The impact of pulmonary tuberculosis on SARS-CoV-2 infection: a nationwide cohort study.

Background: The interaction between COVID-19 and tuberculosis (TB) is not yet fully understood, and large-scale research on the mortality outcome of such dual infection has been limited. This study aimed to investigate the impact of PTB on mortality among patients with COVID-19 within a Korean population by conducting an extensive analysis of a nationwide large dataset. Method: We investigated the mortality and disease severity among COVID-19 patients who had PTB in South Korea. This study analyzed 462,444 out of 566,494 COVID-19 patients identified between January 2020 and December 2021. Result: A total of 203 COVID-19 with PTB patients and 812 matched COVID-19 without PTB were analyzed using 1:4 propensity score matching. COVID-19 patients with PTB exhibited higher in-hospital mortality (odds ratio (OR) 3.02, 95% confidence interval (CI) 1.45-6.27, p-value = 0.003) and were at increased risk of requiring conventional oxygen therapy (OR 1.57, 95% CI 1.10-2.25, p-value = 0.013) as well as high flow nasal cannula (HFNC) or noninvasive ventilation (NIV) oxygen therapy (OR 1.91, 95 CI 1.10-3.32, p-value = 0.022) compared to those without PTB. Compared to matched COVID-19 without PTB, co-infected patients showed increased mortality rates across various timeframes, including during hospitalization, and at 30 day and 90 day intervals. In-hospital mortality rates were particularly elevated among women, individuals with malignancy, and those with lower incomes. Furthermore, the increased in-hospital mortality among PTB patients persisted irrespective of the timing of TB diagnosis or vaccination status against COVID-19. Conclusion: We suggest that physicians be aware of the risk of mortality and severity among COVID-19 patients with PTB; coinfection with COVID-19 is a critical situation that remains to be further explored and needs more attention in countries with an intermediate to high PTB burden.

Synergistic Effect of SARS-CoV-2 Infection and COVID-19 Vaccination on the Risk of Venous Thromboembolism.

BACKGROUND: Previous studies have reported a greater risk of venous thromboembolism among patients infected with SARS-CoV-2 and those who received COVID-19 vaccination. Nevertheless, there is a lack of understanding regarding the interaction effect on the risk of venous thromboembolism occurrence between SARS-CoV-2 infection and COVID-19 vaccination. METHODS: This was a retrospective cohort study including adult patients with confirmed SARS-CoV-2 infection between October 2020 and September 2021. Patients with confirmed SARS-CoV-2 infection were selected and matched 1:1 by age and sex with individuals who were not infected during the same period. A Cox proportional hazards model was used to analyze the venous thromboembolism risk. RESULTS: The study included 422546 individuals who were divided into 4 groups; the interaction group defined by having SARS-CoV-2 infection within 90 days following COVID-19 vaccination, the infection group defined by no vaccination before 90 days of SARS-CoV-2 infection, the vaccination group defined by COVID-19 vaccination without SARS-CoV-2 infection, and the reference group defined by neither COVID-19 vaccination nor SARS-CoV-2 infection. The results showed that the adjusted hazard ratio (aHR) of the interaction group was 29.71 (95% confidence interval [CI], 22.95-38.47), while the aHRs of the infection group and the vaccination group were 6.66 (95% CI, 5.18-8.58) and 2.31 (95% CI, 1.78-3.00), respectively. CONCLUSIONS: A synergistic effect on the risk of venous thromboembolism was suggested when individuals were infected with SARS-CoV-2 within 90 days following COVID-19 vaccination.

Prediction of hearing recovery with deep learning algorithm in sudden sensorineural hearing loss.

This study aimed to establish a deep learning-based predictive model for the prognosis of idiopathic sudden sensorineural hearing loss (SSNHL). Data from 1108 patients with SSNHL between January 2015 and May 2023 were retrospectively analyzed. Patients underwent standardized treatment protocols including high-dose steroid therapy and hearing outcomes were assessed after three months using Siegel's criteria and the American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS) classification. For predicting patient recovery, a two-layered classification process was implemented. Initially, a set of 22 Multilayer Perceptrons (MLP) networks was employed to categorize the patients. The outcomes from this initial categorization were subsequently relayed to a second-layer meta-classifier for final prognosis determination. The validity of this methodology was ascertained through a K-fold cross-validation procedure executed with 10 distinct splits. The prediction model for complete recovery, based on Siegel's criteria, demonstrated an accuracy of 0.892 and area under the curve (AUC) of 0.922. For the class A prediction, according to AAO-HNS classification, the model showed an accuracy of 0.847 and AUC of 0.918. These results suggest that the model may have the potential to contribute to the establishment of tailored patient management strategies by predicting hearing recovery in patients with SSNHL.

Kidney, ureter, and urinary bladder segmentation based on non-contrast enhanced computed tomography images using modified U-Net.

This study was performed to segment the urinary system as the basis for diagnosing urinary system diseases on non-contrast computed tomography (CT). This study was conducted with images obtained between January 2016 and December 2020. During the study period, non-contrast abdominopelvic CT scans of patients and diagnosed and treated with urinary stones at the emergency departments of two institutions were collected. Region of interest extraction was first performed, and urinary system segmentation was performed using a modified U-Net. Thereafter, fivefold cross-validation was performed to evaluate the robustness of the model performance. In fivefold cross-validation results of the segmentation of the urinary system, the average dice coefficient was 0.8673, and the dice coefficients for each class (kidney, ureter, and urinary bladder) were 0.9651, 0.7172, and 0.9196, respectively. In the test dataset, the average dice coefficient of best performing model in fivefold cross validation for whole urinary system was 0.8623, and the dice coefficients for each class (kidney, ureter, and urinary bladder) were 0.9613, 0.7225, and 0.9032, respectively. The segmentation of the urinary system using the modified U-Net proposed in this study could be the basis for the detection of kidney, ureter, and urinary bladder lesions, such as stones and tumours, through machine learning.

Diagnostic Accuracy of Plasma Renin Concentration and Renin Activity in Predicting Mortality and Kidney Outcomes in Patients With Septic Shock and Hypoperfusion or Hypotension: A Multicenter, Prospective, Observational Study.

Background: Lactate is a commonly used biomarker for sepsis, although it has limitations in certain cases, suggesting the need for novel biomarkers. We evaluated the diagnostic accuracy of plasma renin concentration and renin activity for mortality and kidney outcomes in patients with sepsis with hypoperfusion or hypotension. Methods: This was a multicenter, prospective, observational study of 117 patients with septic shock treated at three tertiary emergency departments between September 2021 and October 2022. The accuracy of renin activity, renin, and lactate concentrations in predicting 28-day mortality, acute kidney injury (AKI), and renal replacement requirement was assessed using the area under the ROC curve (AUC) analysis. Results: The AUCs of initial renin activity, renin, and lactate concentrations for predicting 28-day mortality were 0.66 (95% confidence interval [CI], 0.55-0.77), 0.63 (95% CI, 0.52-0.75), and 0.65 (95% CI, 0.53-0.77), respectively, and those at 24 hrs were 0.74 (95% CI, 0.62-0.86), 0.70 (95% CI, 0.56-0.83), and 0.67 (95% CI, 0.54-0.79). Renin concentrations and renin activity outperformed initial lactate concentrations in predicting AKI within 14 days. The AUCs of renin and lactate concentrations were 0.71 (95% CI, 0.61-0.80) and 0.57 (95% CI, 0.46-0.67), respectively (P=0.030). The AUC of renin activity (0.70; 95% CI, 0.60-0.80) was also higher than that of lactate concentration (P=0.044). Conclusions: Renin concentration and renin activity show comparable performance to lactate concentration in predicting 28-day mortality in patients with septic shock but superior performance in predicting AKI.

The Incidence and Outcomes of Out-of-Hospital Cardiac Arrest During the COVID-19 Pandemic in South Korea: Multicenter Registry Study.

BACKGROUND: The COVID-19 pandemic has profoundly affected out-of-hospital cardiac arrest (OHCA) and disrupted the chain of survival. Even after the end of the pandemic, the risk of new variants and surges persists. Analyzing the characteristics of OHCA during the pandemic is important to prepare for the next pandemic and to avoid repeated negative outcomes. However, previous studies have yielded somewhat varied results, depending on the health care system or the specific characteristics of social structures. OBJECTIVE: We aimed to investigate and compare the incidence, outcomes, and characteristics of OHCA during the prepandemic and pandemic periods using data from a nationwide multicenter OHCA registry. METHODS: We conducted a multicenter, retrospective, observational study using data from the Korean Cardiac Arrest Resuscitation Consortium (KoCARC) registry. This study included adult patients with OHCA in South Korea across 3 distinct 1-year periods: the prepandemic period (from January to December 2019), early phase pandemic period (from July 2020 to June 2021), and late phase pandemic period (from July 2021 to June 2022). We extracted and contrasted the characteristics of patients with OHCA, prehospital time factors, and outcomes for the patients across these 3 periods. The primary outcomes were survival to hospital admission and survival to hospital discharge. The secondary outcome was good neurological outcome. RESULTS: From the 3 designated periods, a total of 9031 adult patients with OHCA were eligible for analysis (prepandemic: n=2728; early pandemic: n=2954; and late pandemic: n=3349). Witnessed arrest (P<.001) and arrest at home or residence (P=.001) were significantly more frequent during the pandemic period than during the prepandemic period, and automated external defibrillator use by bystanders was lower in the early phase of the pandemic than during other periods. As the pandemic advanced, the rates of the first monitored shockable rhythm (P=.10) and prehospital endotracheal intubation (P<.001) decreased significantly. Time from cardiac arrest cognition to emergency department arrival increased sequentially (prepandemic: 33 min; early pandemic: 35 min; and late pandemic: 36 min; P<.001). Both survival and neurological outcomes worsened as the pandemic progressed, with survival to discharge showing the largest statistical difference (prepandemic: 385/2728, 14.1%; early pandemic: 355/2954, 12%; and late pandemic: 392/3349, 11.7%; P=.01). Additionally, none of the outcomes differed significantly between the early and late phase pandemic periods (all P>.05). CONCLUSIONS: During the pandemic, especially amid community COVID-19 surges, the incidence of OHCA increased while survival rates and good neurological outcome at discharge decreased. Prehospital OHCA factors, which are directly related to OHCA prognosis, were adversely affected by the pandemic. Ongoing discussions are needed to maintain the chain of survival in the event of a new pandemic. TRIAL REGISTRATION: ClinicalTrials.gov NCT03222999; https://classic.clinicaltrials.gov/ct2/show/NCT03222999.

Correlation between Carboxyhemoglobin Levels Measured by Blood Gas Analysis and by Multiwave Pulse Oximetry.

Carbon monoxide (CO) poisoning is difficult to diagnose owing to its nonspecific symptoms. Multiwave pulse oximetry can be used to quickly screen patients for CO poisoning. However, few studies have analyzed patients with CO poisoning who presented to the emergency department (ED). The primary aim of our study was to determine the correlation between COHb levels measured in blood gas analysis and COHb levels measured in multiwave pulse oximetry. Secondary aims were the sensitivity and specificity of the COHb level cutoff value using multiwave pulse oximetry to predict a 25% COHb level in blood gas analysis. This single-center retrospective observational study included patients with CO poisoning who visited the ED of a university-affiliated hospital in Seoul, Korea between July 2021 and June 2023. COHb poisoning was determined using blood gas analysis and multiwave pulse oximetry. The correlation of COHb levels between the two tests was evaluated using correlation analysis. The area under the receiver operating characteristic curve (AUC) of multiwave pulse oximetry was calculated to predict COHb levels from the blood gas analysis. The optimal cutoff values, sensitivity, and specificity of COHb were determined. A total of 224 patients who had COHb levels measured using both multiwave pulse oximetry and blood gas analysis were included in the analysis. In the correlation analysis, COHb showed a high positive correlation with COHb measured using blood gas analysis (Spearman correlation coefficient = 0.86, p < 0.001). The AUC of COHb measured by multiwave pulse oximetry to predict 25% of the COHb level (which can be an indication of hyperbaric oxygen treatment) measured by blood gas analysis was 0.916. When the COHb levels measured with multiwave pulse oximetry were 20% the sensitivity was 81% and the specificity was 83%, and when the COHb levels were 25% the sensitivity was 50% and the specificity was 95%. The COHb value measured using multiwave pulse oximetry blood gas analysis showed a high correlation. However, additional research using large-scale studies is required for validation.

The prognostic value of the phosphate-to-albumin ratio in patients with OHCA: A multicenter observational study.

PURPOSE: In patients with out-of-hospital cardiac arrest (OHCA), early and accurate outcome prediction is crucial for making treatment decisions and informing their relatives. A previous study reported an association between high phosphate levels and unfavorable neurological outcomes after return of spontaneous circulation (ROSC); however, its prognostic value was insufficient when used independently. Therefore, this study aimed to validate the usefulness of the phosphate-to-albumin ratio (PAR) in predicting neurological outcomes and in-hospital mortality by incorporating albumin, another known prognostic indicator. MATERIALS AND METHODS: This multicenter observational study included adult OHCA survivors from October 2015 to June 2021. The primary endpoint was an unfavorable neurological outcome at hospital discharge, defined as a cerebral performance category score of 3-5. The in-hospital mortality rates were also evaluated. RESULTS: Of the 2397 adult OHCA survivors, PAR differed significantly between the unfavorable and favorable neurological outcome groups, as well as between the non-survival and survival to hospital discharge groups (2.4 vs 1.4, 2.5 vs 1.6, respectively). The area under the receiver operating characteristic curve (AUROC) value of the PAR for predicting unfavorable neurological outcome was 0.81 (95% confidence interval [CI], 0.79-0.83), and the AUROC value for predicting in-hospital mortality was 0.76 (95% CI, 0.74-0.78). In multivariable analysis, the PAR was independently associated with unfavorable neurological outcome (odds ratio [OR] 1.30, 95% CI 1.15-1.37; p < 0.001) and in-hospital mortality (OR 1.24, 95% CI 1.12-1.38; p < 0.001). CONCLUSION: The PAR is a readily obtainable and independent prognostic indicator for patients with ROSC after OHCA, helping healthcare providers in predicting outcomes.

Deep learning-based natural language processing for detecting medical symptoms and histories in emergency patient triage.

OBJECTIVE: The manual recording of electronic health records (EHRs) by clinicians in the emergency department (ED) is time-consuming and challenging. In light of recent advancements in large language models (LLMs) such as GPT and BERT, this study aimed to design and validate LLMs for automatic clinical diagnoses. The models were designed to identify 12 medical symptoms and 2 patient histories from simulated clinician-patient conversations within 6 primary symptom scenarios in emergency triage rooms. MATERIALS AND METHOD: We developed classification models by fine-tuning BERT, a transformer-based pre-trained model. We subsequently analyzed these models using eXplainable artificial intelligence (XAI) and the Shapley additive explanation (SHAP) method. A Turing test was conducted to ascertain the reliability of the XAI results by comparing them to the outcomes of tasks performed and explained by medical workers. An emergency medicine specialist assessed the results of both XAI and the medical workers. RESULTS: We fine-tuned four pre-trained LLMs and compared their classification performance. The KLUE-RoBERTa-based model demonstrated the highest performance (F1-score: 0.965, AUROC: 0.893) on human-transcribed script data. The XAI results using SHAP showed an average Jaccard similarity of 0.722 when compared with explanations of medical workers for 15 samples. The Turing test results revealed a small 6% gap, with XAI and medical workers receiving the mean scores of 3.327 and 3.52, respectively. CONCLUSION: This paper highlights the potential of LLMs for automatic EHR recording in Korean EDs. The KLUE-RoBERTa-based model demonstrated superior classification performance. Furthermore, XAI using SHAP provided reliable explanations for model outputs. The reliability of these explanations was confirmed by a Turing test.

Predicting acute brain lesions on magnetic resonance imaging in acute carbon monoxide poisoning: a multicenter prospective observational study.

An acute brain lesion (ABL) identified by brain magnetic resonance imaging (MRI) after acute carbon monoxide (CO) poisoning is a strong prognostic factor for the development of delayed neuropsychiatric syndrome (DNS). This study aimed to identify predictors of ABLs on MRI in patients with acute CO poisoning. This was a multicenter prospective registry-based observational study conducted at two tertiary hospitals. A total of 1,034 patients were included. Multivariable logistic regression analysis showed that loss of consciousness (LOC) (adjusted odds ratio [aOR] 2.68, 95% Confidence Interval [CI]: 1.49-5.06), Glasgow Coma Scale (GCS) score < 9 (aOR 2.41, 95% CI: 1.49-3.91), troponin-I (TnI) (aOR 1.22, 95% CI: 1.08-1.41), CO exposure duration (aOR 1.09, 95% CI: 1.05-1.13), and white blood cell (WBC) (aOR 1.05, 95% CI: 1.01-1.09) were independent predictors of ABLs on MRI. LOC, GCS score, TnI, CO exposure duration, and WBC count can be useful predictors of ABLs on MRI in patients with acute CO poisoning, helping clinicians decide the need for an MRI scan or transfer the patient to an appropriate institution for MRI or hyperbaric oxygen therapy.

Diagnostic performance of rapid antigen tests for SARS-CoV-2 transmission risk based on cycle threshold values in the emergency department.

BACKGROUND: This study aimed to investigate the diagnostic performance of the rapid antigen test (RAT) for screening patients with cycle threshold (Ct) values of SARS-CoV-2 reverse transcription-polymerase chain reaction (RT-PCR) in the emergency department. Previous studies have shown that Ct values could be used as indicators of infectiousness. Therefore, we considered the Ct value an indicator of potential infectiousness. METHODS: This single-center retrospective observational study was conducted between January 1, 2020, and March 31, 2022. Patients who underwent both RT-PCR and RAT for the diagnosis of COVID-19 were included. Patients with negative RT-PCR results were excluded. Patients with Ct values lower than 26 and 30 were considered potentially infectious for COVID-19. RESULT: A total of 386 patients were analyzed. At Ct value cutoffs of 26 and 30, the result of the RAT showed a sensitivity of 82% and 74%, specificity of 84% and 89%, and area under the curve (AUC) of 0.829 and 0.813, respectively, in the receiver operating characteristic curve. However, the NPV was relatively low at 55% and 25%. CONCLUSION: The RAT might be a rapid screening tool for detecting patients with the infectiousness of SARS-CoV-2. However, considering the low NPV, it is challenging to depend only on a negative test result from an antigen test to terminate quarantine. Clinicians should consider additional factors, such as the duration of symptoms and the immunocompromised state, for SARS-CoV-2 transmission.

Arterial Blood Gas Analysis for Survival Prediction in Pediatric Patients with Out-of-Hospital Cardiac Arrest.

Arterial blood gas analysis (ABGA) is one of the few tests performed during cardiopulmonary resuscitation (CPR). There have been some studies on the prediction of survival outcomes in adult out-of-hospital cardiac arrest (OHCA) patients during CPR using ABGA results. However, in pediatric OHCA patients, the prognosis of survival outcome based on ABGA results during CPR remains unclear. We retrospectively analyzed prospectively collected data from the Korean Cardiac Arrest Resuscitation Consortium (KoCARC) registry, a multicenter OHCA registry of Republic of Korea. We analyzed 108 pediatric (age < 19 years) OHCA patients between October 2015 and June 2022. Using multivariable logistic regression, an adjusted odds ratio (aOR) was obtained to validate the ABGA results of survival to hospital admission and survival to discharge. The variables associated with survival to hospital admission were non-comorbidities (aOR 3.03, 95% confidence interval (CI) 1.22-7.53, p = 0.017) and PaO2 > 45.750 mmHg (aOR 2.69, 95% CI 1.13-6.42, p = 0.026). There was no variable that was statistically significant association with survival to discharge. PaO2 > 47.750 mmHg and non-comorbidities may serve as an independent prognostic factor for survival to hospital admission in pediatric OHCA patients. However, the number of cases analyzed in our study was relatively small, and there have been few studies investigating the association between ABGA results during CPR and the survival outcome of pediatric OHCA patients. Therefore, further large-scale studies are needed.

Association of Socioeconomic Status With Long-Term Outcome in Survivors After Out-of-Hospital Cardiac Arrest: Nationwide Population-Based Longitudinal Study.

BACKGROUND: Out-of-hospital cardiac arrest (OHCA) is a major public health problem and a leading cause of death worldwide. Previous studies have focused on improving the survival of people who have had OHCA by analyzing short-term survival outcomes, such as the return of spontaneous circulation, 30-day survival, and survival to discharge. Research has been conducted on prehospital prognostic factors to improve the survival of patients with OHCA, among which the association between socioeconomic status (SES) and survival has been reported. SES could affect bystander cardiopulmonary resuscitation rates and whether OHCA is witnessed, and low cardiopulmonary resuscitation education rates are associated with low SES. It has been reported that areas with high SES have shorter hospital transfer times and more public defibrillators per person. Previous studies have shown the impact of SES disparities on the short-term survival of patients with OHCA. However, understanding the impact of SES on the long-term prognosis of OHCA survivors remains limited. As long-term outcomes are more indicative of a patient's ongoing health care needs and the burden on public health than short-term outcomes, understanding the long-term prognosis of OHCA survivors is important. OBJECTIVE: This study aimed to identify whether SES influenced the long-term outcomes of OHCA. METHODS: Using health claims data obtained from the National Health Insurance (NHI) service in Korea, we included OHCA survivors who were hospitalized between January 2005 and December 2015. The patients were divided into 2 groups: NHI and Medical Aid (MA) groups, with the MA group defined as having a low SES. Cumulative mortality was estimated using the Kaplan-Meier method, and a Cox proportional hazards model was used to evaluate the impact of SES on long-term mortality. A subgroup analysis was performed based on whether cardiac procedures were performed. RESULTS: We followed 4873 OHCA survivors for up to 14 years (median of 3.3 years). The Kaplan-Meier survival curve showed that the MA group had a significantly decreased long-term survival rate compared to the NHI group. With an adjusted hazard ratio (aHR) of 1.52 (95% CI 1.35-1.72), low SES was associated with increased long-term mortality. The overall mortality rate of the patients who underwent cardiac procedures in the MA group was significantly higher than that of the NHI group (aHR 1.72, 95% CI 1.05-2.82). The overall mortality rate of patients without cardiac procedures was also increased in the MA group compared to the NHI group (aHR 1.39, 95% CI 1.23-1.58). CONCLUSIONS: OHCA survivors with low SES had an increased risk of poor long-term outcomes compared with those with higher SES. OHCA survivors with low SES who have undergone cardiac procedures need considerable care for long-term survival.

Effectiveness of Initial Troponin I and Brain Natriuretic Peptide Levels as Biomarkers for Predicting Delayed Neuropsychiatric Sequelae in Patients with CO Poisoning: A Retrospective Multicenter Observational Study.

BACKGROUND: Delayed neuropsychiatric sequelae (DNS) are a severe complication of carbon monoxide (CO) poisoning, and predicting DNS is difficult. This study aimed to investigate whether cardiac markers can be used as biomarkers to predict DNS occurrence following acute CO poisoning. METHODS: This was a retrospective observational study that included patients with acute CO poisoning who visited two emergency medical centers in Korea from January 2008 to December 2020. The primary outcome was whether the occurrence of DNS was associated with laboratory results. RESULTS: Of the 1327 patients with CO poisoning, 967 patients were included. Troponin I and BNP were significantly higher in the DNS group. As a result of multivariate logistic regression analysis, it was found that troponin I, mentality, creatine kinase, brain natriuretic peptide, and lactate levels independently influenced DNS occurrence in CO poisoning patients. The adjusted odds ratios for DNS occurrence were 2.12 (95% CI 1.31-3.47, p = 0.002) for troponin I and 2.80 (95% CI 1.81-3.47, p < 0.001) for BNP. CONCLUSION: Troponin I and BNP might be useful biomarkers for predicting the occurrence of DNS in patients with acute CO poisoning. This finding can help to identify high-risk patients who require close monitoring and early intervention to prevent DNS.

Analysis of Anxiety or Depression and Long-term Mortality Among Survivors of Out-of-Hospital Cardiac Arrest.

Importance: The recent American Heart Association guidelines added a sixth link in the chain of survival highlighting recovery and emphasized the importance of psychiatric outcome and recovery for survivors of out-of-hospital cardiac arrest (OHCA). The prevalence of psychiatric disorders among this population was higher than that in the general population. Objective: To examine the prevalence of depression or anxiety and the association of these conditions with long-term mortality among individuals who survive OHCA. Design, Setting, and Participants: A longitudinal population-based cohort study was conducted to analyze long-term prognosis in patients hospitalized for OHCA between January 1, 2005, and December 31, 2015, who survived for 1 year or longer. Patients with cardiac arrest due to traumatic or nonmedical causes, such as injuries, poisoning, asphyxiation, burns, or anaphylaxis, were excluded. Data were extracted on depression or anxiety diagnoses in this population within 1 year from the database of the Korean National Health Insurance Service and analyzed April 7, 2022, and reanalyzed January 19 to 20, 2023. Main Outcomes and Measures: Follow-up data were obtained for up to 14 years, and the primary outcome was long-term cumulative mortality. Long-term mortality among patients with and without a diagnosis of depression or anxiety were evaluated. Results: The analysis included 2373 patients; 1860 (78.4%) were male, and the median age was 53.0 (IQR, 44.0-62.0) years . A total of 397 (16.7%) patients were diagnosed with depression or anxiety, 251 (10.6%) were diagnosed with depression, and 227 (9.6%) were diagnosed with anxiety. The incidence of long-term mortality was significantly higher in the group diagnosed with depression or anxiety than in the group without depression or anxiety (141 of 397 [35.5%] vs 534 of 1976 [27.0%]; P = .001). With multivariate Cox proportional hazards regression analysis, the adjusted hazard ratio of long-term mortality for total patients with depression or anxiety was 1.41 (95% CI, 1.17-1.70); depression, 1.44 (95% CI, 1.16-1.79); and anxiety, 1.20 (95% CI, 0.94-1.53). Conclusions and Relevance: In this study, among the patients who experienced OHCA, those diagnosed with depression or anxiety had higher long-term mortality rates than those without depression or anxiety. These findings suggest that psychological and neurologic rehabilitation intervention for survivors of OHCA may be needed to improve long-term survival.

Quercetin Attenuates the Production of Pro-Inflammatory Cytokines in H292 Human Lung Epithelial Cells Infected with Pseudomonas aeruginosa by Modulating ExoS Production.

The type three secretion system (T3SS) is a major virulence system of Pseudomonas aeruginosa (P. aeruginosa). The effector protein Exotoxin S (ExoS) produced by P. aeruginosa is secreted into the host cells via the T3SS. For the purpose of an experiment on inhibitors with regard to ExoS secretion, we developed a sandwich-type enzyme-linked immunosorbent assay (ELISA) system. Quercetin was selected because it has a prominent ExoS inhibition effect and also is known to have anti-inflammatory and antioxidant effects on mammalian cells. In this study, we investigated the effects of quercetin on the expression and secretion of ExoS using ELISA and Western blot analysis methods. The results showed that the secretion of ExoS was significantly decreased by 10 and 20 µM of quercetin. Also, popB, popD, pscF, and pcrV which are composed of the T3SS needle, are reduced by quercetin at the mRNA level. We also confirmed the inhibitory effect of quercetin on cytokines (IL-6, IL-1ß, and IL-18) in P. aeruginosa-infected H292 cells by real-time polymerase chain reaction (PCR) and ELISA. Collectively, quercetin inhibits the secretion of ExoS by reducing both ExoS production and the expression of the needle protein of T3SS. Furthermore, these results suggest that quercetin has the potential to be used as an anti-toxic treatment for the inflammatory disease caused by P. aeruginosa infection.

Effect of Contrast Level and Image Format on a Deep Learning Algorithm for the Detection of Pneumothorax with Chest Radiography.

Under the black-box nature in the deep learning model, it is uncertain how the change in contrast level and format affects the performance. We aimed to investigate the effect of contrast level and image format on the effectiveness of deep learning for diagnosing pneumothorax on chest radiographs. We collected 3316 images (1016 pneumothorax and 2300 normal images), and all images were set to the standard contrast level (100%) and stored in the Digital Imaging and Communication in Medicine and Joint Photographic Experts Group (JPEG) formats. Data were randomly separated into 80% of training and 20% of test sets, and the contrast of images in the test set was changed to 5 levels (50%, 75%, 100%, 125%, and 150%). We trained the model to detect pneumothorax using ResNet-50 with 100% level images and tested with 5-level images in the two formats. While comparing the overall performance between each contrast level in the two formats, the area under the receiver-operating characteristic curve (AUC) was significantly different (all p < 0.001) except between 125 and 150% in JPEG format (p = 0.382). When comparing the two formats at same contrast levels, AUC was significantly different (all p < 0.001) except 50% and 100% (p = 0.079 and p = 0.082, respectively). The contrast level and format of medical images could influence the performance of the deep learning model. It is required to train with various contrast levels and formats of image, and further image processing for improvement and maintenance of the performance.

The impact of the COVID-19 pandemic on in-hospital mortality in patients admitted through the emergency department.

OBJECTIVE: The COVID-19 pandemic might have adversely affected outcomes of patients in emergency departments (EDs). The aim of this study is to evaluate the impact of the COVID-19 pandemic on in patients admitted through the emergency department. METHODS: This study is a single-center, retrospective, observational cohort study. We compared the prognosis of patients admitted through the ED before the COVID-19 pandemic (November 2018 to June 2019) and after COVID-19 (November 2020 to June 2021). The primary outcome was in-hospital mortality. Multivariable logistic regression analysis was performed to determine whether the COVID-19 pandemic was independently associated with patient prognosis. RESULTS: The number of patients admitted through the ED before and after COVID-19 was 5,333 and 4,625, respectively. The mean ED length of stay before and after COVID-19 was 401 and 442 minutes, respectively (P<0.001). The number of in-hospital deaths before and after COVID-19 were 269 (5.0%) and 322 (7.0%), respectively (P<0.001). Multivariable logistic regression analysis showed that the COVID-19 period was significantly associated with higher in-hospital mortality (adjusted odds ratio, 1.37; 95% confidence interval, 1.12-1.67; P=0.002). CONCLUSION: In the COVID-19 period, in-hospital mortality increased compared to that before COVID-19 among hospitalized ED patients.

Feasibility of the Machine Learning Network to Diagnose Tympanic Membrane Lesions without Coding Experience.

A machine learning platform operated without coding knowledge (Teachable machine®) has been introduced. The aims of the present study were to assess the performance of the Teachable machine® for diagnosing tympanic membrane lesions. A total of 3024 tympanic membrane images were used to train and validate the diagnostic performance of the network. Tympanic membrane images were labeled as normal, otitis media with effusion (OME), chronic otitis media (COM), and cholesteatoma. According to the complexity of the categorization, Level I refers to normal versus abnormal tympanic membrane; Level II was defined as normal, OME, or COM + cholesteatoma; and Level III distinguishes between all four pathologies. In addition, eighty representative test images were used to assess the performance. Teachable machine® automatically creates a classification network and presents diagnostic performance when images are uploaded. The mean accuracy of the Teachable machine® for classifying tympanic membranes as normal or abnormal (Level I) was 90.1%. For Level II, the mean accuracy was 89.0% and for Level III it was 86.2%. The overall accuracy of the classification of the 80 representative tympanic membrane images was 78.75%, and the hit rates for normal, OME, COM, and cholesteatoma were 95.0%, 70.0%, 90.0%, and 60.0%, respectively. Teachable machine® could successfully generate the diagnostic network for classifying tympanic membrane.