Implication of diabetic status on platelet reactivity and clinical outcomes after drug-eluting stent implantation: results from the PTRG-DES consortium.

BACKGROUND: Diabetes mellitus (DM) is associated with thrombogenicity, clinically manifested with atherothrombotic events after percutaneous cutaneous intervention (PCI). This study aimed to investigate association between DM status and platelet reactivity, and their prognostic implication in PCI-treated patients. METHODS: The Platelet function and genoType-Related long-term Prognosis-Platelet Function Test (PTRG-PFT) cohort was established to determine the linkage of platelet function test (PFT) with long-term prognosis during dual antiplatelet therapy including clopidogrel in patients treated with drug-eluting stent (DES). We assessed platelet reactivity using VerifyNow and 'high platelet reactivity (HPR)' was defined as ≥ 252 P2Y12 reaction unit (PRU). Major adverse cardiac and cerebrovascular event (MACCE) was a composite of all-cause death, myocardial infarction, stent thrombosis or stroke. RESULTS: Between July 2003 and Aug 2018, DES-treated patients with available PFT were enrolled (n = 11,714). Diabetic patients demonstrated significant higher levels of platelet reactivity (DM vs. non-DM: 225.7 ± 77.5 vs. 213.6 ± 79.1 PRU, P < 0.001) and greater prevalence of HPR compared to non-diabetic patients (38.1% vs. 32.0%, P < 0.001). PRU level and prevalence of HPR were significantly associated with insulin requirement and HbA1c level, as well as diabetic status. DM status and HPR phenotype had a similar prognostic implication, which showed the synergistic clinical impact on MACCE. Association between PRU level and MACCE occurrence seemed higher in diabetic vs. non-diabetic patients. In non-DM patients, HPR phenotype did not significantly increase the risk of MACCE (adjusted hazard ratio [HRadj]: 1.073; 95% confidence interval [CI]: 0.869-1.325; P = 0.511), whereas HPR was an independent determinant for MACCE occurrence among diabetic patients (HRadj: 1.507; 95% CI: 1.193-1.902; P < 0.001). CONCLUSION: The levels of on-clopidogrel platelet reactivity are determined by diabetic status and the severity of DM. In addition, HPR phenotype significantly increases the risk of MACCE only in diabetic patients. CLINICAL TRIAL REGISTRATION: URL: https://www. CLINICALTRIALS: gov . Unique identifier: NCT04734028.

Diagnostic Value of a Wearable Continuous Electrocardiogram Monitoring Device (AT-Patch) for New-Onset Atrial Fibrillation in High-Risk Patients: Prospective Cohort Study.

BACKGROUND: While conventional electrocardiogram monitoring devices are useful for detecting atrial fibrillation, they have considerable drawbacks, including a short monitoring duration and invasive device implantation. The use of patch-type devices circumvents these drawbacks and has shown comparable diagnostic capability for the early detection of atrial fibrillation. OBJECTIVE: We aimed to determine whether a patch-type device (AT-Patch) applied to patients with a high risk of new-onset atrial fibrillation defined by the congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, stroke, vascular disease, age 65-74 years, sex scale (CHA2DS2-VASc) score had increased detection rates. METHODS: In this nonrandomized multicenter prospective cohort study, we enrolled 320 adults aged ≥19 years who had never experienced atrial fibrillation and whose CHA2DS2-VASc score was ≥2. The AT-Patch was attached to each individual for 11 days, and the data were analyzed for arrhythmic events by 2 independent cardiologists. RESULTS: Atrial fibrillation was detected by the AT-Patch in 3.4% (11/320) of patients, as diagnosed by both cardiologists. Interestingly, when participants with or without atrial fibrillation were compared, a previous history of heart failure was significantly more common in the atrial fibrillation group (n=4/11, 36.4% vs n=16/309, 5.2%, respectively; P=.003). When a CHA2DS2-VASc score ≥4 was combined with previous heart failure, the detection rate was significantly increased to 24.4%. Comparison of the recorded electrocardiogram data revealed that supraventricular and ventricular ectopic rhythms were significantly more frequent in the new-onset atrial fibrillation group compared with nonatrial fibrillation group (3.4% vs 0.4%; P=.001 and 5.2% vs 1.2%; P<.001), respectively. CONCLUSIONS: This study detected a moderate number of new-onset atrial fibrillations in high-risk patients using the AT-Patch device. Further studies will aim to investigate the value of early detection of atrial fibrillation, particularly in patients with heart failure as a means of reducing adverse clinical outcomes of atrial fibrillation. TRIAL REGISTRATION: ClinicalTrials.gov NCT04857268; https://classic.clinicaltrials.gov/ct2/show/NCT04857268.

Prediction of functional results of percutaneous coronary interventions with virtual stenting and quantitative flow ratio.

BACKGROUND: The clinical value of residual quantitative flow ratio (rQFR), a novel function of QFR technique, is unknown. AIM: We investigated the clinical value of rQFR, aimed to predict residual ischemia after virtual percutaneous coronary intervention (vPCI). METHODS: This is a substudy of the COE-PERSPECTIVE registry, which investigated the prognostic value of post-PCI fractional flow reserve (FFR). From pre-PCI angiograms, QFR and rQFR were analyzed and their diagnostic performance was assessed at blinded fashion using pre-PCI FFR and post-PCI FFR as reference, respectively. The prognostic value of rQFR after vPCI was assessed according to vessel-oriented composite outcome (VOCO) at 2 years. RESULTS: We analyzed 274 patients (274 vessels) with FFR-based ischemic causing lesions (49%) from 555 screened patients. Pre-PCI QFR and FFR were 0.63 ± 0.10 and 0.66 ± 0.11 (R = 0.756, p < 0.001). rQFR after vPCI and FFR after real PCI were 0.93 ± 0.06 and 0.86 ± 0.07 (R = 0.528, p < 0.001). The mean difference between rQFR and post-PCI FFR was 0.068 (95% limit of agreement: -0.05 to 0.19). Diagnostic performance of rQFR to predict residual ischemia after PCI was good (area under the curve [AUC]: 0.856 [0.804-0.909], p < 0.001). rQFR predicted well the incidence of 2-year VOCO after index PCI (AUC: 0.712 [0.555-0.869], p = 0.041), being similar to that of actual post-PCI FFR (AUC: 0.691 [0.512-0.870], p = 0.061). rQFR ≤0.89 was associated with increased risk of 2-year VOCO (hazard ratio [HR]: 12.9 [2.32-71.3], p = 0.0035). This difference was mainly driven by a higher rate of target vessel revascularization (HR: 16.98 [2.33-123.29], p = 0.0051). CONCLUSIONS: rQFR estimated from pre-PCI angiography and virtual coronary stenting mildly overestimated functional benefit of PCI. However, it well predicted suboptimal functional result and long-term vessel-related clinical events. CLINICAL TRIAL REGISTRATION: Influence of fractional flow reserve on the Clinical OutcomEs of PERcutaneouS Coronary Intervention (COE-PESPECTIVE) Registry, NCT01873560.

Artificial intelligence for detecting electrolyte imbalance using electrocardiography.

INTRODUCTION: The detection and monitoring of electrolyte imbalance is essential for appropriate management of many metabolic diseases; however, there is no tool that detects such imbalances reliably and noninvasively. In this study, we developed a deep learning model (DLM) using electrocardiography (ECG) for detecting electrolyte imbalance and validated its performance in a multicenter study. METHODS AND RESULTS: This retrospective cohort study included two hospitals: 92,140 patients who underwent a laboratory electrolyte examination and an ECG within 30 min were included in this study. A DLM was developed using 83,449 ECGs of 48,356 patients; the internal validation included 12,091 ECGs of 12,091 patients. We conducted an external validation with 31,693 ECGs of 31,693 patients from another hospital, and the result was electrolyte imbalance detection. During internal, the area under the receiving operating characteristic curve (AUC) of a DLM using a 12-lead ECG for detecting hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.945, 0.866, 0.944, 0.885, 0.905, and 0.901, respectively. The values during external validation of the AUC of hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.873, 0.857, 0.839, 0.856, 0.831, and 0.813 respectively. The DLM helped to visualize the important ECG region for detecting each electrolyte imbalance, and it showed how the P wave, QRS complex, or T wave differs in importance in detecting each electrolyte imbalance. CONCLUSION: The proposed DLM demonstrated high performance in detecting electrolyte imbalance. These results suggest that a DLM can be used for detecting and monitoring electrolyte imbalance using ECG on a daily basis.

Detection and classification of arrhythmia using an explainable deep learning model.

BACKGROUND: Early detection and intervention is the cornerstone for appropriate treatment of arrhythmia and prevention of complications and mortality. Although diverse deep learning models have been developed to detect arrhythmia, they have been criticized due to their unexplainable nature. In this study, we developed an explainable deep learning model (XDM) to classify arrhythmia, and validated its performance using diverse external validation data. METHODS: In this retrospective study, the Sejong dataset comprising 86,802 electrocardiograms (ECGs) was used to develop and internally variate the XDM. The XDM based on a neural network-backed ensemble tree was developed with six feature modules that are able to explain the reasons for its decisions. The model was externally validated using data from 36,961 ECGs from four non-restricted datasets. RESULTS: During internal and external validation of the XDM, the average area under the receiver operating characteristic curves (AUCs) using a 12­lead ECG for arrhythmia classification were 0.976 and 0.966, respectively. The XDM outperformed a previous simple multi-classification deep learning model that used the same method. During internal and external validation, the AUCs of explainability were 0.925-0.991. CONCLUSION: Our XDM successfully classified arrhythmia using diverse formats of ECGs and could effectively describe the reason for the decisions. Therefore, an explainable deep learning methodology could improve accuracy compared to conventional deep learning methods, and that the transparency of XDM can be enhanced for its application in clinical practice.

Deep Learning Algorithm to Predict Need for Critical Care in Pediatric Emergency Departments.

BACKGROUND AND OBJECTIVES: Emergency department (ED) overcrowding is a national crisis in which pediatric patients are often prioritized at lower levels. Because the prediction of prognosis for pediatric patients is important but difficult, we developed and validated a deep learning algorithm to predict the need for critical care in pediatric EDs. METHODS: We conducted a retrospective observation cohort study using data from the Korean National Emergency Department Information System, which collected data in real time from 151 EDs. The study subjects were pediatric patients who visited EDs from 2014 to 2016. The data were divided by date into derivation and test data. The primary end point was critical care, and the secondary endpoint was hospitalization. We used age, sex, chief complaint, symptom onset to arrival time, arrival mode, trauma, and vital signs as predicted variables. RESULTS: The study subjects consisted of 2,937,078 pediatric patients of which 18,253 were critical care and 375,078 were hospitalizations. For critical care, the area under the receiver operating characteristics curve of the deep learning algorithm was 0.908 (95% confidence interval, 0.903-0.910). This result significantly outperformed that of the pediatric early warning score (0.812 [0.803-0.819]), conventional triage and acuity system (0.782 [0.773-0.790]), random forest (0.881 [0.874-0.890]), and logistic regression (0.851 [0.844-0.858]). For hospitalization, the deep-learning algorithm (0.782 [0.780-0.783]) significantly outperformed the other methods. CONCLUSIONS: The deep learning algorithm predicted the critical care and hospitalization of pediatric ED patients more accurately than the conventional early warning score, triage tool, and machine learning methods.

Detecting Patient Deterioration Using Artificial Intelligence in a Rapid Response System.

OBJECTIVES: As the performance of a conventional track and trigger system in a rapid response system has been unsatisfactory, we developed and implemented an artificial intelligence for predicting in-hospital cardiac arrest, denoted the deep learning-based early warning system. The purpose of this study was to compare the performance of an artificial intelligence-based early warning system with that of conventional methods in a real hospital situation. DESIGN: Retrospective cohort study. SETTING: This study was conducted at a hospital in which deep learning-based early warning system was implemented. PATIENTS: We reviewed the records of adult patients who were admitted to the general ward of our hospital from April 2018 to March 2019. INTERVENTIONS: The study population included 8,039 adult patients. A total 83 events of deterioration occurred during the study period. The outcome was events of deterioration, defined as cardiac arrest and unexpected ICU admission. We defined a true alarm as an alarm occurring within 0.5-24 hours before a deteriorating event. MEASUREMENTS AND MAIN RESULTS: We used the area under the receiver operating characteristic curve, area under the precision-recall curve, number needed to examine, and mean alarm count per day as comparative measures. The deep learning-based early warning system (area under the receiver operating characteristic curve, 0.865; area under the precision-recall curve, 0.066) outperformed the modified early warning score (area under the receiver operating characteristic curve, 0.682; area under the precision-recall curve, 0.010) and reduced the number needed to examine and mean alarm count per day by 69.2% and 59.6%, respectively. At the same specificity, deep learning-based early warning system had up to 257% higher sensitivity than conventional methods. CONCLUSIONS: The developed artificial intelligence based on deep-learning, deep learning-based early warning system, accurately predicted deterioration of patients in a general ward and outperformed conventional methods. This study showed the potential and effectiveness of artificial intelligence in an rapid response system, which can be applied together with electronic health records. This will be a useful method to identify patients with deterioration and help with precise decision-making in daily practice.

Comparing the performance of artificial intelligence and conventional diagnosis criteria for detecting left ventricular hypertrophy using electrocardiography.

AIMS: Although left ventricular hypertrophy (LVH) has a high incidence and clinical importance, the conventional diagnosis criteria for detecting LVH using electrocardiography (ECG) has not been satisfied. We aimed to develop an artificial intelligence (AI) algorithm for detecting LVH. METHODS AND RESULTS: This retrospective cohort study involved the review of 21 286 patients who were admitted to two hospitals between October 2016 and July 2018 and underwent 12-lead ECG and echocardiography within 4 weeks. The patients in one hospital were divided into a derivation and internal validation dataset, while the patients in the other hospital were included in only an external validation dataset. An AI algorithm based on an ensemble neural network (ENN) combining convolutional and deep neural network was developed using the derivation dataset. And we visualized the ECG area that the AI algorithm used to make the decision. The area under the receiver operating characteristic curve of the AI algorithm based on ENN was 0.880 (95% confidence interval 0.877-0.883) and 0.868 (0.865-0.871) during the internal and external validations. These results significantly outperformed the cardiologist's clinical assessment with Romhilt-Estes point system and Cornell voltage criteria, Sokolov-Lyon criteria, and interpretation of ECG machine. At the same specificity, the AI algorithm based on ENN achieved 159.9%, 177.7%, and 143.8% higher sensitivities than those of the cardiologist's assessment, Sokolov-Lyon criteria, and interpretation of ECG machine. CONCLUSION: An AI algorithm based on ENN was highly able to detect LVH and outperformed cardiologists, conventional methods, and other machine learning techniques.

Artificial intelligence for detecting mitral regurgitation using electrocardiography.

BACKGROUND: Screening and early diagnosis of mitral regurgitation (MR) are crucial for preventing irreversible progression of MR. In this study, we developed and validated an artificial intelligence (AI) algorithm for detecting MR using electrocardiography (ECG). METHODS: This retrospective cohort study included data from two hospital. An AI algorithm was trained using 56,670 ECGs from 24,202 patients. Internal validation of the algorithm was performed with 3174 ECGs of 3174 patients from one hospital, while external validation was performed with 10,865 ECGs of 10,865 patients from another hospital. The endpoint was the diagnosis of significant MR, moderate to severe, confirmed by echocardiography. We used 500 Hz ECG raw data as predictive variables. Additionally, we showed regions of ECG that have the most significant impact on the decision-making of the AI algorithm using a sensitivity map. RESULTS: During the internal and external validation, the area under the receiver operating characteristic curve of the AI algorithm using a 12-lead ECG for detecting MR was 0.816 and 0.877, respectively, while that using a single-lead ECG was 0.758 and 0.850, respectively. In the 3157 non-MR individuals, those patients that the AI defined as high risk had a significantly higher chance of development of MR than the low risk group (13.9% vs. 2.6%, p < 0.001) during the follow-up period. The sensitivity map showed the AI algorithm focused on the P-wave and T-wave for MR patients and QRS complex for non-MR patients. CONCLUSIONS: The proposed AI algorithm demonstrated promising results for MR detecting using 12-lead and single-lead ECGs.

Dialysis-specific factors and incident atrial fibrillation in hemodialysis patients.

BACKGROUND: Atrial fibrillation (AF) is common in end-stage renal disease patients. Besides the traditional risk factors, we aimed to find dialysis-specific factors for developing incident AF. METHODS: From March 2017 to August 2018, we retrospectively reviewed all outpatient-based prevalent hemodialysis patients in our artificial kidney room, and they were followed up until August 2019. Dialysate calcium concentration (3 versus 2.5 mEq/L), time length (4 versus 3.5 h), frequency (thrice weekly versus twice weekly), dialyzer size (effective surface area of 1.4 m2 versus 1.8 m2), membrane permeability (high flux versus low flux), ultrafiltration rate (mL/kg/hour), and blood flow rate (mL/min) were evaluated. RESULTS: Among a total of 84 patients, 15 (17.9%) had newly detected AF with a follow-up period of 21 (13.3-24) months. By performing multivariate Cox regression analysis, blood flow rate (mL/min) and ultrafiltration rate (mL/kg/h) were considered significant factors for developing incident AF (adjusted hazard ratio [HR], 0.977; p = 0.011 and adjusted HR, 1.176; p = 0.013, respectively), while dialysis bath, time length, and frequency, dialyzer size, and membrane type were not considered significant factors. Ultrafiltration cutoff rate of 8.6 mL/kg/h was the best predictive factor for incident AF (area under the curve-receiver operating characteristic [AUC-ROC], 0.746; p < 0.005), while blood flow rate was not considered a significant factor for incident AF in ROC analysis (AUC-ROC, 0.623; p = 0.126). Ultrafiltration rate was largely dependent on interdialytic weight gain (p < 0.005, linear-by-linear association). CONCLUSION: Higher ultrafiltration rate was associated with incident AF in hemodialysis patients.

Deep learning for predicting in-hospital mortality among heart disease patients based on echocardiography.

BACKGROUND: Heart disease (HD) is the leading cause of global death; there are several mortality prediction models of HD for identifying critically-ill patients and for guiding decision making. The existing models, however, cannot be used during initial treatment or screening. This study aimed to derive and validate an echocardiography-based mortality prediction model for HD using deep learning (DL). METHODS: In this multicenter retrospective cohort study, the subjects were admitted adult (age ≥ 18 years) HD patients who underwent echocardiography. The outcome was in-hospital mortality. We extracted predictor variables from echocardiography reports using text mining. We developed deep learning-based prediction model using derivation data of a hospital A. And we conducted external validation using echocardiography report of hospital B. We conducted subgroup analysis of coronary heart disease (CHD) and heart failure (HF) patients of hospital B and compared DL with the currently used predictive models (eg, Global Registry of Acute Coronary Events (GRACE) score, Thrombolysis in Myocardial Infarction score (TIMI), Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score, and Get With The Guidelines-Heart Failure (GWTG-HF) score). RESULTS: The study subjects comprised 25 776 patients with 1026 mortalities. The areas under the receiver operating characteristic curve (AUROC) of the DL model were 0.912, 0.898, 0.958, and 0.913 for internal validation, external validation, CHD, and HF, respectively, and these results significantly outperformed other comparison models. CONCLUSIONS: This echocardiography-based deep learning model predicted in-hospital mortality among HD patients more accurately than existing prediction models and other machine learning models.

Peroxisome proliferator-activated receptor-δ activates endothelial progenitor cells to induce angio-myogenesis through matrix metallo-proteinase-9-mediated insulin-like growth factor-1 paracrine networks.

AIMS: The roles of peroxisome proliferator-activated receptor (PPAR)-δ in vascular biology are mainly unknown. We investigated the effects of PPAR-δ activation on the paracrine networks between endothelial progenitor cells (EPCs) and endothelial cells (ECs)/skeletal muscle. METHODS AND RESULTS: Treatment of EPCs with GW501516, a PPAR-δ agonist, induced specifically matrix metallo-proteinase (MMP)-9 by direct transcriptional activation. Subsequently, this increased-MMP-9 broke down insulin-like growth factor-binding protein (IGFBP)-3, resulting in IGF-1 receptor (IGF-1R) activation in surrounding target cells. Treatment of conditioned medium from GW501516-stimulated EPCs enhanced the number and functions of human umbilical vein ECs and C2C12 myoblasts via MMP-9-mediated IGF-1R activation. Systemic administration of GW501516 in mice increased MMP-9 expression in EPCs, and augmented IGFBP-3 degradation in serum. In a mouse hindlimb ischaemia model, systemic treatment of GW501516 or local transplantation of GW501516-treated EPCs induced IGF-1R phosphorylation in ECs and skeletal muscle in the ischaemic limbs, leading to augmented angiogenesis and skeletal muscle regeneration. It also enhanced wound healing with increased angiogenesis in a mouse skin punch wound model. These pro-angiogenic and muscle-regenerating effects were abolished by MMP-9 knock-out. CONCLUSION: Our results suggest that PPAR-δ is a crucial modulator of angio-myogenesis via the paracrine effects of EPCs, and its agonist is a good candidate as a therapeutic drug for patients with peripheral vascular diseases.

Gelatin microgel-coated balloon catheter with enhanced delivery of everolimus for long-term vascular patency.

In-stent restenosis (ISR) after percutaneous coronary intervention is a major reason for limited long-term patency due to complex neointimal proliferation caused by vascular injury. Drug-coated balloon (DCB) has been developed to treat various cardiovascular diseases including ISR by providing anti-proliferative drugs into blood vessel tissues. However, a significant proportion of the drug is lost during balloon tracking, resulting in ineffective drug delivery to the target region. In this study, we report an everolimus-coated balloon (ECB) using everolimus-loaded gelatin-hydroxyphenyl propionic acid microgel (GM) with enhanced everolimus delivery to vascular walls for long-term patency. GM with high drug loading (> 97%) was simply prepared by homogenizing enzyme-mediated crosslinked hydrogels. The optimal condition to prepare GM-coated ECB (GM-ECB) was established by changing homogenization time and ethanol solvent concentration (30 ∼ 80%). In vitro sustained everolimus release for 30 d, and cellular efficacy using smooth muscle cells and vascular endothelial cells were evaluated. Additionally, an in vivo drug transfer levels of GM-ECB using rabbit femoral arteries were assessed with reduced drug loss and efficient drug delivery capability. Finally, using ISR-induced porcine models, effective in vivo vascular patency 4 weeks after treatment of ECBs was also confirmed. Thus, this study strongly demonstrates that GM can be used as a potential drug delivery platform for DCB application. STATEMENT OF SIGNIFICANCE: We report an ECB using everolimus-loaded GM prepared by homogenization of enzymatic cross-linked hydrogel. GM showed efficient drug loading (> 97 %) and controllable size. GM-ECB exhibited potential to deliver everolimus in a sustained manner to target area with drug efficacy and viability against SMC and EC. Although GM-ECB had much lower drug content compared to controls, animal study demonstrated enhanced drug transfer and reduced drug loss of GM-ECB due to the protection of encapsulated drugs by GM, and the possible interaction between GM and endothelium. Finally, vascular patency and safety were assessed using ISR-induced porcine models. We suggest an advanced DCB strategy to alleviate rapid drug clearance by bloodstream while improving drug delivery for a long-term vascular patency.

CYP3A4 genetic status may be associated with increased vulnerability to the inhibitory effect of calcium-channel blockers on clopidogrel.

BACKGROUND: Calcium-channel blockers (CCBs) inhibit the CYP3A4 enzyme, which is involved in clopidogrel activation. Studies have shown conflicting results regarding the effect of concomitant CCB administration on clopidogrel response. We investigated the relationship between CYP3A4 genotype and the inhibitory effect of CCBs on clopidogrel response. METHODS AND RESULTS: Clopidogrel on-treatment platelet reactivity (OPR) was measured and CYP3A4 (IVS10+12G>A) genotyped in 1,247 consecutive patients with drug-eluting stent implantation. The mean OPR was 231±83 (P2Y12 reaction units: PRU). In total, 332 (26.6%) CCB users had higher OPR compared with 915 (73.4%) non-CCB users (245±84 vs. 227±83 PRU, P=0.001). The distribution of CYP3A4 (IVS10+12G>A) genotype was 63.6%, 32.6% and 3.8% for GG, GA and AA genotypes, respectively. After adjustment for possible confounding factors, the number of A-alleles was associated with increased vulnerability to CCB use (effect of CCB use ΔPRU: +8 PRU, P=0.210, +24 PRU, P=0.012, +50 PRU, P=0.025, for patients with 0, 1, and 2 A-alleles, respectively, +24 PRU, P=0.005 for GA/AA genotypes). Furthermore, only in the GA/AA-genotype did CCB use result in a significantly increased risk for high-OPR (odds ratio 1.84, 95% confidence interval 1.15-2.92, P=0.010). CONCLUSIONS: CCB use is associated with increased OPR. The number of CYP3A4 (IVS10+12G>A) A-alleles may be associated with an increased vulnerability to the effects of CCBs on clopidogrel response variation.

Faster smooth muscle cell coverage in ultrathin-strut drug-eluting stent leads to earlier re-endothelialization.

Background: The ultrathin-strut drug-eluting stent (DES) has shown better clinical results than thin- or thick-strut DES. We investigated if re-endothelialization was different among three types of DES: ultrathin-strut abluminal polymer-coated sirolimus-eluting stent (SES), thin-strut circumferential polymer-coated everolimus-eluting stent (EES), and thick-strut polymer-free biolimus-eluting stent (BES) to gain insight into the effect of stent design on promoting vascular healing. Methods: After implanting three types of DES in the coronary arteries of minipigs, we performed optical coherence tomography (OCT) at weeks 2, 4, and 12 (n = 4, each). Afterward, we harvested the coronary arteries and performed immunofluorescence for endothelial cells (ECs), smooth muscle cells (SMCs), and nuclei. We obtained 3D stack images of the vessel wall and reconstructed the en face view of the inner lumen. We compared re-endothelialization and associated factors among the different types of stents at different time points. Results: SES showed significantly faster and denser re-endothelialization than EES and BES at weeks 2 and 12. Especially in week 2, SES elicited the fastest SMC coverage and greater neointimal cross-sectional area (CSA) compared to EES and BES. A strong correlation between re-endothelialization and SMC coverage was observed in week 2. However, the three stents did not show any difference at weeks 4 and 12 in SMC coverage and neointimal CSA. At weeks 2 and 4, SMC layer morphology showed a significant difference between stents. A sparse SMC layer was associated with denser re-endothelialization and was significantly higher in SES. Unlike the sparse SMC layer, the dense SMC layer did not promote re-endothelialization during the study period. Conclusion: Re-endothelialization after stent implantation was related to SMC coverage and SMC layer differentiation, which were faster in SES. Further investigation is needed to characterize the differences among the SMCs and explore methods for increasing the sparse SMC layer in order to improve stent design and enhance safety and efficacy.

Long-term mortality and cardiovascular events of seven angiotensin receptor blockers in hypertensive patients: Analysis of a national real-world database: A retrospective cohort study.

Background and Aims: Although many angiotensin receptor blockers (ARBs) are widely used, comparative data regarding their impact on clinical outcomes are limited. We aimed to compare the clinical effectiveness of seven ARBs on long-term cardiovascular outcomes in Korean patients with hypertension. Methods: Using the Korean National Health Insurance Service database, the data of 780,785 patients with hypertension without cardiovascular disease (CVD) who initiated ARB treatment (candesartan, fimasartan, irbesartan, losartan, olmesartan, telmisartan, or valsartan) in 2014 and underwent this treatment for more than 6 months, were analyzed. Cox-regression analysis was performed using Losartan as a comparator, as it was the most widely used drug, by adjusting age, sex, diabetes, dyslipidemia, smoking, alcohol drinking, exercise, body mass index, systolic blood pressure, albuminuria, estimated glomerular filtration rate, and concomitant medications. The occurrence of mortality and the rate of major adverse cardiovascular events (MACEs) of the six ARBs was compared with that of losartan. Results: The median follow-up duration was 5.94 (interquartile range, 5.87-5.97) years. In the crude analysis of all-cause mortality and MACEs, fimasartan exhibited the lowest event rates. In the Cox-regression analysis with adjustment, there was no significant difference in all-cause mortality among ARBs. The risk of MACEs with ARBs was similar to that with losartan, although the risks with irbesartan (hazard ratio [HR], 1.079; 95% confidence interval [CI], 1.033-1.127; p = 0.007) and candesartan (HR: 1.066; 95% CI, 1.028-1.106; p = 0.015) were slightly higher. Conclusion: In a Korean population of patients with hypertension without CVD, six different ARBs showed similar efficacy to losartan in terms of long-term mortality and MACEs. Further well-designed prospective studies are required to confirm our findings.

Identifying Atrial Fibrillation With Sinus Rhythm Electrocardiogram in Embolic Stroke of Undetermined Source: A Validation Study With Insertable Cardiac Monitors.

BACKGROUND AND OBJECTIVES: Paroxysmal atrial fibrillation (AF) is a major potential cause of embolic stroke of undetermined source (ESUS). However, identifying AF remains challenging because it occurs sporadically. Deep learning could be used to identify hidden AF based on the sinus rhythm (SR) electrocardiogram (ECG). We combined known AF risk factors and developed a deep learning algorithm (DLA) for predicting AF to optimize diagnostic performance in ESUS patients. METHODS: A DLA was developed to identify AF using SR 12-lead ECG with the database consisting of AF patients and non-AF patients. The accuracy of the DLA was validated in 221 ESUS patients who underwent insertable cardiac monitor (ICM) insertion to identify AF. RESULTS: A total of 44,085 ECGs from 12,666 patient were used for developing the DLA. The internal validation of the DLA revealed 0.862 (95% confidence interval, 0.850-0.873) area under the curve (AUC) in the receiver operating curve analysis. In external validation data from 221 ESUS patients, the diagnostic accuracy of DLA and AUC were 0.811 and 0.827, respectively, and DLA outperformed conventional predictive models, including CHARGE-AF, C2HEST, and HATCH. The combined model, comprising atrial ectopic burden, left atrial diameter and the DLA, showed excellent performance in AF prediction with AUC of 0.906. CONCLUSIONS: The DLA accurately identified paroxysmal AF using 12-lead SR ECG in patients with ESUS and outperformed the conventional models. The DLA model along with the traditional AF risk factors could be a useful tool to identify paroxysmal AF in ESUS patients.

Influence of early dose reduction of ticagrelor on clinical outcomes following percutaneous coronary intervention for complex lesions.

Ticagrelor-based dual antiplatelet therapy (DAPT) provides potent antiplatelet inhibition but may increase the bleeding risk in Asian populations. We investigated the influence of early ticagrelor dose reduction (120 mg) on clinical outcomes in Korean patients undergoing percutaneous coronary intervention (PCI). A multicenter prospective clinical cohort study was conducted with patients who received standard-dose ticagrelor-based DAPT (180 mg) after PCI for complex lesions. Major adverse cardiovascular event (MACE: a composite of cardiovascular death, myocardial infarction, stroke, and repeat revascularization), bleeding, and net adverse clinical events (NACE: a composite of MACE and bleeding) were assessed. Among the 772 patients on standard-dose ticagrelor-based DAPT, 115 (14.8%) switched to low-dose ticagrelor-based DAPT (120 mg) within 6 months. Common reasons for the regimen changes were switching as planned (38.8%), dyspnea (25.5%), and bleeding (23.6%). A multivariable Cox proportional hazard model (CPH) showed that the risks of MACE, bleeding, and NACE were not different between the low-dose and standard-dose groups throughout the entire follow-up period and the period beyond 6 months post-PCI. Time-varying multivariable CPH models of the ticagrelor dose reduction yielded similar results. A reduction of the ticagrelor dose within 6 months after PCI is feasible and safe even in patients with complex lesions harboring a high ischemic event risk.

Enhanced clopidogrel responsiveness in smokers: smokers' paradox is dependent on cytochrome P450 CYP1A2 status.

OBJECTIVE: Observational studies have reported enhanced response to clopidogrel in smokers (the smokers' paradox). We examined whether genetic variations in the cytochrome and drug transporter system are associated with the effect of smoking on clopidogrel response. METHODS AND RESULTS: Clopidogrel on-treatment platelet reactivity (OPR) was measured in 1431 consecutive patients who underwent coronary angiography. Gene samples were available and genotyping was successful in 1123 patients. Nine candidate single-nucleotide polymorphisms in 5 cytochrome genes and 1 drug transporter gene were assessed. The mean OPR of the entire population was 241.9 ± 79.3 (P2Y(12) reaction units). Two hundred forty-nine (17%) smokers had lower OPR compared with 1182 (83%) nonsmokers (227.6 ± 76.0 versus 244.9 ± 79.7, P=0.001). Among the 9 single-nucleotide polymorphisms, only CYP1A2 showed a genotype-dependent change in the effect of smoking on OPR. After adjustment for possible confounding factors, cigarette smoking was associated with a lower OPR by -19 P2Y(12) reaction units (P=0.009) and lower risk for high OPR (odds ratio [OR], 0.48; 95% CI, 0.31 to 0.74) in the AA and CA genotypes but not in the CC genotype. CONCLUSIONS: Enhanced clopidogrel response in smokers, known as the smokers' paradox, is not universal but was observed only in cytochrome P450 CYP1A2 (-163C>A) A-allele carriers, suggesting a genotype-dependent effect of smoking on clopidogrel responsiveness.

Surveillance of Arrhythmia in Patients After Myocardial Infarction Using Wearable Electrocardiogram Patch Devices: Prospective Cohort Study.

BACKGROUND: Acute myocardial infarction may be associated with new-onset arrhythmias. Patients with myocardial infarction may manifest serious arrhythmias such as ventricular tachyarrhythmias or atrial fibrillation. Frequent, prolonged electrocardiogram (ECG) monitoring can prevent devastating outcomes caused by these arrhythmias. OBJECTIVE: We aimed to investigate the incidence of arrhythmias in patients following myocardial infarction using a patch-type device-AT-Patch (ATP-C120; ATsens). METHODS: This study is a nonrandomized, single-center, prospective cohort study. We evaluated 71 patients who had had a myocardial infarction and had been admitted to our hospital. The ATP-C120 device was attached to the patient for 11 days and analyzed by 2 cardiologists for new-onset arrhythmic events. RESULTS: One participant was concordantly diagnosed with atrial fibrillation. The cardiologists diagnosed atrial premature beats in 65 (92%) and 60 (85%) of 71 participants, and ventricular premature beats in 38 (54%) and 44 (62%) participants, respectively. Interestingly, 40 (56%) patients showed less than 2 minutes of sustained paroxysmal atrial tachycardia confirmed by both cardiologists. Among participants with atrial tachycardia, the use of ß-blockers was significantly lower compared with patients without tachycardia (70% vs 90%, P=.04). However, different dosages of ß-blockers did not make a significant difference. CONCLUSIONS: Wearable ECG monitoring patch devices are easy to apply and can correlate symptoms and ECG rhythm disturbances in patients following myocardial infarction. Further study is necessary regarding clinical implications and appropriate therapies for arrhythmias detected early after myocardial infarction to prevent adverse outcomes.