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Background This study aimed to investigate the hemodynamic and anatomic factors associated with sinus thrombosis following transcatheter aortic valve replacement (TAVR), integrating in vivo patient data analysis and in vitro experiments. Methods and Results Postprocedural, 4‐dimensional, multiphase computed tomography data from 211 patients enrolled in the ADAPT‐TAVR (Anticoagulation Versus Dual Antiplatelet Therapy for Prevention of Leaflet Thrombosis and Cerebral Embolization After Transcatheter Aortic Valve Replacement) study were analyzed. The prevalence of native sinus thrombosis was examined in relation to valve type, implant depth, and anatomic features. In vitro experiments used particle image velocimetry to observe changes in sinus flow based on the transcatheter heart valves (23‐mm SAPIEN3, Edwards Lifesciences; and 29‐mm CoreValve, Medtronic) height and coronary artery flow. Native sinus thrombosis was more common in self‐expanding valves (39.1% versus 14.9%, P=0.004). In per‐cusp analysis of in vivo patient data, adjusted transcatheter heart valve implant depth (odds ratio, 1.2 [95% CI, 1.1–1.3]; P

Background: Limited data are available on the long-term outcomes of drug-eluting stents (DES) vs bare-metal stents (BMS) in patients with left main coronary artery (LMCA) disease. Methods: In this observational cohort of the Revascularization for Unprotected Left Main Coronary Artery Stenosis: Comparison of Percutaneous Coronary Angioplasty vs Surgical Revascularization (MAIN-COMPARE) registry, we evaluated patients with unprotected LMCA stenosis who received DES or BMS between January 2000 and June 2006. The primary outcome was a composite of all-cause death or myocardial infarction (MI) at 10 years. Adjusted outcomes were compared using propensity scores and inverse probability of treatment weighting. Results: A total of 1102 patients underwent DES (n = 784) or BMS (n = 318) during the study period. At 10 years, the adjusted rate of the primary outcome was significantly lower in DES group than in the BMS group (27.9% vs 37.0%; hazard ratio [HR], 0.71; 95% confidence interval [CI], 0.53-0.94; P = 0.02). The adjusted 10-year mortality rate was significantly lower in DES group than in the BMS group (20.6% vs 29.6%; HR, 0.65; 95% CI, 0.46-0.91; P = 0.01), whereas the 10-year rate of MI was similar between the 2 groups (9.9% vs 11.0%; HR, 0.93; 95% CI, 0.54-1.59; P = 0.78). DES use was associated with a significant reduction in the rate of target-lesion revascularization (10.2% vs 21.8%; HR, 0.41; 95% CI, 0.27-0.61; P < 0.001). Conclusions: In this 10-year follow-up study in patients with LMCA disease, DES use was associated with a significant reduction in the rate of the composite of death or MI, mortality, and target-lesion revascularization, when compared with BMS. Résumé: Contexte: On dispose de peu de données sur les résultats à long terme de la mise en place d'endoprothèses médicamentées par rapport aux endoprothèses non médicamentées chez les patients atteints d'une maladie de l'artère coronaire principale gauche. Méthodologie: Dans cette cohorte d'observation du registre MAIN-COMPARE (Revascularization for Unprotected Left Main Coronary Artery Stenosis: Comparison of Percutaneous Coronary Angioplasty vs Surgical Revascularization), nous avons évalué les patients présentant une sténose de l'artère coronaire principale gauche non protégée et ayant reçu une endoprothèse médicamentée ou une endoprothèse non médicamentée entre janvier 2000 et juin 2006. Le paramètre d’évaluation principal était composé de la mortalité toutes causes confondues et de l'infarctus du myocarde (IM) à 10 ans. Les résultats ajustés ont été comparés en utilisant des scores de propension et la pondération inverse sur la probabilité d’être traité. Résultats: Au total, 1 102 patients ont reçu des endoprothèses médicamentées (n = 784) ou des endoprothèses non médicamentées (n = 318) pendant la période d’étude. À 10 ans, le taux ajusté de survenue du paramètre d’évaluation principal était nettement plus faible dans le groupe endoprothèses médicamentées que dans le groupe endoprothèses non médicamentées (27,9 % vs 37,0 %; rapport des risques instantanés [RRI] : 0,71; intervalle de confiance [IC] à 95 % : 0,53-0,94; p = 0,02). Le taux de mortalité ajusté à 10 ans était considérablement plus faible dans le groupe endoprothèses médicamentées que dans le groupe endoprothèses non médicamentées (20,6 % vs 29,6 %; RRI : 0,65; IC à 95 % : 0,46-0,91; p = 0,01), tandis que le taux d'IM à 10 ans était similaire dans les deux groupes (9,9 % vs 11,0 %; RRI : 0,93; IC à 95 % : 0,54-1,59; p = 0,78). L'utilisation d'endoprothèses médicamentées était associée à une diminution importante du taux de revascularisation de la lésion cible (10,2 % vs 21,8 %; RRI : 0,41; IC à 95 % : 0,27-0,61; p < 0,001). Conclusions: Dans cette étude de suivi de 10 ans menée auprès de patients atteints d'une maladie de l'artère coronaire principale gauche, l'utilisation d'endoprothèses médicamentées a été associée à une diminution importante du taux des décès ou des IM regroupés, de la mortalité et du taux de revascularisation de la lésion cible, par rapport à l'utilisation d'endoprothèses non médicamentées.

Background: Diabetes mellitus (DM) is a well-known risk factor for adverse cardiovascular events in patients receiving percutaneous coronary intervention (PCI). Limited data are available on the relative performance of different types of contemporary drug-eluting stents (DES) for diabetic patients. Objectives: The authors investigated the effectiveness and safety profiles of several contemporary DES in patients with DM in a “real-world” clinical setting. Methods: Among 24,516 patients enrolled in a multicenter, prospective registry, 7,823 patients with DM were treated with 4 contemporary DES: 2,877 with a cobalt chromium everolimus-eluting stent (EES), 789 with a biodegradable polymer biolimus-eluting stent, 2,286 with a platinum chromium-EES, and 1,871 with a Resolute zotarolimus-eluting stent. The primary outcome was target vessel failure (TVF) (a composite of cardiac death, target vessel myocardial infarction, and target vessel revascularization). Results: The median follow-up duration was 2.9 years. Observed 3-year rates of TVF were not significantly different according to different DES types. On multigroup propensity-score analysis, the adjusted HRs for TVF were similar in between-group comparisons: biodegradable polymer biolimus-eluting stent (HR: 0.94; 95% CI: 0.76-1.16; P = 0.57), platinum chromium-EES (HR: 0.94; 95% CI: 0.81-1.09; P = 0.41), and Resolute zotarolimus-eluting stent (HR: 1.01; 95% CI: 0.86-1.18; P = 0.93) compared with the cobalt chromium-EES (reference). This trend was maintained in patients with non–insulin- and insulin-treated DM. Conclusions: In this multicenter clinical-practice PCI registry, no significant between-group differences were found for a 3-year risk of TVF in patients with DM undergoing PCI with various types of contemporary DES. (Evaluation of the First, Second, and New Drug-Eluting Stents in Routine Clinical Practice [IRIS-DES]; NCT01186133)

Background: Moderate aortic valve stenosis occurs twice as often as severe aortic stenosis (AS) and carries a similarly poor prognosis. Current European and American guidelines offer limited insight into moderate AS (MAS) patients with unexplained symptoms. Measuring valve physiology at rest while most patients experience symptoms during exertion might represent a conceptual limitation in the current grading of AS severity. The stress aortic valve index (SAVI) may delineate hemodynamically significant AS among patients with MAS. Objectives: To investigate the diagnostic value of SAVI in symptomatic MAS patients with normal left ventricular ejection fraction (LVEF ≥ 50%): aortic valve area (AVA) > 1 cm2 plus either mean valve gradient (MG) 15–39 mmHg or maximal aortic valve velocity (AOV max) 2.5–3.9 m/s. Short-term objectives include associations with symptom burden, functional capacity, and cardiac biomarkers. Long-term objectives include clinical outcomes. Methods and results: Multicenter, non-blinded, observational cohort. AS severity will be graded invasively (aortic valve pressure measurements with dobutamine stress testing for SAVI) and non-invasively (echocardiography during dobutamine and exercise stress). Computed tomography (CT) of the aortic valve will be scored for calcium, and hemodynamics simulated using computational fluid dynamics. Cardiac biomarkers and functional parameters will be serially monitored. The primary objective is to see how SAVI and conventional measures (MG, AVA and Vmax) correlate with clinical parameters (quality of life survey, 6-minute walk test [6MWT], and biomarkers). Conclusions: The SAVI-AoS study will extensively evaluate patients with unexplained, symptomatic MAS to determine any added value of SAVI versus traditional, resting valve parameters.

BackgroundAlthough there is a growing interest in prediction models based on electronic medical records (EMRs) to identify patients at risk of adverse cardiac events following invasive coronary treatment, robust models fully utilizing EMR data are limited. ObjectiveWe aimed to develop and validate machine learning (ML) models by using diverse fields of EMR to predict the risk of 30-day adverse cardiac events after percutaneous intervention or bypass surgery. MethodsEMR data of 5,184,565 records of 16,793 patients at a quaternary hospital between 2006 and 2016 were categorized into static basic (eg, demographics), dynamic time-series (eg, laboratory values), and cardiac-specific data (eg, coronary angiography). The data were randomly split into training, tuning, and testing sets in a ratio of 3:1:1. Each model was evaluated with 5-fold cross-validation and with an external EMR-based cohort at a tertiary hospital. Logistic regression (LR), random forest (RF), gradient boosting machine (GBM), and feedforward neural network (FNN) algorithms were applied. The primary outcome was 30-day mortality following invasive treatment. ResultsGBM showed the best performance with area under the receiver operating characteristic curve (AUROC) of 0.99; RF had a similar AUROC of 0.98. AUROCs of FNN and LR were 0.96 and 0.93, respectively. GBM had the highest area under the precision-recall curve (AUPRC) of 0.80, and the AUPRCs of RF, LR, and FNN were 0.73, 0.68, and 0.63, respectively. All models showed low Brier scores of 0.95, contributing majorly to the excellent results. ConclusionsExploiting the diverse fields of the EMR data set, the ML-based 30-day adverse cardiac event prediction models demonstrated outstanding results, and the applied framework could be generalized for various health care prediction models.

Background There are still limited data about the differential effect of sex on long‐term outcomes after percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG) for left main coronary artery disease. This extended follow‐up study of the MAIN‐COMPARE (Ten‐Year Outcomes of Stents Versus Coronary‐Artery Bypass Grafting for Left Main Coronary Artery Disease) registry evaluated clinical outcomes beyond 10 years. Methods and Results Of 2240 patients with unprotected left main coronary artery disease (PCI=1102 and CABG=1138), all‐cause mortality, the composite of death, Q‐wave myocardial infarction, or stroke, and target vessel revascularization were separately evaluated in both sexes. Of 2240 patients, 631 (28.2%) were women and 1609 (71.8%) were men. Women had lower 10‐year incidences of death and serious composite outcomes than men. The adjusted 10‐year risks of adverse outcomes were similar in men. However, the adjusted 10‐year risks were different according to a prespecified period in women. In the short‐term (0–1 year) period, PCI had a significantly lower risk for serious composite outcomes (adjusted hazard ratio [HR], 0.41; 95% CI, 0.19–0.91; P=0.03) compared with CABG. The adjusted risks for death and serious composite outcomes were significantly higher after PCI than after CABG, during the midterm (1–5 years) period (death; adjusted HR, 3.99; 95% CI, 2.01–7.92; P

Background Various ECG criteria for left ventricular hypertrophy (LVH) have been proposed, but their association with clinical outcomes in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement is unknown. We investigated the prevalence of ECG LVH according to different criteria and its prognostic impact on clinical outcomes after transcatheter aortic valve replacement. Methods and Results In this prospective observational cohort, we evaluated 700 patients who underwent transcatheter aortic valve replacement between March 2010 and December 2019. Baseline preprocedural LVH was defined by 3 ECG criteria—Sokolow‐Lyon, Romhilt‐Estes, and Cornell voltage criteria. The primary outcome was major adverse cardiac or cerebrovascular event (MACCE; composite of death, myocardial infarction, stroke, or rehospitalization from cardiovascular cause); the key secondary outcome was all‐cause and cardiovascular mortality. Among 596 eligible patients, the prevalence of LVH was determined as 56.3% by Sokolow‐Lyon, 31.1% by Romhilt‐Estes, and 48.1% by Cornell criteria. Regardless of the criteria, patients with ECG LVH had more severe aortic stenosis hemodynamics and higher left ventricular mass index. After multivariate adjustment, the presence of LVH by the Cornell criteria was significantly associated with lower risks of MACCE (adjusted hazard ratio [HR], 0.68; 95% CI, 0.51–0.91; P=0.009), all‐cause mortality (adjusted HR, 0.55; 95% CI, 0.34–0.90 [P=0.017]), and cardiovascular mortality (adjusted HR, 0.40; 95% CI, 0.20–0.79 [P=0.008]). However, this association was absent with the Sokolow‐Lyon and Romhilt‐Estes criteria. Conclusions ECG LVH by Cornell criteria only was significantly associated with lower risks of MACCE and all‐cause or cardiovascular mortality. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT03298178.

Introduction Optimal antithrombotic strategy following transcatheter aortic valve replacement (TAVR) is still unknown. We hypothesised that the direct factor Xa inhibitor edoxaban can potentially prevent subclinical leaflet thrombosis and cerebral embolisation compared with conventional dual antiplatelet therapy (DAPT) in patients undergoing TAVR.Methods and analysis The ADAPT-TAVR trial is an international, multicentre, randomised, open-label, superiority trial comparing edoxaban-based strategy and DAPT strategy in patients without an indication for oral anticoagulation who underwent successful TAVR. A total of 220 patients are randomised (1:1 ratio), 1–7 days after successful TAVR, to receive either edoxaban (60 mg daily or 30 mg daily if patients had dose-reduction criteria) or DAPT using aspirin (100 mg daily) plus clopidogrel (75 mg daily) for 6 months. The primary endpoint was an incidence of leaflet thrombosis on four-dimensional, volume-rendered cardiac CT imaging at 6 months post-TAVR. The key secondary endpoints were the number of new lesions and new lesion volume on brain diffusion-weighted MRI and the changes in neurological and neurocognitive function assessment between immediate post-TAVR and 6 months of study drug administration. Detailed clinical information on thromboembolic and bleeding events were also assessed.Ethics and dissemination Ethic approval has been obtained from the Ethics Committee/Institutional Review Board of Asan Medical Center (approval number: 2017–1317) and this trial is also approved by National Institute of Food and Drug Safety Evaluation of Republic of Korea (approval number: 31511). Results of this study will be disseminated in scientific publication in reputed journals.Trial registration number NCT03284827.

Background Although guidelines recommend the use of coronary computed tomographic angiography (CTA) in patients with stable pain syndromes, the clinical benefits of the use of coronary CTA in a broad spectrum of patients is unknown. We evaluated the contemporary practice pattern and diagnostic yield of coronary CTA and their impact on the subsequent diagnostic‐therapeutic cascade and clinical outcomes. Methods and Results We identified 39 906 patients without known coronary artery disease (CAD) who underwent coronary CTA between January 2007 and December 2013. The patients' demographic characteristics, risk factors, symptoms, results of coronary CTA, the appropriateness of downstream diagnostic and therapeutic interventions, and long‐term outcomes (death or myocardial infarction) were evaluated. The number of coronary CTAs had increased over time, especially in asymptomatic patients. Coronary CTA revealed that 6108 patients (15.3%) had obstructive CAD (23.7% of symptomatic and 9.3% of asymptomatic patients). Subsequent cardiac catheterization was performed in 19.2% of symptomatic patients (appropriate, 80.6%) and in 3.9% of asymptomatic patients (appropriate, 7.9%). The 5‐year rate of death or myocardial infarction was significantly higher in patients with obstructive CAD on CTA than those without (7.2% versus 3.0%; P

Background Data are still limited regarding whether there are differential long‐term outcomes after percutaneous coronary intervention versus coronary artery bypass grafting (CABG) for left main coronary artery disease with or without diabetes mellitus (DM). Methods and Results Using the 10‐year data from the MAIN‐COMPARE (Revascularization for Unprotected Left Main Coronary Artery Stenosis: Comparison of Percutaneous Coronary Angioplasty Versus Surgical Revascularization) registry, we sought to examine the effect of DM on comparative outcomes after percutaneous coronary intervention or CABG in patients with unprotected left main coronary artery disease. The outcomes of interest were all‐cause mortality; a composite of death, Q‐wave myocardial infarction, or stroke; and target‐vessel revascularization. The primary adjusted analyses were performed with the use of propensity scores and inverse‐probability weighting. Of 2240 patients with left main coronary artery revascularization, 722 (32%) had DM. In the overall population, the adjusted 10‐year risks of death and composite outcome were similar between percutaneous coronary intervention and CABG, irrespective of DM status (Pinteraction: 0.41, mortality; 0.40, composite outcome). However, in the cohort of bare‐metal stents and concurrent CABG, we observed differential outcomes after stenting and CABG by DM status (Pinteraction: 0.09, mortality; 0.04, composite outcome), favoring CABG in patients with DM. In the cohort of drug‐eluting stents and concurrent CABG, the better effect of CABG over stenting was narrowed in patients with DM without a significant interaction (Pinteraction: 0.63, mortality; 0.47, composite outcome). Conclusions In this cohort of patients with longest follow‐up who underwent left main coronary artery revascularization, the clinical impact of DM favoring CABG over percutaneous coronary intervention has diminished over time from the bare‐metal stent to the drug‐eluting stent era. Registration URL: http://www.clini​caltr​ials.gov. Unique identifier: NCT02791412.

ObjectiveTo investigate factors associated with enrollment and participation in cardiac rehabilitation (CR) in Korea.MethodsPatients admitted to four university hospitals with acute coronary syndrome between June 2014 and May 2016 were enrolled. The Cardiac Rehabilitation Barriers Scale (CRBS) made of 21-item questionnaire and divided in four subdomains was administered during admission. CRBS items used a 5-point Likert scale and ≥2.5 was considered as a barrier. Differences between CR non-attender and CR attender, or CR non-enroller and CR enroller in subscale and each items of CRBS were examined using the chi-square test.ResultsThe CR participation rate in four hospitals was 31% (170 of the 552). Logistical factors (odds ratio [OR]=7.61; 95% confidence interval [CI], 4.62–12.55) and comorbidities/functional status (OR=6.60; 95% CI, 3.95–11.01) were identified as a barrier to CR enrollment in the subdomain analysis. Among patients who were enrolled (agreed to participate in CR during admission), only work/time conflict was a significant barrier to CR participation (OR=2.17; 95% CI, 1.29–3.66).ConclusionDiverse barriers to CR participation were identified in patients with acute coronary syndrome. Providing the tailored model for CR according to the individual patient's barrier could improve the CR utilization. Further multicenter study with large sample size including other CR indication is required.

Background An angiography‐based supervised machine learning (ML) algorithm was developed to classify lesions as having fractional flow reserve ≤0.80 versus >0.80. Methods and Results With a 4:1 ratio, 1501 patients with 1501 intermediate lesions were randomized into training versus test sets. Between the ostium and 10 mm distal to the target lesion, a series of angiographic lumen diameter measurements along the centerline was plotted. The 24 computed angiographic features based on the diameter plot and 4 clinical features (age, sex, body surface area, and involve segment) were used for ML by XGBoost. The model was independently trained and tested by 2000 bootstrap iterations. External validation with 79 patients was conducted. Including all 28 features, the ML model with 5‐fold cross‐validation in the 1204 training samples predicted fractional flow reserve ≤0.80 with overall diagnostic accuracy of 78±4% (averaged area under the curve: 0.84±0.03). The 12 high‐ranking features selected by scatter search were involved segment; body surface area; distal lumen diameter; minimal lumen diameter; length of a lumen diameter 70%. Using those 12 features, the ML predicted fractional flow reserve ≤0.80 in the test set with sensitivity of 84%, specificity of 80%, and overall accuracy of 82% (area under the curve: 0.87). The averaged diagnostic accuracy in bootstrap replicates was 81±1% (averaged area under the curve: 0.87±0.01). External validation showed accuracy of 85% (area under the curve: 0.87). Conclusions Angiography‐based ML showed good diagnostic performance in identifying ischemia‐producing lesions and reduced the need for pressure wires.

BACKGROUND:Invasive fractional flow reserve (FFR) is a standard tool for identifying ischemia-producing coronary stenosis. However, in clinical practice, over 70% of treatment decisions still rely on visual estimation of angiographic stenosis, which has limited accuracy (about 60%-65%) for the prediction of FFR < 0.80. One of the reasons for the visual-functional mismatch is that myocardial ischemia can be affected by the supplied myocardial size, which is not always evident by coronary angiography. The aims of this study were to develop an angiography-based machine learning (ML) algorithm for predicting the supplied myocardial volume for a stenosis, as measured using coronary computed tomography angiography (CCTA), and then to build an angiography-based classifier for the lesions with an FFR < 0.80 versus ≥ 0.80. METHODS AND FINDINGS:A retrospective study was conducted using data from 1,132 stable and unstable angina patients with 1,132 intermediate lesions who underwent invasive coronary angiography, FFR, and CCTA at the Asan Medical Center, Seoul, Korea, between 1 May 2012 and 30 November 2015. The mean age was 63 ± 10 years, 76% were men, and 72% of the patients presented with stable angina. Of these, 932 patients (assessed before 31 January 2015) constituted the training set for the algorithm, and 200 patients (assessed after 1 February 2015) served as a test cohort to validate its diagnostic performance. Additionally, external validation with 79 patients from two centers (CHA University, Seongnam, Korea, and Ajou University, Suwon, Korea) was conducted. After automatic contour calibration using the caliber of guiding catheter, quantitative coronary angiography was performed using the edge-detection algorithms (CAAS-5, Pie-Medical). Clinical information was provided by the Asan BiomedicaL Research Environment (ABLE) system. The CCTA-based myocardial segmentation (CAMS)-derived myocardial volume supplied by each vessel (right coronary artery [RCA], left anterior descending [LAD], left circumflex [LCX]) and the myocardial volume subtended to a stenotic segment (CAMS-%Vsub) were measured for labeling. The ML for (1) predicting vessel territories (CAMS-%LAD, CAMS-%LCX, and CAMS-%RCA) and CAMS-%Vsub and (2) identifying the lesions with an FFR < 0.80 was constructed. Angiography-based ML, employing a light gradient boosting machine (GBM), showed mean absolute errors (MAEs) of 5.42%, 8.57%, and 4.54% for predicting CAMS-%LAD, CAMS-%LCX, and CAMS-%RCA, respectively. The percent myocardial volumes predicted by ML were used to predict the CAMS-%Vsub. With 5-fold cross validation, the MAEs between ML-predicted percent myocardial volume subtended to a stenotic segment (ML-%Vsub) and CAMS-%Vsub were minimized by the elastic net (6.26% ± 0.55% for LAD, 5.79% ± 0.68% for LCX, and 2.95% ± 0.14% for RCA lesions). Using all attributes (age, sex, involved vessel segment, and angiographic features affecting the myocardial territory and stenosis degree), the ML classifiers (L2 penalized logistic regression, support vector machine, and random forest) predicted an FFR < 0.80 with an accuracy of approximately 80% (area under the curve [AUC] = 0.84-0.87, 95% confidence intervals 0.71-0.94) in the test set, which was greater than that of diameter stenosis (DS) > 53% (66%, AUC = 0.71, 95% confidence intervals 0.65-0.78). The external validation showed 84% accuracy (AUC = 0.89, 95% confidence intervals 0.83-0.95). The retrospective design, single ethnicity, and the lack of clinical outcomes may limit this prediction model's generalized application. CONCLUSION:We found that angiography-based ML is useful to predict subtended myocardial territories and ischemia-producing lesions by mitigating the visual-functional mismatch between angiographic and FFR. Assessment of clinical utility requires further validation in a large, prospective cohort study.

BackgroundThe pathophysiology underlying very late drug‐eluting stent (DES) thrombosis is not sufficiently understood. Using optical coherence tomography, we investigated characteristics of very late stent thrombosis (VLST) according to different onset times. Methods and ResultsA total of 98 patients from 10 South Korean hospitals who underwent optical coherence tomography for evaluation of very late DES thrombosis were retrospectively included in analyses. VLST occurred at a median of 55.1 months after DES implantation. All patients were divided into 2 equal groups of earlier versus delayed presentation of VLST, according to median onset time. In total, 27 patients were treated with next‐generation DES and 71 with first‐generation DES. Based on optical coherence tomography findings at thrombotic sites, main VLST mechanisms were as follows, in descending order: neoatherosclerosis (34.7%), stent malapposition (33.7%), and uncovered struts without stent malapposition or evagination (24.5%). Compared with patients with earlier VLST, patients with delayed VLST had lower frequency of uncovered struts without stent malapposition or evagination (34.7% versus 14.3%, respectively; P=0.019). Conversely, the frequency of neoatherosclerosis was higher in patients with delayed versus earlier VLST (44.9% versus 24.5%, respectively; P=0.034). The frequency of stent malapposition was not different between patients with earlier and delayed VLST (34.7% versus 32.7%, respectively; P=0.831). The frequency of stent malapposition, evagination, and uncovered struts was still half of delayed VLST. ConclusionsThe pathological mechanisms of very late DES thrombosis changed over time. Delayed neointimal healing remained a substantial substrate for VLST, even long after DES implantation.

Abnormal climate phenomena that exceed conventional weather observations occur worldwide, such as severe floods, droughts, and environmental issues, and have attracted increasing attention. To avoid indiscriminate industrialization and achieve sustainable development, environmental experts have presented various opinions based on climate change scenario data. In this study, shared socioeconomic pathway (SSP) climate change scenarios that consider socioeconomic conditions were used to assess the environmental impact of extreme climate conditions on watersheds. Using the UK Earth System Modelling (UKESM1) SSP scenarios, we analyzed the following: 1) Heat index for each SSP climate change scenario using the H-Index; 2) extreme climate for each SSP climate change scenario using the standardized precipitation index (SPI); 3) the impact of extreme climate on non-point pollution using event mean concentration (EMC); and 4) the environmental flow caused by extreme climate by combining the K-water Distributed RUnoff Model (K-DRUM) and global environmental flow calculator (GEFC) models. According to the heat index analysis regarding SSPs climate change scenarios, the heatwave index will continue to rise if high carbon emissions persist. Temperature serves as an important indicator that has the most meaningful impact on ecosystems. The SPI analysis showed an increase in “extremely dry” conditions, and overall, more severe droughts are likely to occur due to high carbon emission-induced climate change. The nonpoint source pollution is also higher in climate change scenarios with high carbon emissions. The extreme drought event assessment using SPI and environmental flow revealed a shift from the “moist conditions” category to grade C. Our assessments in this study conclusively indicate that drought frequency will increase along with nonpoint source pollution. Furthermore, environmental flows will shift to grade C, resulting in the disappearance of some sensitive ecological species and an increase in invasive species. These analyses determined that high carbon emission-induced climate change caused leads to severe droughts and significant alterations in the overall water circulation, thereby complicating water resource management. Furthermore, we identified watersheds that are highly vulnerable to climate change and designated these as “mid-watersheds” that first require non-point pollution management. Here, the aquatic ecosystem environment can be affected by climate change without any artificial environmental influence. Various research-based analysis methods based on modeling have been presented using SSP climate change scenarios, and are highly beneficial for establishing policies and coping strategies for environmental preservation and sustainable development.

Harmful algal blooms (HABs) are a serious threat to ecosystems and human health. The accurate prediction of HABs is crucial for their proactive preparation and management. While mechanism-based numerical modeling, such as the Environmental Fluid Dynamics Code (EFDC), has been widely used in the past, the recent development of machine learning technology with data-based processing capabilities has opened up new possibilities for HABs prediction. In this study, we developed and evaluated two types of machine learning-based models for HABs prediction: Gradient Boosting models (XGBoost, LightGBM, CatBoost) and attention-based CNN-LSTM models. We used Bayesian optimization techniques for hyperparameter tuning, and applied bagging and stacking ensemble techniques to obtain the final prediction results. The final prediction result was derived by applying the optimal hyperparameter and bagging and stacking ensemble techniques, and the applicability of prediction to HABs was evaluated. When predicting HABs with an ensemble technique, it is judged that the overall prediction performance can be improved by complementing the advantages of each model and averaging errors such as overfitting of individual models. Our study highlights the potential of machine learning-based models for HABs prediction and emphasizes the need to incorporate the latest technology into this important field.

Harmful algal blooms (HABs) caused by harmful cyanobacteria adversely impact the water quality in aquatic ecosystems and burden socioecological systems that are based on water utilization. Currently, Korea uses the Environmental Fluid Dynamics Code-National Institute of Environmental Research (EFDC-NIER) model to predict algae conditions and respond to algal blooms through the HAB alert system. This study aimed to establish an additional deep learning model to effectively respond to algal blooms. The prediction model is based on a deep neural network (DNN), which is a type of artificial neural network widely used for HAB prediction. By applying the synthetic minority over-sampling technique (SMOTE) to resolve the imbalance in the data, the DNN model showed improved performance during validation for predicting the number of cyanobacteria cells. The R-squared increased from 0.7 to 0.78, MAE decreased from 0.7 to 0.6, and RMSE decreased from 0.9 to 0.7, indicating an enhancement in the model’s performance. Furthermore, regarding the HAB alert levels, the R-squared increased from 0.18 to 0.79, MAE decreased from 0.2 to 0.1, and RMSE decreased from 0.3 to 0.2, indicating improved performance as well. According to the results, the constructed data-based model reasonably predicted algae conditions in the summer when algal bloom-induced damage occurs and accurately predicted the HAB alert levels for immediate decision-making. The main objective of this study was to develop a new technology for predicting and managing HABs in river environments, aiming for a sustainable future for the aquatic ecosystem.

Green algae play an important role in ecosystems as primary producers, but they can cause algal blooms, which are socio-environmental burdens as responding to them requires water resources from dam reservoirs. This study proposes an alternative for reducing algal blooms through dam operation without using additional water resources. A novel oscillation flow concept was suggested: oscillating discharge of dam for irregular flow. To examine its effect, the Environmental Fluid Dynamics Code—National Institute of Environment Research (EFDC-NIER) model was constructed and calibrated for the Namhan River, South Korea, from downstream of the Chungju Dam to downstream of Gangcheon Weir. The water quality in the study area were simulated and analyzed for August 2019, which is when the largest number of harmful cyanobacteria had been reported in recent years. Our results showed that the oscillation flow produced significant variance of flow velocity, and algal bloom density in the Namhan River was reduced by 20–30% through the operation of the Chungju Dam. However, further study and investigation are required before practical application.

Cyanobacterial Harmful Algal Blooms (CyanoHABs) produce toxins and odors in public water bodies and drinking water. Current process-based models predict algal blooms by modeling chlorophyll-a concentrations. However, chlorophyll-a concentrations represent all algae and hence, a method for predicting the proportion of harmful cyanobacteria is required. We proposed a technique to predict harmful cyanobacteria concentrations based on the source codes of the Environmental Fluid Dynamics Code from the National Institute of Environmental Research. A graphical user interface was developed to generate information about general water quality and algae which was subsequently used in the model to predict harmful cyanobacteria concentrations. Predictive modeling was performed for the Hapcheon-Changnyeong Weir–Changnyeong-Haman Weir section of the Nakdong River, South Korea, from May to October 2019, the season in which CyanoHABs predominantly occur. To evaluate the success rate of the proposed model, a detailed five-step classification of harmful cyanobacteria levels was proposed. The modeling results demonstrated high prediction accuracy (62%) for harmful cyanobacteria. For the management of CyanoHABs, rather than chlorophyll-a, harmful cyanobacteria should be used as the index, to allow for a direct inference of their cell densities (cells/mL). The proposed method may help improve the existing Harmful Algae Alert System in South Korea.

Process-based modeling for predicting harmful cyanobacteria is affected by a variety of factors, including the initial conditions, boundary conditions (tributary inflows and atmosphere), and mechanisms related to cyanobacteria growth and death. While the initial conditions do not significantly affect long-term predictions, the initial cyanobacterial distribution in water is particularly important for short-term predictions. Point-based observation data have typically been used for cyanobacteria prediction of initial conditions. These initial conditions are determined through the linear interpolation of point-based observation data and may differ from the actual cyanobacteria distribution. This study presents an optimal method of applying hyperspectral images to establish the Environmental Fluid Dynamics Code-National Institute of Environment Research (EFDC-NIER) model initial conditions. Utilizing hyperspectral images to determine the EFDC-NIER model initial conditions involves four steps that are performed sequentially and automated in MATLAB. The EFDC-NIER model is established using three grid resolution cases for the Changnyeong-Haman weir section of the Nakdong River Basin, where Microcystis dominates during the summer (July to September). The effects of grid resolution on (1) water quality modeling and (2) initial conditions determined using cumulative distribution functions are evaluated. Additionally, the differences in Microcystis values are compared when applying initial conditions using hyperspectral images and point-based evaluation data. Hyperspectral images allow detailed initial conditions to be applied in the EFDC-NIER model based on the plane-unit cyanobacterial information observed in grids, which can reduce uncertainties in water quality (cyanobacteria) modeling.

PurposePrevious studies have revealed many inconsistent results regarding the relationship between vitamin D and metabolic syndrome. The purpose of our study was to investigate the association between serum 25-hydroxyvitamin D (25(OH)D) concentration and factors that characterize metabolic syndrome in Korean children and adolescents.MethodsWe analyzed data from 2,880 children and adolescents aged 10-18 years collected from the 2008-2010 Korean National Health and Nutrition Examination Survey. We investigated the data according to quartiles of 25(OH)D concentrations.ResultsSystolic blood pressure and diastolic blood pressure with adjustment for sex and age differed significantly between the serum 25(OH)D groups and exhibited negative trend as 25(OH)D concentrations increased. The number of subjects with metabolic syndrome was greater in the low 25(OH)D groups (I, II, and III quartile) than in the high 25(OH)D group (IV quartile) (P=0.029). BMI and waist circumference were lower in the high 25(OH)D group. Serum 25(OH)D concentration correlated negatively with homeostasis model assessment estimate of insulin resistance (ρ=-0.073, P

PurposeShort stature is a very common reason for visits to pediatric endocrine clinics. It could be the first sign of an underlying disease. The purpose of this study is to investigate the etiologies and general characteristics of subjects who visited an outpatient clinic due to short stature.MethodsWe retrospectively reviewed the medical records of 3,371 patients who visited Severance Children's Hospital with the chief complaint of short stature from 2010 to 2012. Medical history, auxological data, and laboratory tests including bone age were collected and analyzed. Chromosome studies or combined pituitary function tests were performed if needed.ResultsApproximately 89.4% of the subjects with the chief complaint of short stature who visited the outpatient clinic were of normal height, and only 10.6% of subjects were identified as having short stature. Of the subject of short stature, 44.7% were classified as having normal variant short stature; that is, familial short stature (23.0%), constitutional delay in growth (17.7%), and mixed form (3.9%). Pathological short stature was found in 193 subjects (54.2%). Among pathological short stature, most common etiology was growth hormone deficiency (GHD) (38.9%).ConclusionA majority of children had a normal height. Among children with short stature, pathological short stature and normal variants occupied a similar percentage. GHD was the most common cause of pathological short stature and found in about 20% of the children with short stature. In pathological short stature, the height, height velocity, and IGF-1 level were lower than in normal variants.

PurposeThe rapid increase in the incidence of precocious puberty in Korea has clinical and social significance. Gonadotropin-releasing hormone (GnRH) stimulation test is required to diagnose central precocious puberty (CPP), however this test is expensive and time-consuming. This study aimed to identify factors that can predict a positive response to the GnRH stimulation test.MethodsClinical and laboratory parameters, including basal serum luteinizing hormone (LH), follicle-stimulating hormone (FSH), and estradiol (E2), were measured in 540 girls with clinical signs of CPP.ResultsTwo hundred twenty-nine of 540 girls with suspected CPP had a peak serum LH level higher than 5 IU/L (the CPP group). The CPP group had advanced bone age (P

Multifunctional weirs can be used to maintain water supply during dry seasons and to improve downstream water quality during drought conditions through discharge based on retained flux. Sixteen multifunctional weirs were recently constructed in four river systems as part of the Four Rivers Restoration Project. In this study, three multifunctional weirs in the Geum River Basin were investigated to analyze the environmental effects of multifunctional weir operation on downstream flow. To determine seasonal vulnerability to drought, the basin was evaluated using the Palmer Drought Severity Index (PDSI). Furthermore, the downstream flow regime and the effect on water quality improvement of a coordinated dam–multifunctional weir operation controlled by: (a) a rainfall–runoff model; (b) a reservoir optimization model; and (c) a water quality model, were examined. A runoff estimate at each major location in the Geum River Basin was performed using the water quality model, and examined variation in downstream water quality depending on the operational scenario of each irrigation facility such as dams and weirs. Although the water quality was improved by the coordinated operation of the dams and weirs, when the discharged water quality is poor, the downstream water quality is not improved. Therefore, it is necessary to first improve the discharged water quality on the lower Geum River. Improvement of the water quality of main stream in the Geum River is important, but water quality from tributaries should also be improved. By applying the estimated runoff data to the reservoir optimization model, these scenarios will be utilized as basic parameters for assessing the optimal operation of the river.