Background: This study was performed to evaluate the prognostic utility of a positive T wave in lead augmented vector right (TaVR) and elevated E/e′ ratio in predicting major adverse cardiovascular events (MACE) in patients receiving maintenance hemodialysis. Methods: We retrospectively examined 296 adults on thrice-weekly hemodialysis with baseline electrocardiography and transthoracic echocardiography (October 2018–April 2024). TaVR positivity was T-wave amplitude, >0 mV and high E/e′, ≥19. Primary outcome was the first MACE—cardiovascular death, myocardial infarction, stroke, heart-failure admission, or revascularization. Multivariable Cox models adjusted for clinical covariates; incremental value was gauged with Harrell’s C-index, integrated discrimination improvement (IDI), and continuous net reclassification improvement (NRI). Sensitivity analysis was performed using a guideline-recommended E/e′ threshold of ≥15 to assess robustness. Results: Over 56.5 months (1,325 patient-years), 118 MACE occurred (8.9/100 patient-years). Incidence was higher with TaVR positivity than negativity (16.0/100 patient-years vs. 3.7/100 patient-years; log-rank p < 0.001). Adjusted hazard ratios were 3.19 (95% confidence interval [CI], 2.00–5.08) for TaVR and 2.92 (95% CI, 1.71–4.96) for high E/e′. Adding both markers to the clinical model increased the C-index from 0.65 to 0.75 (Δ 0.10) and improved IDI (0.10) and NRI (0.16) (all p < 0.01). A significant negative interaction (hazard ratio, 0.21; p = 0.01) indicated complementary but partly overlapping information. Sensitivity results were similar. Conclusion TaVR positivity is a strong independent electrocardiography predictor of cardiovascular events in hemodialysis. Combining TaVR with E/e′ adds prognostic value and supports a pragmatic two-step strategy— electrocardiography triage followed by focused echocardiography—for cardiovascular risk stratification in this high-risk population.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Purpose: Biliary atresia (BA) is a rare but progressive cholangiopathy and the leading cause of pediatric liver transplantation worldwide. The Korean Association of Pediatric Surgeons (KAPS) has conducted three national surveys (2001, 2011, and 2023) to assess long-term trends in the diagnosis, treatment, and outcomes of BA. This study provides a comparative analysis of the 2nd and 3rd national surveys, with reference to selected findings from the 1st survey. Methods: This study included 453 patients from the 3rd national survey (2011–2021) and 435 patients from the 2nd survey (2001–2010), all of whom underwent Kasai portoenterostomy. Data were collected via electronic case report forms from pediatric surgical centers nationwide. Comparisons were made regarding demographics, clinical features, diagnostic patterns, operative details, follow-up outcomes, and survival. Kaplan–Meier analysis was used to evaluate long-term survival. Results: The mean number of BA patients per year remained stable between surveys (43.5 in the 2nd, 41.18 in the 3rd), though centralization of care increased, with 61.5% of cases managed by two major institutions in the 3rd survey. The median age at surgery decreased, and the use of preoperative imaging (especially magnetic resonance cholangiopancreatography) increased. The 10-year native liver survival rate declined from 59.8% to 53.7%, while overall 10-year survival improved slightly (92.9% to 93.2%). Postoperative complications, such as cholangitis and liver failure, persisted but were better categorized. The 3rd survey also reported improved mortality (4.9%) and reduced follow-up loss (11.5%) compared to the 2nd survey. Conclusion While overall survival after Kasai operation has remained high and even improved, native liver survival has slightly declined. The findings reflect earlier diagnosis, more consistent diagnostic imaging, and increasing centralization of care. These trends underscore the importance of long-term nationwide data collection in guiding future strategies for BA management in Korea.

Purpose: This study aims to investigate and compare the incidence, demographic characteristics, clinical manifestations, preoperative diagnostic methods, anatomical classifications, associated anomalies, operative treatments, and postoperative outcomes of patients with intestinal atresia treated by the members of the Korean Association of Pediatric Surgeons (KAPS) through three nationwide surveys. Methods: KAPS conducted 3 national surveys in 1998, 2010, and 2024 to examine the patients diagnosed with intestinal atresia. In preparation for the survey, we developed a customized case registration form to obtain data on patient sex, birth weight, gestational age, clinical manifestations, preoperative diagnostic methods, anatomical types, associated anomalies, operative treatments, and postoperative outcomes. Authorized KAPS members completed the case registration form. Results: The first, second, and third national surveys included 218, 222, and 236 individuals diagnosed with intestinal atresia, respectively. The male-to-female ratios were 1.5:1, 1.1:1, and 1.1:1, respectively. The first, second, and third national surveys revealed that 34.3%, 43.3%, and 53.4% of patients were born before 37 weeks of gestation, respectively. Additionally, 28.7%, 32.0%, and 40.7% of patients had a birth weight under 2,500 g. In the third national survey, duodenoduodenostomy was the most common procedure, performed in 70 out of 82 patients diagnosed with duodenal atresia. Resection and anastomosis were the main surgical procedures conducted in 47 out of 54 cases of jejunal atresia and 74 out of 92 cases of ileal atresia. The mortality rates in the first, second, and third national surveys were 13.8%, 3.6%, and 1.3% respectively, with the lowest rate observed in the third national survey. Conclusion These national surveys offer valuable insights into the current state of intestinal atresia, including specific surgical interventions and postoperative outcomes in South Korea. For pediatric surgeons aiming to enhance their understanding of intestinal atresia and its treatment options, these surveys could be an indispensable resource and guide.

Objective: The Scales for Outcomes in Parkinson’s Disease–Cognition (SCOPA-Cog) was developed to assess cognition in patients with Parkinson’s disease (PD). In this study, we aimed to evaluate the validity and reliability of the Korean version of the SCOPACog (K-SCOPA-Cog). Methods: We enrolled 129 PD patients with movement disorders from 31 clinics in South Korea. The original version of the SCOPA-Cog was translated into Korean using the translation-retranslation method. The test–retest method with an intraclass correlation coefficient (ICC) and Cronbach’s alpha coefficient were used to assess reliability. Spearman’s rank correlation analysis with the Montreal Cognitive Assessment-Korean version (MOCA-K) and the Korean Mini-Mental State Examination (K-MMSE) were used to assess concurrent validity. Results: The Cronbach’s alpha coefficient was 0.797, and the ICC was 0.887. Spearman’s rank correlation analysis revealed a significant correlation with the K-MMSE and MOCA-K scores (r = 0.546 and r = 0.683, respectively). Conclusion Our results demonstrate that the K-SCOPA-Cog has good reliability and validity.

Background: Hand hygiene is considered the simplest and most cost-effective method of infection prevention. Regular observation and feedback on hand hygiene compliance are key strategies for its enhancement. This study evaluated the effectiveness of hand hygiene surveillance, including direct observation and feedback, by comprehensively analyzing the reported hand hygiene compliance within the Korean National Healthcare-Associated Infections Surveillance System from 2019 to 2022. Methods: Participating medical institutions included general hospitals and hospitals with infection control departments that consented to participate. Hand hygiene surveillance was conducted using direct observation. Collected data, including healthcare workers, clinical areas, hand hygiene moments, and hand hygiene compliance, were recorded to calculate hand hygiene compliance rates. Additionally, the volume of alcohol-based hand sanitizers used per patient per day was investigated as an indirect indicator of hand hygiene compliance. The study was conducted from July 2019 to December 2022. Results: Hand hygiene compliance increased from 87.2% in Q3 2019 to 89.9% in 2022. Nurses and medical technologists showed the highest compliance rates, whereas doctors showed the lowest compliance rates. Intensive care units excelled in compliance, whereas emergency de partments lagged. Compliance was highest after patient contact and lowest when the patient’s surroundings were touched. Larger hospitals consumed more alcohol-based hand sanitizers than smaller hospitals did. Conclusion This study confirmed an improvement in hand hygiene compliance through sustained surveillance, indicating its contribution not only to preventing infection transfer within healthcare facilities but also to fostering a culture of hand hygiene in the country.

Purpose: This study provides insights into the prevalence at birth, clinical characteristics, and outcomes of gastroschisis and omphalocele in Korea over the past decade, addressing the lack of localized data despite advanced healthcare capabilities. Methods: The study retrospectively analyzed data from 20 pediatric surgical centers in Korea from January 2012 to December 2021, including 269 patients diagnosed with gastroschisis or omphalocele. Data variables included gender, gestational age, birth weight, associated anomalies, type of defect, surgical interventions, and outcomes. Results: The study covered 269 patients, with 80 gastroschisis and 189 omphalocele cases. Gastroschisis prevalence at birth remained stable at 2.15 per 100,000 live births, while omphalocele increased to 5.08 per 100,000. Both conditions had similar gender ratios (0.95). Gastroschisis patients had lower birth weights (2,463.90±505.50 g) and smaller head circumferences (31.97±1.86 cm) compared to omphalocele patients (2,757.65±761.24 g, 32.78±2.64 cm). Omphalocele cases had more associated anomalies, especially cardiovascular issues. Prenatal diagnosis rates were high: 93.7% for gastroschisis and 86.4% for omphalocele. About 96.3% of gastroschisis and 84.1% of omphalocele patients were born in their treatment hospitals. Gastroschisis patients underwent surgery sooner (average 3.5 days) and started feeding later (16.5 days) than omphalocele patients (average 56.5 days to surgery, 6.6 days to start feeding). Hospital stays and follow-up durations were similar, averaging around 782.6 days for gastroschisis and 800.3 days for omphalocele patients. Survival rates were 89.7% for gastroschisis and 87.1% for omphalocele. Conclusion The study highlights the need for early diagnosis, centralized care, and specialized surgical approaches to optimize outcomes for gastroschisis and omphalocele patients in Korea. Enhanced prenatal screening and surgical protocols are recommended to improve these patients' prognosis.