Jo H, et al. Yonsei Med J. 2021 Jun;62(6):510-519. https://doi.org/10.3349/ymj.2021.62.6.510

Objectives This study investigated the risk associated with interhospital transfer of patients with acute myocardial infarction (AMI) and clinical outcomes according to the location of the patient’ residence. Design A nationwide longitudinal cohort. Setting National Health Insurance Service database of South Korea. Participants This study included 69 899 patients with AMI who visited an emergency centre from 2013 to 2015, as per the Korea National Health Insurance Service database. Primary outcome measure The clinical outcome of a patient with AMI was defined as mortality within 7 days, 30 days and 1 year. Results Clinical outcomes were analysed and compared with respect to the location of the patient’s residence and occurrence of interhospital transfer. We concluded that the HR of mortality within 7 days was 1.49 times higher (95% CI 1.18 to 1.87) in rural patients than in urban patients not subjected to interhospital transfer and 1.90 times higher (95% CI 1.13 to 3.19) in transferred rural patients than in non-transferred urban patients. Conclusions To reduce health inequality in rural areas, a healthcare policy considering regional characteristics, rather than a central government-led, catch-all approach to healthcare policy, must be formulated. Additionally, a local medical emergency delivery system, based on allocation of roles between different medical facilities in the region, must be established.

Metabolic syndrome (MetS) is a major public health problem related to increased risks of cardiovascular disease and type 2 diabetes. We investigated whether recommended levels of leisure time physical activity (LTPA) decrease the risk of MetS in a community-based prospective cohort study (Ansung and Ansan cohort). A total of 3910 adults (1890 men, 2020 women) without MetS examined in 2001-2002 (baseline) were included in this study and followed up from 2013 to 2014. We measured LTPA energy expenditure using the standard metabolic equivalent value [metabolic equivalent of task (MET)-h/wk]. Individuals were categorized into five groups (physically inactive, 0.1 to <7.5, 7.5 to <15.0, 15.0 to <22.5, 22.5 to <40.0, and ≥40.0 MET-h/wk). The mean follow-up period was 11.8 years. Among the cohort, 482 men (25.5%) and 541 women (26.8%) were diagnosed with newly developed MetS. Multivariate logistic analysis revealed a 4.4% lower MetS risk among those performing two to three times the recommended minimum of 7.5 MET-h/wk [multivariate relative risk (RR), 0.956; 95% confidence interval (CI), 0.654-1.398] and a 21.9% lower risk among those performing three to four times the physical activity minimum (multivariate RR, 0.781; 95% CI, 0.574-1.063). This association was only significant in females performing three to four times the minimum (crude RR, 0.655; 95% CI, 0.432-0.994). We noted that physical activity at more than 2 times the recommended minimum prevents MetS. Healthcare professionals should encourage inactive adults to be physically active during their leisure time to take advantage of its health benefits.

Kim J, et al. J Korean Med Sci. 2023 Apr;38(19):e146. https://doi.org/10.3346/jkms.2023.38.e146

Myocardial infarction (MI) is a common cardiovascular disease, the early diagnosis of which is essential for effective treatment and reduced mortality. Therefore, novel methods are required for automatic screening or early diagnosis of MI, and many studies have proposed diverse conventional methods for its detection. In this study, we aimed to develop a sleep-myocardial infarction (sleepMI) algorithm for automatic screening of MI based on nocturnal electrocardiography (ECG) findings from diagnostic polysomnography (PSG) data using artificial intelligence (AI) models. The proposed sleepMI algorithm was designed using representation and ensemble learning methods and optimized via dropout and batch normalization. In the sleepMI algorithm, a deep convolutional neural network and light gradient boost machine (LightGBM) models were mixed to obtain robust and stable performance for screening MI from nocturnal ECG findings. The nocturnal ECG signal was extracted from 2,691 participants (2,331 healthy individuals and 360 patients with MI) from the PSG data of the second follow-up stage of the Sleep Heart Health Study. The nocturnal ECG signal was extracted 3 h after sleep onset and segmented at 30-s intervals for each participant. All ECG datasets were divided into training, validation, and test sets consisting of 574,729, 143,683, and 718,412 segments, respectively. The proposed sleepMI model exhibited very high performance with precision, recall, and F1-score of 99.38%, 99.38%, and 99.38%, respectively. The total mean accuracy for automatic screening of MI using a nocturnal single-lead ECG was 99.387%. MI events can be detected using conventional 12-lead ECG signals and polysomnographic ECG recordings using our model.