Association of Medicaid coverage with emergency department utilization after self-harm in Korea: A nationwide registry-based study.

BACKGROUND: Self-harm presents an important public health challenge. It imposes a notable burden on the utilization of emergency department (ED) services and medical expenses from patients and family. The Medicaid system is vital in providing financial support for individuals who struggle with medical expenses. This study explored the association of Medicaid coverage with ED visits following incidents of self-harm, utilizing nationwide ED surveillance data in Korea. METHODS: Data of all patients older than 14 years who presented to EDs following incidents of self-harm irrespective of intention to end their life, including cases of self-poisoning, were gathered from the National ED Information System (NEDIS). The annual self-harm visit rate (SHVR) per 100,000 people was calculated for each province and a generalized linear model analysis was conducted, with SHVR as a dependent variable and factors related to Medicaid coverage as independent variables. RESULTS: A 1% increase in Medicaid enrollment rate was linked to a significant decrease of 14% in SHVR. Each additional 1,000 Korean Won of Medicaid spending per enrollee was correlated with a 1% reduction in SHVR. However, an increase in Medicaid visits per enrollee and an extension of Medicaid coverage days were associated with an increase in SHVR. SHVR exhibited a stronger associated with parameters of Medicaid coverage in adolescents and young adults than in older adult population. CONCLUSION: Expansion of Medicaid coverage coupled with careful monitoring of shifts in Medicaid utilization patterns can mitigate ED overloading by reducing visits related to self-harm.

Electrocardiogram-based deep learning model to screen peripartum cardiomyopathy.

BACKGROUND: Peripartum cardiomyopathy, one of the most fatal conditions during delivery, results in heart failure secondary to left ventricular systolic dysfunction. Left ventricular dysfunction can result in abnormalities in electrocardiography. However, the usefulness of electrocardiography in the identification of peripartum cardiomyopathy in pregnant women remains unclear. OBJECTIVE: This study aimed to evaluate the effectiveness of a 12-lead electrocardiography-based artificial intelligence/machine learning-based software as a medical device for screening peripartum cardiomyopathy. STUDY DESIGN: This retrospective cohort study included pregnant women who underwent transthoracic echocardiography between a month before and 5 months after delivery and underwent 12-lead electrocardiography within 30 days of echocardiography between December 2011 and May 2022 at Seoul National University Hospital. The performance of 12-lead electrocardiography-based artificial intelligence/machine learning analysis (AiTiALVSD software; version 1.00.00, which was developed to screen for left ventricular systolic dysfunction in the general population) was evaluated for the identification of peripartum cardiomyopathy. In addition, the performance of another artificial intelligence/machine learning algorithm using only 1-lead electrocardiography to detect left ventricular systolic dysfunction was evaluated in identifying peripartum cardiomyopathy. The results were obtained under a 95% confidence interval and considered significant when P<.05. RESULTS: Among the 14,557 women who delivered during the study period, 204 (1.4%) underwent transthoracic echocardiography a month before and 5 months after delivery. Among them, 12 (5.8%) were diagnosed with peripartum cardiomyopathy. The results showed that AiTiALVSD for 12-lead electrocardiography was highly effective in detecting peripartum cardiomyopathy, with an area under the receiver operating characteristic of 0.979 (95% confidence interval, 0.953-1.000), an area under the precision-recall curve of 0.715 (95% confidence interval, 0.499-0.951), a sensitivity of 0.917 (95% confidence interval, 0.760-1.000), a specificity of 0.927 (95% confidence interval, 0.890-0.964), a positive predictive value of 0.440 (95% confidence interval, 0.245-0.635), and a negative predictive value of 0.994 (95% confidence interval, 0.983-1.000). In addition, a 1-lead (lead I) artificial intelligence/machine learning algorithm showed excellent performance; the area under the receiver operating characteristic, area under the precision-recall curve, sensitivity, specificity, positive predictive value, and negative predictive value were 0.944 (95% confidence interval, 0.895-0.993), 0.520 (95% confidence interval, 0.319-0.801), 0.833 (95% confidence interval, 0.622-1.000), 0.880 (95% confidence interval, 0.834-0.926), 0.303 (95% confidence interval, 0.146-0.460), and 0.988 (95% confidence interval, 0.972-1.000), respectively. CONCLUSION: The 12-lead electrocardiography-based artificial intelligence/machine learning-based software as a medical device (AiTiALVSD) and 1-lead algorithm are noninvasive and effective ways of identifying cardiomyopathies occurring during the peripartum period, and they could potentially be used as highly sensitive screening tools for peripartum cardiomyopathy.

BODIPY-Coumarin Conjugate as an Endoplasmic Reticulum Membrane Fluidity Sensor and Its Application to ER Stress Models.

An endoplasmic reticulum (ER) membrane-selective chemosensor composed of BODIPY and coumarin moieties and a long alkyl chain (n-C18) was synthesized. The emission ratio of BODIPY to coumarin depends on the solution viscosity. The probe is localized to the ER membrane and was applied to reveal the reduced ER membrane fluidity under ER stress conditions.