Association between the Korean Triage and Acuity Scale level and severity of children with dyspnea in the emergency department

Purpose@#Triage tools play a vital role in classifying the severity of children in emergency departments (EDs). We investigated the association between the Korean Triage and Acuity Scale (KTAS) and severity of dyspnea in the ED. @*Methods@#We conducted a retrospective study of children aged 3-14 years with dyspnea who visited the ED from January 2015 through December 2021. They were divided into severe (KTAS level 1-3) and non-severe (KTAS level 4-5) groups. Between the groups, we compared the clinical characteristics, including age, sex, associated symptoms, vital signs, route of visit, treatment at ED, and outcomes. @*Results@#Among a total of 468 children with dyspnea, 267 and 201 were assigned to the severe and non-severe groups, respectively. The severe group had higher frequencies of fever (21.7% vs. 13.9%; P = 0.031), cough (53.2% vs. 43.3%; P = 0.034), systemic steroids (42.3% vs. 25.9%; P < 0.001), intravenous fluids (47.6% vs. 25.4%; P < 0.001), oxygen therapy (16.5% vs. 6.5%; P = 0.001), inotropics (4.1% vs. 1.0%; P = 0.042), and hospitalization (24.7% vs. 11.9%; P = 0.002). The severe group also showed a higher mean heart rate, respiratory rate, and temperature, and lower mean oxygen saturation (all Ps < 0.001). Among these findings, fever, heart rate, respiratory rate, temperature, intravenous fluids, oxygen therapy, inotropics, and hospitalization remained significantly different between the groups after defining the severe group as a KTAS level 1-2. @*Conclusion@#This study shows the association between KTAS and severity of dyspnea in the ED. Therefore, KTAS may reflect not only the initial clinical conditions but also emergency measures and outcomes in children with dyspnea who visit EDs.

Diagnostic Performance of Deep Learning-Based Lesion Detection Algorithm in CT for Detecting Hepatic Metastasis from Colorectal Cancer

Objective@#To compare the performance of the deep learning-based lesion detection algorithm (DLLD) in detecting liver metastasis with that of radiologists. @*Materials and Methods@#This clinical retrospective study used 4386-slice computed tomography (CT) images and labels from a training cohort (502 patients with colorectal cancer [CRC] from November 2005 to December 2010) to train the DLLD for detecting liver metastasis, and used CT images of a validation cohort (40 patients with 99 liver metastatic lesions and 45 patients without liver metastasis from January 2011 to December 2011) for comparing the performance of the DLLD with that of readers (three abdominal radiologists and three radiology residents). For per-lesion binary classification, the sensitivity and false positives per patient were measured. @*Results@#A total of 85 patients with CRC were included in the validation cohort. In the comparison based on per-lesion binary classification, the sensitivity of DLLD (81.82%, [81/99]) was comparable to that of abdominal radiologists (80.81%, p = 0.80) and radiology residents (79.46%, p = 0.57). However, the false positives per patient with DLLD (1.330) was higher than that of abdominal radiologists (0.357, p < 0.001) and radiology residents (0.667, p < 0.001). @*Conclusion@#DLLD showed a sensitivity comparable to that of radiologists when detecting liver metastasis in patients initially diagnosed with CRC. However, the false positives of DLLD were higher than those of radiologists. Therefore, DLLD could serve as an assistant tool for detecting liver metastasis instead of a standalone diagnostic tool.

Diagnostic Performance of Deep Learning-Based Lesion Detection Algorithm in CT for Detecting Hepatic Metastasis from Colorectal Cancer

Objective@#To compare the performance of the deep learning-based lesion detection algorithm (DLLD) in detecting liver metastasis with that of radiologists. @*Materials and Methods@#This clinical retrospective study used 4386-slice computed tomography (CT) images and labels from a training cohort (502 patients with colorectal cancer [CRC] from November 2005 to December 2010) to train the DLLD for detecting liver metastasis, and used CT images of a validation cohort (40 patients with 99 liver metastatic lesions and 45 patients without liver metastasis from January 2011 to December 2011) for comparing the performance of the DLLD with that of readers (three abdominal radiologists and three radiology residents). For per-lesion binary classification, the sensitivity and false positives per patient were measured. @*Results@#A total of 85 patients with CRC were included in the validation cohort. In the comparison based on per-lesion binary classification, the sensitivity of DLLD (81.82%, [81/99]) was comparable to that of abdominal radiologists (80.81%, p = 0.80) and radiology residents (79.46%, p = 0.57). However, the false positives per patient with DLLD (1.330) was higher than that of abdominal radiologists (0.357, p < 0.001) and radiology residents (0.667, p < 0.001). @*Conclusion@#DLLD showed a sensitivity comparable to that of radiologists when detecting liver metastasis in patients initially diagnosed with CRC. However, the false positives of DLLD were higher than those of radiologists. Therefore, DLLD could serve as an assistant tool for detecting liver metastasis instead of a standalone diagnostic tool.

Clinical application of S-Detect to breast masses on ultrasonography: a study evaluating the diagnostic performance and agreement with a dedicated breast radiologist

PURPOSE: The purpose of this study was to evaluate the diagnostic performance of S-Detect when applied to breast ultrasonography (US), and the agreement with an experienced radiologist specializing in breast imaging. METHODS: From June to August 2015, 192 breast masses in 175 women were included. US features of the breast masses were retrospectively analyzed by a radiologist who specializes in breast imaging and S-Detect, according to the fourth edition of the American College of Radiology Breast Imaging Reporting and Data System lexicon and final assessment categories. Final assessments from S-Detect were in dichotomized form: possibly benign and possibly malignant. Kappa statistics were used to analyze the agreement between the radiologist and S-Detect. Diagnostic performance of the radiologist and S-Detect was calculated, including sensitivity, specificity, positive predictive value (PPV), negative predictive value, accuracy, and area under the receiving operator characteristics curve. RESULTS: Of the 192 breast masses, 72 (37.5%) were malignant, and 120 (62.5%) were benign. Benign masses among category 4a had higher rates of possibly benign assessment on S-Detect for the radiologist, 63.5% to 36.5%, respectively (P=0.797). When the cutoff was set at category 4a, the specificity, PPV, and accuracy was significantly higher in S-Detect compared to the radiologist (all P<0.05), with a higher area under the receiver operator characteristics curve of 0.725 compared to 0.653 (P=0.038). Moderate agreement (k=0.58) was seen in the final assessment between the radiologist and S-Detect. CONCLUSION: S-Detect may be used as an additional diagnostic tool to improve the specificity of breast US in clinical practice, and guide in decision making for breast masses detected on US.

Rhinovirus Associated Severe Respiratory Failure in Immunocompetent Adult Patient / 결핵및호흡기질환

Rhinovirus infection is typically associated with the common cold and has rarely been reported as a cause of severe pneumonia in immunocompetent adults. A 55-year-old previous healthy woman, who consumed half a bottle of alcohol daily, presented with respiratory failure after one week of upper respiratory infection symptoms. Radiography revealed bilateral, diffuse ground glass opacity with patchy consolidation in the whole lung field; bronchoalveolar lavage fluid analysis indicated that rhinovirus was the causative organism. After five days of conservative support, the symptoms and radiographic findings began to improve. We report this rare case of rhinovirus pneumonia in an otherwise healthy host along with a review of references.