Epstein-Barr virus (EBV) causes a variety of central nervous system infections, including encephalitis. EBV encephalitis accounts for approximately 5.8% of all viral encephalitis, with fever, headache, convulsions, and decreased consciousness as common symptoms. Brain magnetic resonance imaging (MRI) in EBV encephalitis shows high signal intensity mainly in the parieto-occipital cortex, subcortical white matter, and deep gray matter nuclei with reversibility. In this report, we present a case of bilateral corticospinal tract involvement (wineglass sign) identified on brain MRI in a patient with diagnosed with EBV encephalitis.

Epstein-Barr virus (EBV) causes a variety of central nervous system infections, including encephalitis. EBV encephalitis accounts for approximately 5.8% of all viral encephalitis, with fever, headache, convulsions, and decreased consciousness as common symptoms. Brain magnetic resonance imaging (MRI) in EBV encephalitis shows high signal intensity mainly in the parieto-occipital cortex, subcortical white matter, and deep gray matter nuclei with reversibility. In this report, we present a case of bilateral corticospinal tract involvement (wineglass sign) identified on brain MRI in a patient with diagnosed with EBV encephalitis.

Epstein-Barr virus (EBV) causes a variety of central nervous system infections, including encephalitis. EBV encephalitis accounts for approximately 5.8% of all viral encephalitis, with fever, headache, convulsions, and decreased consciousness as common symptoms. Brain magnetic resonance imaging (MRI) in EBV encephalitis shows high signal intensity mainly in the parieto-occipital cortex, subcortical white matter, and deep gray matter nuclei with reversibility. In this report, we present a case of bilateral corticospinal tract involvement (wineglass sign) identified on brain MRI in a patient with diagnosed with EBV encephalitis.

Background: The quick sequential organ failure assessment (qSOFA) score is suggested to use for screening patients with a high risk of clinical deterioration in the general wards, which could simply be regarded as a general early warning score. However, comparison of unselected admissions to highlight the benefits of introducing qSOFA in hospitals already using Modified Early Warning Score (MEWS) remains unclear. We sought to compare qSOFA with MEWS for predicting clinical deterioration in general ward patients regardless of suspected infection. Methods: The predictive performance of qSOFA and MEWS for in-hospital cardiac arrest (IHCA) or unexpected intensive care unit (ICU) transfer was compared with the areas under the receiver operating characteristic curve (AUC) analysis using the databases of vital signs collected from consecutive hospitalized adult patients over 12 months in five participating hospitals in Korea. Results: Of 173,057 hospitalized patients included for analysis, 668 (0.39%) experienced the composite outcome. The discrimination for the composite outcome for MEWS (AUC, 0.777;95% confidence interval [CI], 0.770–0.781) was higher than that for qSOFA (AUC, 0.684;95% CI, 0.676–0.686; P < 0.001). In addition, MEWS was better for prediction of IHCA (AUC, 0.792; 95% CI, 0.781–0.795 vs. AUC, 0.640; 95% CI, 0.625–0.645; P < 0.001) and unexpected ICU transfer (AUC, 0.767; 95% CI, 0.760–0.773 vs. AUC, 0.716; 95% CI, 0.707–0.718; P < 0.001) than qSOFA. Using the MEWS at a cutoff of ≥ 5 would correctly reclassify 3.7% of patients from qSOFA score ≥ 2. Most patients met MEWS ≥ 5 criteria 13 hours before the composite outcome compared with 11 hours for qSOFA score ≥ 2. Conclusion MEWS is more accurate that qSOFA score for predicting IHCA or unexpected ICU transfer in patients outside the ICU. Our study suggests that qSOFA should not replace MEWS for identifying patients in the general wards at risk of poor outcome.

BACKGROUND AND OBJECTIVES: This study aimed to confirm the effects of traditional holidays on the incidence and outcomes of out-of-hospital cardiac arrest (OHCA) in South Korea. METHODS: We studied 95,066 OHCAs of cardiac cause from a nationwide, prospective study from the Korea OHCA Registry from January 2012 to December 2016. We compared the incidence of OHCA, in-hospital mortality, and neurologic outcomes between traditional holidays, Seollal (Lunar New Year's Day) and Chuseok (Korean Thanksgiving Day), and other day types (weekday, weekend, and public holiday). RESULTS: OHCA occurred more frequently on traditional holidays than on the other days. The median OHCA incidence were 51.0 (interquartile range [IQR], 44.0–58.0), 53.0 (IQR, 46.0–60.5), 52.5 (IQR, 45.3–59.8), and 60.0 (IQR, 52.0–69.0) cases/day on weekday, weekend, public holiday, and traditional holiday, respectively (p<0.001). The OHCA occurred more often at home rather than in public place, lesser bystander cardiopulmonary resuscitation (CPR) was performed, and the rate of cessation of CPR within 20 minutes without recovery of spontaneous circulation was higher on traditional holiday. After multivariable adjustment, traditional holiday was associated with higher in-hospital mortality (adjusted hazard ratio [HR], 1.339; 95% confidence interval [CI], 1.058–1.704; p=0.016) but better neurologic outcomes (adjusted HR, 0.503; 95% CI, 0.281–0.894; p=0.020) than weekdays. CONCLUSIONS: The incidence of OHCAs was associated with day types in a year. It occurred more frequently on traditional holidays than on other day types. It was associated with higher in-hospital mortality and favorable neurologic outcomes than weekday.
Cardiopulmonary Resuscitation ; Epidemiology ; Heart Arrest ; Holidays ; Hospital Mortality ; Incidence ; Korea ; Mortality ; Out-of-Hospital Cardiac Arrest ; Prospective Studies

BACKGROUND AND OBJECTIVES: Screening and early diagnosis for heart failure (HF) are critical. However, conventional screening diagnostic methods have limitations, and electrocardiography (ECG)-based HF identification may be helpful. This study aimed to develop and validate a deep-learning algorithm for ECG-based HF identification (DEHF). METHODS: The study involved 2 hospitals and 55,163 ECGs of 22,765 patients who performed echocardiography within 4 weeks were study subjects. ECGs were divided into derivation and validation data. Demographic and ECG features were used as predictive variables. The primary endpoint was detection of HF with reduced ejection fraction (HFrEF; ejection fraction [EF]≤40%), and the secondary endpoint was HF with mid-range to reduced EF (≤50%). We developed the DEHF using derivation data and the algorithm representing the risk of HF between 0 and 1. We confirmed accuracy and compared logistic regression (LR) and random forest (RF) analyses using validation data. RESULTS: The area under the receiver operating characteristic curves (AUROCs) of DEHF for identification of HFrEF were 0.843 (95% confidence interval, 0.840–0.845) and 0.889 (0.887–0.891) for internal and external validation, respectively, and these results significantly outperformed those of LR (0.800 [0.797–0.803], 0.847 [0.844–0.850]) and RF (0.807 [0.804–0.810], 0.853 [0.850–0.855]) analyses. The AUROCs of deep learning for identification of the secondary endpoint was 0.821 (0.819–0.823) and 0.850 (0.848–0.852) for internal and external validation, respectively, and these results significantly outperformed those of LR and RF. CONCLUSIONS: The deep-learning algorithm accurately identified HF using ECG features and outperformed other machine-learning methods.
Artificial Intelligence ; Early Diagnosis ; Echocardiography ; Electrocardiography ; Forests ; Heart Failure ; Heart ; Humans ; Learning ; Logistic Models ; Machine Learning ; Mass Screening ; ROC Curve

BACKGROUND AND OBJECTIVES: Screening and early diagnosis for heart failure (HF) are critical. However, conventional screening diagnostic methods have limitations, and electrocardiography (ECG)-based HF identification may be helpful. This study aimed to develop and validate a deep-learning algorithm for ECG-based HF identification (DEHF). METHODS: The study involved 2 hospitals and 55,163 ECGs of 22,765 patients who performed echocardiography within 4 weeks were study subjects. ECGs were divided into derivation and validation data. Demographic and ECG features were used as predictive variables. The primary endpoint was detection of HF with reduced ejection fraction (HFrEF; ejection fraction [EF]≤40%), and the secondary endpoint was HF with mid-range to reduced EF (≤50%). We developed the DEHF using derivation data and the algorithm representing the risk of HF between 0 and 1. We confirmed accuracy and compared logistic regression (LR) and random forest (RF) analyses using validation data. RESULTS: The area under the receiver operating characteristic curves (AUROCs) of DEHF for identification of HFrEF were 0.843 (95% confidence interval, 0.840–0.845) and 0.889 (0.887–0.891) for internal and external validation, respectively, and these results significantly outperformed those of LR (0.800 [0.797–0.803], 0.847 [0.844–0.850]) and RF (0.807 [0.804–0.810], 0.853 [0.850–0.855]) analyses. The AUROCs of deep learning for identification of the secondary endpoint was 0.821 (0.819–0.823) and 0.850 (0.848–0.852) for internal and external validation, respectively, and these results significantly outperformed those of LR and RF. CONCLUSIONS The deep-learning algorithm accurately identified HF using ECG features and outperformed other machine-learning methods.

BACKGROUND AND OBJECTIVES: This study aimed to confirm the effects of traditional holidays on the incidence and outcomes of out-of-hospital cardiac arrest (OHCA) in South Korea. METHODS: We studied 95,066 OHCAs of cardiac cause from a nationwide, prospective study from the Korea OHCA Registry from January 2012 to December 2016. We compared the incidence of OHCA, in-hospital mortality, and neurologic outcomes between traditional holidays, Seollal (Lunar New Year's Day) and Chuseok (Korean Thanksgiving Day), and other day types (weekday, weekend, and public holiday). RESULTS: OHCA occurred more frequently on traditional holidays than on the other days. The median OHCA incidence were 51.0 (interquartile range [IQR], 44.0–58.0), 53.0 (IQR, 46.0–60.5), 52.5 (IQR, 45.3–59.8), and 60.0 (IQR, 52.0–69.0) cases/day on weekday, weekend, public holiday, and traditional holiday, respectively (p<0.001). The OHCA occurred more often at home rather than in public place, lesser bystander cardiopulmonary resuscitation (CPR) was performed, and the rate of cessation of CPR within 20 minutes without recovery of spontaneous circulation was higher on traditional holiday. After multivariable adjustment, traditional holiday was associated with higher in-hospital mortality (adjusted hazard ratio [HR], 1.339; 95% confidence interval [CI], 1.058–1.704; p=0.016) but better neurologic outcomes (adjusted HR, 0.503; 95% CI, 0.281–0.894; p=0.020) than weekdays. CONCLUSIONS The incidence of OHCAs was associated with day types in a year. It occurred more frequently on traditional holidays than on other day types. It was associated with higher in-hospital mortality and favorable neurologic outcomes than weekday.

A 42-year-old man was diagnosed with cancer of the right lower lung lobe with a posteparterial type of tracheal bronchus, in which the posterior segmental bronchus of the right upper lobe arose from the distal bronchus intermedius. A mass involved the distal bronchus intermedius, requiring a right lower bilobectomy with an additional posterior segmental resection of the right upper lung lobe. Thus, we performed a right lower bilobectomy and sleeve anastomosis of the posterior segmental bronchus of the right upper lobe to the proximal bronchus intermedius, sparing the pulmonary parenchyma of the same lobe.
Adult ; Bronchi* ; Humans ; Lung Neoplasms* ; Lung* ; Respiratory System Abnormalities ; Trachea