Newly Developed Sex-Specific Z Score Model for Coronary Artery Diameter in a Pediatric Population.

BACKGROUND: This study aimed to generate a Z score calculation model for coronary artery diameter of normal children and adolescents to be adopted as the standard calculation method with consensus in clinical practice. METHODS: This study was a retrospective, multicenter study that collected data from multiple institutions across South Korea. Data were analyzed to determine the model that best fit the relationship between the diameter of coronary arteries and independent demographic parameters. Linear, power, logarithmic, exponential, and square root polynomial models were tested for best fit. RESULTS: Data of 2,030 subjects were collected from 16 institutions. Separate calculation models for each sex were developed because the impact of demographic variables on the diameter of coronary arteries differs according to sex. The final model was the polynomial formula with an exponential relationship between the diameter of coronary arteries and body surface area using the DuBois formula. CONCLUSION: A new coronary artery diameter Z score model was developed and is anticipated to be applicable in clinical practice. The new model will help establish a consensus-based Z score model.

Improvement in Yield of Extracellular Vesicles Derived from Edelweiss Callus Treated with LED Light and Enhancement of Skin Anti-Aging Indicators.

The process of skin aging is currently recognized as a disease, and extracellular vesicles (EVs) are being used to care for it. While various EVs are present in the market, there is a growing need for research on improving skin conditions through microbial and plant-derived EVs. Edelweiss is a medicinal plant and is currently an endangered species. Callus culture is a method used to protect rare medicinal plants, and recently, research on EVs using callus culture has been underway. In this study, the researchers used LED light to increase the productivity of Edelweiss EVs and confirmed that productivity was enhanced by LED exposure. Additionally, improvements in skin anti-aging indicators were observed. Notably, M-LED significantly elevated callus fresh and dry weight, with a DW/FW ratio of 4.11%, indicating enhanced proliferation. Furthermore, M-LED boosted secondary metabolite production, including a 20% increase in total flavonoids and phenolics. The study explores the influence of M-LED on EV production, revealing a 2.6-fold increase in concentration compared to darkness. This effect is consistent across different plant species (Centella asiatica, Panax ginseng), demonstrating the universality of the phenomenon. M-LED-treated EVs exhibit a concentration-dependent inhibition of reactive oxygen species (ROS) production, surpassing dark-cultured EVs. Extracellular melanin content analysis reveals M-LED-cultured EVs' efficacy in reducing melanin production. Additionally, the expression of key skin proteins (FLG, AQP3, COL1) is significantly higher in fibroblasts treated with M-LED-cultured EVs. These results are expected to provide valuable insights into research on improving the productivity of plant-derived EVs and enhancing skin treatment using plant-derived EVs.

Development and validation of a deep learning model to diagnose COVID-19 using time-series heart rate values before the onset of symptoms.

One of the effective ways to minimize the spread of COVID-19 infection is to diagnose it as early as possible before the onset of symptoms. In addition, if the infection can be simply diagnosed using a smartwatch, the effectiveness of preventing the spread will be greatly increased. In this study, we aimed to develop a deep learning model to diagnose COVID-19 before the onset of symptoms using heart rate (HR) data obtained from a smartwatch. In the deep learning model for the diagnosis, we proposed a transformer model that learns HR variability patterns in presymptom by tracking relationships in sequential HR data. In the cross-validation (CV) results from the COVID-19 unvaccinated patients, our proposed deep learning model exhibited high accuracy metrics: sensitivity of 84.38%, specificity of 85.25%, accuracy of 84.85%, balanced accuracy of 84.81%, and area under the receiver operating characteristics (AUROC) of 0.8778. Furthermore, we validated our model using external multiple datasets including healthy subjects, COVID-19 patients, as well as vaccinated patients. In the external healthy subject group, our model also achieved high specificity of 77.80%. In the external COVID-19 unvaccinated patient group, our model also provided similar accuracy metrics to those from the CV: balanced accuracy of 87.23% and AUROC of 0.8897. In the COVID-19 vaccinated patients, the balanced accuracy and AUROC dropped by 66.67% and 0.8072, respectively. The first finding in this study is that our proposed deep learning model can simply and accurately diagnose COVID-19 patients using HRs obtained from a smartwatch before the onset of symptoms. The second finding is that the model trained from unvaccinated patients may provide less accurate diagnosis performance compared with the vaccinated patients. The last finding is that the model trained in a certain period of time may provide degraded diagnosis performances as the virus continues to mutate.

Outcomes of surgery for coarctation of the aorta based on a new classification system.

OBJECTIVES: To evaluate early- and long-term outcomes of the surgical treatment for coarctation of the aorta based on a new classification system. METHODS: A retrospective clinical review of 111 patients with coarctation of the aorta who underwent surgery (March 2011 to August 2020) was performed. We categorised coarctation of the aorta into type I, with all three head vessels tightly packed; type II, with the left subclavian artery separated from the two other head vessels; and type III, with all three head vessels separated from one another. Each type included subtype a, with a short isthmic portion, and subtype b, with a long isthmic portion. RESULTS: The median patient age and weight at operation were 8 (range, 1-1490) days and 3.2 (range, 1.9-18.5) kg, respectively. Extended end-to-end anastomosis was performed via sternotomy in 54, via thoracotomy in 12, end-to-side anastomosis in 31, autologous main pulmonary artery patch augmentation in 12, and modified end-to-end anastomosis combined with subclavian artery flap aortoplasty in two patients. There was one (0.9%) case of early mortality and 12 (10.8%) cases of post-operative complications. Two (1.8%) late deaths occurred during follow-up. Five (4.5%) patients underwent balloon dilatation and three (2.7%) underwent reoperation for restenosis of coarctation of the aorta. All patients with type Ia (21 patients, 18.9%) underwent extended end-to-end anastomosis via sternotomy or thoracotomy. CONCLUSIONS: According to the early and late outcomes observed in this study, surgical treatment of coarctation of the aorta using the new classification system could be safe and low risk.

Coronary artery spasm due to acute myocarditis in an adolescent: a case report.

BACKGROUND: Myocarditis refers to the inflammation of the myocardium caused by infection or autoimmune disease that may or may not present with clinical manifestations, such as gastrointestinal symptoms, dyspnea, chest pain, or sudden death. Although myocarditis and coronary artery vasospasm may mimic ST-segment elevation myocardial infarction (STEMI) with normal coronary arteries on angiography, acute myocarditis rarely causes coronary artery spasm. Here, we report a case of coronary artery spasm with reversible electrocardiographic changes mimicking STEMI in an adolescent with acute myocarditis. CASE PRESENTATION: A 15-year-old boy present with sudden-onset repeated chest pain following a 3-day history of flu-like illness. Cardiac biomarkers were significantly elevated. Electrocardiography showed ST-segment elevation in the absence of detectable vasospasm on coronary angiography. These findings were consistent with the diagnosis of coronary artery spasm secondary to acute myocarditis. Treatment with immunoglobulin for 2 days improved his condition. The patient was discharged on the 12th day with complete resolution of symptoms and normalization of electrocardiogram findings. CONCLUSIONS: We reported a case of coronary artery spasm due to acute myocarditis. This study highlights the importance of considering coronary artery spasm due to acute myocarditis as a differential diagnosis in patients presenting with signs of STEMI as these diseases have different medical management strategies.

A Deep Residual U-Net Algorithm for Automatic Detection and Quantification of Ascites on Abdominopelvic Computed Tomography Images Acquired in the Emergency Department: Model Development and Validation.

BACKGROUND: Detection and quantification of intra-abdominal free fluid (ie, ascites) on computed tomography (CT) images are essential processes for finding emergent or urgent conditions in patients. In an emergency department, automatic detection and quantification of ascites will be beneficial. OBJECTIVE: We aimed to develop an artificial intelligence (AI) algorithm for the automatic detection and quantification of ascites simultaneously using a single deep learning model (DLM). METHODS: We developed 2D DLMs based on deep residual U-Net, U-Net, bidirectional U-Net, and recurrent residual U-Net (R2U-Net) algorithms to segment areas of ascites on abdominopelvic CT images. Based on segmentation results, the DLMs detected ascites by classifying CT images into ascites images and nonascites images. The AI algorithms were trained using 6337 CT images from 160 subjects (80 with ascites and 80 without ascites) and tested using 1635 CT images from 40 subjects (20 with ascites and 20 without ascites). The performance of the AI algorithms was evaluated for diagnostic accuracy of ascites detection and for segmentation accuracy of ascites areas. Of these DLMs, we proposed an AI algorithm with the best performance. RESULTS: The segmentation accuracy was the highest for the deep residual U-Net model with a mean intersection over union (mIoU) value of 0.87, followed by U-Net, bidirectional U-Net, and R2U-Net models (mIoU values of 0.80, 0.77, and 0.67, respectively). The detection accuracy was the highest for the deep residual U-Net model (0.96), followed by U-Net, bidirectional U-Net, and R2U-Net models (0.90, 0.88, and 0.82, respectively). The deep residual U-Net model also achieved high sensitivity (0.96) and high specificity (0.96). CONCLUSIONS: We propose a deep residual U-Net-based AI algorithm for automatic detection and quantification of ascites on abdominopelvic CT scans, which provides excellent performance.

Model for Predicting In-Hospital Mortality of Physical Trauma Patients Using Artificial Intelligence Techniques: Nationwide Population-Based Study in Korea.

BACKGROUND: Physical trauma-related mortality places a heavy burden on society. Estimating the mortality risk in physical trauma patients is crucial to enhance treatment efficiency and reduce this burden. The most popular and accurate model is the Injury Severity Score (ISS), which is based on the Abbreviated Injury Scale (AIS), an anatomical injury severity scoring system. However, the AIS requires specialists to code the injury scale by reviewing a patient's medical record; therefore, applying the model to every hospital is impossible. OBJECTIVE: We aimed to develop an artificial intelligence (AI) model to predict in-hospital mortality in physical trauma patients using the International Classification of Disease 10th Revision (ICD-10), triage scale, procedure codes, and other clinical features. METHODS: We used the Korean National Emergency Department Information System (NEDIS) data set (N=778,111) compiled from over 400 hospitals between 2016 and 2019. To predict in-hospital mortality, we used the following as input features: ICD-10, patient age, gender, intentionality, injury mechanism, and emergent symptom, Alert/Verbal/Painful/Unresponsive (AVPU) scale, Korean Triage and Acuity Scale (KTAS), and procedure codes. We proposed the ensemble of deep neural networks (EDNN) via 5-fold cross-validation and compared them with other state-of-the-art machine learning models, including traditional prediction models. We further investigated the effect of the features. RESULTS: Our proposed EDNN with all features provided the highest area under the receiver operating characteristic (AUROC) curve of 0.9507, outperforming other state-of-the-art models, including the following traditional prediction models: Adaptive Boosting (AdaBoost; AUROC of 0.9433), Extreme Gradient Boosting (XGBoost; AUROC of 0.9331), ICD-based ISS (AUROC of 0.8699 for an inclusive model and AUROC of 0.8224 for an exclusive model), and KTAS (AUROC of 0.1841). In addition, using all features yielded a higher AUROC than any other partial features, namely, EDNN with the features of ICD-10 only (AUROC of 0.8964) and EDNN with the features excluding ICD-10 (AUROC of 0.9383). CONCLUSIONS: Our proposed EDNN with all features outperforms other state-of-the-art models, including the traditional diagnostic code-based prediction model and triage scale.

Prediction and Feature Importance Analysis for Severity of COVID-19 in South Korea Using Artificial Intelligence: Model Development and Validation.

BACKGROUND: The number of deaths from COVID-19 continues to surge worldwide. In particular, if a patient's condition is sufficiently severe to require invasive ventilation, it is more likely to lead to death than to recovery. OBJECTIVE: The goal of our study was to analyze the factors related to COVID-19 severity in patients and to develop an artificial intelligence (AI) model to predict the severity of COVID-19 at an early stage. METHODS: We developed an AI model that predicts severity based on data from 5601 COVID-19 patients from all national and regional hospitals across South Korea as of April 2020. The clinical severity of COVID-19 was divided into two categories: low and high severity. The condition of patients in the low-severity group corresponded to no limit of activity, oxygen support with nasal prong or facial mask, and noninvasive ventilation. The condition of patients in the high-severity group corresponded to invasive ventilation, multi-organ failure with extracorporeal membrane oxygenation required, and death. For the AI model input, we used 37 variables from the medical records, including basic patient information, a physical index, initial examination findings, clinical findings, comorbid diseases, and general blood test results at an early stage. Feature importance analysis was performed with AdaBoost, random forest, and eXtreme Gradient Boosting (XGBoost); the AI model for predicting COVID-19 severity among patients was developed with a 5-layer deep neural network (DNN) with the 20 most important features, which were selected based on ranked feature importance analysis of 37 features from the comprehensive data set. The selection procedure was performed using sensitivity, specificity, accuracy, balanced accuracy, and area under the curve (AUC). RESULTS: We found that age was the most important factor for predicting disease severity, followed by lymphocyte level, platelet count, and shortness of breath or dyspnea. Our proposed 5-layer DNN with the 20 most important features provided high sensitivity (90.2%), specificity (90.4%), accuracy (90.4%), balanced accuracy (90.3%), and AUC (0.96). CONCLUSIONS: Our proposed AI model with the selected features was able to predict the severity of COVID-19 accurately. We also made a web application so that anyone can access the model. We believe that sharing the AI model with the public will be helpful in validating and improving its performance.

Effectiveness of posterior aortopexy for the left pulmonary vein obstruction between the left atrium and the descending aorta.

BACKGROUND: Left pulmonary vein (PV) obstruction can occur due to compression between the left atrium (LA) and the descending aorta (DA). One of the effective solutions for this problem is posterior aortopexy. In this study, we have reported five cases of posterior aortopexy to relieve left PV obstruction between the LA and the DA. METHODS: Since August 2012, five patients have undergone posterior aortopexy for compression of the left PV between the LA and the DA. The median age and weight of the patients at the time of operation were 5.5 months (range, 1-131 months) and 5.2 kg (range, 4.2-29.5 kg), respectively. The left PV obstruction was initially diagnosed on echocardiography in four patients and computed tomography angiography in one patient. The median peak pressure gradient across the obstructed left PV was 7.3 mmHg (range, 4-20 mmHg). Concomitant procedures were ventricular septal defect closure in one patient and patent ductus arteriosus ligation in one patient. RESULTS: There was no PV obstruction on echocardiography in any of the patients after the operation except in the case of one patient who had diffuse pulmonary vein stenosis. The median follow-up duration was 34 months (range, 14-89 months), and during follow-up no incidence of the left PV obstruction was observed in any of the surviving patients. CONCLUSIONS: The posterior aortopexy technique could be a good surgical option for the left PV obstruction caused by compression between the LA and the anteriorly positioned DA.

Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System.

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).

Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering.

Emotion information represents a user's current emotional state and can be used in a variety of applications, such as cultural content services that recommend music according to user emotional states and user emotion monitoring. To increase user satisfaction, recommendation methods must understand and reflect user characteristics and circumstances, such as individual preferences and emotions. However, most recommendation methods do not reflect such characteristics accurately and are unable to increase user satisfaction. In this paper, six human emotions (neutral, happy, sad, angry, surprised, and bored) are broadly defined to consider user speech emotion information and recommend matching content. The "genetic algorithms as a feature selection method" (GAFS) algorithm was used to classify normalized speech according to speech emotion information. We used a support vector machine (SVM) algorithm and selected an optimal kernel function for recognizing the six target emotions. Performance evaluation results for each kernel function revealed that the radial basis function (RBF) kernel function yielded the highest emotion recognition accuracy of 86.98%. Additionally, content data (images and music) were classified based on emotion information using factor analysis, correspondence analysis, and Euclidean distance. Finally, speech information that was classified based on emotions and emotion information that was recognized through a collaborative filtering technique were used to predict user emotional preferences and recommend content that matched user emotions in a mobile application.

COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation.

BACKGROUND: Coronavirus disease (COVID-19) has spread explosively worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) is a relevant screening tool due to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely occupied fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. OBJECTIVE: We aimed to rapidly develop an AI technique to diagnose COVID-19 pneumonia in CT images and differentiate it from non-COVID-19 pneumonia and nonpneumonia diseases. METHODS: A simple 2D deep learning framework, named the fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning using one of four state-of-the-art pretrained deep learning models (VGG16, ResNet-50, Inception-v3, or Xception) as a backbone. For training and testing of FCONet, we collected 3993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and nonpneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training set and a testing set at a ratio of 8:2. For the testing data set, the diagnostic performance of the four pretrained FCONet models to diagnose COVID-19 pneumonia was compared. In addition, we tested the FCONet models on an external testing data set extracted from embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. RESULTS: Among the four pretrained models of FCONet, ResNet-50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100.00%, and accuracy 99.87%) and outperformed the other three pretrained models in the testing data set. In the additional external testing data set using low-quality CT images, the detection accuracy of the ResNet-50 model was the highest (96.97%), followed by Xception, Inception-v3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). CONCLUSIONS: FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing data set, the FCONet model based on ResNet-50 appears to be the best model, as it outperformed other FCONet models based on VGG16, Xception, and Inception-v3.

An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples: Development and Validation of an Ensemble Model.

BACKGROUND: COVID-19, which is accompanied by acute respiratory distress, multiple organ failure, and death, has spread worldwide much faster than previously thought. However, at present, it has limited treatments. OBJECTIVE: To overcome this issue, we developed an artificial intelligence (AI) model of COVID-19, named EDRnet (ensemble learning model based on deep neural network and random forest models), to predict in-hospital mortality using a routine blood sample at the time of hospital admission. METHODS: We selected 28 blood biomarkers and used the age and gender information of patients as model inputs. To improve the mortality prediction, we adopted an ensemble approach combining deep neural network and random forest models. We trained our model with a database of blood samples from 361 COVID-19 patients in Wuhan, China, and applied it to 106 COVID-19 patients in three Korean medical institutions. RESULTS: In the testing data sets, EDRnet provided high sensitivity (100%), specificity (91%), and accuracy (92%). To extend the number of patient data points, we developed a web application (BeatCOVID19) where anyone can access the model to predict mortality and can register his or her own blood laboratory results. CONCLUSIONS: Our new AI model, EDRnet, accurately predicts the mortality rate for COVID-19. It is publicly available and aims to help health care providers fight COVID-19 and improve patients' outcomes.

Intelligent Data Management and Security in Cloud Computing.

This paper will present the authors' own techniques of secret data management and protection, with particular attention paid to techniques securing data services. Among the solutions discussed, there will be information-sharing protocols dedicated to the tasks of secret (confidential) data sharing. Such solutions will be presented in an algorithmic form, aimed at solving the tasks of protecting and securing data against unauthorized acquisition. Data-sharing protocols will execute the tasks of securing a special type of information, i.e., data services. The area of data protection will be defined for various levels, within which will be executed the tasks of data management and protection. The authors' solution concerning securing data with the use of cryptographic threshold techniques used to split the secret among a specified group of secret trustees, simultaneously enhanced by the application of linguistic methods of description of the shared secret, forms a new class of protocols, i.e., intelligent linguistic threshold schemes. The solutions presented in this paper referring to the service management and securing will be dedicated to various levels of data management. These levels could be differentiated both in the structure of a given entity and in its environment. There is a special example thereof, i.e., the cloud management processes. These will also be subject to the assessment of feasibility of application of the discussed protocols in these areas. Presented solutions will be based on the application of an innovative approach, in which we can use a special formal graph for the creation of a secret representation, which can then be divided and transmitted over a distributed network.

Recognition System Using Fusion Normalization Based on Morphological Features of Post-Exercise ECG for Intelligent Biometrics.

Although biometrics systems using an electrocardiogram (ECG) have been actively researched, there is a characteristic that the morphological features of the ECG signal are measured differently depending on the measurement environment. In general, post-exercise ECG is not matched with the morphological features of the pre-exercise ECG because of the temporary tachycardia. This can degrade the user recognition performance. Although normalization studies have been conducted to match the post- and pre-exercise ECG, limitations related to the distortion of the P wave, QRS complexes, and T wave, which are morphological features, often arise. In this paper, we propose a method for matching pre- and post-exercise ECG cycles based on time and frequency fusion normalization in consideration of morphological features and classifying users with high performance by an optimized system. One cycle of post-exercise ECG is expanded by linear interpolation and filtered with an optimized frequency through the fusion normalization method. The fusion normalization method aims to match one post-exercise ECG cycle to one pre-exercise ECG cycle. The experimental results show that the average similarity between the pre- and post-exercise states improves by 25.6% after normalization, for 30 ECG cycles. Additionally, the normalization algorithm improves the maximum user recognition performance from 96.4 to 98%.

Risk Factors for Prolonged Pleural Effusion After Extracardiac Fontan Operation.

Prolonged pleural effusion after Fontan operation is a significant morbidity that leads to long hospital stays. We investigated the association of multiple risk factors, including clinical characteristics, hemodynamic parameters, and preoperative, operative, and postoperative factors, with prolonged pleural effusion after Fontan operation. Eighty-five patients who underwent a Fontan operation between January 2005 and June 2018 in our center were included in this retrospective study. Patients were divided into two groups: group 1 (n = 36, 42.4%) included those with prolonged pleural effusion, defined as lasting > 14 days after the Fontan operation, and group 2 included patients without prolonged pleural effusion. Patients with hypoplastic left heart syndrome (HLHS) were more prevalent in group 1 (n = 15, P = 0.006). No differences in age at Fontan operation, central venous pressure at Fontan operation, or hemodynamic parameters during the pre-Fontan evaluation were found between the two groups. In multivariable analysis, HLHS (P = 0.002), non-fenestration (P = 0.018), and high central venous pressure at bidirectional cavopulmonary shunt (BCPS) operation (P = 0.043) were independent risk factors for prolonged pleural effusion after Fontan operation. Adverse outcomes such as death, need for heart transplantation, and Fontan failure were not associated with prolonged pleural effusion. In conclusion, patients with HLHS and higher central venous pressure at BCPS were more likely to have a prolonged pleural effusion after Fontan operation, but fenestration was more likely to decrease prolonged effusion. We should consider closer management of fluid status before, during, and after surgery in patients with these risk factors after Fontan operation.

Does Coronary Reimplantation After Neoaortic Reconstruction Increase Aortic Regurgitation?

Coronary reimplantation after neoaortic reconstruction (CRANR) in the arterial switch operation (ASO) allows easy selection of accurate coronary transfer sites in the distended neoaorta. However, neoaortic valve injury may occur during coronary reimplantation. We determined whether the CRANR procedure increased the incidence of aortic valve regurgitation (AR) after ASO. Between March 1994 and August 2017, 227 patients underwent ASO. Since September 2000 CRANR has been performed on 155 patients and open coronary reimplantation (OCR) on 72. Patients who had undergone aortocoronary flaps procedures (n = 13), had early or late mortality (n = 27), or lacked data (n = 11) were excluded. We enrolled and retrospectively reviewed the medical records of 176 patients who were followed up for postoperative AR: 38 underwent OCR and 138 underwent CRANR. We compared the incidences of early and late postoperative AR in both groups. We defined mild or greater AR as "significant AR." The groups did not differ in body weight at operation, great artery relationship, and coronary artery anatomy. The incidences of significant AR at discharge were 21.1% (8/38) in the OCR group and 16.6% (23/138) in the CRANR group (p = 0.53). The freedom from significant AR at 5 years was 59.9% in the OCR group and 62.4% in the CRANR group with no difference between the two groups (p = 0.73). Moderate AR occurred in one patient in the CRANR group. No surgical intervention was required for the aortic valve in either group. ASO using the CRANR technique did not increase the incidence of postoperative early and late AR.

Late results of right ventricular outflow tract reconstruction with a bicuspid expanded polytetrafluoroethylene valved conduit.

BACKGROUND AND AIM TO READ: We report the results of a bicuspid expanded polytetrafluoroethylene (ePTFE) valved conduit used for right ventricular outflow tract reconstruction (RVOTR). METHODS: Between November 2005 and February 2009, 12 conduits were used for RVOTR. The mean age and weight of patients were 43.5 ± 46.4 months and 13.4 ± 8.6 kg. The main diagnosis was tetralogy of Fallot with pulmonary atresia in eight patients. The most common conduit size was 18 mm. The mean follow-up was 88.0 ± 35.9 months. RESULTS: There were no operative and late mortalities. At discharge, the mean peak systolic pressure gradient across the RVOT was 14.1 ± 11.3 mmHg. There was no conduit valve regurgitation in nine patients. At the latest echocardiography (mean follow-up: 84.3 ± 35.5 months), the mean peak systolic pressure gradient across the RVOT was 59.7 ± 20.2 mmHg, and there was no conduit valve regurgitation in six patients. Freedom from conduit malfunction was 100% and 83.3%, at 1 and 8 years, respectively. Two conduits were explanted due to sternal compression and four from conduit malfunction. Freedom from explantation was 83.3% and 74.2% at 2 and 8 years, respectively. CONCLUSIONS: ePTFE bicuspid valved conduit has good late function in terms of valve regurgitation, but the pressure gradient across the conduit increases with time, which is the main cause of conduit failure and explantation.

Right Ventricle-to-Pulmonary Artery Shunt in Pulmonary Atresia with a Ventricular Septal Defect: A Word of Caution.

The management of pulmonary atresia with a ventricular septal defect (PA/VSD) depends on the anatomy of the pulmonary artery or on the surgical strategy used at individual institutions. In our institution, we adopted a right ventricle-to-pulmonary artery (RV-PA) shunt in 2011 as a palliative procedure for PA/VSD to overcome the disadvantages of a Blalock-Taussig shunt. We evaluated the results of the RV-PA shunt as initial palliative surgery for PA/VSD. Thirteen patients with ductus-dependent PA/VSD from August 2011 to August 2015 were enrolled. The mean age at surgery was 17.9 ± 15.3 (range 5-60) days, and the mean body weight was 2.9 ± 0.6 (range 2.2-4.0) kg. A Gore-Tex tube graft was used in all patients. We retrospectively observed intra- and postoperative complications, early and late mortality, and palliation duration to definitive repair. Left pulmonary artery angioplasty was performed as a concomitant procedure in three patients. There were no early hospital mortalities, although two inter-stage deaths occurred 34 and 47 days postoperatively: one patient died of aspiration and the other of right ventricular outflow tract (RVOT) pseudoaneurysm rupture. Two patients (15.4%) required the extracorporeal membrane oxygenation support postoperatively: one because of failure to wean from a bypass caused by persistent hypoxemia and the other because of sudden massive bleeding from the RVOT suture line in the intensive care unit 2 days postoperatively. These two patients underwent second-stage definitive repair successfully. Five patients (41.7%) required catheter intervention, for juxtaductal left pulmonary artery stenosis in three patients, right pulmonary artery stenosis in one, and shunt inflow stenosis in one. Two patients (15.4%) required re-operation because of shunt inflow stenosis and RVOT pseudoaneurysm, respectively. All patients who survived the RV-PA shunt underwent total correction at a mean interval of 13.1 months. A RV-PA shunt is an option for the initial palliation of ductus-dependent PA/VSD. Major complications can occur, including RVOT pseudoaneurysm, shunt inflow stenosis, persistent hypoxemia during the immediate postoperative period, and dehiscence of the shunt anastomosis site. Caution should be taken when performing the RV-PA shunt for palliation of PA/VSD.