conclusion DNN model had satisfactory performance and a higher accuracy than pulmonologists on classifying patients with presumptive mycobacterial lung diseases. DNN model could be a complementary first-line screening tool.

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“…In 1883, this bacterium was first isolated by Friedlander from fatal cases of pneumonia; thus, the disease has also been known as Friedlander pneumonia. The potential risk factors for Friedlander pneumonia include diabetes mellitus, smoking, COPD and alcoholism [3, 4]. Characteristic radiological features of Friedlander pneumonia include a bulging fissure sign from chest radiography and thorax CT scan, which typically causes lobar infiltrates in the posterior aspect of the right upper lung.…”

Section: Discussionmentioning

confidence: 99%