Applying masked autoencoder-based self-supervised learning for high-capability vision transformers of electrocardiographies.

The generalization of deep neural network algorithms to a broader population is an important challenge in the medical field. We aimed to apply self-supervised learning using masked autoencoders (MAEs) to improve the performance of the 12-lead electrocardiography (ECG) analysis model using limited ECG data. We pretrained Vision Transformer (ViT) models by reconstructing the masked ECG data with MAE. We fine-tuned this MAE-based ECG pretrained model on ECG-echocardiography data from The University of Tokyo Hospital (UTokyo) for the detection of left ventricular systolic dysfunction (LVSD), and then evaluated it using multi-center external validation data from seven institutions, employing the area under the receiver operating characteristic curve (AUROC) for assessment. We included 38,245 ECG-echocardiography pairs from UTokyo and 229,439 pairs from all institutions. The performances of MAE-based ECG models pretrained using ECG data from UTokyo were significantly higher than that of other Deep Neural Network models across all external validation cohorts (AUROC, 0.913-0.962 for LVSD, p < 0.001). Moreover, we also found improvements for the MAE-based ECG analysis model depending on the model capacity and the amount of training data. Additionally, the MAE-based ECG analysis model maintained high performance even on the ECG benchmark dataset (PTB-XL). Our proposed method developed high performance MAE-based ECG analysis models using limited ECG data.

Monitoring Berlin Heart EXCOR by computer vision: A preliminary implementation and evaluation.

BACKGROUND: EXCOR Pediatric is one of the most commonly used ventricular assist devices (VAD) for small children; it requires visual inspection of the diaphragm movement to assess its operating status. Although this visual inspection can only be performed by trained medical professionals, it can also be attempted by the recent advances in computer vision technology. METHODS: Movement of the diaphragm in the operating EXCOR VAD was recorded as movies and annotated frame-by-frame in three classes according to the state of the diaphragm: "fill," "mid," and "empty." Three models, MobileNetV3, EfficientNetV2, and MobileViT, were trained using the frames, and their performance was evaluated based on the accuracy and area under the receiver operating characteristic curve (AROC). RESULTS: A total of 152 movies were available from two participants. Only the 10 mL pumps were used. Ninety-eight movies were used for annotation and frame extraction, and 7949 frames per class were included in the analysis. The macro-average accuracies of the three models were 0.88, 0.91, and 0.93, and the AROC were 0.99, 0.98, and 0.99 for MobileNetV3, EfficientNetV2, and MobileViT, respectively. CONCLUSION: Image recognition models based on lightweight deep neural networks could detect the diaphragm state of EXCOR VAD with sufficient accuracy, although there were limited variations in the dataset. This suggests the potential of computer vision for the automated monitoring of the EXCOR diaphragm position.

The potential of the transformer-based survival analysis model, SurvTrace, for predicting recurrent cardiovascular events and stratifying high-risk patients with ischemic heart disease.

INTRODUCTION: Ischemic heart disease is a leading cause of death worldwide, and its importance is increasing with the aging population. The aim of this study was to evaluate the accuracy of SurvTrace, a survival analysis model using the Transformer-a state-of-the-art deep learning method-for predicting recurrent cardiovascular events and stratifying high-risk patients. The model's performance was compared to that of a conventional scoring system utilizing real-world data from cardiovascular patients. METHODS: This study consecutively enrolled patients who underwent percutaneous coronary intervention (PCI) at the Department of Cardiovascular Medicine, University of Tokyo Hospital, between 2005 and 2019. Each patient's initial PCI at our hospital was designated as the index procedure, and a composite of major adverse cardiovascular events (MACE) was monitored for up to two years post-index event. Data regarding patient background, clinical presentation, medical history, medications, and perioperative complications were collected to predict MACE. The performance of two models-a conventional scoring system proposed by Wilson et al. and the Transformer-based model SurvTrace-was evaluated using Harrell's c-index, Kaplan-Meier curves, and log-rank tests. RESULTS: A total of 3938 cases were included in the study, with 394 used as the test dataset and the remaining 3544 used for model training. SurvTrace exhibited a mean c-index of 0.72 (95% confidence intervals (CI): 0.69-0.76), which indicated higher prognostic accuracy compared with the conventional scoring system's 0.64 (95% CI: 0.64-0.64). Moreover, SurvTrace demonstrated superior risk stratification ability, effectively distinguishing between the high-risk group and other risk categories in terms of event occurrence. In contrast, the conventional system only showed a significant difference between the low-risk and high-risk groups. CONCLUSION: This study based on real-world cardiovascular patient data underscores the potential of the Transformer-based survival analysis model, SurvTrace, for predicting recurrent cardiovascular events and stratifying high-risk patients.

[Development of a Deep Learning Model for Judging Late Gadolinium-enhancement in Cardiac MRI].

PURPOSE: To verify the usefulness of a deep learning model for determining the presence or absence of contrast-enhanced myocardium in late gadolinium-enhancement images in cardiac MRI. METHODS: We used 174 late gadolinium-enhancement myocardial short-axis images obtained from contrast-enhanced cardiac MRI performed using a 3.0T MRI system at the University of Tokyo Hospital. Of these, 144 images were used for training, extracting a region of interest targeting the heart, scaling signal intensity, and data augmentation were performed to obtain 3312 training images. The interpretation report of two cardiology specialists of our hospital was used as the correct label. A learning model was constructed using a convolutional neural network and applied to 30 test data. In all cases, the acquired mean age was 56.4±12.1 years, and the male-to-female ratio was 1 : 0.82. RESULTS: Before and after data augmentation, sensitivity remained consistent at 93.3%, specificity improved from 0.0% to 100.0%, and accuracy improved from 46.7% to 96.7%. CONCLUSION: The prediction accuracy of the deep learning model developed in this research is high, suggesting its high usefulness.

Multimodality Risk Assessment of Patients with Ischemic Heart Disease Using Deep Learning Models Applied to Electrocardiograms and Chest X-rays.

Comprehensive management approaches for patients with ischemic heart disease (IHD) are important aids for prognostication and treatment planning. While single-modality deep neural networks (DNNs) have shown promising performance for detecting cardiac abnormalities, the potential benefits of using DNNs for multimodality risk assessment in patients with IHD have not been reported. The purpose of this study was to investigate the effectiveness of multimodality risk assessment in patients with IHD using a DNN that utilizes 12-lead electrocardiograms (ECGs) and chest X-rays (CXRs), with the prediction of major adverse cardiovascular events (MACEs) being of particular concern.DNN models were applied to detection of left ventricular systolic dysfunction (LVSD) on ECGs and identification of cardiomegaly findings on CXRs. A total of 2107 patients who underwent elective percutaneous coronary intervention were categorized into 4 groups according to the models' outputs: Dual-modality high-risk (n = 105), ECG high-risk (n = 181), CXR high-risk (n = 392), and No-risk (n = 1,429).A total of 342 MACEs were observed. The incidence of a MACE was the highest in the Dual-modality high-risk group (P < 0.001). Multivariate Cox hazards analysis for predicting MACE revealed that the Dual-modality high-risk group had a significantly higher risk of MACE than the No-risk group (hazard ratio (HR): 2.370, P < 0.001), the ECG high-risk group (HR: 1.906, P = 0.010), and the CXR high-risk group (HR: 1.624, P = 0.018), after controlling for confounding factors.The results suggest the usefulness of multimodality risk assessment using DNN models applied to 12-lead ECG and CXR data from patients with IHD.

Forecasting the Acute Heart Failure Admissions: Development of Deep Learning Prediction Model Incorporating the Climate Information.

BACKGROUND: Climate is known to influence the incidence of cardiovascular events. However, their prediction with traditional statistical models remains imprecise. METHODS AND RESULTS: We analyzed 27,799 acute heart failure (AHF) admissions within the Tokyo CCU Network Database from January 2014 to December 2019. High-risk AHF (HR-AHF) day was defined as a day with the upper 10th percentile of AHF admission volume. Deep neural network (DNN) and traditional regression models were developed using the admissions in 2014-2018 and tested in 2019. Explanatory variables included 17 meteorological parameters. Shapley additive explanations were used to evaluate their importance. The median number of incidences of AHF was 12 (9-16) per day in 2014-2018 and 11 (9-15) per day in 2019. The predicted AHF admissions correlated well with the observed numbers (DNN: R2 = 0.413, linear regression: R2 = 0.387). The DNN model was superior in predicting HR-AHF days compared with the logistic regression model [c-statistics: 0.888 (95% CI: 0.818-0.958) vs 0.827 (95% CI: 0.745-0.910): P = .0013]. Notably, the strongest predictive variable was the 7-day moving average of the lowest ambient temperatures. CONCLUSIONS: The DNN model had good prediction ability for incident AHF using climate information. Forecasting AHF admissions could be useful for the effective management of AHF.

Clinical valve thrombosis and arterial embolism in a cancer patient after transcatheter aortic valve replacement.

The number of cancer patients with severe aortic stenosis and atrial fibrillation (AF) is increasing in the aging population. Transcatheter aortic valve replacement (TAVR) is an established treatment option for severe aortic stenosis with high surgical risk, including individuals with cancer. Antithrombotic therapy should be considered for post-TAVR or AF patients. However, antithrombotic management in cancer patients remains challenging due to the increased risk of both thromboembolism and bleeding. We present a case of clinical valve thrombosis and arterial embolism after transcatheter aortic valve replacement in an elderly patient with a history of metastatic pancreatic cancer and permanent atrial fibrillation under treatment of single antiplatelet therapy. Warfarin treatment after successful surgical thrombectomy to the occluded arteries improved clinical valve thrombosis, although the long-term outcome remains unclear. This case demonstrates that novel management algorithms for thromboembolism and bleeding in elderly cancer patients with AF and valvular heart disease are urgently needed.

Deep Learning Models for Predicting Left Heart Abnormalities From Single-Lead Electrocardiogram for the Development of Wearable Devices.

BACKGROUND: Left heart abnormalities are risk factors for heart failure. However, echocardiography is not always available. Electrocardiograms (ECGs), which are now available from wearable devices, have the potential to detect these abnormalities. Nevertheless, whether a model can detect left heart abnormalities from single Lead I ECG data remains unclear.Methods and Results: We developed Lead I ECG models to detect low ejection fraction (EF), wall motion abnormality, left ventricular hypertrophy (LVH), left ventricular dilatation, and left atrial dilatation. We used a dataset comprising 229,439 paired sets of ECG and echocardiography data from 8 facilities, and validated the model using external verification with data from 2 facilities. The area under the receiver operating characteristic curves of our model was 0.913 for low EF, 0.832 for wall motion abnormality, 0.797 for LVH, 0.838 for left ventricular dilatation, and 0.802 for left atrial dilatation. In interpretation tests with 12 cardiologists, the accuracy of the model was 78.3% for low EF and 68.3% for LVH. Compared with cardiologists who read the 12-lead ECGs, the model's performance was superior for LVH and similar for low EF. CONCLUSIONS: From a multicenter study dataset, we developed models to predict left heart abnormalities using Lead I on the ECG. The Lead I ECG models show superior or equivalent performance to cardiologists using 12-lead ECGs.

Clinical feasibility of endovascular recanalization with intravascular ultrasound-guided wiring for chronic total occlusion of below-the-knee arteries.

BACKGROUND: Revascularization with endovascular therapy (EVT) for complex below-the-knee (BTK) chronic total occlusion (CTO) remains a challenging problem. The Japanese-BTK (J-BTK) CTO score is reported as an indicator of the difficulty of BTK CTO, with the guidewire (GW) passage success rate decreasing as the grade increases. We previously reported an effective GW crossing method for the intravascular ultrasound (IVUS)-guided parallel wiring of complex BTK CTO. In this study, we investigated the feasibility of EVT using IVUS-guided wiring for BTK CTO. MATERIALS AND METHODS: This single center, retrospective study analyzed 65 consecutive BTK CTO vessels in which IVUS-guided wiring was attempted after the failure of a conventional antegrade wiring approach from November 2020 to November 2022. The primary endpoint was the clinical success of the target CTO vessel. The secondary endpoints were the GW success rate per grade based on the J-BTK CTO score, number of GW used for CTO crossing, fluoroscopy time, and complications. RESULTS: Target vessels were the anterior tibial artery (66.2% of cases), peroneal artery (9.2%), and posterior tibial artery (24.6%). Blunt type CTO entry was performed in 55.4% of cases, calcification of entry was observed in 24.6% of cases, the mean occlusion length was 228.2 ± 93.7 mm, mean reference vessel diameter was 2.1 ± 0.71 mm, and outflow was absent in 38.5% of cases. J-BTK CTO scores of 0/1 (grade A), 2/3 (grade B), 4/5 (grade C), and 6 (grade D) were seen in 18.5%, 43.1%, 36.3%, and 1.5% of cases, respectively. The clinical success rate was 95.4%. The GW success rate by J-BTK CTO grade was as follows: grade A (100%), B (100%), C (91.7%), and D (0%). The mean number of GW used was 3.4 ± 1.4, the mean fluoroscopy time was 72.3 ± 32.5 min, and complications occurred in 7.7% of cases. CONCLUSION: This study showed a very high clinical success rate despite the difficulty of BTK CTO. IVUS-guided EVT might be a feasible strategy for complex BTK CTO.

An explainable artificial intelligence-enabled electrocardiogram analysis model for the classification of reduced left ventricular function.

Aims: The black box nature of artificial intelligence (AI) hinders the development of interpretable AI models that are applicable in clinical practice. We aimed to develop an AI model for classifying patients of reduced left ventricular ejection fraction (LVEF) from 12-lead electrocardiograms (ECG) with the decision-interpretability. Methods and results: We acquired paired ECG and echocardiography datasets from the central and co-operative institutions. For the central institution dataset, a random forest model was trained to identify patients with reduced LVEF among 29 907 ECGs. Shapley additive explanations were applied to 7196 ECGs. To extract the model's decision criteria, the calculated Shapley additive explanations values were clustered for 192 non-paced rhythm patients in which reduced LVEF was predicted. Although the extracted criteria were different for each cluster, these criteria generally comprised a combination of six ECG findings: negative T-wave inversion in I/V5-6 leads, low voltage in I/II/V4-6 leads, Q wave in V3-6 leads, ventricular activation time prolongation in I/V5-6 leads, S-wave prolongation in V2-3 leads, and corrected QT interval prolongation. Similarly, for the co-operative institution dataset, the extracted criteria comprised a combination of the same six ECG findings. Furthermore, the accuracy of seven cardiologists' ECG readings improved significantly after watching a video explaining the interpretation of these criteria (before, 62.9% ± 3.9% vs. after, 73.9% ± 2.4%; P = 0.02). Conclusion: We visually interpreted the model's decision criteria to evaluate its validity, thereby developing a model that provided the decision-interpretability required for clinical application.

Efficacy and Safety of Percutaneous Venoarterial Extracorporeal Membrane Oxygenation Decannulation Using Endovascular Balloon Dilation and Perclose ProGlide Closure Device: Results from the Multicenter SKYLINE Study.

BACKGROUND: The venoarterial extracorporeal membrane oxygenation (VA-ECMO) cannula can be surgically removed, but postoperative complications and surgical staffing issues can be problematic. We previously reported a method of percutaneously removing the arterial cannula of VA-ECMO by combining intravascular balloon dilation and the Perclose ProGlide (PP) closure device. In this study, we investigated the efficacy and safety of this percutaneous decannulation of the VA-ECMO. METHODS: This multicenter, retrospective study involved consecutive patients who underwent percutaneous VA-ECMO decannulation at 2 cardiovascular centers from September 2019 to December 2021. We analyzed 37 patients in whom the VA-ECMO cannula was removed by the percutaneous procedure with balloon dilation and the PP. The primary end point was procedural success of hemostasis. The secondary end points were the procedural time, procedure-related complications, and rate of surgical conversion. RESULTS: The patients' mean age was 65.4 years. The approach site of the endovascular therapy (EVT) procedures were the transradial approach (56.8%), transfemoral approach (27.8%), and transbrachial approach (18.9%). The mean balloon diameter was 7.3 ± 0.68 mm, and the mean balloon inflation time was 14.8 ± 7.3 min. The mean procedure time was 58.5 ± 27.0 min. The procedure success rate was 94.6%, procedure-related complication rate was 10.8%, procedure-related death and postprocedural infection rate was 0.0%, surgical conversion rate was 0.0%, and EVT access site complication rate was 2.7%. CONCLUSIONS: We concluded that percutaneous VA-ECMO decannulation using a combination of intravascular balloon dilation in EVT and the PP appears to be a safe, minimally invasive, and effective procedure.

Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates.

Microvascular thrombosis is a typical symptom of COVID-19 and shows similarities to thrombosis. Using a microfluidic imaging flow cytometer, we measured the blood of 181 COVID-19 samples and 101 non-COVID-19 thrombosis samples, resulting in a total of 6.3 million bright-field images. We trained a convolutional neural network to distinguish single platelets, platelet aggregates, and white blood cells and performed classical image analysis for each subpopulation individually. Based on derived single-cell features for each population, we trained machine learning models for classification between COVID-19 and non-COVID-19 thrombosis, resulting in a patient testing accuracy of 75%. This result indicates that platelet formation differs between COVID-19 and non-COVID-19 thrombosis. All analysis steps were optimized for efficiency and implemented in an easy-to-use plugin for the image viewer napari, allowing the entire analysis to be performed within seconds on mid-range computers, which could be used for real-time diagnosis.

Deep learning-based age estimation from chest X-rays indicates cardiovascular prognosis.

BACKGROUND: In recent years, there has been considerable research on the use of artificial intelligence to estimate age and disease status from medical images. However, age estimation from chest X-ray (CXR) images has not been well studied and the clinical significance of estimated age has not been fully determined. METHODS: To address this, we trained a deep neural network (DNN) model using more than 100,000 CXRs to estimate the patients' age solely from CXRs. We applied our DNN to CXRs of 1562 consecutive hospitalized heart failure patients, and 3586 patients admitted to the intensive care unit with cardiovascular disease. RESULTS: The DNN's estimated age (X-ray age) showed a strong significant correlation with chronological age on the hold-out test data and independent test data. Elevated X-ray age is associated with worse clinical outcomes (heart failure readmission and all-cause death) for heart failure. Additionally, elevated X-ray age was associated with a worse prognosis in 3586 patients admitted to the intensive care unit with cardiovascular disease. CONCLUSIONS: Our results suggest that X-ray age can serve as a useful indicator of cardiovascular abnormalities, which will help clinicians to predict, prevent and manage cardiovascular diseases.

Chest X-ray is one of the most widely used medical imaging tests worldwide to diagnose and manage heart and lung diseases. In this study, we developed a computer-based tool to predict patients' age from chest X-rays. The tool precisely estimated patients' age from chest X-rays. Furthermore, in patients with heart failure and those admitted to the intensive care unit for cardiovascular disease, elevated X-ray age estimated by our tool was associated with poor clinical outcomes, including readmission for heart failure or death from any cause. With further testing, our tool may help clinicians to predict outcomes in patients with heart disease based on a simple chest X-ray.

Combining transradial access and sheathless femoral access for complex iliac artery chronic total occlusions.

BACKGROUND: The transradial approach (TRA) is associated with fewer serious access site-related complications compared with the transfemoral or transbrachial approach. However, TRA has associated problems in complex aortoiliac (AI) lesions, including the procedural difficulty. A bidirectional approach was used combining TRA with a sheathless technique for femoral artery (FA) puncture to treat complex AI lesions, as a minimally-invasive approach. This report describes a representative cases with AI chronic total occlusion in which the combination of TRA and a sheathless technique for FA puncture was useful for guidewire crossing. CASE PRESENTATION: Case 1 was a 71-year-old man with intermittent claudication (IC). Control angiography showed total occlusion of the left common iliac artery (CIA) ostium to the distal external iliac artery (EIA). Guidewire externalization was achieved by combining TRA using a 6Fr guiding sheath and a sheathless technique for the left FA. Two nitinol stents were deployed in the CIA to EIA. Case 2 was a 63-year-old man with IC. Control angiography revealed total occlusion of the right CIA ostium to the common femoral artery (CFA) with severe calcification. The antegrade wire could not pass through the CTO lesion because of the calcified CFA occlusion. A 21-G metal needle was used to penetrate the CFA calcification through the distal true lumen of the CFA, and the wire was inserted into the EIA for wire externalization. Three nitinol stents were deployed in the CIA to EIA, and a drug-coated balloon was dilated in the CFA with hemostasis of the distal puncture site. In both cases, the retrograde puncture site was hemostatic during the procedure and postoperative bed rest was not required. CONCLUSIONS: TRA combined with a sheathless technique from the FA has the potential to treat AI complex lesions in a less invasive manner.

Age prediction from coronary angiography using a deep neural network: Age as a potential label to extract prognosis-related imaging features.

Coronary angiography (CAG) is still considered the reference standard for coronary artery assessment, especially in the treatment of acute coronary syndrome (ACS). Although aging causes changes in coronary arteries, the age-related imaging features on CAG and their prognostic relevance have not been fully characterized. We hypothesized that a deep neural network (DNN) model could be trained to estimate vascular age only using CAG and that this age prediction from CAG could show significant associations with clinical outcomes of ACS. A DNN was trained to estimate vascular age using ten separate frames from each of 5,923 CAG videos from 572 patients. It was then tested on 1,437 CAG videos from 144 patients. Subsequently, 298 ACS patients who underwent percutaneous coronary intervention (PCI) were analysed to assess whether predicted age by DNN was associated with clinical outcomes. Age predicted as a continuous variable showed mean absolute error of 4 years with R squared of 0.72 (r = 0.856). Among the ACS patients stratified by predicted age from CAG images before PCI, major adverse cardiovascular events (MACE) were more frequently observed in the older vascular age group than in the younger vascular age group (p = 0.017). Furthermore, after controlling for actual age, gender, peak creatine kinase, and history of heart failure, the older vascular age group independently suffered from more MACE (hazard ratio 2.14, 95% CI 1.07 to 4.29, p = 0.032). The vascular age estimated based on CAG imaging by DNN showed high predictive value. The age predicted from CAG images by DNN could have significant associations with clinical outcomes in patients with ACS.

Automatic Detection of Left Ventricular Dilatation and Hypertrophy from Electrocardiograms Using Deep Learning.

Left ventricular dilatation (LVD) and left ventricular hypertrophy (LVH) are risk factors for heart failure, and their detection improves heart failure screening. This study aimed to investigate the ability of deep learning to detect LVD and LVH from a 12-lead electrocardiogram (ECG). Using ECG and echocardiographic data, we developed deep learning and machine learning models to detect LVD and LVH. We also examined conventional ECG criteria for the diagnosis of LVH. We calculated the area under the receiver operating characteristic (AUROC) curve, sensitivity, specificity, and accuracy of each model and compared the performance of the models. We analyzed data for 18,954 patients (mean age (standard deviation): 64.2 (16.5) years, men: 56.7%). For the detection of LVD, the value (95% confidence interval) of the AUROC was 0.810 (0.801-0.819) for the deep learning model, and this was significantly higher than that of the logistic regression and random forest methods (P < 0.001). The AUROCs for the logistic regression and random forest methods (machine learning models) were 0.770 (0.761-0.779) and 0.757 (0.747-0.767), respectively. For the detection of LVH, the AUROC was 0.784 (0.777-0.791) for the deep learning model, and this was significantly higher than that of the logistic regression and random forest methods and conventional ECG criteria (P < 0.001). The AUROCs for the logistic regression and random forest methods were 0.758 (0.751-0.765) and 0.716 (0.708-0.724), respectively. This study suggests that deep learning is a useful method to detect LVD and LVH from 12-lead ECGs.

Cost-Effectiveness of Dapagliflozin for Chronic Kidney Disease in Japan.

BACKGROUND: The cost-effectiveness of sodium-glucose cotransporter 2 (SGLT2) inhibitors for chronic kidney disease (CKD) has not been evaluated in Japan, so we analyzed the cost-effectiveness of dapagliflozin, an SGLT2 inhibitor, for CKD stages 3a and 3b.Methods and Results: We used the Markov model for CKD to assess the costs and benefits associated with and without dapagliflozin from a health system perspective. We estimated the incremental cost-effectiveness ratio (ICER), expressed as per quality-adjusted life-years (QALYs). An ICER <5 million Japanese yen (JPY)/QALY was judged to be cost-effective. The effect of dapagliflozin on renal and cardiovascular events was based on published clinical trials. In patients with CKD stage 3a, the ICER of dapagliflozin over standard treatment was 4.03 million JPY/QALY gained. With a cost-effectiveness threshold of 5 million JPY/QALY gained, the cost-effectiveness probability of dapagliflozin over standard treatment was 52.6%. In patients with CKD stage 3b, the ICER of dapagliflozin over standard treatment was 0.12 million JPY/QALY gained. The cost-effectiveness probability of dapagliflozin over standard treatment was 75.2%. CONCLUSIONS: The results seemed to show acceptable cost-effectiveness when dapagliflozin was used for CKD stage 3b. On the other hand, cost-effectiveness of dapagliflozin for CKD stage 3a was ambiguous, and further validation is needed.

Prediction of Atrial Fibrillation Being Asymptomatic at First Onset by Cardiac Pacing.

Asymptomatic or silent atrial fibrillation (AF) has long been a clinical problem due to the incidence of ischemic stroke. A method is needed to predict the development of silent AF before the occurrence of ischemic stroke. This study was focused on the symptoms of AF, especially palpitation, in pacemaker patients. We assessed the hypothesis that absence of palpitation during rapid ventricular pacing could be a predictor of future onset AF being asymptomatic.In this study, we assessed the presence of symptoms during RV pacing and AF symptoms on 145 pacemaker patients at the outpatient clinic by VVI pacing at 120 ppm. The relationship between symptoms during RV pacing and symptom during AF was assessed. The predictive value of absence of symptom during RV pacing on AF being asymptomatic was assessed.Of 145 patients, 74 had previous AF episode. Among the AF patients, absence of symptom during VVI pacing was associated with AF being asymptomatic.Of 145 patients, 71 had no previous AF events. There were 14 patients who had new-onset AF or atrial flutter (AFL) after the device implantation. Four of the 14 patients (28.6%) were symptomatic during first AF/AFL episode, and 10 (71.4%) were asymptomatic during first-onset AF. All ten patients who were asymptomatic during cardiac pacing test were asymptomatic during their initial episodes of AF as well.This study showed that absence of symptoms during rapid ventricular pacing was associated with first-onset AF being asymptomatic.

Association of ambient temperature and acute heart failure with preserved and reduced ejection fraction.

AIMS: Evidence on the association between ambient temperature and the onset of acute heart failure (AHF) is scarce and mixed. We sought to investigate the incidence of AHF admissions based on ambient temperature change, with particular interest in detecting the difference between AHF with preserved (HFpEF), mildly reduced (HFmrEF), and reduced ejection fraction (HFrEF). METHODS AND RESULTS: Individualized AHF admission data from January 2015 to December 2016 were obtained from a multicentre registry (Tokyo CCU Network Database). The primary event was the daily number of admissions. A linear regression model, using the lowest ambient temperature as the explanatory variable, was selected for the best-estimate model. We also applied the cubic spline model using five knots according to the percentiles of the distribution of the lowest ambient temperature. We divided the entire population into HFpEF + HFmrEF and HFrEF for comparison. In addition, the in-hospital treatment and mortality rates were obtained according to the interquartile ranges (IQRs) of the lowest ambient temperature (IQR1 <5.5°C; IQR25.5-13.3°C; IQR3 13.3-19.7°C; and IQR4 >19.7°C). The number of admissions for HFpEF, HFmrEF and HFrEF were 2736 (36%), 1539 (20%), and 3354 (44%), respectively. The lowest ambient temperature on the admission day was inversely correlated with the admission frequency for both HFpEF + HFmrEF and HFrEF patients, with a stronger correlation in patients with HFpEF + HFmrEF (R2  = 0.25 vs. 0.05, P < 0.001). In the sensitivity analysis, the decrease in the ambient temperature was associated with the greatest incremental increases in HFpEF, followed by HFmrEF and HFrEF patients (3.5% vs. 2.8% vs. 1.5% per -1°C, P < 0.001), with marked increase in admissions of hypertensive patients (systolic blood pressure >140 mmHg vs. 140-100 mmHg vs. <100 mmHg, 3.0% vs. 2.0% vs. 0.8% per -1°C, P for interaction <0.001). A mediator analysis indicated the presence of the mediator effect of systolic blood pressure. The in-hospital mortality rate (7.5%) did not significantly change according to ambient temperature (P = 0.62). CONCLUSIONS: Lower ambient temperature was associated with higher frequency of AHF admissions, and the effect was more pronounced in HFpEF and HFmrEF patients than in those with HFrEF.

Severe aortic stenosis during leptin replacement therapy in a patient with generalized lipodystrophy-associated progeroid syndrome due to an LMNA variant: A case report.

Leptin replacement therapy (LRT) has drastically improved the prognosis of patients with lipodystrophy, but pro-inflammatory properties of leptin could become evident in the long term. Here, we report a 30-year-old Japanese woman with generalized lipodystrophy-associated progeroid syndrome due to a heterozygous LMNA variant (c.29C > T; p.T10I), who was diagnosed with severe aortic stenosis (AS) after more than a decade of LRT, which required transcatheter aortic valve implantation. Given her marked hypoadiponectinemia and the LMNA variant, our patient might have been susceptible to progeria-associated disorders, including aortic stenosis, which could have been exaggerated by the prolonged 'imbalanced adipokines' caused by LRT between pro-inflammatory leptin and anti-inflammatory adiponectin. Thus, long-term LRT could be associated with AS in patients with the LMNA variant to cause generalized lipodystrophy-associated progeroid syndrome and hypoadiponectinemia.