Prediction of longitudinal clinical outcomes after acute myocardial infarction using a dynamic machine learning algorithm.

Several regression-based models for predicting outcomes after acute myocardial infarction (AMI) have been developed. However, prediction models that encompass diverse patient-related factors over time are limited. This study aimed to develop a machine learning-based model to predict longitudinal outcomes after AMI. This study was based on a nationwide prospective registry of AMI in Korea (n = 13,104). Seventy-seven predictor candidates from prehospitalization to 1 year of follow-up were included, and six machine learning approaches were analyzed. Primary outcome was defined as 1-year all-cause death. Secondary outcomes included all-cause deaths, cardiovascular deaths, and major adverse cardiovascular event (MACE) at the 1-year and 3-year follow-ups. Random forest resulted best performance in predicting the primary outcome, exhibiting a 99.6% accuracy along with an area under the receiver-operating characteristic curve of 0.874. Top 10 predictors for the primary outcome included peak troponin-I (variable importance value = 0.048), in-hospital duration (0.047), total cholesterol (0.047), maintenance of antiplatelet at 1 year (0.045), coronary lesion classification (0.043), N-terminal pro-brain natriuretic peptide levels (0.039), body mass index (BMI) (0.037), door-to-balloon time (0.035), vascular approach (0.033), and use of glycoprotein IIb/IIIa inhibitor (0.032). Notably, BMI was identified as one of the most important predictors of major outcomes after AMI. BMI revealed distinct effects on each outcome, highlighting a U-shaped influence on 1-year and 3-year MACE and 3-year all-cause death. Diverse time-dependent variables from prehospitalization to the postdischarge period influenced the major outcomes after AMI. Understanding the complexity and dynamic associations of risk factors may facilitate clinical interventions in patients with AMI.

Association of Antiarrhythmic Drug Therapy With Syncope and Pacemaker Implantation in Patients With Atrial Fibrillation.

BACKGROUND: Early rhythm control therapy mainly with antiarrhythmic drugs (AADs) for new-onset atrial fibrillation (AF) reduces major adverse cardiovascular events. However, negative dromotropic effects of AADs via ion channel blocking may cause bradyarrhythmias. OBJECTIVES: This study aimed to evaluate the association between AAD use and the risk of pacemaker implantation or syncope in patients with new-onset AF receiving early rhythm control therapy with AADs. METHODS: This study was based on data from the Korean National Health Insurance Service system. We screened all new-onset AF diagnoses that occurred from 2013 to 2019 and identified patients who were prescribed AADs within 1 year of AF diagnosis. The risk of pacemaker implantation or syncope was compared between AAD users and nonusers. RESULTS: A total of 770,977 new-onset AF cases were identified and 142,141 patients were prescribed AADs. After multivariate adjustment, use of AADs was associated with 3.5-, 2.0-, and 5.0-fold increased risk of pacemaker implantation or syncope, syncope, and pacemaker implantation, respectively. Propensity score-matched analysis revealed similar results, demonstrating a significant association between AAD use and the risk of pacemaker implantation or syncope. This association was consistent across various subgroups. Women were more susceptible to adverse effects of AADs than men. CONCLUSIONS: This study showed an association between AADs and risk of pacemaker implantation or syncope, a consistent finding across various subgroups. Precise evaluation of such risk should be undertaken before prescription of AADs.

Prediction of In-Hospital Mortality for Ischemic Cardiogenic Shock Requiring Venoarterial Extracorporeal Membrane Oxygenation.

BACKGROUND: Clinical outcome of ischemic cardiogenic shock (CS) requiring extracorporeal membrane oxygenation is highly variable, necessitating appropriate assessment of prognosis. However, a systemic predictive model estimating the mortality of refractory ischemic CS is lacking. The PRECISE (Prediction of In-Hospital Mortality for Patients With Refractory Ischemic Cardiogenic Shock Requiring Veno-Arterial Extracorporeal Membrane Oxygenation Support) score was developed to predict the prognosis of refractory ischemic CS due to acute myocardial infarction. METHODS AND RESULTS: Data were obtained from the multicenter CS registry RESCUE (Retrospective and Prospective Observational Study to Investigate Clinical Outcomes and Efficacy of Left Ventricular Assist Device for Korean Patients With Cardiogenic Shock) that consists of 322 patients with acute myocardial infarction complicated by refractory ischemic CS requiring extracorporeal membrane oxygenation support. Fifteen parameters were selected to assess in-hospital mortality. The developed model was validated internally and externally using an independent external cohort (n=138). Among 322 patients, 138 (42.9%) survived postdischarge. Fifteen predictors were included for model development: age, diastolic blood pressure, hypertension, chronic kidney disease, peak lactic acid, serum creatinine, lowest left ventricular ejection fraction, vasoactive inotropic score, shock to extracorporeal membrane oxygenation insertion time, extracorporeal cardiopulmonary resuscitation, use of intra-aortic balloon pump, continuous renal replacement therapy, mechanical ventilator, successful coronary revascularization, and staged percutaneous coronary intervention. The PRECISE score yielded a high area under the receiver-operating characteristic curve (0.894 [95% CI, 0.860-0.927]). External validation and calibration resulted in competent sensitivity (area under the receiver-operating characteristic curve, 0.895 [95% CI, 0.853-0.930]). CONCLUSIONS: The PRECISE score demonstrated high predictive performance and directly translates into the expected in-hospital mortality rate. The PRECISE score may be used to support clinical decision-making in ischemic CS (www.theprecisescore.com). REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02985008.

Feasibility of extracting cancer stage and metastasis codes from health insurance claims of outpatients and expressibility in ICD-11: a cross-sectional study using national health insurance data from South Korea.

OBJECTIVES: This study aimed to evaluate the incidence of health insurance claims recording the cancer stage and TNM codes representing tumor extension size (T), lymph node metastasis (N), and distant metastasis (M) for patients diagnosed with cancer and to determine whether this extracted data could be applied to the new ICD-11 codes. DESIGN: A cross-sectional study design was used, with the units of analysis as individual outpatients. Two dependent variables were extraction feasibility of cancer stage and TNM metastasis information from each claim. Expressibility of the two variables in ICD-11 was descriptively analysed. SETTING AND PARTICIPANTS: The study was conducted in South Korea and study participants were outpatients: lung cancer (LC) (46616), stomach cancer (SC) (50103) and colorectal cancer (CC) (54707). The data set consisted of the first health insurance claim of each patient visiting a hospital from 1 July to 31 December 2021. RESULTS: The absolute extraction success rates for cancer stage based on claims with cancer stage was 33.3%. The rates for stage for LC, SC and CC were 30.1%, 35.5% and 34.0%, respectively. The rate for TNM was 11.0%. The relative extraction success rates for stage compared with that for CC (the reference group) were lower for patients with LC (adjusted OR (aOR), 0.803; 95% CI 0.782 to 0.825; p<0.0001) but higher for SC (aOR 1.073; 95% CI 1.046 to 1.101; p<0.0001). The rates of TNM compared that for CC were 40.7% lower for LC (aOR, 0.593; 95% CI 0.569 to 0.617; p<0.0001) and 43.0% lower for SC (aOR 0.570; 95% CI 0.548 to 0.593; p<0.0001). There were limits to expressibility in ICD-11 regarding the detailed cancer stage and TNM metastasis codes. CONCLUSION: Extracting cancer stage and TNM codes from health insurance claims were feasible, but expressibility in ICD-11 codes was limited. WHO may need to create specific cancer stage and TNM extension codes for ICD-11 due to the absence of current rules in ICD-11.

Seat belt use and cardiac arrest immediately after motor vehicle collision: Nationwide observational study.

Objective: Motor vehicle collisions (MVCs) are known to cause traumatic cardiac arrest; it is unclear whether seat belts prevent this. This study aimed to evaluate the association between seat belt use and immediate cardiac arrest in cases of MVCs. Method: This cross-sectional observational study used data from a nationwide EMS-based severe trauma registry in South Korea. The sample comprised adult patients with EMS-assessed severe trauma due to MVCs between 2018 and 2019. The primary, secondary, and tertiary outcomes were immediate cardiac arrest, in-hospital mortality, and death or severe disability, respectively. We calculated the adjusted odds ratios (AORs) of immediate cardiac arrest with seat belt use after adjusting for potential confounders. Results: Among the 8178 eligible patients, 6314 (77.2 %) and 1864 (29.5 %) were wearing and not wearing seat belts, respectively. Immediate cardiac arrest, mortality, and death/severe disability rates were higher in the "no seat belt use" group than in the "seat belt use" group (9.4 % vs. 4.0 %, 12.4 % vs. 6.2 %, 17.7 % vs. 9.9 %, respectively; p < 0.001). The former group was more likely to experience immediate cardiac arrest (AOR [95 %CI]: 3.29 [2.65-4.08]), in-hospital mortality (AOR [95 %CI]: 2.72 [2.26-3.27]), and death or severe disability (AOR [95 %CI]: 2.40 [2.05-2.80]). Conclusion: There was an association between wearing seat belts during MVCs and a reduced risk of immediate cardiac arrest.

Alcohol and the risk of all-cause death, atrial fibrillation, ventricular arrhythmia, and sudden cardiac arrest.

The risk of having atrial fibrillation (AF) is associated with alcohol intake. However, it is not clear whether sudden cardiac arrest (SCA) and ventricular arrhythmia (VA) including ventricular tachycardia, flutter, or fibrillation have similar associations with alcohol. We aimed to evaluate the association of alcohol intake with all-cause death, new-onset AF, VA, and SCA using single cohort with a sufficient sample size. A total of 3,990,373 people without a prior history of AF, VAs, or SCA was enrolled in this study based on nationwide health check-up in 2009. We classified the participants into four groups according to weekly alcohol consumption, and evaluated the association of alcohol consumption with each outcome. We observed a significant association between mild (hazard ratio [HR] = 0.826; 95% confidence interval [CI] = 0.815-0.838) to moderate (HR = 0.930; 95% CI = 0.912-0.947) drinking with decreased risk of all-cause mortality. However heavy drinking (HR = 1.108; 95% CI = 1.087-1.129) was associated with increased all-cause death. The risk of new-onset AF was significantly associated with moderate (HR = 1.129; 95% CI = 1.097-1.161) and heavy (HR = 1.298; 95% CI = 1.261-1.337) drinking. However, the risk of SCA showed negative association with all degrees of alcohol intake: 20% (HR = 0.803; 95% CI = 0.769-0.839), 15% (HR = 0.853; 95% CI = 0.806-0.902), and 8% (HR = 0.918; 95% CI = 0.866-0.974) lower risk for mild, moderate, and heavy drinkers, respectively. Mild drinking was associated with reduced risk of VA with moderate and heavy drinking having no associations. In conclusion, the association between alcohol and various outcomes in this study were heterogeneous. Alcohol might have different influences on various cardiac disorders.

Isoflavone-enriched soybean leaves (Glycine max) restore loss of dermal collagen fibers induced by ovariectomy in the Sprague Dawley rats.

Phytoestrogens, such as isoflavones, are known for their capacity to simulate various physiological impacts of estrogen in the human body. Our research evaluated the effects of isoflavone-enriched soybean leaves (IESL) on collagen fiber loss prompted by ovariectomy in Sprague Dawley (SD) rats, thereby simulating menopausal changes in women. IESL, bolstered with an increased concentration of isoflavones through a metabolite farming process, contained a significantly higher amount of isoflavones than regular soybean leaves. Our results indicate that the administration of IESL can counteract the decrease in relative optical density and dermal thickness of collagen fibers caused by ovariectomy in SD rats, with more pronounced effects observed at higher isoflavone dosages. These outcomes suggest that soybean leaves rich in isoflavones may hold potential benefits in combating collagen degradation and skin aging symptoms related to menopause. Further research is needed to fully understand the exact molecular pathways at play and the potential clinical relevance of these findings.

Association of temporal change in body mass index with sudden cardiac arrest in diabetes mellitus.

BACKGROUND: Underweight imposes significant burden on cardiovascular outcomes in patients with diabetes mellitus. However, less is known about the impact of serial change in body weight status measured as body mass index (BMI) on the risk of sudden cardiac arrest (SCA). This study investigated the association between SCA and temporal change in BMI among patients with diabetes mellitus. METHODS: Based on Korean National Health Insurance Service database, participants with diabetes mellitus who underwent health examination between 2009 and 2012 and had prior health examination data (four years ago, 2005-2008) were retrospectively analyzed. BMI was measured at baseline (2005-2008) and 4-year follow-up health examination (2009-2012). Patients were classified in four groups according to the body weight status and its temporal change: sustained non-underweight, sustained underweight, previous underweight, and newly developed underweight. Primary outcome was defined as occurrence of SCA. RESULTS: A total of 1,355,746 patients with diabetes mellitus were included for analysis, and SCA occurred in 12,554 cases. SCA was most common in newly developed underweight (incidence rate = 4.45 per 1,000 person-years), followed by sustained underweight (incidence rate = 3.90), previous underweight (incidence rate = 3.03), and sustained non-underweight (incidence rate = 1.34). Adjustment of covariates resulted highest risk of SCA in sustained underweight (adjusted hazard ratio = 2.60, 95% confidence interval [2.25-3.00], sustained non-underweight as a reference), followed by newly developed underweight (2.42, [2.15-2.74]), and previous underweight (2.12, [1.77-2.53]). CONCLUSIONS: In diabetes mellitus, sustained underweight as well as decrease in body weight during 4-year follow-up imposes substantial risk on SCA. Recovery from underweight over time had relatively lower, but yet increased risk of SCA. Both underweight and dynamic decrease in BMI can be associated with increased risk of SCA.

Temporal elevation of blood pressure is associated with increased risk of sudden cardiac arrest.

Hypertension is a known risk factor for sudden cardiac arrest (SCA). However, the role of temporal changes in blood pressure on the risk of SCA is not fully understood. This study was conducted to determine whether a temporal increase or decrease in blood pressure is associated with the risk of SCA. This study was based on nationwide healthcare insurance data. Individuals who underwent nationwide health check-ups in 2009 and 2011 were analyzed. A total of 2,801,153 individuals were evaluated for 8100 SCA events during the 17, 740, 420 person-years of follow-up. In a multivariate analysis, there were linear association between the degree of temporal elevation of systolic blood pressure (SBP) and the risk of SCA: (i) adjusted-hazard ratio (HR) 1.11 (p = 0.001) in 10 ≤ ΔSBP < 20 (mmHg) group; (ii) adjusted-HR 1.40 (p < 0.001) in 20 ≤ ΔSBP < 40 group; and (iii) adjusted-HR 1.88 (p < 0.001) in 40 ≤ ΔSBP group as compared with the reference group (- 10 ≤ ΔSBP < 10). Temporal increase in diastolic blood pressure (DBP) also a showed significant association with SCA risk with the highest risk observed in ∆DBP ≥ 25 group (adjusted-HR 1.61; p < 0.001) as compared with the reference group (- 5 ≤ ΔDBP < 5). The association between SBP and SCA was not affected by age, sex, presence of diabetes mellitus, or baseline SBP. In conclusion, a temporal increase in blood pressure was significantly associated with the occurrence of SCA, and this association was consistent across all subgroups. However, a temporary decrease in blood pressure does not reduce the risk of SCA. Prevention of elevated blood pressure may play an important role in preventing SCA.

Association between Case Volumes of Extracorporeal Life Support and Clinical Outcome in Out-of-Hospital Cardiac Arrest.

AIM: Extracorporeal life support (ECLS) for out-of-hospital cardiac arrest (OHCA) is increasing. There is little evidence identifying the association between hospital ECLS case volumes and outcomes in different populations receiving ECLS or conventional cardiopulmonary resuscitation (CPR). The goal of this investigation was to identify the association between ECLS case volumes and clinical outcomes of OHCA patients. METHODS: This cross-sectional observational study used the National OHCA Registry for adult OHCA cases in Seoul, Korea between January 2015 and December 2019. If the ECLS volume during the study period was >20, the institution was defined as a high-volume ECLS center. Others were defined as low-volume ECLS centers. Outcomes were good neurologic recovery (cerebral performance category 1 or 2) and survival to discharge. We performed multivariate logistic regression and interaction analyses to assess the association between case volume and clinical outcome. RESULTS: Of the 17,248 OHCA cases, 3,731 were transported to high-volume centers. Among the patients who underwent ECLS, those at high-volume centers had a higher neurologic recovery rate than those at low-volume centers (17.0% vs. 12.0%), and the adjusted OR for good neurologic recovery was 2.22 (95% confidence interval (CI): 1.15-4.28) in high-volume centers compared to low-volume centers. For patients who received conventional CPR, high-volume centers also showed higher survival-to-discharge rates (adjusted OR of 1.16, 95%CI: 1.01-1.34). CONCLUSIONS: High-volume ECLS centers showed better neurological recovery in patients who underwent ECLS. High-volume centers also had better survival-to-discharge rates than low-volume centers for patients not receiving ECLS.

Automated quantification of lipid contents of Lipomyces starkeyi using deep-learning-based image segmentation.

Intracellular lipid droplets (LDs), subcellular organelles playing a role in long-term carbon storage, have immense potential in biofuel and dietary lipid production. Monitoring the state of LDs in living cells is of utmost importance for quick biomass harvest and screening promising isolates. Here, a deep-learning-based segmentation model was developed for automatic detection and segmentation of LDs using the model yeast species Lipomyces starkeyi, leading to fast and accurate quantification of lipid contents in liquid cultures. The trained model detected the yeast's cell and LDs in light microscopic images with an accuracy of 98% and 92%, respectively. Lipid content prediction using pixel numbers counted in segmented LDs showed high similarity to lipid quantification results obtained with gas chromatography-mass spectrometry. This automated quantification can highly reduce cost and time in real-time monitoring of lipid production, thereby providing an efficient tool in bio-fermentation.

Growth, Distribution, and Photosynthesis of Chlamydomonas Reinhardtii in 3D Hydrogels.

Engineered living materials (ELMs) are a novel class of functional materials that typically feature spatial confinement of living components within an inert polymer matrix to recreate biological functions. Understanding the growth and spatial configuration of cellular populations within a matrix is crucial to predicting and improving their responsive potential and functionality. Here, this work investigates the growth, spatial distribution, and photosynthetic productivity of eukaryotic microalga Chlamydomonas reinhardtii (C. reinhardtii) in three-dimensionally shaped hydrogels in dependence of geometry and size. The embedded C. reinhardtii cells photosynthesize and form confined cell clusters, which grow faster when located close to the ELM periphery due to favorable gas exchange and light conditions. Taking advantage of location-specific growth patterns, this work successfully designs and prints photosynthetic ELMs with increased CO2 capturing rate, featuring high surface to volume ratio. This strategy to control cell growth for higher productivity of ELMs resembles the already established adaptations found in multicellular plant leaves.

Impact of an Emergency Department Isolation Policy for Patients With Suspected COVID-19 on Door-to-Electrocardiography Time and Clinical Outcomes in Patients With Acute Myocardial Infarction.

BACKGROUND: Rapid electrocardiography diagnosis within 10 minutes of presentation is critical for acute myocardial infarction (AMI) patients in the emergency department (ED). However, the coronavirus disease 2019 (COVID-19) pandemic has significantly impacted the emergency care system. Screening for COVID-19 symptoms and implementing isolation policies in EDs may delay the door-to-electrocardiography (DTE) time. METHODS: We conducted a cross-sectional study of 1,458 AMI patients who presented to a single ED in South Korea from January 2019 to December 2021. We used multivariate logistic regression analysis to assess the impact of COVID-19 pandemic and ED isolation policies on DTE time and clinical outcomes. RESULTS: We found that the mean DTE time increased significantly from 5.5 to 11.9 minutes (P < 0.01) in ST segment elevation myocardial infarction (STEMI) patients and 22.3 to 26.7 minutes (P < 0.01) in non-ST segment elevation myocardial infarction (NSTEMI) patients. Isolated patients had a longer mean DTE time compared to non-isolated patients in both STEMI (9.2 vs. 24.4 minutes) and NSTEMI (22.4 vs. 61.7 minutes) groups (P < 0.01). The adjusted odds ratio (aOR) for the effect of COVID-19 duration on DTE ≥ 10 minutes was 1.93 (95% confidence interval [CI], 1.51-2.47), and the aOR for isolation status was 5.62 (95% CI, 3.54-8.93) in all patients. We did not find a significant association between in-hospital mortality and the duration of COVID-19 (aOR, 0.9; 95% CI, 0.52-1.56) or isolation status (aOR, 1.62; 95% CI, 0.71-3.68). CONCLUSION: Our study showed that ED screening or isolation policies in response to the COVID-19 pandemic could lead to delays in DTE time. Timely evaluation and treatment of emergency patients during pandemics are essential to prevent potential delays that may impact their clinical outcomes.

Development and validation of an artificial intelligence algorithm for detecting vocal cords in video laryngoscopy.

Airway procedures in life-threatening situations are vital for saving lives. Video laryngoscopy (VL) is commonly performed during endotracheal intubation (ETI) in the emergency department. Artificial intelligence (AI) is widely used in the medical field, particularly to detect anatomical structures. This study aimed to develop an AI algorithm that detects vocal cords from VL images acquired during emergent situations. This retrospective study used VL images acquired in the emergency department to facilitate the ETI. The vocal cord image was labeled with a ground-truth bounding box. The dataset was divided into training and validation datasets. The algorithm was developed from a training dataset using the YOLOv4 model. The performance of the algorithm was evaluated using a test set. The test set was further divided into specific environments during the ETI for clinical subgroup analysis. In total, 20,161 images from 84 patients were used in this study. A total of 10,287, 5766, and 4108 images were used for the model training, validation, and test sets, respectively. The developed algorithm achieved F1 score 0.906, sensitivity 0.963, and specificity 0.842 in the validation set. The performance in the test set was F1 score 0.808, sensitivity 0.823, and specificity 0.804. We developed and validated an AI algorithm to detect vocal cords in VL. This algorithm demonstrated a high performance. The algorithm can be used to determine the vocal cord to ensure safe ETI.

Hyperkalemia Detection in Emergency Departments Using Initial ECGs: A Smartphone AI ECG Analyzer vs. Board-Certified Physicians.

BACKGROUND: Hyperkalemia is a potentially fatal condition that mandates rapid identification in emergency departments (EDs). Although a 12-lead electrocardiogram (ECG) can indicate hyperkalemia, subtle changes in the ECG often pose detection challenges. An artificial intelligence application that accurately assesses hyperkalemia risk from ECGs could revolutionize patient screening and treatment. We aimed to evaluate the efficacy and reliability of a smartphone application, which utilizes camera-captured ECG images, in quantifying hyperkalemia risk compared to human experts. METHODS: We performed a retrospective analysis of ED hyperkalemic patients (serum potassium ≥ 6 mmol/L) and their age- and sex-matched non-hyperkalemic controls. The application was tested by five users and its performance was compared to five board-certified emergency physicians (EPs). RESULTS: Our study included 125 patients. The area under the curve (AUC)-receiver operating characteristic of the application's output was nearly identical among the users, ranging from 0.898 to 0.904 (median: 0.902), indicating almost perfect interrater agreement (Fleiss' kappa 0.948). The application demonstrated high sensitivity (0.797), specificity (0.934), negative predictive value (NPV) (0.815), and positive predictive value (PPV) (0.927). In contrast, the EPs showed moderate interrater agreement (Fleiss' kappa 0.551), and their consensus score had a significantly lower AUC of 0.662. The physicians' consensus demonstrated a sensitivity of 0.203, specificity of 0.934, NPV of 0.527, and PPV of 0.765. Notably, this performance difference remained significant regardless of patients' sex and age (P < 0.001 for both). CONCLUSION: Our findings suggest that a smartphone application can accurately and reliably quantify hyperkalemia risk using initial ECGs in the ED.

Identification of concealed cardiomyopathy using next-generation sequencing-based genetic testing in Korean patients initially diagnosed with idiopathic ventricular fibrillation.

AIMS: Idiopathic ventricular fibrillation (IVF) is a disease in which the cause of ventricular fibrillation cannot be identified despite comprehensive clinical evaluation. This study aimed to investigate the clinical yield and implications of genetic testing for IVF. METHODS AND RESULTS: This study was based on the multi-centre inherited arrhythmia syndrome registry in South Korea from 2014 to 2017. Next-generation sequencing-based genetic testing was performed that included 174 genes previously linked to cardiovascular disease. A total of 96 patients were clinically diagnosed with IVF. The mean age of the onset was 41.2 ± 12.7 years, and 79 patients were males (82.3%). Of these, 74 underwent genetic testing and four (5.4%) of the IVF probands had pathogenic or likely pathogenic variants (each having one of MYBPC3, MYH7, DSP, and TNNI3). All pathogenic or likely pathogenic variants were located in genes with definite evidence of a cardiomyopathy phenotype, either hypertrophic cardiomyopathy or arrhythmogenic right ventricular cardiomyopathy. CONCLUSION: Next-generation sequencing-based genetic testing identified pathogenic or likely pathogenic variants in 5.4% of patients initially diagnosed with IVF, suggesting that genetic testing with definite evidence genes of cardiomyopathy may enable molecular diagnosis in a minority of patients with IVF. Further clinical evaluation and follow-up of patients with IVF with positive genotypes are needed to unveil concealed phenotypes, such as the pre-clinical phase of cardiomyopathy.

Mask R-CNN based multiclass segmentation model for endotracheal intubation using video laryngoscope.

Objective: Endotracheal intubation (ETI) is critical to secure the airway in emergent situations. Although artificial intelligence algorithms are frequently used to analyze medical images, their application to evaluating intraoral structures based on images captured during emergent ETI remains limited. The aim of this study is to develop an artificial intelligence model for segmenting structures in the oral cavity using video laryngoscope (VL) images. Methods: From 54 VL videos, clinicians manually labeled images that include motion blur, foggy vision, blood, mucus, and vomitus. Anatomical structures of interest included the tongue, epiglottis, vocal cord, and corniculate cartilage. EfficientNet-B5 with DeepLabv3+, EffecientNet-B5 with U-Net, and Configured Mask R-Convolution Neural Network (CNN) were used; EffecientNet-B5 was pretrained on ImageNet. Dice similarity coefficient (DSC) was used to measure the segmentation performance of the model. Accuracy, recall, specificity, and F1 score were used to evaluate the model's performance in targeting the structure from the value of the intersection over union between the ground truth and prediction mask. Results: The DSC of tongue, epiglottis, vocal cord, and corniculate cartilage obtained from the EfficientNet-B5 with DeepLabv3+, EfficientNet-B5 with U-Net, and Configured Mask R-CNN model were 0.3351/0.7675/0.766/0.6539, 0.0/0.7581/0.7395/0.6906, and 0.1167/0.7677/0.7207/0.57, respectively. Furthermore, the processing speeds (frames per second) of the three models stood at 3, 24, and 32, respectively. Conclusions: The algorithm developed in this study can assist medical providers performing ETI in emergent situations.

Predictors of in-hospital mortality after successful weaning of venoarterial extracorporeal membrane oxygenation in cardiogenic shock.

Limited knowledge exists regarding the predictors of mortality after successful weaning of venoarterial extracorporeal membrane oxygenation (ECMO). We aimed to identify predictors of in-hospital mortality in patients with cardiogenic shock (CS) after successful weaning from ECMO. Data were obtained from a multicenter registry of CS. Successful ECMO weaning was defined as survival with minimal mean arterial pressure (> 65 mmHg) for > 24 h after ECMO removal. The primary outcome was in-hospital mortality after successful ECMO weaning. Among 1247 patients with CS, 485 received ECMO, and 262 were successfully weaned from ECMO. In-hospital mortality occurred in 48 patients (18.3%). Survivors at discharge differed significantly from non-survivors in age, cardiovascular comorbidities, cause of CS, left ventricular ejection fraction, and use of adjunctive therapy. Five independent predictors for in-hospital mortality were identified: use of continuous renal replacement therapy (odds ratio 5.429, 95% confidence interval [CI] 2.468-11.940; p < 0.001), use of intra-aortic balloon pump (3.204, 1.105-9.287; p = 0.032), diabetes mellitus (3.152, 1.414-7.023; p = 0.005), age (1.050, 1.016-1.084; p = 0.003), and left ventricular ejection fraction after ECMO insertion (0.957, 0.927-0.987; p = 0.006). Even after successful weaning of ECMO, patients with irreversible risk factors should be recognized, and careful monitoring should be done for sign of deconditioning.

Clinical and Genetic Features of Korean Inherited Arrhythmia Probands.

BACKGROUND AND OBJECTIVES: Inherited arrhythmia (IA) is a more common cause of sudden cardiac death in Asian population, but little is known about the genetic background of Asian IA probands. We aimed to investigate the clinical characteristics and analyze the genetic underpinnings of IA in a Korean cohort. METHODS: This study was conducted in a multicenter cohort of the Korean IA Registry from 2014 to 2017. Genetic testing was performed using a next-generation sequencing panel including 174 causative genes of cardiovascular disease. RESULTS: Among the 265 IA probands, idiopathic ventricular fibrillation (IVF) and Brugada Syndrome (BrS) was the most prevalent diseases (96 and 95 cases respectively), followed by long QT syndrome (LQTS, n=54). Two-hundred-sixteen probands underwent genetic testing, and 69 probands (31.9%) were detected with genetic variant, with yield of pathogenic or likely pathogenic variant as 6.4%. Left ventricular ejection fraction was significantly lower in genotype positive probands (54.7±11.3 vs. 59.3±9.2%, p=0.005). IVF probands showed highest yield of positive genotype (54.0%), followed by LQTS (23.8%), and BrS (19.5%). CONCLUSIONS: There were significant differences in clinical characteristics and genetic yields among BrS, LQTS, and IVF. Genetic testing did not provide better yield for BrS and LQTS. On the other hand, in IVF, genetic testing using multiple gene panel might enable the molecular diagnosis of concealed genotype, which may alter future clinical diagnosis and management strategies.