Journal of the American Society of Echocardiography最新文献

{"title":"Machine Learning Identifies Clinically Distinct Phenotypes in Patients With Aortic Regurgitation","authors":"Brototo Deb MD, MIDS ,&nbsp;Christopher G. Scott MS ,&nbsp;Hector I. Michelena MD, PhD ,&nbsp;Sorin V. Pislaru MD, PhD ,&nbsp;Vuyisile T. Nkomo MD, MPH ,&nbsp;Garvan C. Kane MD, PhD ,&nbsp;Juan A. Crestanello MD ,&nbsp;Patricia A. Pellikka MD ,&nbsp;Vidhu Anand MD","doi":"10.1016/j.echo.2024.10.019","DOIUrl":"10.1016/j.echo.2024.10.019","url":null,"abstract":"<div><h3>Background</h3><div>Aortic regurgitation (AR) is a prevalent valve disease with a long latent period before symptoms appear. Recent data has suggested the role of novel markers of myocardial overload in assessing onset of decompensation.</div></div><div><h3>Methods</h3><div>The aim of this study was to evaluate the role of unsupervised cluster analyses in identifying different clinical clusters, including clinical status, and a large number of echocardiographic variables including left ventricular volumes, and their associations with mortality. Patients with moderate to severe or greater chronic AR identified using echocardiography at the Mayo Clinic in Rochester, Minnesota, were retrospectively analyzed. The primary outcome was all-cause mortality censored at aortic valve surgery. Uniform manifold approximation and projection with the <em>k</em>-means algorithm was used to cluster patients using clinical and echocardiographic variables at the time of presentation. Missing data were imputed using the multiple imputation by chained equations method. A supervised approach trained on the training set was used to find cluster membership in a hold-out validation set. Log-rank tests were used to assess differences in mortality rates among the clusters in both the training and validation sets.</div></div><div><h3>Results</h3><div>Three distinct clusters were identified among 1,100 patients (log-rank <em>P</em> for survival &lt; .001). Cluster 1 (<em>n</em> = 337), which included younger males with severe AR but fewer symptoms, showed the best survival at 75.6% (95% CI, 69.5%-82.3%). Cluster 2 (<em>n</em> = 235), including older patients and more females with elevated filling pressures, showed intermediate survival of 64.2% (95% CI, 56.8%-72.5%). Cluster 3 (<em>n</em> = 253), characterized by severe symptomatic AR, demonstrated the lowest survival of 45.3% (95% CI, 34.4%-59.8%) at 5 years. Similar clusters were identified in the internal validation cohort.</div></div><div><h3>Conclusions</h3><div>Distinct clusters with variable echocardiographic features and mortality differences exist within patients with chronic moderate to severe or greater AR. Recognizing these clusters can refine individual risk stratification and clinical decision-making after verification in future prospective studies.</div></div>","PeriodicalId":50011,"journal":{"name":"Journal of the American Society of Echocardiography","volume":"38 4","pages":"Pages 300-309"},"PeriodicalIF":5.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utility of an Echocardiographic Machine Learning Model to Predict Outcomes in Intensive Cardiac Care Unit Patients","authors":"Samy Aghezzaf MD ,&nbsp;Augustin Coisne MD, PhD ,&nbsp;Kenza Hamzi MSc ,&nbsp;Solenn Toupin PhD ,&nbsp;Claire Bouleti MD, PhD ,&nbsp;Charles Fauvel MD ,&nbsp;Jean-Baptiste Brette MD ,&nbsp;David Montaigne MD, PhD ,&nbsp;Reza Rossanaly Vasram MD ,&nbsp;Antonin Trimaille MD ,&nbsp;Gilles Lemesle MD, PhD ,&nbsp;Guillaume Schurtz MD ,&nbsp;Edouard Gerbaud MD, PhD ,&nbsp;Clément Delmas MD, PhD ,&nbsp;Marc Bedossa MD ,&nbsp;Jean-Claude Dib MD ,&nbsp;Vincent Roule MD, PhD ,&nbsp;Etienne Puymirat MD, PhD ,&nbsp;Martine Gilard MD, PhD ,&nbsp;Marouane Boukhris MD ,&nbsp;Theo Pezel MD, PhD","doi":"10.1016/j.echo.2024.11.014","DOIUrl":"10.1016/j.echo.2024.11.014","url":null,"abstract":"<div><h3>Introduction</h3><div>The risk stratification at admission to the intensive cardiac care unit (ICCU) is crucial and remains challenging.</div></div><div><h3>Objectives</h3><div>We aimed to investigate the accuracy of a machine learning (ML)-model based on initial transthoracic echocardiography (TTE) to predict in-hospital major adverse events (MAEs) in a broad spectrum of patients admitted to ICCU.</div></div><div><h3>Methods</h3><div>All consecutive patients hospitalized in ICCUs with a complete TTE performed within the first 24 hours of admission were included in this prospective multicenter study (39 centers). Sixteen TTE parameters were evaluated. The ML model involved automated feature selection by random survival forest and model building with an extreme gradient boosting (XGBoost) algorithm. The primary outcome was in-hospital MAEs defined as all-cause death, resuscitated cardiac arrest, or cardiogenic shock.</div></div><div><h3>Results</h3><div>Of 1,499 consecutive patients (63 ± 15 years, 70% male), MAEs occurred in 67 patients (4.5%). The 5 TTE parameters selected in the model were left ventricular outflow tract velocity-time integral, E/e’ ratio, systolic pulmonary artery pressure, tricuspid annular plane systolic excursion, and left ventricular ejection fraction. Using the XGBoost, the ML model exhibited a higher area under the receiver operating curve compared with any existing scores (ML model, 0.83 vs logistic regression, 0.76, ACUTE-HF score:,0.66; thrombolysis in myocardial infarction score, 0.60; Global Registry of Acute Coronary Events score, 0.58, all <em>P</em> &lt; .001). The ML model had an incremental prognostic value for predicting MAE over a traditional model including clinical and biological data (<em>C</em> index 0.80 vs 0.73, <em>P</em> = .012; chi-square 59.7 vs 32.4; <em>P</em> &lt; .001).</div></div><div><h3>Conclusion</h3><div>The ML model based on initial TTE exhibited a higher prognostic value to predict in-hospital MAEs compared with existing scores or clinical and biological data in the ICCU.</div></div>","PeriodicalId":50011,"journal":{"name":"Journal of the American Society of Echocardiography","volume":"38 4","pages":"Pages 320-330"},"PeriodicalIF":5.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142839656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Limited Concordance of Left Ventricular Ejection Fraction and Chamber Dimensions With Automated Assessments in Hypertrophic Cardiomyopathy: A Substudy From VALOR-HCM","authors":"Gary Parizher MD,&nbsp;Alice Haouzi MD,&nbsp;Wael A. Jaber MD,&nbsp;Anjali Owens MD,&nbsp;Kathy Wolski MPH,&nbsp;Jeffrey B. Geske MD,&nbsp;Sara Saberi MD, MS,&nbsp;Andrew Wang MD,&nbsp;Mark Sherrid MD,&nbsp;Neal K. Lakdawala MD,&nbsp;Albree Tower-Rader MD,&nbsp;David Fermin MD,&nbsp;Srihari S. Naidu MD,&nbsp;Zoran B. Popovic MD, PhD,&nbsp;Nicholas G. Smedira MD, MBA,&nbsp;Hartzell Schaff MD,&nbsp;Ellen McErlean RN, MSN,&nbsp;Christina Sewell RN,&nbsp;Kathy Lampl MD,&nbsp;Amy J. Sehnert MD,&nbsp;Paul C. Cremer MD, MS","doi":"10.1016/j.echo.2024.12.009","DOIUrl":"10.1016/j.echo.2024.12.009","url":null,"abstract":"","PeriodicalId":50011,"journal":{"name":"Journal of the American Society of Echocardiography","volume":"38 4","pages":"Pages 356-358"},"PeriodicalIF":5.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142903994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blood Pressure and Echocardiographic Interpretation: Guideline Revision Needed","authors":"Christopher Lee MD,&nbsp;Theodore P. Abraham MD,&nbsp;Nelson B. Schiller MD","doi":"10.1016/j.echo.2025.01.003","DOIUrl":"10.1016/j.echo.2025.01.003","url":null,"abstract":"","PeriodicalId":50011,"journal":{"name":"Journal of the American Society of Echocardiography","volume":"38 4","pages":"Page 364"},"PeriodicalIF":5.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thank You, JASE Reviewer Team!","authors":"Patricia A. Pellikka MD (Editor-in-Chief)","doi":"10.1016/j.echo.2025.02.009","DOIUrl":"10.1016/j.echo.2025.02.009","url":null,"abstract":"","PeriodicalId":50011,"journal":{"name":"Journal of the American Society of Echocardiography","volume":"38 4","pages":"Pages 295-299"},"PeriodicalIF":5.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Height-Based Pediatric Echocardiographic Z Scores Are Valid in Patients With Normal Body Mass Index and May Be Advantageous in Obese Patients","authors":"David Ezon MD,&nbsp;Son Q. Duong MD, MS,&nbsp;Guillaume Stoffels PhD,&nbsp;Leo Lopez MD,&nbsp;Joseph Mahgerefteh MD","doi":"10.1016/j.echo.2024.10.021","DOIUrl":"10.1016/j.echo.2024.10.021","url":null,"abstract":"","PeriodicalId":50011,"journal":{"name":"Journal of the American Society of Echocardiography","volume":"38 4","pages":"Pages 358-361"},"PeriodicalIF":5.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142640105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Continuing Education and Meeting Calendar","authors":"","doi":"10.1016/j.echo.2025.02.003","DOIUrl":"10.1016/j.echo.2025.02.003","url":null,"abstract":"","PeriodicalId":50011,"journal":{"name":"Journal of the American Society of Echocardiography","volume":"38 4","pages":"Page A23"},"PeriodicalIF":5.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Life Beyond ISCHEMIA in Stress Echocardiography.","authors":"Eugenio Picano","doi":"10.1016/j.echo.2025.03.013","DOIUrl":"https://doi.org/10.1016/j.echo.2025.03.013","url":null,"abstract":"","PeriodicalId":50011,"journal":{"name":"Journal of the American Society of Echocardiography","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143774675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating Left Atrial Reservoir Strain into the First-line Assessment of Diastolic Function: Prognostic Implications in a Community-Based Cohort With Normal Left Ventricular Systolic Function.","authors":"Máté Tolvaj, Fjolla Zhubi Bakija, Alexandra Fábián, Andrea Ferencz, Bálint Lakatos, Zsuzsanna Ladányi, Ádám Szijártó, Edvi Borbála, Loretta Kiss, Zsolt Szelid, Pál Soós, Béla Merkely, Zsolt Bagyura, Márton Tokodi, Attila Kovács","doi":"10.1016/j.echo.2025.03.012","DOIUrl":"https://doi.org/10.1016/j.echo.2025.03.012","url":null,"abstract":"<p><strong>Background: </strong>Left atrial reservoir strain (LASr) has emerged as a sensitive marker of LA function and elevated filling pressures, even though its role in detecting diastolic dysfunction (DD) and the subsequent risk stratification has remained relatively underexplored. Accordingly, we aimed to investigate the prognostic implications of replacing left atrial volume index (LAVi) with LASr in the 2016 ASE/EACVI algorithm for diagnosing DD, compared to the 2024 BSE algorithm, in individuals with normal left ventricular (LV) systolic function.</p><p><strong>Methods: </strong>We retrospectively identified 1180 volunteers from a population-based screening program with normal LV systolic function and no evidence of myocardial disease. Echocardiographic measurements comprised recommended parameters of diastolic function and LASr by speckle tracking. Diastolic function was assessed using the BSE algorithm and the modified ASE/EACVI algorithm, in which LAVi >34 ml/m² was replaced with LASr <23%. The primary endpoint was the composite of all-cause mortality and heart failure hospitalization.</p><p><strong>Results: </strong>During a median follow-up of 11 years, 133 (11%) individuals met the primary endpoint. Using the BSE algorithm, there was no difference in the risk of meeting the primary endpoint between individuals with normal diastolic function and those with impaired diastolic function with normal filling pressures. In univariable analysis, individuals having impaired diastolic function with elevated filling pressures exhibited a significantly higher risk than those in the other two groups (unadjusted HRs: 4.408 [95% CI: 2.376 - 8.179], p<0.001, and 5.137 [95% CI: 1.138 - 23.181], p=0.033, respectively). However, these differences were no longer significant after adjusting for relevant covariates. In contrast, the modified ASE/EACVI algorithm identified three groups with distinct risk profiles, and even in multivariable analysis, individuals with DD had a higher risk of meeting the primary endpoint than those with normal diastolic function (adjusted HR: 3.199 [95% CI: 1.534 - 6.671], p=0.002).</p><p><strong>Conclusion: </strong>In a community-based cohort with normal LV function, integrating LASr into the first-line echocardiographic assessment of diastolic function improved both classification and subsequent risk stratification.</p>","PeriodicalId":50011,"journal":{"name":"Journal of the American Society of Echocardiography","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143744193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Echocardiographic Machine Learning Model to Predict Outcomes in Intensive Cardiac Care Unit.","authors":"Rohan G Reddy, David A Danford, Shelby Kutty","doi":"10.1016/j.echo.2025.03.008","DOIUrl":"https://doi.org/10.1016/j.echo.2025.03.008","url":null,"abstract":"","PeriodicalId":50011,"journal":{"name":"Journal of the American Society of Echocardiography","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}