Reducing echocardiographic examination time through routine use of fully automated software: a comparative study of measurement and report creation time.

BACKGROUND: Manual interpretation of echocardiographic data is time-consuming and operator-dependent. With the advent of artificial intelligence (AI), there is a growing interest in its potential to streamline echocardiographic interpretation and reduce variability. This study aimed to compare the time taken for measurements by AI to that by human experts after converting the acquired dynamic images into DICOM data. METHODS: Twenty-three consecutive patients were examined by a single operator, with varying image quality and different medical conditions. Echocardiographic parameters were independently evaluated by human expert using the manual method and the fully automated US2.ai software. The automated processes facilitated by the US2.ai software encompass real-time processing of 2D and Doppler data, measurement of clinically important variables (such as LV function and geometry), automated parameter assessment, and report generation with findings and comments aligned with guidelines. We assessed the duration required for echocardiographic measurements and report creation. RESULTS: The AI significantly reduced the measurement time compared to the manual method (159 ± 66 vs. 325 ± 94 s, p < 0.01). In the report creation step, AI was also significantly faster compared to the manual method (71 ± 39 vs. 429 ± 128 s, p < 0.01). The incorporation of AI into echocardiographic analysis led to a 70% reduction in measurement and report creation time compared to manual methods. In cases with fair or poor image quality, AI required more corrections and extended measurement time than in cases of good image quality. Report creation time was longer in cases with increased report complexity due to human confirmation of AI-generated findings. CONCLUSIONS: This fully automated software has the potential to serve as an efficient tool for echocardiographic analysis, offering results that enhance clinical workflow by providing rapid, zero-click reports, thereby adding significant value.

Unsupervised cluster analysis reveals different phenotypes in patients after transcatheter aortic valve replacement.

Aims: The aim of this study was to identify phenotypes with potential prognostic significance in aortic stenosis (AS) patients after transcatheter aortic valve replacement (TAVR) through a clustering approach. Methods and results: This multi-centre retrospective study included 1365 patients with severe AS who underwent TAVR between January 2015 and March 2019. Among demographics, laboratory, and echocardiography parameters, 20 variables were selected through dimension reduction and used for unsupervised clustering. Phenotypes and outcomes were compared between clusters. Patients were randomly divided into a derivation cohort (n = 1092: 80%) and a validation cohort (n = 273: 20%). Three clusters with markedly different features were identified. Cluster 1 was associated predominantly with elderly age, a high aortic valve gradient, and left ventricular (LV) hypertrophy; Cluster 2 consisted of preserved LV ejection fraction, larger aortic valve area, and high blood pressure; and Cluster 3 demonstrated tachycardia and low flow/low gradient AS. Adverse outcomes differed significantly among clusters during a median of 2.2 years of follow-up (P < 0.001). After adjustment for clinical and echocardiographic data in a Cox proportional hazards model, Cluster 3 (hazard ratio, 4.18; 95% confidence interval, 1.76-9.94; P = 0.001) was associated with increased risk of adverse outcomes. In sequential Cox models, a model based on clinical data and echocardiographic variables (χ2 = 18.4) was improved by Cluster 3 (χ2 = 31.5; P = 0.001) in the validation cohort. Conclusion: Unsupervised cluster analysis of patients after TAVR revealed three different groups for assessment of prognosis. This provides a new perspective in the categorization of patients after TAVR that considers comorbidities and extravalvular cardiac dysfunction.

Pulmonary pressure-flow responses to exercise in heart failure treated with angiotensin receptor neprilysin inhibitor.

BACKGROUND: The role of the angiotensin receptor neprilysin inhibitor (ARNI) in cardiac function, particularly its impact on pulmonary circulation, remains underexplored. Recent studies have described abnormal mean pulmonary artery pressure (mPAP)-cardiac output (CO) responses as having the potential to assess the disease state. The aim of this study was to assess the effects of ARNI on pulmonary circulation in heart failure. We measured echocardiographic parameters post 6-min walk (6 MW) and compared the changes with baseline and follow-up. Our hypothesis was that pulmonary pressure-flow relationship of the pulmonary circulation obtained by 6 MW stress echocardiography would be improved with treatment. METHODS: We prospectively enrolled 39 heart failure patients and conducted the 6 MW test indoors. Post-6 MW echocardiography measured echocardiographic variables, and CO was derived from electric cardiometry. Individualized ARNI doses were optimized, with follow-up echocardiographic evaluations after 1 year. RESULTS: Left ventricular (LV) volume were significantly reduced (160.7 ± 49.6 mL vs 136.0 ± 54.3 mL, P < 0.001), and LV ejection fraction was significantly improved (37.6 ± 11.3% vs 44.9 ± 11.5%, P < 0.001). Among the 31 patients who underwent 6 MW stress echocardiographic study at baseline and 1 year later, 6 MW distance increased after treatment (380 m vs 430 m, P = 0.003). The ΔmPAP/ΔCO by 6 MW stress decreased with treatment (6.9 mmHg/L/min vs 2.8 mmHg/L/min, P = 0.002). The left atrial volume index was associated with the response group receiving ARNI treatment for pulmonary circulation. CONCLUSIONS: Initiation of ARNI was associated with improvement of left ventricular size and LVEF. Additionally, the 6 MW distance increased and the ΔmPAP/ΔCO was improved to within normal range with treatment.

Echocardiographic artificial intelligence for pulmonary hypertension classification.

OBJECTIVE: The classification of pulmonary hypertension (PH) is crucial for determining the appropriate therapeutic strategy. We investigated whether machine learning (ML) algorithms may assist in echocardiographic PH prediction, where current guidelines recommend integrating several different parameters. METHODS: We obtained physical and echocardiographic data from 885 patients who underwent right heart catheterisation (RHC). Patients were classified into three groups: non-PH, precapillary PH and postcapillary PH, based on values obtained from RHC. Using 24 parameters, we created predictive models employing four different classifiers and selected the one with the highest area under the curve. We then calculated the macro-average classification accuracy for PH on the derivation cohort (n=720) and prospective validation data set (n=165), comparing the results with guideline-based echocardiographic assessment obtained from each cohort. RESULTS: Logistic regression with elastic net regularisation had the highest classification accuracy, with areas under the curves of 0.789, 0.766 and 0.742 for normal, precapillary PH and postcapillary PH, respectively. The ML model demonstrated significantly better predictive accuracy than the guideline-based echocardiographic assessment in the derivation cohort (59.4% vs 51.6%, p<0.01). In the independent validation data set, the ML model's accuracy was comparable to the guideline-based PH classification (59.4% vs 57.8%, p=0.638). CONCLUSIONS: This preliminary study suggests promising potential for our ML model in predicting echocardiographic PH. Further research and validation are needed to fully assess its clinical utility in PH diagnosis and treatment decision-making.

Correlation between energy loss index and B-type natriuretic peptide: a vector flow mapping study.

BACKGROUND: Vector Flow Mapping (VFM) and Energy Loss (EL) evaluation are emerging echocardiographic techniques that offer detailed insights into cardiac function. This study aimed to explore the relationship between EL parameters and B-type natriuretic peptide (BNP) levels, a well-established marker of heart failure severity. METHODS: Our study prospectively enrolled 62 patients experiencing shortness of breath and suspected heart failure, who underwent echocardiography and had BNP levels measured between January 2018 and August 2020. Patients were stratified based on BNP levels, and their clinical and echocardiographic characteristics were evaluated. Univariate and multivariate regression analyses were performed to assess the correlation between BNP levels and various echocardiographic variables, including VFM parameters. RESULTS: Patients were stratified into two groups based on their BNP levels: BNP < 200 pg/ml (n = 53) and BNP ≥ 200 pg/ml (n = 9). Patients with BNP ≥ 200 pg/ml presented significantly different clinical and echocardiographic characteristics, such as older age, larger left ventricular mass and volume indices, higher pulmonary artery systolic pressure, higher E/e' ratio, and larger EL parameters. Multivariate regression analysis demonstrated the E/e' ratio and ELA (EL during Atrial contraction phase/A wave ratio as significant determinants of logBNP. Receiver operating characteristic curve analysis showed ELA/A > 36.0 J/m2 as a significant predictor of high BNP with 89% sensitivity and 85% specificity. ELA/A demonstrated an incremental diagnostic value over elevated left atrial pressure for predicting high BNP (C statistic = 0.98 vs 0.74, P = 0.006). CONCLUSION: This study provides novel insights into the potential utility of EL parameters as auxiliary indicators of cardiac load, thereby enhancing our understanding of heart failure.

Deep learning approach for analyzing chest x-rays to predict cardiac events in heart failure.

Background: A deep learning (DL) model based on a chest x-ray was reported to predict elevated pulmonary artery wedge pressure (PAWP) as heart failure (HF). Objectives: The aim of this study was to (1) investigate the role of probability of elevated PAWP for the prediction of clinical outcomes in association with other parameters, and (2) to evaluate whether probability of elevated PAWP based on DL added prognostic information to other conventional clinical prognostic factors in HF. Methods: We evaluated 192 patients hospitalized with HF. We used a previously developed AI model to predict HF and calculated probability of elevated PAWP. Readmission following HF and cardiac mortality were the primary endpoints. Results: Probability of elevated PAWP was associated with diastolic function by echocardiography. During a median follow-up period of 58 months, 57 individuals either died or were readmitted. Probability of elevated PAWP appeared to be associated with worse clinical outcomes. After adjustment for readmission score and laboratory data in a Cox proportional-hazards model, probability of elevated PAWP at pre-discharge was associated with event free survival, independent of elevated left atrial pressure (LAP) based on echocardiographic guidelines (p < 0.001). In sequential Cox models, a model based on clinical data was improved by elevated LAP (p = 0.005), and increased further by probability of elevated PAWP (p < 0.001). In contrast, the addition of pulmonary congestion interpreted by a doctor did not statistically improve the ability of a model containing clinical variables (compared p = 0.086). Conclusions: This study showed the potential of using a DL model on a chest x-ray to predict PAWP and its ability to add prognostic information to other conventional clinical prognostic factors in HF. The results may help to enhance the accuracy of prediction models used to evaluate the risk of clinical outcomes in HF, potentially resulting in more informed clinical decision-making and better care for patients.

Classification of chest X-ray images by incorporation of medical domain knowledge into operation branch networks.

BACKGROUND: This study was conducted to alleviate a common difficulty in chest X-ray image diagnosis: The attention region in a convolutional neural network (CNN) does not often match the doctor's point of focus. The method presented herein, which guides the area of attention in CNN to a medically plausible region, can thereby improve diagnostic capabilities. METHODS: The model is based on an attention branch network, which has excellent interpretability of the classification model. This model has an additional new operation branch that guides the attention region to the lung field and heart in chest X-ray images. We also used three chest X-ray image datasets (Teikyo, Tokushima, and ChestX-ray14) to evaluate the CNN attention area of interest in these fields. Additionally, after devising a quantitative method of evaluating improvement of a CNN's region of interest, we applied it to evaluation of the proposed model. RESULTS: Operation branch networks maintain or improve the area under the curve to a greater degree than conventional CNNs do. Furthermore, the network better emphasizes reasonable anatomical parts in chest X-ray images. CONCLUSIONS: The proposed network better emphasizes the reasonable anatomical parts in chest X-ray images. This method can enhance capabilities for image interpretation based on judgment.

Isoproterenol loading transesophageal echocardiography in atrial fibrillation.

In patients with sludge or severe spontaneous echo contrast (SEC) in the left atrial appendage (LAA), cases with isoproterenol loading transesophageal echocardiography (ISP-TEE) have been reported to identify the presence of thrombus in the LAA. This study aimed to assess the validity and hemodynamic changes of ISP-TEE in the LAA. We prospectively enrolled patients with atrial fibrillation (AF) who underwent ISP-TEE. The degree of sludge/SEC was categorized as being either absent (grade 0), mild SEC (grade 1), moderate SEC (grade 2), severe SEC or sludge (grade 3). The hemodynamic evaluation was performed by measuring LAA flow velocity, LAA tissue Doppler imaging (LAA-TDI) velocity, and pulmonary vein systolic forward flow velocity (PVS). In total, 35 patients (mean age 71 ± 7 years; 71% male) underwent ISP-TEE. Among 35 patients, 30 patients had grade 3 or 2 SEC, 5 patients had grade 1 SEC. After ISP loading, 23 patients (66% of all patients) showed improved sludge/SEC and one patient was diagnosed with thrombus in the LAA. There were 25 patients with grade 1 SEC, or no SEC (classified as Group1), 10 patients had residual sludge or grade 2 to 3 SEC (classified as Group2) after ISP administration. LAA flow, LAA-TDI, and PVS velocities were significantly higher in group 1 than in group 2 after ISP administration. There was no complication during the examination and after 24 h and 3 months. ISP infusion may be a potential tool to recognize LAA thrombus under the sludge/SEC during TEE in AF.

Clinical course and decision-making in heart failure by preload stress echocardiography: a preliminary study.

AIMS: Abnormal left ventricular diastolic response to preload stress can be an early marker of heart failure (HF). The aim of this study was to assess clinical course in patients with HF with preserved ejection fraction (HFpEF) who underwent preload stress echocardiography. In the subgroup analysis, we assessed the prognosis of patients with unstable signs during preload stress classified by treatment strategies. METHODS AND RESULTS: We prospectively conducted preload stress echocardiographic studies between January 2006 and December 2013 in 211 patients with HFpEF. Fifty-eight patients had abnormal diastolic reserve during preload stress (unstable impaired relaxation: unstable IR). Of 58 patients with unstable IR, 19 patients were assigned to additional therapy by increased or additional therapy and 39 patients were assigned to standard therapy. Composite outcomes were prespecified as the primary endpoint of death and hospitalization for deteriorating HF. During a median period of 6.9 years, 19 patients (33%) reached the composite outcome. Unstable group with standard therapy had significantly shorter event-free survival than stable group. Patients with uptitration of therapy had longer event-free survival than those with standard therapy group after adjustment of laboratory data (hazard ratio, 0.20, 95% confidence interval, 0.05-0.90; P = 0.036); the 10 year event-free survival in patients with and without uptitration of therapy was 93% and 51%, respectively (P = 0.023). CONCLUSIONS: Patients with unstable sign had significantly shorter event-free survival than patients with stable sign. After additional therapy, the prognosis of patients with unstable signs improved. This technique may impact decision-making for improving their prognosis.

Deep Learning for Detection of Exercise-Induced Pulmonary Hypertension Using Chest X-Ray Images.

Background: Stress echocardiography is an emerging tool used to detect exercise-induced pulmonary hypertension (EIPH). However, facilities that can perform stress echocardiography are limited by issues such as cost and equipment. Objective: We evaluated the usefulness of a deep learning (DL) approach based on a chest X-ray (CXR) to predict EIPH in 6-min walk stress echocardiography. Methods: The study enrolled 142 patients with scleroderma or mixed connective tissue disease with scleroderma features who performed a 6-min walk stress echocardiographic test. EIPH was defined by abnormal cardiac output (CO) responses that involved an increase in mean pulmonary artery pressure (mPAP). We used the previously developed AI model to predict PH and calculated PH probability in this cohort. Results: EIPH defined as ΔmPAP/ΔCO >3.3 and exercise mPAP >25 mmHg was observed in 52 patients, while non-EIPH was observed in 90 patients. The patients with EIPH had a higher mPAP at rest than those without EIPH. The probability of PH based on the DL model was significantly higher in patients with EIPH than in those without EIPH. Multivariate analysis showed that gender, mean PAP at rest, and the probability of PH based on the DL model were independent predictors of EIPH. A model based on baseline parameters (age, gender, and mPAP at rest) was improved by adding the probability of PH predicted by the DL model (AUC: from 0.65 to 0.74; p = 0.046). Conclusion: Applying the DL model based on a CXR may have a potential for detection of EIPH in the clinical setting.

Association between cardiovascular risk factors and left ventricular strain distribution in patients without previous cardiovascular disease.

BACKGROUND: Some cardiovascular (CV) risk factors, such as hypertension and diabetes mellitus, have been reported to reduce left ventricular (LV) longitudinal strain (LS) even in patients with preserved LV ejection fraction. We hypothesized that multiple CV risk factors might cause changes in myocardial strain. Our study aimed to assess the association between multiple CV risk factors and strain in patients without previous CV disease (CVD). METHODS: We retrospectively evaluated 137 patients without CVD, who underwent echocardiography at our institution between May 2017 and February 2020. They were divided into four groups based on the number of risk factors (group 0: no risk factor, group 1: one risk factor, group 2: two risk factors, and groups 3: three or four risk factors). Risk factors were hypertension, dyslipidemia, diabetes mellitus, and chronic kidney disease. Absolute values of global LS (GLS) and relative apical LS ratio (RALSR) defined using the equation: average apical LS/(average basal LS + average mid LS) and was used as a marker of strain distribution. RESULTS: Out of 137 patients, group 0 had 35 patients, group 1 had 35 patients, group 2 had 32 patients, and group 3 had 35 patients. GLS was 22.4 ± 2.0%, 21.7 ± 2.1%, 21.3 ± 1.8%, 20.7 ± 2.2%, and RALSR was 0.64 ± 0.06, 0.66 ± 0.06, 0.68 ± 0.08, 0.69 ± 0.07 in groups 0-3, respectively. The one-way ANOVA detected significant differences between groups in GLS (p = 0.005) and RALSR (p = 0.037), respectively. Group 3 had a significantly lower GLS and higher RALSR than group 0 (p < 0.05). CONCLUSION: In patients without previous CVD, LS decreased especially from the basal segment as the number of cardiovascular risks increased. The segmental LS may be markers of occult LV dysfunction in patients with CV risk factors.

Effects of Radiofrequency Catheter Ablation on Cardiac Reserve Using Preload Stress Echocardiography in Paroxysmal and Persistent Atrial Fibrillation.

The effects of catheter ablation on exercise tolerance and quality of life in patients with atrial fibrillation (AF) have been reported. We assessed cardiac function in more detail using the leg positive pressure (LPP) technique and found that contractile reserve is particularly important in relation to exercise tolerance and prognosis. In this study, we used the LPP technique to examine changes in contractile reserve immediately after ablation and 6 months later. We prospectively enrolled patients who underwent catheter ablation for AF at 2 institutes. We performed LPP stress echocardiography 2 to 3 days after (FU-1) and 6 months after (FU-2) ablation to examine changes in cardiac function indexes. The primary end point was improvement in contractile reserve. Ultimately, 109 patients (mean age 67.4 ± 9.6 years; 70% men) underwent 2 sessions of LPP stress echocardiography. The median CHA2DS2-VASC score was 2 (interquartile range 13). From FU-1 to FU-2, the change in the stroke volume index after the LPP maneuver increased in patients with paroxysmal and persistent AF with low CHA2DS2-VASC scores (both p <0.05). Regardless of AF subtype, contractile reserve at FU-2 improved in patients with low CHA2DS2-VASC scores compared with that at FU-1. In contrast, patients with high CHA2DS2-VASC scores had no change. In conclusion, patients with AF with a low CHA2DS2-VASC score had improved contractile reserve after ablation, whereas patients with high scores did not show any improvement. Aggressive interventions in patients with high scores may lead to better management after catheter ablation.

Predictive value of left atrial function for latent paroxysmal atrial fibrillation as the cause of embolic stroke of undetermined source.

BACKGROUND: In patients with embolic stroke of undetermined source (ESUS), paroxysmal atrial fibrillation (AF) is often diagnosed, however, the risk of paroxysmal AF in ESUS has not been well described. Several studies have suggested a linkage between left atrial (LA) functional parameters and risk of AF in stroke patients. The aim of this study was to assess the role of LA functional parameters as predictors of latent paroxysmal AF in ESUS on admission. METHODS: Between January 2015 and December 2019, consecutive stroke patients with suspected ESUS at admission were prospectively included in this study. They were under hospital electrocardiographic monitoring for detection of new-onset AF. Various echocardiographic parameters including left atrial strain were assessed for association with new-onset AF. RESULTS: We gathered 1082 consecutive patients with ischemic stroke. After exclusions, 121 patients with suspected ESUS at admission formed the study cohort. New-onset AF was detected in 46 (38%) patients during hospital electrocardiographic monitoring (median follow-up: 18 days). LA pump and reservoir strains were significantly and independently associated with new-onset AF. Receiver operating characteristic analysis for the association with new-onset AF showed that the areas under the curve (AUCs) of clinical parameters plus one of each strain (LA pump strain: AUC: 0.86±0.04 and LA reservoir strain: AUC: 0.76±0.05) models were significantly better than plus LA volume index (AUC: 0.68±0.04, compared p-values <0.05). CONCLUSIONS: LA strain was significantly associated with new development of AF. Patients with impaired LA function at admission should be carefully monitored to find AF.

Clinical Utility of Overlap Time for Incomplete Relaxation to Predict Cardiac Events in Heart Failure.

BACKGROUND: The overlap time of transmitral flow can be a novel marker of subclinical left ventricular dysfunction for predicting adverse events in heart failure (HF). We aimed to (1) investigate the role of overlap time of the E-A wave in association with clinical parameters and (2) evaluate whether the overlap time could add prognostic information with respect to other conventional clinical prognosticators in HF. METHODS: We prospectively evaluated 153 patients hospitalized with HF (mean age 68 ± 15 years; 63% male). The primary endpoint was readmission following HF or cardiac death. RESULTS: During a median period of 25 months, 43 patients were readmitted or died. Overlap time appeared to be associated with worse outcomes. After adjustment for readmission scores and ratios of diastolic filling period and cardiac cycle length in a Cox proportional-hazards model, overlap time was associated with event-free survival, independent of elevated left atrial pressure based on guidelines. When overlap time was added to the model based on clinical variables and elevated left atrial pressure, the C-statistic significantly improved from 0.70 (95% CI: 0.63-0.77) to 0.77 (95% CI: 0.69-0.83, compared) (P = 0.035). CONCLUSION: This preliminary study suggested that prolonged overlap time may have potential for predicting readmission and cardiac mortality risk assessment in patients with HF.

Left Atrial Strain Associated with Functional Recovery in Patients Receiving Optimal Treatment for Heart Failure.

BACKGROUND: Heart failure with recovered ejection fraction (HFrecEF) has been reported in several previous studies to have a better prognosis than heart failure with reduced ejection fraction (HFrEF). However, the factors associated with HFrecEF have not been identified. The aim of this study was to test the hypothesis that left atrial (LA) strain could help identify patients with recovered ejection fraction (EF) among those with heart failure (HF) with low EF on admission. METHODS: One hundred consecutive patients hospitalized for the first time for new-onset HF were enrolled. Patients were clinically diagnosed with HFrEF on admission (left ventricular EF < 40%) and received optimal treatment for HF. Twenty-eight patients improved to HFrecEF during 6 months of follow-up. RESULTS: Regarding clinical background, there were significantly more women and a lower rate of atrial fibrillation in the HFrecEF group than in the HFrEF group. In a multivariate logistic regression analysis, LA strain was an independent predictor of HFrecEF, even after adjustment for gender and left ventricular EF (odds ratio: 4.06; 95% CI: 2.04-8.07; P < .001). A cutoff value of 10.8% for LA strain showed high sensitivity (96%) and specificity (82%) in identifying HFrecEF in patients with HF presenting with low EF on admission. During a follow-up period of 24 ± 13 months, 31 patients (31%) had cardiovascular death or readmission for HF. Patients with reduced LA strain (<10.8%) had significantly shorter event-free survival than those with preserved LA strain (P = .02). CONCLUSIONS: LA strain is a useful indicator for predicting HFrecEF and should be considered as a routine measurement in patients with HFrEF on admission.

Cardiac reserve by 6-minute walk stress echocardiography in systemic sclerosis.

OBJECTIVES: There is a high prevalence of left ventricular diastolic dysfunction (LVDD) in systemic sclerosis (SSc) which is associated with high mortality. Thus, early detection of LVDD could be important in management of SSc. We hypothesised that exercise echocardiography in SSc patients with normal resting haemodynamics may expose early phase LVDD, which could affect its prognosis, defined as cardiovascular death and unplanned hospitalisation for heart failure. METHODS: Between January 2014 and December 2018, we prospectively enrolled 140 patients with SSc who underwent 6-minute walk (6MW) stress echocardiographic studies with normal range of estimated mean pulmonary arterial pressure (mPAP) (<25 mm Hg) and mean pulmonary artery wedge pressure (mPAWP) (<15 mm Hg) at rest. We used ΔmPAP/Δcardiac output (CO) to assess pulmonary vascular reserve and ΔmPAWP/ΔCO to assess LV cardiac reserve between resting and post-6MW. RESULTS: During a median period of 3.6 years (IQR 2.0-5.1 years), 25 patients (18%) reached the composite outcome. Both ΔmPAP/ΔCO and ΔmPAWP/ΔCO in patients with events were significantly greater than in those without events (8.9±3.8 mm Hg/L/min vs 3.0±1.7 mm Hg/L/min; p=0.002, and 2.2±0.9 mm Hg/L/min vs 0.9±0.5 mm Hg/L/min; p<0.001, respectively). Patients with both impaired LV cardiac reserve (ΔmPAWP/ΔCO>1.4 mm Hg/L/min) and impaired pulmonary vascular reserve (ΔmPAP/ΔCO>3.0 mm Hg/L/min) had worse outcomes compared with those without these abnormalities (p<0.001). CONCLUSION: The 6MW stress echocardiography revealed impaired LV cardiac reserve in SSc patients with normal resting haemodynamics. Furthermore, LV cardiac reserve independently associates with clinical worsening in SSc, providing incremental prognostic utility, in addition to pulmonary vascular parameters.

Deep Learning for Detection of Elevated Pulmonary Artery Wedge Pressure Using Standard Chest X-Ray.

BACKGROUND: To accurately diagnose and control heart failure (HF), it is important to carry out a simple assessment of elevated pulmonary arterial wedge pressure (PAWP). The aim of this study was to develop and validate an objective method for detecting elevated PAWP by applying deep learning (DL) to a chest x-ray (CXR). METHODS: We enrolled 1013 consecutive patients with a right-heart catheter between October 2009 and February 2020. We developed a convolutional neural network to identify patients with elevated PAWP (> 18 mm Hg) as the actual value of PAWP to be used in the dataset for training. In the prospective validation dataset used to detect elevated PAWP, the area under the receiver operating characteristic curve (AUC) was calculated using the DL model that evaluated the CXR. RESULTS: In the prospective validation dataset, the AUC of the DL model with CXR was not significantly different from the AUC produced by brain natriuretic peptide (BNP) and the echocardiographic left-ventricular diastolic dysfunction (DD) algorithm (DL model: 0.77 vs BNP: 0.77 vs DD algorithm: 0.70; respectively; P = NS for all comparisons); it was, however, significantly higher than the AUC of the cardiothoracic ratio (DL model vs cardiothoracic ratio [CTR]: 0.66, P = 0.044). The model based on 3 parameters (BNP, DD algorithm, and CTR) was improved by adding the DL model (AUC: from 0.80 to 0.86; P = 0.041). CONCLUSIONS: Applying the DL model based on a CXR (a classical, universal, and low-cost test) is useful for screening for elevated PAWP.

Deep learning to predict elevated pulmonary artery pressure in patients with suspected pulmonary hypertension using standard chest X ray.

Accurate diagnosis of pulmonary hypertension (PH) is crucial to ensure that patients receive timely treatment. We hypothesized that application of artificial intelligence (AI) to the chest X-ray (CXR) could identify elevated pulmonary artery pressure (PAP) and stratify the risk of heart failure hospitalization with PH. We retrospectively enrolled a total of 900 consecutive patients with suspected PH. We trained a convolutional neural network to identify patients with elevated PAP (> 20 mmHg) as the actual value of PAP. The endpoints in this study were admission or occurrence of heart failure with elevated PAP. In an independent evaluation set for detection of elevated PAP, the area under curve (AUC) by the AI algorithm was significantly higher than the AUC by measurements of CXR images and human observers (0.71 vs. 0.60 and vs. 0.63, all p < 0.05). In patients with AI predicted PH had 2-times the risk of heart failure with PH compared with those without AI predicted PH. This preliminary work suggests that applying AI to the CXR in high risk groups has limited performance when used alone in identifying elevated PAP. We believe that this report can serve as an impetus for a future large study.

Association between Sarcopenia/Lower Muscle Mass and Short-Term Regression of Deep Vein Thrombosis Using Direct Oral Anticoagulants.

Advanced age, obesity, and muscle weakness are independent factors in the onset of deep vein thrombosis (DVT). Recently, an association between sarcopenia and DVT has been reported. We hypothesized that sarcopenia related factors, observed by ultrasonography, are associated with the regression effect on the thrombus following anticoagulation therapy. The present study focused on gastrocnemius muscle (GCM) thickness and the GCM's internal echogenic brightness. We examined the association with DVT regression following direct oral anticoagulants (DOACs) treatment.The prospective cohort study period was between October 2017 and August 2018. We enrolled 46 patients diagnosed with DVT by ultrasonography, who were aged >60 years old and treated with DOACs. Sarcopenia was evaluated using the Asian Working Group for Sarcopenia flowchart. The average DOACs treatment period was 94 days, and 29 patients exhibited thrombus regression. On univariate logistic regression analysis, sarcopenia, average GCM diameter index, and gastrocnemius integrated backscatter index were significantly associated with thrombus regression. In a multivariate model, only the average GCM diameter index correlated with thrombus regression.The average GCM diameter index is associated with DVT regression treated with DOACs. Considering the GCM diameter during DVT treatment can be a marker to make a decision for the treatment of DVT.