Background: Even amid the coronavirus disease-19 (COVID-19) pandemic, the spread of multidrug-resistant bacteria and infection control are still important tasks. After recognizing the carbapenem-resistant Acinetobacter baumannii (CRAB) outbreak that occurred in the isolation room for COVID-19, we would like to introduce what infection control measures were implemented to eradicate it. Materials and Methods: All COVID-19 patients with CRAB in any specimen admitted to the COVID-19 isolation ward of the tertiary hospital in Korea from October to November 2021 were analyzed. Results: During the outbreak, 23 patients with COVID-19 and CRAB infections were identified. The index case was an 85-year-old female referred from a long-term care facility. CRAB was identified in sputum culture in most patients (91.3%). The CRAB outbreak occurred mainly in the rooms around the index case. Environmental cultures on the floor, air inlet, air outlet, and window frame of the rooms were performed. The antimicrobial resistance patterns of CRAB from patients and the environment were identical; whole-genome sequencing analyses revealed isolated clonality. Infection control measures with enhanced environmental cleaning using 1,000 ppm sodium hypochlorite and phenolic compounds, enhanced hand hygiene, additional education, and mandatory additional gowning and gloving of COVID-19 personal protective equipment (PPE) were applied on 29 October. No CRAB infection cases occurred from 2 November for two weeks. Conclusion In addition to applying PPE and COVID-19 precautions in COVID-19 isolation wards, adhering to strict contact precautions along with environmental control can help prevent the spread of multidrug-resistant bacteria.

Background and Objectives: Evidence regarding the efficacy and safety of intracardiac echocardiography (ICE) for guidance during transcatheter aortic valve replacement (TAVR) is limited. This study aimed to compare the clinical efficacy and safety of ICE versus transesophageal echocardiography (TEE) for guiding TAVR. Methods: This prospective cohort study included patients who underwent TAVR from August 18, 2015, to June 31, 2021. Eligible patients were stratified by echocardiographic modality (ICE or TEE) and anesthesia mode (monitored anesthesia care [MAC] or general anesthesia [GA]). Primary outcome was the 1-year composite of all-cause mortality, rehospitalization for cardiovascular cause, or stroke, according to the Valve Academic Research Consortium-3 (VARC-3) definition. Propensity score matching was performed, and study outcomes were analyzed for the matched cohorts. Results: Of the 359 eligible patients, 120 patients were matched for the ICE-MAC and TEEGA groups, respectively. The incidence of primary outcome was similar between matched groups (18.3% vs. 20.0%; adjusted hazard ratio, 0.94; 95% confidence interval [CI], 0.53– 1.68; p=0.843). ICE-MAC and TEE-GA also had similar incidences of moderate-to-severe paravalvular regurgitation (PVR) (4.2% vs. 5.0%; adjusted odds ratio, 0.83; 95% CI, 0.23– 2.82; p=0.758), new permanent pacemaker implantation, and VARC-3 types 2–4 bleeding. Conclusions ICE was comparable to TEE for guidance during TAVR for the composite clinical efficacy outcome, with similar incidences of moderate-to-severe PVR, new permanent pacemaker implantation, and major bleeding. These results suggest that ICE could be a safe and effective alternative echocardiographic modality to TEE for guiding TAVR.

Background: The purpose of this study was to investigate the characteristics of osteoporotic vertebral compression fractures (OVCFs) in Korean postmenopausal women and the association between OVCFs and clinical factors such as age, bone mineral density (BMD), and quality of life. Methods: According to the population distribution in four regions in Korea, 1,281 postmenopausal female patients were recruited from nationwide orthopedic outpatient clinics. Radiologic, asymptomatic, and within 3 months of OVCF groups were analyzed based on age, fracture location, and prevalence according to BMD. In addition, BMD, T-score, body mass index, and European Quality of Life-5 Dimensions (EQ-5D) were investigated in the three groups, and the differences between groups were compared and analyzed. Results: The prevalence of radiologic OVCFs at the T11–L1 was 3.7 times higher in the 70s group (44.0%) than in the 50s group (11.9%). Femur and total hip BMD were significantly lower in patients with thoracolumbar junction fractures than in patients with L2–5 fractures, whereas no difference was observed in lumbar spine BMD. Of the three OVCF groups, the within 3 months of OVCF group had the lowest lumbar spine T-score of –2.445. The asymptomatic OVCF group also showed significantly lower lumbar spine T-score than did the group without radiologic OVCFs (p < 0.001). The EQ-5D showed a significant decrease in the radiologic OVCF group (p < 0.001) and within 3 months of OVCF group (p < 0.001). Conclusions The prevalence of OVCFs in the thoracolumbar junction rapidly increases with old age and low BMD in Korean postmenopausal women. Femur and total hip BMD are more predictive of thoracolumbar junction fractures than lumbar spine BMD.Patients with radiologic OVCFs had a significantly lower quality of life than no OVCF group. Therefore, this study shows it is important to treat and prevent osteoporosis before an OVCF occurs.

Background: Ultrasonography is an effective noninvasive imaging modality for the diagnosis of subcutaneous masses. To date, few studies have reported skin ultrasonography using deep convolutional neural networks (DCNNs).We investigated the accuracy of DCNNs for the diagnosis of epidermal cysts, lipomas, and other subcutaneous masses. Objective: The purpose of this study was to evaluate whether DCNNs could diagnose subcutaneous masses with ultrasonographic images at level of competence comparable to dermatologists. Methods: We created a dataset of 1,361 skin ultrasonography images obtained from 202 patients diagnosed with epidermal cysts, lipomas, and other subcutaneous masses, to train the DCNNs using ResNet18. Performance was compared with another set of 93 ultrasonographic images (24 epidermal cysts, 25 lipomas, and 44 other subcutaneous masses) from open-access articles. Results: The DCNNs yielded 87.10% classification accuracy and 86.10% F1-scores. The area under the curve, sensitivity, and specificity were 0.92 (95% confidence interval [CI] 0.86∼0.98), 75.00%, and 98.55% for epidermal cysts; 0.93 (95% CI 0.88∼0.98), 80.00%, and 94.12% for lipomas; and 0.97 (95% CI 0.93∼1.00), 97.73%, and 85.71% for other subcutaneous masses, respectively. Analysis using gradient-weighted class activation mapping revealed that the DCNNs could detect specific ultrasonographic findings of epidermal cysts and lipomas. Conclusion We propose that DCNNs combined with ultrasonography may aid in the diagnosis of subcutaneous masses in outpatient settings.

Background: The purpose of this study was to investigate the characteristics of osteoporotic vertebral compression fractures (OVCFs) in Korean postmenopausal women and the association between OVCFs and clinical factors such as age, bone mineral density (BMD), and quality of life. Methods: According to the population distribution in four regions in Korea, 1,281 postmenopausal female patients were recruited from nationwide orthopedic outpatient clinics. Radiologic, asymptomatic, and within 3 months of OVCF groups were analyzed based on age, fracture location, and prevalence according to BMD. In addition, BMD, T-score, body mass index, and European Quality of Life-5 Dimensions (EQ-5D) were investigated in the three groups, and the differences between groups were compared and analyzed. Results: The prevalence of radiologic OVCFs at the T11–L1 was 3.7 times higher in the 70s group (44.0%) than in the 50s group (11.9%). Femur and total hip BMD were significantly lower in patients with thoracolumbar junction fractures than in patients with L2–5 fractures, whereas no difference was observed in lumbar spine BMD. Of the three OVCF groups, the within 3 months of OVCF group had the lowest lumbar spine T-score of –2.445. The asymptomatic OVCF group also showed significantly lower lumbar spine T-score than did the group without radiologic OVCFs (p < 0.001). The EQ-5D showed a significant decrease in the radiologic OVCF group (p < 0.001) and within 3 months of OVCF group (p < 0.001). Conclusions The prevalence of OVCFs in the thoracolumbar junction rapidly increases with old age and low BMD in Korean postmenopausal women. Femur and total hip BMD are more predictive of thoracolumbar junction fractures than lumbar spine BMD.Patients with radiologic OVCFs had a significantly lower quality of life than no OVCF group. Therefore, this study shows it is important to treat and prevent osteoporosis before an OVCF occurs.

Background: Ultrasonography is an effective noninvasive imaging modality for the diagnosis of subcutaneous masses. To date, few studies have reported skin ultrasonography using deep convolutional neural networks (DCNNs).We investigated the accuracy of DCNNs for the diagnosis of epidermal cysts, lipomas, and other subcutaneous masses. Objective: The purpose of this study was to evaluate whether DCNNs could diagnose subcutaneous masses with ultrasonographic images at level of competence comparable to dermatologists. Methods: We created a dataset of 1,361 skin ultrasonography images obtained from 202 patients diagnosed with epidermal cysts, lipomas, and other subcutaneous masses, to train the DCNNs using ResNet18. Performance was compared with another set of 93 ultrasonographic images (24 epidermal cysts, 25 lipomas, and 44 other subcutaneous masses) from open-access articles. Results: The DCNNs yielded 87.10% classification accuracy and 86.10% F1-scores. The area under the curve, sensitivity, and specificity were 0.92 (95% confidence interval [CI] 0.86∼0.98), 75.00%, and 98.55% for epidermal cysts; 0.93 (95% CI 0.88∼0.98), 80.00%, and 94.12% for lipomas; and 0.97 (95% CI 0.93∼1.00), 97.73%, and 85.71% for other subcutaneous masses, respectively. Analysis using gradient-weighted class activation mapping revealed that the DCNNs could detect specific ultrasonographic findings of epidermal cysts and lipomas. Conclusion We propose that DCNNs combined with ultrasonography may aid in the diagnosis of subcutaneous masses in outpatient settings.

No abstract available.
Chordoma

OBJECTIVE: The purpose of this study was to evaluate the diagnostic performance of ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) for chronic empyema-associated malignancy (CEAM). MATERIALS AND METHODS: We retrospectively reviewed the ¹⁸F-FDG PET/CT images of 33 patients with chronic empyema, and analyzed the following findings: 1) shape of the empyema cavity, 2) presence of fistula, 3) maximum standardized uptake value (SUV) of the empyema cavity, 4) uptake pattern of the empyema cavity, 5) presence of a protruding soft tissue mass within the empyema cavity, and 6) involvement of adjacent structures. Final diagnosis was determined based on histopathology or clinical follow-up for at least 6 months. The abovementioned findings were compared between the ¹⁸F-FDG PET/CT images of CEAM and chronic empyema. A receiver operating characteristic (ROC) analysis was also performed. RESULTS: Six lesions were histopathologically proven as malignant; there were three cases of diffuse large B-cell lymphoma, two of squamous cell carcinoma, and one of poorly differentiated carcinoma. Maximum SUV within the empyema cavity (p < 0.001) presence of a protruding soft tissue mass (p = 0.002), and involvement of the adjacent structures (p < 0.001) were significantly different between the CEAM and chronic empyema images. The maximum SUV exhibited the highest diagnostic performance, with the highest specificity (96.3%, 26/27), positive predictive value (85.7%, 6/7), and accuracy (97.0%, 32/33) among all criteria. On ROC analysis, the area under the curve of maximum SUV was 0.994. CONCLUSION: ¹⁸F-FDG PET/CT can be useful for diagnosing CEAM in patients with chronic empyema. The maximum SUV within the empyema cavity is the most accurate ¹⁸F-FDG PET/CT diagnostic criterion for CEAM.
Carcinoma, Squamous Cell ; Diagnosis ; Electrons ; Empyema ; Fistula ; Follow-Up Studies ; Humans ; Lymphoma, B-Cell ; Positron-Emission Tomography and Computed Tomography ; Retrospective Studies ; ROC Curve ; Sensitivity and Specificity

BACKGROUND: Alopecia areata (AA), a chronic, relapsing hair-loss disorder, is considered to be a T-cell-mediated autoimmune disease. Cold-inducible RNA-binding protein (CIRP) belongs to a family of cold-shock proteins that respond to cold stress, and has been identified as a damage-associated molecular pattern (DAMP) molecule that triggers the inflammatory response. Recent studies have shown that high-mobility group box 1, another DAMP molecule, is elevated in serum and scalp tissue of AA patients, suggesting a relationship between DAMP molecules and the pathogenesis of AA. OBJECTIVE: To investigate the clinical significance of serum CIRP levels in AA. METHODS: The serum levels of CIRP were compared between 68 patients with AA and 20 healthy controls. Additionally, the correlation between CIRP level and various clinical parameters was evaluated. RESULTS: The serum CIRP levels were significantly higher in AA patients compared to healthy subjects. Moreover, there was an association between the serum CIRP level and clinical characteristics, such as disease duration and disease activity. However, there was no significant difference in the serum CIRP level among the clinical types of AA (AA multiplex, alopecia totalis, and alopecia universalis). CONCLUSION: These results suggest that CIRP may play a significant role in the pathogenesis of AA and could be a potential biologic marker for monitoring the disease activity of AA.
Alopecia Areata ; Alopecia ; Autoimmune Diseases ; Biomarkers ; Healthy Volunteers ; Humans ; Inflammation ; RNA-Binding Proteins ; Scalp

PURPOSE: We developed predictive models using different programming languages and different computing platforms for machine learning (ML) and deep learning (DL) that classify clinical diagnoses in patients with epiphora. We evaluated the diagnostic performance of these models.METHODS: Between January 2016 and September 2017, 250 patients with epiphora who underwent dacryocystography (DCG) and lacrimal scintigraphy (LS) were included in the study. We developed five different predictive models using ML tools, Python-based TensorFlow, R, and Microsoft Azure Machine Learning Studio (MAMLS). A total of 27 clinical characteristics and parameters including variables related to epiphora (VE) and variables related to dacryocystography (VDCG) were used as input data. Apart from this, we developed two predictive convolutional neural network (CNN) models for diagnosing LS images. We conducted this study using supervised learning.RESULTS: Among 500 eyes of 250 patients, 59 eyes had anatomical obstruction, 338 eyes had functional obstruction, and the remaining 103 eyes were normal. For the data set that excluded VE and VDCG, the test accuracies in Python-based TensorFlow, R, multiclass logistic regression in MAMLS, multiclass neural network in MAMLS, and nuclear medicine physician were 81.70%, 80.60%, 81.70%, 73.10%, and 80.60%, respectively. The test accuracies of CNN models in three-class classification diagnosis and binary classification diagnosis were 72.00% and 77.42%, respectively.CONCLUSIONS: ML-based predictive models using different programming languages and different computing platforms were useful for classifying clinical diagnoses in patients with epiphora and were similar to a clinician's diagnostic ability.
Classification ; Dataset ; Diagnosis ; Humans ; Lacrimal Apparatus Diseases ; Learning ; Logistic Models ; Machine Learning ; Nuclear Medicine ; Programming Languages ; Radionuclide Imaging