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International Journal of Arrhythmia ; : 11-2022.
Article in English | WPRIM | ID: wpr-937684


Background@#Conventional modality requires several days observation by Holter monitor to differentiate atrial fibril‑ lation (AF) between Paroxysmal atrial fibrillation (PAF) and Non-paroxysmal atrial fibrillation (Non-PAF). Rapid and practical differentiating approach is needed. @*Objective@#To develop a machine learning model that observes 10-s of standard 12-lead electrocardiograph (ECG) for real-time classification of AF between PAF versus Non-PAF. @*Methods@#In this multicenter, retrospective cohort study, the model training and cross-validation was performed on a dataset consisting of 741 patients enrolled from Severance Hospital, South Korea. For cross-institutional validation, the trained model was applied to an independent data set of 600 patients enrolled from Ewha University Hospital, South Korea. Lasso regression was applied to develop the model. @*Results@#In the primary analysis, the Area Under the Receiver Operating Characteristic Curve (AUC) on the test set for the model that predicted AF subtype only using ECG was 0.72 (95% CI 0.65–0.80). In the secondary analysis, AUC only using baseline characteristics was 0.53 (95% CI 0.45–0.61), while the model that employed both baseline characteris‑ tics and ECG parameters was 0.72 (95% CI 0.65–0.80). Moreover, the model that incorporated baseline characteristics, ECG, and Echocardiographic parameters achieved an AUC of 0.76 (95% CI 0.678–0.855) on the test set. @*Conclusions@#Our machine learning model using ECG has potential for automatic differentiation of AF between PAF versus Non-PAF achieving high accuracy. The inclusion of  Echocardiographic parameters further increases model per‑ formance. Further studies are needed to clarify the next steps towards clinical translation of the proposed algorithm.

Yonsei Medical Journal ; : 1-11, 2021.
Article in English | WPRIM | ID: wpr-875608


Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a worldwide pandemic. The first reports of patients with COVID-19 were provided to World Health Organization on December 21, 2019 and were presumably associated with seafood markets in Wuhan, China. As of October 25, 2020, more than 42 million cases have been confirmed worldwide, with more than 1.1 million deaths. Asymptomatic transmission contributes significantly to transmission, and clinical features are non-specific to the disease. Thus, the diagnosis of COVID-19 requires specific viral RNA testing. The disease demonstrates extensive human-to-human transmissibility and has infected healthcare workers at high rates. Clinical awareness of the epidemiology and the risk factors for nosocomial transmission of COVID-19 is essential to preventing infection. Moreover, effective control measures should be further identified by comprehensive evaluation of hospital and community responses. In this review, we provide a comprehensive update on the epidemiology, presentation, transmission, risk factors, and public health measures associated with COVID-19. We also review past insights from previous coronavirus epidemics [i.e., severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS)] to suggest measures to reduce transmission.

Yonsei Medical Journal ; : 349-358, 2020.
Article | WPRIM | ID: wpr-833377


Globally and in Africa specifically, female sex workers (FSWs) are at an extraordinarily high risk of contracting human immunodeficiencyvirus (HIV). Pre-exposure prophylaxis (PrEP) has emerged as an effective and ethical method with which to preventHIV infection among FSWs. PrEP efficacy is, however, closely linked to adherence, and adherence to PrEP among FSWs is a complexand interrelated process that has been shown to be of importance to public health policies and HIV control and interventionprograms. This comprehensive review categorizes barriers to and facilitators of adherence to HIV PrEP for FSWs, and describes fivestrategies for promoting PrEP adherence among FSWs. These strategies encompass 1) a long-term educational effort to decreasethe stigma associated with sex work and PrEP use, 2) education on how PrEP works, 3) lifestyle modification, 4) research on nextgenerationPrEP products to address the inconvenience of taking daily pills, and 5) integration of PrEP into existing services, suchas social services and routine primary care visits, to reduce the economic burden of seeking the medication. Our review is expectedto be useful for the design of future PrEP intervention programs. Multidisciplinary intervention should be considered to promotePrEP adherence among FSWs in order to help control the HIV epidemic.