Factors Associated with Worsening Oxygenation in Patients with Non-severe COVID-19 Pneumonia / 결핵및호흡기질환

Background@#This study aimed to determine the parameters for worsening oxygenation in non-severe coronavirus disease 2019 (COVID-19) pneumonia. @*Methods@#This retrospective cohort study included cases of confirmed COVID-19 pneumonia in a public hospital in South Korea. The worsening oxygenation group was defined as that with SpO2 ≤94% or received oxygen or mechanical ventilation (MV) throughout the clinical course versus the non-worsening oxygenation group that did not experience any respiratory event. Parameters were compared, and the extent of viral pneumonia from an initial chest computed tomography (CT) was calculated using artificial intelligence (AI) and measured visually by a radiologist. @*Results@#We included 136 patients, with 32 (23.5%) patients in the worsening oxygenation group; of whom, two needed MV and one died. Initial vital signs and duration of symptoms showed no difference between the two groups; however, univariate logistic regression analysis revealed that a variety of parameters on admission were associated with an increased risk of a desaturation event. A subset of patients was studied to eliminate potential bias, that ferritin ≥280 μg/L (p=0.029), lactate dehydrogenase ≥240 U/L (p=0.029), pneumonia volume (p=0.021), and extent (p=0.030) by AI, and visual severity scores (p=0.042) were the predictive parameters for worsening oxygenation in a sex-, age-, and comorbid illness-matched case-control study using propensity score (n=52). @*Conclusion@#Our study suggests that initial CT evaluated by AI or visual severity scoring as well as serum markers of inflammation on admission are significantly associated with worsening oxygenation in this COVID-19 pneumonia cohort.

The individual and neighborhood factors associated with the use of emergency medical services in patients with ST-elevation myocardial infarction

Objective@#The utilization of emergency medical services (EMS) varies widely among communities. In this study, we aimed to evaluate the relationship between the use of EMS by patients with ST-elevation myocardial infarction (STEMI) and the individual and neighborhood characteristics of these patients. @*Methods@#We performed a secondary analysis of data from the Cardiovascular Disease Surveillance project, which included patients diagnosed with STEMI at 29 emergency centers in South Korea. Our analysis included only patients living in Seoul, and the primary outcome measured was the use of EMS. While the clinical variables of the patients were collected from the Cardiovascular Disease Surveillance registry, the 2010 National Census data was used to identify neighborhood variables such as population density, income, age, and residence type. We used a 3-level hierarchical logistic regression to estimate the effects of neighborhood-level factors on EMS use by individual patients. @*Results@#We evaluated 1,634 patients with STEMI from 2007 to 2012. The neighborhoods were grouped into 25 counties. The regional rates of EMS use varied from 18.3% to 46.5%. The final adjusted logistic model revealed that the use of EMS was significantly associated with the average number of households (neighborhood level factor) and symptoms of syncope, cardiac arrest, and history of cardiovascular disease (individual level factors). @*Conclusion@#The individual levels factors had a greater influence on the use of EMS compared to the neighborhood-level factors.

The individual and neighborhood factors associated with the use of emergency medical services in patients with ST-elevation myocardial infarction

Objective@#The utilization of emergency medical services (EMS) varies widely among communities. In this study, we aimed to evaluate the relationship between the use of EMS by patients with ST-elevation myocardial infarction (STEMI) and the individual and neighborhood characteristics of these patients. @*Methods@#We performed a secondary analysis of data from the Cardiovascular Disease Surveillance project, which included patients diagnosed with STEMI at 29 emergency centers in South Korea. Our analysis included only patients living in Seoul, and the primary outcome measured was the use of EMS. While the clinical variables of the patients were collected from the Cardiovascular Disease Surveillance registry, the 2010 National Census data was used to identify neighborhood variables such as population density, income, age, and residence type. We used a 3-level hierarchical logistic regression to estimate the effects of neighborhood-level factors on EMS use by individual patients. @*Results@#We evaluated 1,634 patients with STEMI from 2007 to 2012. The neighborhoods were grouped into 25 counties. The regional rates of EMS use varied from 18.3% to 46.5%. The final adjusted logistic model revealed that the use of EMS was significantly associated with the average number of households (neighborhood level factor) and symptoms of syncope, cardiac arrest, and history of cardiovascular disease (individual level factors). @*Conclusion@#The individual levels factors had a greater influence on the use of EMS compared to the neighborhood-level factors.

Analysis of Delayed Arrival Time According to the Residences Type of Elderly Acute Ischemic Stroke Patients

PURPOSE: We made a clinical comparison of elderly patients from home and residential aged care facilities (RACFs) who visited the emergency department and were hospitalized with acute ischemic stroke. In addition, we investigated the factors associated with prehospital delay in acute ischemic stroke. METHODS: A retrospective study was conducted in a public hospital between January 2013 and December 2016. Information regarding the patients was registered including gender, age, comorbidities, symptoms at onset, use of emergency medical services, National Institute of Health Stroke Scale (NIHSS) at the emergency department, symptom-to-door time, etc. Characteristics of the patients were analyzed and logistic regression analysis was conducted to identify factors associated with symptom-to-door time. RESULTS: A total of 402 patients were enrolled during the study period. Overall, 339 elderly patients visited from home and 63 patients from RACFs, and patients from home were divided into two groups, living with family (n=274) and living alone (n=65). Patients from RACFs were older (≤0.001) and had higher NIHSS (p=0.007) than patients from home, but there were no significant relationships between symptom-to-door time and age (p=0.525), NIHSS (p=0.428). There was no difference in symptom-to-door time between patients living with family and patients from RACFs, but patients living alone had delayed symptom-to-door time (p < 0.001). CONCLUSION: Elderly patients living alone were among the three groups that had the most delayed symptom-to-door time. Therefore, it is necessary to expand and supplement support for elderly patients living alone, as well as to improve education regarding acute ischemic stroke.

The Effect of Emergency Department Expansion on the Emergency Department Length of Stay in a Tertiary Hospital

PURPOSE: In this study, we aimed to measure the effects of emergency department (ED) expansion at a tertiary hospital on overall ED length of stay (LOS). METHODS: This study was a before and after study using data from a tertiary medical center in Seoul, a large metropolis. We used electronic medical records and administrative databases obtained from the ED. The control period (before expansion) was defined as from January 1 to April 31, 2016. The study period (after expansion) was defined as from May 1 to August 31, 2016. The number of ED beds increased from 42 to 74 after the expansion. After adjusting individual characteristics and institutional characteristics, multivariate regression analysis was carried out to evaluate the effects of expansion on overall ED LOS. RESULTS: A total of 45,632 patients visited the emergency medical center: 20,592 patients before and 25,040 after the expansion. Although the absolute number of patients increased, the portion of medical patients, portion of non-referral patients, and ambulatory patients decreased during the study period (all p<0.001). Average visit number increased from 170.2 (standard deviation [SD], 27.3) to 203.6 (SD, 21.3) (p<0.001). The overall ED LOS increased from 332.2 (SD, 473.4) to 391.0 minutes (SD, 649.5). After adjusting for potential confounders, we found that ED expansion was associated with an increase in ED LOS by 75.8 minutes (95% confidence interval, 63.5 to 88.2). CONCLUSION: We found that the ED expansion was associated with a significant increase in ED LOS.

The Relationship between the Number of Community Acquired Pneumonia Patients and the Weather among the Patients Who Visit ER: A Poisson Regression with Variable Selection Via Elastic net

PURPOSE: This study shows the relationship between meteorological factors and the number of community acquired pneumonia (CAP) patients in the emergency room and lag effect of meteorological factors affecting CAP. METHODS: A retrospective study was conducted. Patients diagnosed with CAP in the emergency room between January 2012 and December 2014 were enrolled. The patients were over 18 years old and lived in Seoul, Korea. Meteorological factors (highest daily temperature, lowest temperature, mean temperature, diurnal temperature, rainfall, relative humidity, amount of sunshine, and powdery dust under 10 microg/m3 (PM10)) between December 2011 and December 2014 in Seoul were acquired from the Korea Meteorological Administration. Multiple Poisson regression (Generalized Linear Model) was used with daily patient's number of CAP as the response variable and meteorological factors as the explanatory variable. Variable selection was performed via Elastic net. RESULTS: A total of 568 CAP patients were checked. Highest temperature (before 6 days), rainfall (before 1 day), relative humidity (before 20, 15, 13, 6, 2, and 1 days), and PM10 (before 27, 24, 17, and 13 days) showed relationship and lag effect with the incidence of CAP. CONCLUSION: This study showed that meteorological factors (highest temperature, rainfall, relative humidity, and PM10) had relationship and lag effect with the incidence of CAP. We can make a prediction model with health weather index for prevention of CAP and redistribution of medical facilities and resources.