A Pilot Study Evaluating LV Diastolic Function with M-Mode Measurement of Mitral Valve Movement in the Parasternal Long Axis View.

This pilot study aimed to develop a new, reliable, and easy-to-use method for the evaluation of diastolic function through the M-mode measurement of mitral valve (MV) movement in the parasternal long axis (PSLA), similar to E-point septal separation (EPSS) used for systolic function estimation. Thirty healthy volunteers from a tertiary emergency department (ED) underwent M-mode measurements of the MV anterior leaflet in the PSLA view. EPSS, A-point septal separation (APSS), A-point opening length (APOL), and E-point opening length (EPOL) were measured in the PSLA view, along with the E and A velocities and e' velocity in the apical four-chamber view. Correlation analyses were performed to assess the relationship between M-mode and Doppler measurements, and the measurement time was evaluated. No significant correlations were found between M-mode and Doppler measurements in the study. However, M-mode measurements exhibited high reproducibility and faster acquisition, and the EPOL value consistently exceeded the APOL value, resembling the E and A pattern. These findings suggest that visually assessing the M-mode pattern on the MV anterior leaflet in the PSLA view may be a practical approach to estimating diastolic function in the ED. Further investigations with a larger and more diverse patient population are needed to validate these findings.

Online Learning versus Hands-On Learning of Basic Ocular Ultrasound Skills: A Randomized Controlled Non-Inferiority Trial.

Background and objectives: Ocular ultrasound is a core application of point-of-care ultrasound (POCUS) to assist physicians in promptly identifying various ocular diseases at the bedside; however, hands-on POCUS training is challenging during a pandemic. Materials and Methods: A randomized controlled non-inferiority trial was conducted in an academic emergency department from October 2020 to April 2021. Thirty-two participants were randomly assigned to one of two groups. Group H (hands-on learning group) participated individually in a hands-on session with a standardized patient for 30 min, whereas Group O (online learning group) learned training materials and video clips for 20 min. They scanned four eyeballs of two standardized patients sequentially following the ocular POCUS scan protocol. Repeated POCUS scans were performed 2 weeks later to assess skill maintenance. Both groups completed the pre- and post-surveys and knowledge tests. Two emergency medicine faculty members blindly evaluated the data and assigned a score of 0−25. The primary endpoint was the initial total score of scan quality evaluated using non-inferiority analysis (generalized estimating equation). The secondary endpoints were total scores for scan quality after 2 weeks, scan time, and knowledge test scores. Results: The least squares means of the total scores were 21.7 (0.35) for Group O and 21.3 (0.25) for Group H, and the lower bound of the 95% confidence interval (CI) was greater than the non-inferiority margin of minus 2 (95% CI: −0.48−1.17). The second scan scores were not significantly different from those of the first scan. The groups did not differ in scanning time or knowledge test results; however, Group H showed higher subjective satisfaction with the training method (p < 0.001). Conclusion: This study showed that basic online ocular ultrasound education was not inferior to hands-on education, suggesting that it could be a useful educational approach in the pandemic era.

Factors Associated with Emergency Department Visits and Consequent Hospitalization and Death in Korea Using a Population-Based National Health Database.

We aim to investigate common diagnoses and risk factors for emergency department (ED) visits as well as those for hospitalization and death after ED visits. This study describes the clinical course of ED visits by using the 2014-2015 population data retrieved from the National Health Insurance Service. Sociodemographic, medical, and behavioral factors were analyzed through multiple logistic regression. Older people were more likely to be hospitalized or to die after an ED visit, but younger people showed a higher risk for ED visits. Females were at a higher risk for ED visits, but males were at a higher risk for ED-associated hospitalization and death. Individuals in the highest quartile of income had a lower risk of ED death relative to lowest income level individuals. Disabilities, comorbidities, and medical issues, including previous ED visits or prior hospitalizations, were risk factors for all ED-related outcomes. Unhealthy behaviors, including current smoking, heavy alcohol consumption, and not engaging in regular exercise, were also significantly associated with ED visits, hospitalization, and death. Common diagnoses and risk factors for ED visits and post-visit hospitalization and death found in this study provide a perspective from which to establish health polices for the emergency medical care system.

Impact of Insurance Benefits and Education on Point-of-Care Ultrasound Use in a Single Emergency Department: An Interrupted Time Series Analysis.

Background and Objectives: Point-of-care ultrasound (POCUS) is a useful tool that helps clinicians properly treat patients in emergency department (ED). This study aimed to evaluate the impact of specific interventions on the use of POCUS in the ED. Materials and Methods: This retrospective study used an interrupted time series analysis to assess how interventions changed the use of POCUS in the emergency department of a tertiary medical institute in South Korea from October 2016 to February 2021. We chose two main interventions-expansion of benefit coverage of the National Health Insurance (NHI) for emergency ultrasound (EUS) and annual ultrasound educational workshops. The primary variable was the EUS rate, defined as the number of EUS scans per 1000 eligible patients per month. We compared the level and slope of EUS rates before and after interventions. Results: A total of 5188 scanned records were included. Before interventions, the EUS rate had increased gradually. After interventions, except for the first workshop, the EUS rate immediately increased significantly (p < 0.05). The difference in the EUS rate according to the expansion of the NHI was estimated to be the largest (p < 0.001). However, the change in slope significantly decreased after the third workshop during the coronavirus disease 2019 pandemic (p = 0.004). The EUS rate increased significantly in the presence of physicians participating in intensive POCUS training (p < 0.001). Conclusion: This study found that expansion of insurance coverage for EUS and ultrasound education led to a significant and immediate increase in the use of POCUS, suggesting that POCUS use can be increased by improving education and insurance benefits.

Identifying prescription patterns with a topic model of diseases and medications.

Wide variance exists among individuals and institutions for treating patients with medicine. This paper analyzes prescription patterns using a topic model with more than four million prescriptions. Specifically, we propose the disease-medicine pattern model (DMPM) to extract patterns from a large collection of insurance data by considering disease codes joined with prescribed medicines. We analyzed insurance prescription data from 2011 with DMPM and found prescription patterns that could not be identified by traditional simple disease classification, such as the International Classification of Diseases (ICD). We analyzed the identified prescription patterns from multiple aspects. First, we found that our model better explain unseen prescriptions than other probabilistic models. Second, we analyzed the similarities of the extracted patterns to identify their characteristics. Third, we compared the identified patterns from DMPM to the known disease categorization, ICD. This comparison showed what additional information can be provided by the data-oriented bottom-up patterns in contrast to the knowledge-based top-down categorization. The comparison results showed that the bottom-up categorization allowed for the identification of (1) diverse treatment options for the same disease symptoms, and (2) diverse disease cases sharing the same prescription options. Additionally, the extracted bottom-up patterns revealed treatment differences based on basic patient information better than the top-down categorization. We conclude that this data-oriented analysis will be an effective alternative method for analyzing the complex interwoven disease-prescription relationship.