Background/purpose: Isolated intrathoracic lymphadenopathy (IT-LAP) is clinically challenging because of the difficult anatomic location and wide range of associated diseases, including tuberculosis (TB). Although sampling via endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) for histopathology is a major development, there is still room for improvement. This study aimed to investigate an algorithmic approach driven by EBUS-TBNA and conventional bronchoscopy to streamline the management of IT-LAP.
Methods: Eighty-three prospectively enrolled patients with IT-LAP were subjected to an EBUS-TBNA diagnostic panel test (histopathology, cytology, and microbiology) and underwent conventional bronchoscopy for bronchoalveolar lavage. The results were structured into an algorithmic approach to direct patient treatment, workup, or follow-up.
Results: The diagnostic yields of EBUS-TBNA based on histopathology were similar for each disease entity: 77.8% for malignancy, 70.0% for TB, 75.0% for sarcoidosis, 80.0% for anthracosis, and 70.0% for lymphoid hyperplasia (p = 0.96). The incidence of malignancy was 10.8% for total IT-LAP patients, and 12.0% and 33.7% for patients with TB and sarcoidosis, respectively. Thirty-five (42.2%) patients were symptomatic. The leading diagnosis was sarcoidosis (60%), followed by TB (20%), malignancy (11.4%), lymphoid hyperplasia (5.7%), and anthracosis (2.9%). By logistic regression analysis, granulomatous disease (odds ratio: 13.45; 95% confidence interval: 4.45-40.67, p < 0.001) was an independent predictor of symptoms. Seven (8.4%) and three (3.6%) IT-LAP patients diagnosed active TB and suggestive of TB with household contact history, respectively, were all placed on anti-TB treatment.
Conclusion: The algorithmic approach streamlines patient management. It enables early detection of malignancy, correctly places nonmalignant patients on an appropriate treatment regimen, and particularly identifies candidates at high risk of TB reactivation for anti-TB chemoprophylaxis.
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http://dx.doi.org/10.1016/j.jfma.2013.06.012 | DOI Listing |
J Med Syst
January 2025
Department of Computing, University of North Florida, 1 UNF Dr., Jacksonville, 32246, FL, USA.
The "no-show" problem in healthcare refers to the prevalent phenomenon where patients schedule appointments with healthcare providers but fail to attend them without prior cancellation or rescheduling. In addressing this issue, our study delves into a multivariate analysis over a five-year period involving 21,969 patients. Our study introduces a predictive model framework that offers a holistic approach to managing the no-show problem in healthcare, incorporating elements into the objective function that address not only the accurate prediction of no-shows but also the management of service capacity, overbooking, and idle resource allocation resulting from mispredictions.
View Article and Find Full Text PDFJ Med Syst
January 2025
Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
Optimizing operating room (OR) utilization is critical for enhancing hospital management and operational efficiency. Accurate surgical case duration predictions are essential for achieving this optimization. Our study aimed to refine the accuracy of these predictions beyond traditional estimation methods by developing Random Forest models tailored to specific surgical departments.
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
Department of Natural Resource Management, College of Agriculture and Veterinary Medicine, Jimma University, Jimma, Ethiopia.
Assessing the impacts of forest cover change on carbon stock and soil moisture dynamics is critical for understanding environmental degradation and guiding sustainable land management. This study evaluates the effects of forest cover change on carbon stock and soil moisture dynamics in Nensebo Forest from 1993 to 2023 using geospatial techniques. Landsat imagery including TM (1993), ETM + (2009), and OLI/TIRS (2023) were used.
View Article and Find Full Text PDFMed Biol Eng Comput
January 2025
Non-Invasive Imaging and Diagnostic Laboratory, Department of Applied Mechanics and Biomedical Engineering, Indian Institute of Technology Madras, Chennai, India.
Detection of early mild cognitive impairment (EMCI) is clinically challenging as it involves subtle alterations in multiple brain sub-anatomic regions. Among different brain regions, the corpus callosum and lateral ventricles are primarily affected due to EMCI. In this study, an improved deep canonical correlation analysis (CCA) based framework is proposed to fuse magnetic resonance (MR) image features from lateral ventricular and corpus callosal structures for the detection of EMCI condition.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Departamento de Genética del Desarrollo y Fisiología Molecular, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos 62210, México.
This study addresses the challenging task of identifying viruses within metagenomic data, which encompasses a broad array of biological samples, including animal reservoirs, environmental sources, and the human body. Traditional methods for virus identification often face limitations due to the diversity and rapid evolution of viral genomes. In response, recent efforts have focused on leveraging artificial intelligence (AI) techniques to enhance accuracy and efficiency in virus detection.
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