Delayed Isolation of Active Pulmonary Tuberculosis in Hospitalized Patients: A Pivotal Role of Radiologic Evaluation

Deep Learning-based Diagnosis of Pulmonary Tuberculosis on Chest X-ray in the Emergency Department: A Retrospective Study

Prompt and correct detection of pulmonary tuberculosis (PTB) is critical in preventing its spread. We aimed to develop a deep learning-based algorithm for detecting PTB on chest X-ray (CXRs) in the emergency department. This retrospective study included 3498 CXRs acquired from the National Taiwan University Hospital (NTUH). The images were chronologically split into a training dataset, NTUH-1519 (images acquired during the years 2015 to 2019; n = 2144), and a testing dataset, NTUH-20 (images acquired during the year 2020; n = 1354). Public databases, including the NIH ChestX-ray14 dataset (model training; 112,120 images), Montgomery County (model testing; 138 images), and Shenzhen (model testing; 662 images), were also used in model development. EfficientNetV2 was the basic architecture of the algorithm. Images from ChestX-ray14 were employed for pseudo-labelling to perform semi-supervised learning. The algorithm demonstrated excellent performance in detecting PTB (area under the receiver operating characteristic curve [AUC] 0.878, 95% confidence interval [CI] 0.854-0.900) in NTUH-20. The algorithm showed significantly better performance in posterior-anterior (PA) CXR (AUC 0.940, 95% CI 0.912-0.965, p-value < 0.001) compared with anterior-posterior (AUC 0.782, 95% CI 0.644-0.897) or portable anterior-posterior (AUC 0.869, 95% CI 0.814-0.918) CXR. The algorithm accurately detected cases of bacteriologically confirmed PTB (AUC 0.854, 95% CI 0.823-0.883). Finally, the algorithm tested favourably in Montgomery County (AUC 0.838, 95% CI 0.765-0.904) and Shenzhen (AUC 0.806, 95% CI 0.771-0.839). A deep learning-based algorithm could detect PTB on CXR with excellent performance, which may help shorten the interval between detection and airborne isolation for patients with PTB.

Predictive Factors and Clinical Impacts of Delayed Isolation of Tuberculosis during Hospital Admission

Delayed isolation of tuberculosis (TB) can cause unexpected exposure of healthcare workers (HCWs). This study identified the predictive factors and clinical impact of delayed isolation. We retrospectively reviewed the electronic medical records of index patients and HCWs who underwent contact investigation after TB exposure during hospitalization at the National Medical Center, between January 2018 and July 2021. Among the 25 index patients, 23 (92.0%) were diagnosed with TB based on the molecular assay, and 18 (72.0%) had a negative acid-fast bacilli smear. Sixteen (64.0%) patients were hospitalized via the emergency room, and 18 (72.0%) were admitted to a non-pulmonology/infectious disease department. According to the patterns of delayed isolation, patients were classified into five categories. Among 157 close-contact events in 125 HCWs, 75 (47.8%) occurred in Category A. Twenty-five (20%) HCWs had multiple TB exposures (n = 57 events), of whom 37 (64.9%) belonged to Category A (missed during emergency situations). After contact tracing, latent TB infection was diagnosed in one (1.2%) HCW in Category A, who was exposed during intubation. Delayed isolation and TB exposure mostly occurred during pre-admission in emergency situations. Effective TB screening and infection control are necessary to protect HCWs, especially those who routinely contact new patients in high-risk departments.

The Accuracy of Emergency Physicians' Suspicions of Active Pulmonary Tuberculosis

Objective: To investigate factors associated with recognition and delayed isolation of pulmonary tuberculosis (PTB). Background: Precise identification of PTB in the emergency department (ED) remains challenging. Methods: Retrospectively reviewed PTB suspects admitted via the ED were divided into three groups based on the acid-fast bacilli culture report and whether they were isolated initially in the ED or general ward. Factors related to recognition and delayed isolation were statistically compared. Results: Only 24.94% (100/401) of PTB suspects were truly active PTB and 33.77% (51/151) of active PTB were unrecognized in the ED. Weight loss (p = 0.022), absence of dyspnea (p = 0.021), and left upper lobe field (p = 0.024) lesions on chest radiographs were related to truly active PTB. Malignancy (p = 0.015), chronic kidney disease (p = 0.047), absence of a history of PTB (p = 0.013), and lack of right upper lung (p ≤ 0.001) and left upper lung (p = 0.020) lesions were associated with PTB being missed in the ED. Conclusions: Weight loss, absence of dyspnea, and left upper lobe field lesions on chest radiographs were related to truly active PTB. Malignancy, chronic kidney disease, absence of a history of PTB, and absence of right and/or left upper lung lesions on chest radiography were associated with isolation delay.