ABSTRACT
Background: Coronavirus disease (COVID-19), first reported at the end of 2019, is characterized by a broad spectrum of clinical manifestations ranging from asymptomatic to multi-organ dysfunction. These symptoms may persist even after the acute phase has passed. Post-acute COVID-19 syndrome (long-COVID) is a condition characterized by COVID-19 symptoms that persist for longer than two months after infection. Fatigue, muscle and joint pain, dyspnea, cognitive impairment, and anxiety are the most common symptoms of long-COVID. Given the substantial impact of COVID-19 sequelae on the quality of life of its survivors, as well as its socioeconomic burden, proactive measures are required. Current Concepts: Following the identification of long-COVID characteristics and symptoms, patient-centered care based on vaccination, COVID-19 medications, and digital healthcare is recommended. Furthermore, people who are more vulnerable to long-COVID, such as those with respiratory dysfunctions or the older adults, require more specialized and attentive management. Big data and artificial intelligence will hopefully enable a more Discussion and Conclusion: Infectious diseases threaten our lives constantly, as evidenced by the recent COVID-19 pandemic and its lingering consequences. A novel virus can emerge at any time and place, resulting in substantial clinical and economic loss. At this stage, it is crucial to establish prompt and effective strategies against long-COVID, as well as against potential pandemics.
ABSTRACT
Background Although the number of patients presenting with non-ST elevation myocardial infarction (NSTEMI) has drastically reduced in the coronavirus-19 pandemic era, increased mortality was reported. A plausible explanation for increased mortality was suggested as the delay of arrival at the hospital due to patients’ reticence of their symptoms. However, evidence to support the suggested explanation is lacking. Methods From the nationwide prospective registry, we evaluated 6,544 patients with NSTEMI. Study patients were categorized into two groups according to their symptom-to-door (StD) time (<24 h or ≥24 h). The primary outcome was 3-year all-cause mortality, and the secondary outcome was 3-year composite of all-cause mortality, recurrent MI, and hospitalization for heart failure. Results Overall, 27.9% patients were classified into the StD time ≥24 h group. The StD time ≥24 h group had higher all-cause mortality (17.0% vs. 10.5%, p<0.001) and incidence of secondary outcome (23.3% vs. 15.7%, p<0.001) than the StD time <24 h group. In the multivariable analysis, independent predictors of delayed arrival at the hospital were the elderly, female, non-specific symptoms such as atypical chest pain or dyspnea, diabetes, and no use of emergency medical services. Conclusion Delayed arrival (StD time ≥24 h) is associated with an increased risk of 3-year all-cause mortality and composite outcomes in patients with NSTEMI. [Formula presented]
ABSTRACT
Photoplethysmography (PPG) is often used in telemedicine because it enables convenient measurement and provides data related to cardiopulmonary function. However PPG is difficult analyze using an automated algorithm because of its vulnerability to motion artefacts and the diversity of the waveforms according to the characteristics of individuals and diseases. Recently, as the use of telemedicine has become more frequent due to the outbreak of COVID19, the application of deep neural network (DNN) technology in the analysis of PPG and selection of reliable data has increased. In this study, PPG was analyzed using DNN techniques to reproduce the long-term potential (LTP) phenomenon in the brain. Moreover, the reliability of measuring saturation pulse oxymetry (SPO2) simultaneously was evaluated using the LTP-DNN. The LTP-DNN was able to evaluate faultless data by inspecting 58 PPG datasets, including 29 fault data, and could determine the possibility of failure in SPO2 measurement as well. Even in a moving situation, the LTP-DNN provides more accurate heartrate (HR) measurements than commercial SPO2 devices do. It can also be used to normalize the PPG waveform to identify waveform differences between individuals.