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The small heterodimer partner (SHP) regulates fatty acid oxidation and lipogenesis in the liver by regulating peroxisome proliferator-activated receptor (PPAR) γ expression. SHP is also abundantly expressed in the myocardium. We investigated the effect of SHP expression on myocardia assessing not only heart structure and function but also lipid metabolism and related gene expression in a SHP deletion animal model. Transcriptional profiling with a microarray revealed that genes participating in cell growth, cytokine signalling, phospholipid metabolism, and extracellular matrix are up-regulated in the myocardia of SHP knockout (KO) mice compared to those of wild-type (WT) mice (nominal p value < 0.05). Consistent with these gene expression changes, the left ventricular masses of SHP KO mice were significantly higher than WT mice (76.8 ± 20.5 mg vs. 52.8 ± 6.8 mg, P = 0.0093). After 12 weeks of high fat diet (HFD), SHP KO mice gained less weight and exhibited less elevation in serum-free fatty acid and less ectopic lipid accumulation in the myocardium than WT mice. According to microarray analysis, genes regulated by PPARγ1 and PPARα were down-regulated in myocardia of SHP KO mice compared to their expression in WT mice after HFD, suggesting that the reduction in lipid accumulation in the myocardium resulted from a decrease in lipogenesis regulated by PPARγ. We confirmed the reduced expression of PPARγ1 and PPARα target genes such as CD36, medium-chain acyl-CoA dehydrogenase, long-chain acyl-CoA dehydrogenase, and very long-chain acyl-CoA dehydrogenase by SHP KO after HFD.

BackgroundDespite the essential role of ectopic osteogenic calcium-phosphate metabolism in the development of calcific aortic valve disease (CAVD), the implications of high serum phosphate levels in CAVD development are not fully understood.MethodsAsymptomatic individuals who underwent health screening using serial cardiac computed tomography (CT) and echocardiography were selected from a multicenter registry. CAVD was identified and quantified on CT images using the aortic valve calcification (AVC) score. The associations between initial serum phosphate levels and the presence of baseline CAVD, development of new CAVD, and the AVC score progression rate were investigated using multivariable regression models.ResultsA total of 736 individuals were selected for analysis, and the median interscan duration was 36.4 months. On initial CT, 83 (13.7%) participants had baseline CAVD, while 52 (7.0%) individuals developed new CAVD during follow-up. Serum phosphate levels were not associated with a higher probability of baseline CAVD but were predictive of newly developed CAVD (odds ratio per 1 mg/dl, 1.05; 95% confidence interval, 1.01–1.10; p = 0.02). Higher phosphate levels were also associated with a faster AVC score progression in those with baseline CAVD (regression coefficient per 1 mg/dl, 15.55 Agatston units/year; 95% confidence interval, 6.02–25.07; p

Abstract Background This study aimed to develop a prediction model for transferring patients to an inappropriate hospital for suspected cardiovascular emergency diseases at the pre-hospital stage, using variables obtained from an integrated nationwide dataset, and to assess the performance of this model. Methods We integrated three nationwide datasets and developed a two-step prediction model utilizing a machine learning algorithm. Ninety-eight clinical characteristics of patients identified at the pre-hospital stage and 13 hospital components were used as input data for the model. The primary endpoint of the model was the prediction of transfer to an inappropriate hospital. Results A total of 94,256 transferred patients in the public pre-hospital care system matched the National Emergency Department Information System data of patients with a pre-hospital cardiovascular registry created in South Korea between July 2017 and December 2018. Of these, 1,770 (6.26%) patients failed to be transferred to a capable hospital. The area under the receiver operating characteristic curve of the final predictive model was 0.813 (0.800–0.825), and the area under the receiver precision-recall curve was 0.286 (0.265–0.308). Conclusions Our prediction model used machine learning to show favorable performance in transferring patients with suspected cardiovascular disease to a capable hospital. For our results to lead to changes in the pre-hospital care system, a digital platform for sharing real-time information should be developed.

In coronary computed tomography angiography (CCTA), motion artifacts due to heartbeats can obscure coronary artery diagnoses. In this study, we introduce a cycle-consistent adversarial-network-based method for motion artifact correction in CCTA. Our methodology involves extracting image patches and using style transfer for synthetic ground truth creation, followed by CycleGAN network training for motion compensation. We employ Dynamic Time Warping (DTW) to align extracted image patches along the artery centerline with their corresponding motion-free phase patches, ensuring matched pixel correspondences and similar anatomical features for accuracy in subsequent processing steps. Our quantitative analysis, using metrics like the Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD), demonstrates CycleGAN’s superior performance in reducing motion artifacts, with improvements in image quality and clarity. An observer study using a 5-point Likert scale further validates the reduction of motion artifacts and improved visibility of coronary arteries. Additionally, we present a quantitative analysis on clinical data, affirming the correction of motion artifacts through metric-based evaluations.

Abstract In this retrospective observational study, we aimed to develop a machine-learning model using data obtained at the prehospital stage to predict in-hospital cardiac arrest in the emergency department (ED) of patients transferred via emergency medical services. The dataset was constructed by attaching the prehospital information from the National Fire Agency and hospital factors to data from the National Emergency Department Information System. Machine-learning models were developed using patient variables, with and without hospital factors. We validated model performance and used the SHapley Additive exPlanation model interpretation. In-hospital cardiac arrest occurred in 5431 of the 1,350,693 patients (0.4%). The extreme gradient boosting model showed the best performance with area under receiver operating curve of 0.9267 when incorporating the hospital factor. Oxygen supply, age, oxygen saturation, systolic blood pressure, the number of ED beds, ED occupancy, and pulse rate were the most influential variables, in that order. ED occupancy and in-hospital cardiac arrest occurrence were positively correlated, and the impact of ED occupancy appeared greater in small hospitals. The machine-learning predictive model using the integrated information acquired in the prehospital stage effectively predicted in-hospital cardiac arrest in the ED and can contribute to the efficient operation of emergency medical systems.

Abstract Background and Objective Macitentan is approved for treating pulmonary arterial hypertension. However, the real-world evidence of macitentan use is limited. Therefore, we evaluated the safety and clinical outcomes of macitentan use in clinical practice under a post-marketing surveillance. Methods Patients with pulmonary arterial hypertension receiving macitentan treatment were prospectively and consecutively enrolled from 2014 to 2020 at 50 medical centers in Korea. Safety and clinical outcomes were monitored from baseline to the nearest timepoint of 24 weeks after macitentan initiation. The adverse events and adverse drug reactions were identified. Changes in the World Health Organization functional class were assessed as the primary clinical outcome, which was used to estimate the final effectiveness (both improved and maintained). Factors associated with safety and final effectiveness were identified. Results Among 474 patients enrolled in the study, 467 and 440 were included in the safety and clinical outcome analyses, respectively. Dyspnea, nasopharyngitis, and worsening pulmonary arterial hypertension were the most frequent adverse events with incidences of 5%, 3%, and 3%, respectively. The final effectiveness rate was 93%. Older age (adjusted odds ratio [aOR] = 1.021, p = 0.003) and higher level (III vs II) of baseline World Health Organization functional class (aOR = 1.784; p = 0.022) were significantly associated with a higher adverse event occurrence. Younger age (aOR = 0.947; p = 0.001) and shorter disease duration (aOR = 0.991; p = 0.010) were significantly associated with positive final effectiveness. Conclusions This real-world study demonstrated the safety and clinical outcomes of macitentan use in Korean patients with pulmonary arterial hypertension. Macitentan was well tolerated and significantly effective with no new safety concerns during the 24 weeks.

Abstract Background The baseline coronary plaque burden is the most important factor for rapid plaque progression (RPP) in the coronary artery. However, data on the independent predictors of RPP in the absence of a baseline coronary plaque burden are limited. Thus, this study aimed to investigate the predictors for RPP in patients without coronary plaques on baseline coronary computed tomography angiography (CCTA) images. Methods A total of 402 patients (mean age: 57.6 ± 10.0 years, 49.3% men) without coronary plaques at baseline who underwent serial coronary CCTA were identified from the Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging (PARADIGM) registry and included in this retrospective study. RPP was defined as an annual change of ≥ 1.0%/year in the percentage atheroma volume (PAV). Results During a median inter-scan period of 3.6 years (interquartile range: 2.7–5.0 years), newly developed coronary plaques and RPP were observed in 35.6% and 4.2% of the patients, respectively. The baseline traditional risk factors, i.e., advanced age (≥ 60 years), male sex, hypertension, diabetes mellitus, hyperlipidemia, obesity, and current smoking status, were not significantly associated with the risk of RPP. Multivariate linear regression analysis showed that the serum hemoglobin A1c level (per 1% increase) measured at follow-up CCTA was independently associated with the annual change in the PAV (β: 0.098, 95% confidence interval [CI]: 0.048–0.149; P

Abstract Background The triglyceride glucose (TyG) index has been suggested as a reliable surrogate marker of insulin resistance which is a substantial risk factor for atherosclerotic cardiovascular disease (ASCVD). Several recent studies have shown the relationship between the TyG index and cardiovascular disease; however, the role of the TyG index in coronary artery calcification (CAC) progression has not been extensively assessed especially in low-risk population. Methods We enrolled 5775 Korean adults who had at least two CAC evaluations. We determined the TyG index using ln (fasting triglycerides [mg/dL] x fasting glucose [mg/dL]/2). The CAC progression was defined as either incident CAC in a CAC-free population at baseline or an increase of ≥ 2.5 units between the square roots of the baseline and follow-up coronary artery calcium scores (CACSs) of subjects with detectable CAC at baseline. Results CAC progression was seen in 1,382 subjects (23.9%) during mean 3.5 years follow-up. Based on the TyG index, subjects were stratified into four groups. Follow-up CACS and incidence of CAC progression were markedly elevated with rising TyG index quartiles (group I [lowest]:17.6% vs. group II:22.2% vs. group III:24.6% vs. group IV [highest]: 31.3%, p

BackgroundDespite the importance of attaining optimal lipid levels from a young age to secure long-term cardiovascular health, the detailed impact of non-optimal lipid levels in young adults on coronary artery calcification (CAC) is not fully explored. We sought to investigate the risk of CAC progression as per lipid profiles and to demonstrate lipid optimality in young adults.MethodsFrom the KOrea Initiative on Coronary Artery calcification (KOICA) registry that was established in six large volume healthcare centers in Korea, 2,940 statin-naïve participants aged 20–45 years who underwent serial coronary calcium scans for routine health check-ups between 2002 and 2017 were included. The study outcome was CAC progression, which was assessed by the square root method. The risk of CAC progression was analyzed according to the lipid optimality and each lipid parameter.ResultsIn this retrospective cohort (mean age, 41.3 years; men 82.4%), 477 participants (16.2%) had an optimal lipid profile, defined as triglycerides 60 mg/dl. During follow-up (median, 39.7 months), CAC progression was observed in 434 participants (14.8%), and more frequent in the non-optimal lipid group (16.5% vs. 5.7%; p

Background Previously developed prediction models for type 2 diabetes mellitus (T2DM) have limited performance. We developed a deep learning (DL) based model using a cohort representative of the Korean population. Methods This study was conducted on the basis of the National Health Insurance Service-Health Screening (NHIS-HEALS) cohort of Korea. Overall, 335,302 subjects without T2DM at baseline were included. We developed the model based on 80% of the subjects, and verified the power in the remainder. Predictive models for T2DM were constructed using the recurrent neural network long short-term memory (RNN-LSTM) network and the Cox longitudinal summary model. The performance of both models over a 10-year period was compared using a time dependent area under the curve. Results During a mean follow-up of 10.4±1.7 years, the mean frequency of periodic health check-ups was 2.9±1.0 per subject. During the observation period, T2DM was newly observed in 8.7% of the subjects. The annual performance of the model created using the RNN-LSTM network was superior to that of the Cox model, and the risk factors for T2DM, derived using the two models were similar; however, certain results differed. Conclusion The DL-based T2DM prediction model, constructed using a cohort representative of the population, performs better than the conventional model. After pilot tests, this model will be provided to all Korean national health screening recipients in the future.

Background: The prognostic value of whole vessel plaque quantification has not been fully understood. Objectives: We aimed to investigate the clinical relevance of whole vessel plaque quantification on coronary computed tomography angiography. Methods: In a total of 1,013 vessels with fractional flow reserve (FFR) measurement and available coronary computed tomography angiography, high-risk plaque characteristics (HRPC) included minimum lumen area 0.80, the number of HRVC was significantly associated with the risk of VOCO (HR: 2.54; 95% CI: 1.77-3.64) and enhanced the predictability for VOCO of % diameter stenosis and the number of HRPC (P < 0.001). In a landmark analysis at 2 years, the number of HRVC showed sustained prognostic implications beyond 2 years, but the number of HRPC did not. Conclusions: Whole vessel plaque quantification can provide incremental predictability for low FFR and additive prognostic value in deferred vessels with high FFR over anatomical severity and lesion plaque characteristics. (CCTA-FFR Registry for Risk Prediction; NCT04037163)

Abstract Background The triglyceride glucose (TyG) index is a noninsulin-based marker for insulin resistance (IR) in general practice. Although smoking and heavy drinking have been regarded as major risk factors for various chronic diseases, there is limited evidence regarding the combined effects of smoking and alcohol consumption on IR. This study aimed to investigate the relationship between the TyG index and smoking and alcohol consumption using two Korean population-based datasets. Methods This study included 10,568 adults in the Korean National Health and Nutrition Examination Survey (KNHANES) and 9586 adults in the Korean Initiatives on Coronary Artery Calcification (KOICA) registry datasets. Multivariate logistic analysis was conducted to explore the relationship between smoking and alcohol consumption and the TyG index. To assess the predictive value of smoking and alcohol consumption on high TyG index, the area under the curve (AUC) were compared and net reclassification improvement (NRI) and integrated discrimination improvement (IDI) analyses were derived. Results The combined effect of smoking and alcohol consumption was an independent risk factor of a higher TyG index in the KNHANES (adjusted odds ratio: 4.33, P

Abstract Background Hypertension (HTN), diabetes mellitus (DM), and dyslipidemia (DL) are well-known risk factors of cardiovascular disease (CVD), but not all patients develop CVDs. Studies have been limited investigating genetic risk of CVDs specific to individuals with metabolic diseases. This study aimed to identify disease-specific and/or common genetic loci associated with CVD susceptibility in chronic metabolic disease patients. Methods We conducted a genome-wide association study (GWAS) of a multiple case–control design with data from the City Cohort within Health EXAminees subcohort of the Korean Genome and Epidemiology Study (KoGES_HEXA). KoGES_HEXA is a population-based prospective cohort of 173,357 urban Korean adults that had health examinations at medical centers. 42,393 participants (16,309 HTN; 5,314 DM; 20,770 DL) were analyzed, and each metabolic disease group was divided into three CVD case-controls: coronary artery disease (CAD), ischemic stroke (IS), and cardio-cerebrovascular disease (CCD). GWASs were conducted for each case–control group with 7,975,321 imputed single nucleotide polymorphisms using the Phase 3 Asian panel from 1000 Genomes Project, by logistic regression and controlled for confounding variables. Genome-wide significant levels were implemented to identify important susceptibility loci. Results Totaling 42,393 individuals, this study included 16,309 HTN (mean age [SD], 57.28 [7.45]; 816 CAD, 398 IS, and 1,185 CCD cases), 5,314 DM (57.79 [7.39]; 361 CAD, 153 IS, and 497 CCD cases), and 20,770 DL patients (55.34 [7.63]; 768 CAD, 295 IS, and 1,039 CCD cases). Six genome-wide significant CVD risk loci were identified, with relatively large effect sizes: 1 locus in HTN (HTN-CAD: 17q25.3/CBX8-CBX4 [OR, 2.607; P = 6.37 × 10−9]), 2 in DM (DM-IS: 4q32.3/MARCH1-LINC01207 [OR, 5.587; P = 1.34 × 10−8], and DM-CCD: 17q25.3/RPTOR [OR, 3.511; P = 1.99 × 10−8]), and 3 in DL (DL-CAD: 9q22.2/UNQ6494-LOC101927847 [OR, 2.282; P = 7.78 × 10−9], DL-IS: 3p22.1/ULK4 [OR, 2.162; P = 2.97 × 10−8], and DL-CCD: 2p22.2/CYP1B1-CYP1B1-AS1 [OR, 2.027; P = 4.24 × 10−8]). Conclusions This study identified 6 susceptibility loci and positional candidate genes for CVDs in HTN, DM, and DL patients using an unprecedented study design. 1 locus (17q25.3) was commonly associated with CAD. These associations warrant validation in additional studies for potential therapeutic applications.

Purpose: In a preclinical study using a swine myocardial infarction (MI) model, a delayed enhancement (DE)-multi-detector computed tomography (MDCT) scan was performed using a hybrid system alongside diagnostic invasive coronary angiography (ICA) without the additional use of a contrast agent, and demonstrated an excellent correlation in the infarct area compared with histopathologic specimens. In the present investigation, we evaluated the feasibility and diagnostic accuracy of a myocardial viability assessment by DE-MDCT using a hybrid system comprising ICA and MDCT alongside diagnostic ICA without the additional use of a contrast agent. Materials and Methods: We prospectively enrolled 13 patients (median age: 67 years) with a previous MI (>6 months) scheduled to undergo ICA. All patients underwent cardiac magnetic resonance (CMR) imaging before diagnostic ICA. MDCT viability scans were performed concurrently with diagnostic ICA without the use of additional contrast. The total myocardial scar volume per patient and average transmurality per myocardial segment measured by DE-MDCT were compared with those from DE-CMR. Results: The DE volume measured by MDCT showed an excellent correlation with the volume measured by CMR (r=0.986, p <0.0001). The transmurality per segment by MDCT was well-correlated with CMR (r=0.900, p<0.0001); the diagnostic performance of MDCT in differentiating non-viable from viable myocardium using a 50% transmurality criterion was good with a sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 87.5%, 99.5%, 87.5%, 99.5%, and 99.1%, respectively. Conclusion: The feasibility of the DE-MDCT viability assessment acquired simultaneously with conventional ICA was proven in patients with chronic MI using DE-CMR as the reference standard. [ABSTRACT FROM AUTHOR]

Abstract We investigated the optimal combinations of systolic blood pressure (SBP) and diastolic blood pressure (DBP) levels for lowest mortality in participants not taking hypertensive medication at the study baseline using nationwide representative databases. Survival rates and hazard ratios (HRs) were calculated using Kaplan‐Meier curves and multivariable Cox regression analyses. The discriminatory ability for clinical outcomes was assessed by Harrell's C‐index analysis. A survival spline curve was presented, and Classification and Regression Tree (CART) analysis was performed. SBP ≥ 140 group and DBP ≥ 90 group had the highest risk of mortality. Within SBP

Models for predicting acute myocardial infarction (AMI) at the prehospital stage were developed and their efficacy compared, based on variables identified from a nationwide systematic emergency medical service (EMS) registry using conventional statistical methods and machine learning algorithms. Patients in the EMS cardiovascular registry aged >15 years who were transferred from the public EMS to emergency departments in Korea from January 2016 to December 2018 were enrolled. Two datasets were constructed according to the hierarchical structure of the registry. A total of 184,577 patients (Dataset 1) were included in the final analysis. Among them, 72,439 patients (Dataset 2) were suspected to have AMI at prehospital stage. Between the models derived using the conventional logistic regression method, the B-type model incorporated AMI-specific variables from the A-type model and exhibited a superior discriminative ability (p = 0.02). The models that used extreme gradient boosting and a multilayer perceptron yielded a higher predictive performance than the conventional logistic regression-based models for analyses that used both datasets. Each machine learning algorithm yielded different classification lists of the 10 most important features. Therefore, prediction models that use nationwide prehospital data and are developed with appropriate structures can improve the identification of patients who require timely AMI management.

Even with increasing awareness of sex-related differences in atherosclerotic cardiovascular disease (ASCVD), it remains unclear whether the progression of coronary atherosclerosis differs between women and men. We sought to compare coronary artery calcium (CAC) progression between women and men. From a retrospective, multicentre registry of consecutive asymptomatic individuals who underwent CAC scoring, we identified 9,675 men and 1,709 women with follow-up CAC scoring. At baseline, men were more likely to have a CAC score >0 than were women (47.8% vs. 28.6%). The probability of CAC progression at 5 years, defined as [√CAC score (follow-up)-√CAC score (baseline)] ≥2.5, was 47.4% in men and 29.7% in women (p

Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography–determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP. Methods and Results Qualitative and quantitative coronary computed tomography angiography plaque characterization was performed in 1083 patients who underwent serial coronary computed tomography angiography from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. RPP was defined as an annual progression of percentage atheroma volume ≥1.0%. We employed the following ML models: model 1, clinical variables; model 2, model 1 plus qualitative plaque features; model 3, model 2 plus quantitative plaque features. ML models were compared with the atherosclerotic cardiovascular disease risk score, Duke coronary artery disease score, and a logistic regression statistical model. 224 patients (21%) were identified as RPP. Feature selection in ML identifies that quantitative computed tomography variables were higher‐ranking features, followed by qualitative computed tomography variables and clinical/laboratory variables. ML model 3 exhibited the highest discriminatory performance to identify individuals who would experience RPP when compared with atherosclerotic cardiovascular disease risk score, the other ML models, and the statistical model (area under the receiver operating characteristic curve in ML model 3, 0.83 [95% CI 0.78–0.89], versus atherosclerotic cardiovascular disease risk score, 0.60 [0.52–0.67]; Duke coronary artery disease score, 0.74 [0.68–0.79]; ML model 1, 0.62 [0.55–0.69]; ML model 2, 0.73 [0.67–0.80]; all P

OBJECTIVES:To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation. BACKGROUND:Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images. METHODS:Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split. RESULTS:The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups. CONCLUSIONS:An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level.

BackgroundPulmonary arterial hypertension (PAH) is a progressive chronic disease with poor outcomes. One reason for poor prognosis is the lack of understanding regarding individual variability in response to treatment. Idiopathic PAH (IPAH) patients with bone morphogenetic protein receptor type 2 (BMPR2) mutations have distinct phenotypes that are crucial for individualized therapy but evidence regarding their prevalence and clinical features in the Korean population is lacking. Therefore, the present study aimed to screen Korean IPAH patients for BMPR2 mutations and analyze their clinical phenotypes.MethodsWe enrolled 73 unrelated IPAH patients for BMPR2 mutation screening between March 2010 to November 2015 from 11 hospitals in Korea. Thirty-three lineal family members from 6 families of BMPR2 mutation carriers were also screened.ResultsAmong 73 patients, 16 (22%) had BMPR2 mutations. Mutation carriers were younger (27 vs. 47 years; p = 0.02) and had a higher mean pulmonary arterial pressure (mPAP) than non-carriers (64 vs. 51 mmHg; pConclusionThe prevalence of BMPR2 mutations in Korean IPAH patients was 22%. Mutation carriers were younger and had a poorer hemodynamic profile compared with the non-carriers.Clinical trial registrationClinicaltrials.gov NCT01054105.

BackgroundMachine learning (ML) is able to extract patterns and develop algorithms to construct data-driven models. We use ML models to gain insight into the relative importance of variables to predict obstructive coronary artery disease (CAD) using the Coronary Computed Tomographic Angiography for Selective Cardiac Catheterization (CONSERVE) study, as well as to compare prediction of obstructive CAD to the CAD consortium clinical score (CAD2). We further perform ML analysis to gain insight into the role of imaging and clinical variables for revascularization.MethodsFor prediction of obstructive CAD, the entire ICA arm of the study, comprising 719 patients was used. For revascularization, 1,028 patients were randomized to invasive coronary angiography (ICA) or coronary computed tomographic angiography (CCTA). Data was randomly split into 80% training 20% test sets for building and validation. Models used extreme gradient boosting (XGBoost).ResultsMean age was 60.6 ± 11.5 years and 64.3% were female. For the prediction of obstructive CAD, the AUC was significantly higher for ML at 0.779 (95% CI: 0.672-0.886) than for CAD2 (0.696 [95% CI: 0.594-0.798]) (P = 0.01). BMI, age, and angina severity were the most important variables. For revascularization, the model obtained an overall area under the receiver-operation curve (AUC) of 0.958 (95% CI = 0.933-0.983). Performance did not differ whether the imaging parameters used were from ICA (AUC 0.947, 95% CI = 0.903-0.990) or CCTA (AUC 0.941, 95% CI = 0.895-0.988) (P = 0.90). The ML model obtained sensitivity and specificity of 89.2% and 92.9%, respectively. Number of vessels with ≥70% stenosis, maximum segment stenosis severity (SSS) and body mass index (BMI) were the most important variables. Exclusion of imaging variables resulted in performance deterioration, with an AUC of 0.705 (95% CI 0.614-0.795) (P ConclusionsFor obstructive CAD, the ML model outperformed CAD2. BMI is an important variable, although currently not included in most scores. In this ML model, imaging variables were most associated with revascularization. Imaging modality did not influence model performance. Removal of imaging variables reduced model performance.

Emergency medical service (EMS) occurs in a high-pressure and error-prone environment, where paramedics must provide prompt decisions in care while recording information with limited time, incomplete data, restricted resources, and competing priorities. The EMS requires cooperative workflows between patients or caregivers, paramedics and medical centers in the community. In a conventional EMS, they have difficulties in obtaining causes of emergencies and personal medical histories, which are important for a rapid and proper response. We analyzed the requirement of a smart EMS (SEMS) system and derived the key components in connected care environments leveraging information and communication technology. A survey of paramedics (n=113) revealed that a SEMS system using IoT technology should integrate personal lifelogs, electronic medical records, and patient monitoring in ambulances into pre-hospital care recording systems. It also addressed context-awareness in the EMS accelerates first responder's activities, while supporting personalized care not only at the scene of the emergency but also during the entire hospital stay. Based on requirement analysis, we designed and implemented SEMS using health information standards to provide interoperability between devices and systems. As an application of SEMS, an example service is introduced: lifelog-connected EMS for stroke patients with a real-time location service for managing timeline of treatment.

Abstract Background Arterial stiffness increases pressure load to the left ventricle (LV), leading to LV hypertrophy and subendocardial ischemia. Neurohormones stimulate myocardial fibrosis and LV dysfunction. We aimed to explore the associations of arterial stiffness and plasma aldosterone with multi-directional, layer-specific LV, and left atrial (LA) mechanical function in hypertensive patients. Methods Layer-specific LV global longitudinal strain (GLS-trans, GLS-endo, GLS-epi), global circumferential strain (GCS-trans, GCS-endo, GCS-epi), LV torsional parameters, and LA global longitudinal strain (LA GLS) were analyzed by two-dimensional speckle tracking echocardiography in 195 hypertensive patients (110 men, mean age 55 years). Pulse wave velocity (PWV) was analyzed as a measure of arterial stiffness, and plasma aldosterone was measured for evaluation of neurohormonal activation. Results In a simple correlation, PWV significantly correlated with LV GLS-endo and LA GLS. Log aldosterone correlated with both LV GCS-endo and LV GCS-trans. Multiple regression analysis revealed that LV GLS-endo (β = 0.223, p = 0.031) and LA GLS (β = −0.311, p = 0.002) were independently correlated with PWV even after controlling for confounding factors. Conclusions In hypertensive patients without clinically apparent target organ damage, LV GLS, especially endocardium, and LA GLS were more dominantly affected by arterial stiffness because, among the three myocardial layers, the endocardium is most susceptible to pressure overload. Two-dimensional layer-specific speckle-tracking echocardiography sensitively detects LV mechanical dysfunction and provides pathophysiologic insights into LV mechanical adaptations in hypertension.