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.

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.

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 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.

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.

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.

Clinical outcome of drug-coated balloon versus scaffold device in patients with superficial femoral artery chronic total occlusion.

The demand for endovascular therapy is increasing in an aging society, but the problem of restenosis in the chronic phase has not been resolved in femoropopliteal occlusive disease. Few studies have compared drug-coated balloon (DCB) and scaffold devices in chronic total occlusion (CTO) of the superficial femoral artery (SFA). This study aimed to compare DCBs with scaffold in patients with CTO of the SFA. This was a single-center, retrospective study. From June 2018 to December 2019, we compared 31 patients and 33 limbs treated with DCBs and 44 patients and 45 limbs treated with a stent or stent-graft (scaffold) for SFA CTO. The primary endpoint was 12-month primary patency. The secondary endpoints were 12-month freedom from clinically driven target lesion revascularization (CD-TLR) and 12-month freedom from re-occlusion. The DCBs were performed using an intravascular ultrasound (IVUS)-guided approach or a non-loop wire technique. Baseline characteristics were similar between the groups. An intraluminal approach was performed to use all DCBs. The bailout stent rate was 0% in the DCB group. Kaplan-Meier analysis showed that rates of 12-month primary patency tended to be higher in the DCB than in the scaffold group (92.7 vs. 76.6%, p = 0.073) and that freedom from CD-TLR also did not differ significantly between the two groups (96.8 vs. 86.3%, p = 0.17). Kaplan-Meier analysis also showed that the 12-month freedom from re-occlusion rate was significantly less in the Scaffold than in the DCB group (96.8 vs. 79.3%, p = 0.045). Therefore, we concluded that in treatment for CTO of the SFA, a DCB with intraluminal angioplasty without bailout stenting was less re-occlusion compared with scaffold.

Agreement between single plane and biplane derived angiographic fractional flow reserve in patients with intermediate coronary artery stenosis.

Fractional flow reserve (FFR) is often used to evaluate the physiological severity of intermediate coronary stenoses, but less-invasive assessment methods are desirable. We evaluated the feasibility of angiographic FFR (angioFFR) calculated from two projections acquired simultaneously by a biplane C-arm system and angioFFR calculated from two projections acquired independently by one plane of the same biplane C-arm system. AngioFFR was validated against FFR in terms of detection of hemodynamically relevant coronary artery stenoses. Twenty-two Patients who underwent angiography and FFR for coronary artery disease were included. We used a non-commercial prototype to calculate biplane angioFFR for 22 vessels (19 LAD, 1 LCx, 2 RCA) and single plane angioFFR for 17 of the same 22 vessels. FFR < 0.8 was measured in 8 vessels. The Pearson correlation coefficients with FFR were 0.55 for single plane angioFFR and 0.61 for biplane angioFFR and the diagnostic accuracies were 88% (95% CI 73-100%) for single plane angioFFR and 86% (95% CI 72-100%) for biplane angioFFR. Bland-Altman plots revealed that compared with FFR, the limits of agreement for single plane angioFFR were - 0.07 to 0.19 (mean difference 0.06, p = 0.002) and the limits of agreement for biplane FFR were - 0.09 to 0.15 (mean difference 0.03, p = 0.03). In conclusion, angioFFR calculated from single or biplane acquisitions by a biplane C-arm is feasible and may evolve to a tool for less invasive imaging-based assessment of myocardial ischemia.

Cost-Effectiveness of Management for Hospitalized Patients.

The cost and/or cost-effectiveness for inpatient management according to the gender of attending physicians remain to be elucidated.Hospitalization costs were extracted from the Diagnosis Procedure Combination (DPC) -based payment system. Using a dataset of 7,457 hospitalized patients with cardiovascular diseases in our hospital from 2012 to 2018, we compared the actual cost of inpatient management by female cardiologists with that by male cardiologists. Next, we estimated the cost-effectiveness of inpatient management according to the gender of the attending cardiologist. The cost of initial hospitalization per patient was similar between the patients treated by a female or male middle-grade cardiologist ($17,527 ± 14,158, versus $17,358 ± 15,183, P = 0.69). As an analysis on cost-effectiveness, the incremental cost of hospitalization managed by male middle-grade cardiologists was $67 per patient as compared with female middle-grade cardiologists. Concordantly, evaluation of the incremental cost-effectiveness ratio per quality-adjusted life year gained showed that the inpatient management by female cardiologists was dominant over that by male cardiologists.Inpatient management by female cardiologists was more cost-effective as compared with that by male cardiologists. Physician gender might have a considerable effect on medical economics.

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.

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.

A Real-World Comparison of 1-Year Survival and Expenditures for Transcatheter Aortic Valve Replacements: SAPIEN 3 Versus CoreValve Versus Evolut R.

OBJECTIVES: New versions of balloon-expandable and self-expandable valves for transcatheter aortic valve replacement (TAVR) have been developed, but few studies have examined the outcomes associated with these devices using national-level data. This study aimed to elucidate the clinical and economic outcomes of TAVR for aortic stenosis in Japan through an analysis of real-world data. METHODS: This retrospective cohort study was performed using data from patients with aortic stenosis who had undergone transfemoral TAVR with Edwards SAPIEN 3, Medtronic CoreValve, or Medtronic Evolut R valves throughout Japan from April 2016 to March 2018. Pacemaker implantation, mortality, and health expenditure were examined for each valve type during hospitalization and at 1 month, 3 months, 6 months, and 1 year. Generalized linear regression models and Cox proportional hazards models were used to examine the associations between the valve types and outcomes. RESULTS: We analyzed 7244 TAVR cases (SAPIEN 3: 5276, CoreValve: 418, and Evolut R: 1550) across 145 hospitals. The adjusted 1-year expenditures for SAPIEN 3, CoreValve, and Evolut R were $79 402, $76 125, and $75 527, respectively; SAPIEN 3 was significantly more expensive than the other valves (P < .05). The pacemaker implantation hazard ratios (95% confidence intervals) for CoreValve and Evolut R were significantly higher (P < .001) than SAPIEN 3 at 2.61 (2.07-3.27) and 1.80 (1.53-2.12), respectively. The mortality hazard ratios (95% confidence intervals) for CoreValve and Evolut R were not significant at 1.11 (0.84-1.46) and 1.22 (0.97-1.54), respectively. CONCLUSIONS: SAPIEN 3 users had generally lower pacemaker implantation and mortality but higher expenditures than CoreValve and Evolut R users.

Deep Learning Algorithm to Detect Cardiac Sarcoidosis From Echocardiographic Movies.

BACKGROUND: Because the early diagnosis of subclinical cardiac sarcoidosis (CS) remains difficult, we developed a deep learning algorithm to distinguish CS patients from healthy subjects using echocardiographic movies.Methods and Results:Among the patients who underwent echocardiography from January 2015 to December 2019, we chose 151 echocardiographic movies from 50 CS patients and 151 from 149 healthy subjects. We trained two 3D convolutional neural networks (3D-CNN) to identify CS patients using a dataset of 212 echocardiographic movies with and without a transfer learning method (Pretrained algorithm and Non-pretrained algorithm). On an independent set of 41 echocardiographic movies, the area under the receiver-operating characteristic curve (AUC) of the Pretrained algorithm was greater than that of Non-pretrained algorithm (0.842, 95% confidence interval (CI): 0.722-0.962 vs. 0.724, 95% CI: 0.566-0.882, P=0.253). The AUC from the interpretation of the same set of 41 echocardiographic movies by 5 cardiologists was not significantly different from that of the Pretrained algorithm (0.855, 95% CI: 0.735-0.975 vs. 0.842, 95% CI: 0.722-0.962, P=0.885). A sensitivity map demonstrated that the Pretrained algorithm focused on the area of the mitral valve. CONCLUSIONS: A 3D-CNN with a transfer learning method may be a promising tool for detecting CS using an echocardiographic movie.

Efficacy of three-dimensional road mapping by fusion of computed tomography angiography and fluoroscopy in endovascular treatment of aorto-iliac chronic total occlusion.

The efficacy of multimodality image fusion road-mapping technique for endovascular therapy has been reported recently. Our aim was to evaluate the efficacy of endovascular therapy (EVT) with three-dimensional (3D) road mapping by fusing computed tomography (CT) and angiographical volumetric data for aorto-iliac chronic total occlusion (CTO). We retrospectively analyzed 36 patients with aorto-iliac CTO from June 2017 to November 2019 and classified them into two groups: EVT using a CT fused 3D roadmap (CTf3D-RM; 3D group, n = 14) and standard EVT (standard group, n = 22). Primary endpoint was wiring time and secondary endpoints were procedural success rate, number of guidewires, procedure time, radiation dose, contrast medium dose, and complications. Wiring time was significantly shorter in the 3D group than the standard group (3D, 15.6 ± 10.23 min vs. standard, 44.6 ± 35.3 min; p = 0.0052). Both groups had high procedural success rates (3D, 100% vs. standard, 100%) and low complication rates (3D, 0.0% vs. standard, 9.1%; p = 0.51). There were significantly fewer guidewires in the 3D group than the standard group (3D, 2.78 ± 1.31 vs. standard, 4.36 ± 2.01; p = 0.0138). The 3D group trended towards shorter procedural time (3D, 78.8 ± 32.5 min vs. standard, 107.5 ± 52.5 min; p = 0.076), lower radiation dose (3D, 28.6 ± 18.9 Gycm2 vs. standard, 48.9 ± 49.2 Gycm2; p = 0.15), and lower contrast medium dose (3D, 102.2 ± 30.6 vs. standard, 127.5 ± 51.3; p = 0.11) than the standard group. Therefore, we concluded that EVT with CTf3D-RM is effective for aorto-iliac CTO. This method may improve the quality of aorto-iliac CTO interventions.

Efficacy and Safety of Angiography-Guided Retrograde Posterior Popliteal Puncture Technique in the Supine Position.

BACKGROUND: Various techniques are needed for successful treatment of femoropopliteal long chronic total occlusion (CTO). Previous reports have described popliteal puncture by duplex echo guidance, but the safety of angiography-guided popliteal puncture remains unclear. This study was performed to examine the efficacy and safety of angiography-guided retrograde posterior popliteal puncture in the supine position. METHODS: We analyzed 44 consecutive patients treated with retrograde femoropopliteal puncture for femoropopliteal CTO from October 2013 to February 2019. We compared 24 patients who underwent angiography-guided posterior popliteal puncture (posterior group) and 20 patients who underwent frontal superficial femoral artery/popliteal artery puncture (frontal group). The primary end point was successful revascularization. The secondary end points were the puncture time, hemostasis time, and procedure-related complications. RESULTS: The posterior group tended to have a higher procedural success rate than the frontal group (100% vs. 90.0%, respectively; P = 0.20). The hemostasis time was significantly shorter in the posterior than frontal group (527.1 ± 146.5 vs. 710.0 ± 337.8 sec, respectively; P = 0.021). There was no significant difference in the puncture time between the posterior and frontal groups (8.19 ± 9.22 vs. 7.50 ± 9.68 min, respectively; P = 0.81). Both groups had a low complication rate (posterior, 0.0% vs. frontal, 5.0%; P = 0.4). CONCLUSIONS: Angiography-guided posterior popliteal puncture in the supine position can facilitate hemostasis and may improve the success rate of the procedure. This technique may contribute to the treatment of complex femoropopliteal CTO as one option for a retrograde approach.

An Effective Method for Percutaneous Removal of Venoarterial Extracorporeal Membrane Oxygenation by a Combination of Balloon Dilatation in Endovascular Therapy and the Perclose Proglide™ Closure Device.

BACKGROUND: Venoarterial extracorporeal membrane oxygenation (VA-ECMO) can be undertaken surgically, but there are various problems with this method. METHODS AND CASE REPORT: We removed the arterial cannula of VA-ECMO percutaneously by combining intravascular balloon dilatation and the Perclose ProGlide™ (PP) closure device in a short amount of time and the extent of bleeding was extremely small. Simultaneously, the venous cannula was removed by suturing and manual compression. We report a series of cases of percutaneous removal of VA-ECMO using intravascular balloon dilatation and PP. CONCLUSIONS: By using this method, VA-ECMO removal was possible in a very short amount of time with minimal invasiveness.

Percutaneous Mitral Valve Intervention Using MitraClip for Functional Mitral Regurgitation and Heart Failure.

Functional mitral regurgitation (FMR) frequently coexists with left ventricular systolic dysfunction and advanced heart failure, and typically has poor clinical outcomes. Although various therapeutic options including cardiac resynchronization therapy and surgical mitral intervention, have been proposed, an optimal treatment strategy for functional mitral regurgitation has not yet been established. Over the last decade, transcatheter mitral valve repair using MitraClip has emerged as a novel alternative therapeutic option for functional mitral regurgitation. In 2018, the COAPT trial demonstrated that MitraClip treatment reduced rehospitalization due to heart failure and all-cause death in patients with functional mitral regurgitation and heart failure. As a consequence, the MitraClip has become a very promising potential treatment for functional mitral regurgitation. In this review, we discuss and summarize the current status and future perspectives of the treatment for functional mitral regurgitation and heart failure.

The Effectiveness of a Deep Learning Model to Detect Left Ventricular Systolic Dysfunction from Electrocardiograms.

Deep learning models can be applied to electrocardiograms (ECGs) to detect left ventricular (LV) dysfunction. We hypothesized that applying a deep learning model may improve the diagnostic accuracy of cardiologists in predicting LV dysfunction from ECGs. We acquired 37,103 paired ECG and echocardiography data records of patients who underwent echocardiography between January 2015 and December 2019. We trained a convolutional neural network to identify the data records of patients with LV dysfunction (ejection fraction < 40%) using a dataset of 23,801 ECGs. When tested on an independent set of 7,196 ECGs, we found the area under the receiver operating characteristic curve was 0.945 (95% confidence interval: 0.936-0.954). When 7 cardiologists interpreted 50 randomly selected ECGs from the test dataset of 7,196 ECGs, their accuracy for predicting LV dysfunction was 78.0% ± 6.0%. By referring to the model's output, the cardiologist accuracy improved to 88.0% ± 3.7%, which indicates that model support significantly improved the cardiologist diagnostic accuracy (P = 0.02). A sensitivity map demonstrated that the model focused on the QRS complex when detecting LV dysfunction on ECGs. We developed a deep learning model that can detect LV dysfunction on ECGs with high accuracy. Furthermore, we demonstrated that support from a deep learning model can help cardiologists to identify LV dysfunction on ECGs.

Efficacy of Fractional Flow Reserve-Guided Percutaneous Cornary Intervention for Patients with Angina Pectoris.

Evaluation of hemodynamic parameters, such as fractional flow reserve (FFR), is recommended before percutaneous coronary intervention (PCI) for patients with angina pectoris (AP). However, the advantage of FFR-guided PCI has not been fully established. This study was performed to confirm whether FFR-guided PCI improves the prognosis compared with other treatments. Multiple databases were searched for studies published from 2000 to 2018, and a network meta-analysis (NMA) was performed to compare outcomes of FFR-guided PCI, non-FFR-guided PCI, coronary artery bypass grafting (CABG), and medical treatment (MT) for AP based on estimated odds ratios (ORs). The study included 18,093 patients from 15 randomized controlled trials (RCTs). No evidence of inconsistency was observed among the studies. The NMA showed that the all-cause mortality of FFR-guided PCI was not significantly different from that of the other treatment groups (CABG: OR, 1.1; 95% confidence interval [CI], 0.67-1.7; non-FFR-guided PCI: OR, 0.85; 95% CI, 0.53-1.4; and MT: OR, 0.83; 95% CI, 0.52-1.3). The NMA for the composite of all-cause mortality and myocardial infarction, which included 15,454 patients from 12 RCTs, showed that FFR-guided PCI significantly reduced the composite outcome compared with non-FFR-guided PCI and MT (non-FFR-guided PCI: OR, 0.66; 95% CI, 0.46-0.95 and MT: OR, 0.66; 95% CI, 0.46-0.95). Although FFR-guided PCI for AP did not show significant prognostic improvement compared with non-FFR-guided PCI, CABG, and MT, FFR-guided PCI may significantly reduce the composite of all-cause mortality and myocardial infarction compared with non-FFR-guided PCI and MT.

Time-Dependent Changes in Porcelain Aorta and Aortic Stenosis in a Patient with Systemic Lupus Erythematosus.

Porcelain aorta, defined as extensive calcification of the ascending aorta or aortic arch, is a reported risk factor for embolic stroke during cardiac surgery. However, the time course of the progression of aortic calcification leading to porcelain aorta has not been elucidated. We herein describe a 70-year-old woman who was followed up for systemic lupus erythematosus and antiphospholipid syndrome for approximately 20 years. A routine computed tomography scan revealed progression of ascending aortic calcification to porcelain aorta. The calcification was absent during the preceding 12 years, partial 6 years later, and total after another 3 years. Computed tomography also demonstrated aortic and mitral valve calcification in the development of porcelain aorta. During the 3 years prior to the last admission, annual echocardiography examinations showed progression of calcific aortic stenosis with symptoms. The patient was admitted to our institution for aortic valve replacement. Considering the high risk of perioperative stroke associated with porcelain aorta, transcatheter aortic valve implantation was performed. Postoperative transthoracic echocardiography revealed improvement of the aortic stenosis with no symptoms. The present case revealed aortic calcific progression to porcelain aorta during an approximately 10-year period with deterioration of aortic stenosis within a short time. The aortic and valvular calcification could be attributed to the inflammatory process of systemic lupus erythematosus and antiphospholipid syndrome. The presence of aortic and mitral annular calcification in the serial imaging can provide information on aortic and valvular atherosclerotic progression, which may be modifiable by early steroid-lowering therapy.