JACC advances最新文献

{"title":"Full issue PDF","authors":"","doi":"10.1016/S2772-963X(25)00454-5","DOIUrl":"10.1016/S2772-963X(25)00454-5","url":null,"abstract":"","PeriodicalId":73527,"journal":{"name":"JACC advances","volume":"4 7","pages":"Article 102030"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial Board/Officers","authors":"","doi":"10.1016/S2772-963X(25)00447-8","DOIUrl":"10.1016/S2772-963X(25)00447-8","url":null,"abstract":"","PeriodicalId":73527,"journal":{"name":"JACC advances","volume":"4 7","pages":"Article 102023"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of Atrioventricular Interval Modulation Therapy in Subjects With Hypertension and Diastolic Dysfunction","authors":"Marat Fudim MD, MHS , Yuval Mika PhD , Avi Fischer MD , Steven J. Evans MD , Ziv Belsky MSc , Karl-Heinz Kuck MD","doi":"10.1016/j.jacadv.2025.101896","DOIUrl":"10.1016/j.jacadv.2025.101896","url":null,"abstract":"","PeriodicalId":73527,"journal":{"name":"JACC advances","volume":"4 7","pages":"Article 101896"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medical Malpractice Claims for Sports Cardiology Cases Among Young Athletes","authors":"Samantha L. Weller DO ,&nbsp;Masihullah Barat MD ,&nbsp;Zachary Weller MS, MBA ,&nbsp;Francis Phan MD ,&nbsp;Nathaniel Moulson MD ,&nbsp;Timothy W. Churchill MD ,&nbsp;Kimberly G. Harmon MD ,&nbsp;Jonathan A. Drezner MD ,&nbsp;Aaron L. Baggish MD ,&nbsp;Ahmad Masri MD, MS ,&nbsp;Bradley J. Petek MD","doi":"10.1016/j.jacadv.2025.101915","DOIUrl":"10.1016/j.jacadv.2025.101915","url":null,"abstract":"<div><h3>Background</h3><div>Sudden cardiac arrest/death (SCA/D) is the leading medical cause of fatalities among young competitive athletes. Sports participation among athletes with cardiovascular disease has become more frequent, raising concerns regarding the medicolegal risk and adequacy of emergency medical response plans.</div></div><div><h3>Objectives</h3><div>The purpose of this study was to analyze the frequency and characteristics of medical malpractice/negligence claims related to sports cardiology cases among young competitive athletes in the United States.</div></div><div><h3>Methods</h3><div>A comprehensive retrospective review of medical malpractice/negligence lawsuits from inception to October 2024 was performed using 4 search strategies. Cases involving young competitive athletes aged 12 to 40 years competing at the middle school, high school, competitive club, collegiate, semiprofessional/professional, or national/international level who experienced SCA/D or had a diagnosis associated with SCA/D were included. Medical malpractice/negligence case frequency, location, demographics, allegations, defendant profiles, and case outcomes/awards were identified.</div></div><div><h3>Results</h3><div>A total of 35/586 (6%) cases met inclusion criteria from 1978 to 2022. There was a favorable plaintiff outcome or settlement in 10/35 (29%) cases with known settlements or awards ranging from $600,000 to $24,000,000; a favorable defendant outcome or dismissal in 16/35 (46%) cases; and the case outcome was undisclosed/unknown in 9/35 (26%) cases. The most common primary allegation for lawsuits was a negligent emergency medical response (13/35, 37%) followed by failure to diagnose cardiovascular disease (9/35, 26%).</div></div><div><h3>Conclusions</h3><div>Medical malpractice/negligence claims regarding cardiac cases in young competitive athletes in the United States were rare (&lt;1 case/y), although the financial settlements were significant. This study supports ongoing efforts to improve emergency preparedness and the cardiac emergency medical response for young competitive athletes.</div></div>","PeriodicalId":73527,"journal":{"name":"JACC advances","volume":"4 8","pages":"Article 101915"},"PeriodicalIF":0.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence–Enabled Electrocardiograms","authors":"Joshua Mayourian MD, PhD,&nbsp;Sunil Ghelani MD,&nbsp;John K. Triedman MD","doi":"10.1016/j.jacadv.2025.101866","DOIUrl":"10.1016/j.jacadv.2025.101866","url":null,"abstract":"","PeriodicalId":73527,"journal":{"name":"JACC advances","volume":"4 7","pages":"Article 101866"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Right Ventricular Myocardial Work Predicts Pulmonary Capillary Wedge Pressure Rise During Exercise in Heart Failure","authors":"Kuan-Chih Huang MD, PhD ,&nbsp;Ting-Tse Lin MD, PhD ,&nbsp;Lung-Chun Lin MD, PhD ,&nbsp;Lian-Yu Lin MD, PhD ,&nbsp;Cho-kai Wu MD, PhD","doi":"10.1016/j.jacadv.2025.101905","DOIUrl":"10.1016/j.jacadv.2025.101905","url":null,"abstract":"<div><h3>Background</h3><div>Symptoms of heart failure with preserved ejection fraction (HFpEF) are closely related to exercise-induced elevation in pulmonary capillary wedge pressure (PCWP). However, the diagnostic role of right ventricular (RV) myocardial work in HFpEF remains unclear.</div></div><div><h3>Objectives</h3><div>The purpose of this study was to evaluate the diagnostic utility of RV myocardial work in HFpEF and their correlation with PCWP during exercise.</div></div><div><h3>Methods</h3><div>Patients with unexplained dyspnea underwent invasive cardiopulmonary exercise tests to identify HFpEF. Echocardiography assessed left and right ventricular parameters. RV myocardial work was calculated using strain rate and pressure curves, matched with electrocardiography data. RV global constructive work, RV global work index, RV global wasted work (RVGWW), and RV global work efficiency (RVGWE) were analyzed.</div></div><div><h3>Results</h3><div>Forty-one patients with adequate data were enrolled, with 21 diagnosed with HFpEF. No significant differences in various echocardiographic parameters were found between HFpEF and non-HFpEF groups, except higher postexercise PCWP and mean pulmonary artery pressure in HFpEF patients. HFpEF patients had higher RVGWW and lower RVGWE. RVGWW and RVGWE demonstrated superior diagnostic performance for HFpEF compared to other echocardiographic parameters, with areas under the receiver operating characteristic curve of 0.85 (95% CI: 0.73-0.97) and 0.83 (95% CI: 0.70-0.96), respectively. RV global constructive work (r = 0.504; <em>P</em> = 0.001) and RVGWW (r = 0.621; <em>P</em> &lt; 0.001) correlated with postexercise ΔPCWP and exercise PCWP, with RVGWW independently associated with both after adjustment for confounding factors.</div></div><div><h3>Conclusions</h3><div>RVGWW is a novel predictive parameter that provides a better explanation of RV performance regarding postexercise ΔPCWP than other standard echocardiographic parameters in HFpEF.</div></div>","PeriodicalId":73527,"journal":{"name":"JACC advances","volume":"4 8","pages":"Article 101905"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Real-Time Percutaneous Coronary Intervention Risk Model Leveraging Electronic Health Records","authors":"Mandeep Singh MD, MPH ,&nbsp;Adelaide M. Arruda-Olson MD, PhD ,&nbsp;Bradley R. Lewis MS ,&nbsp;Bradley K. Johnson BS ,&nbsp;Rajeev Chaudhry MD, MPH ,&nbsp;Arman Arghami MD, MPH ,&nbsp;Mohamad Alkhouli MD, MBA ,&nbsp;Charanjit S. Rihal MD, MBA","doi":"10.1016/j.jacadv.2025.101865","DOIUrl":"10.1016/j.jacadv.2025.101865","url":null,"abstract":"<div><h3>Background</h3><div>Automated individualized risk prediction tools linked to electronic health records (EHRs) are not available for patients undergoing percutaneous coronary interventions (PCIs).</div></div><div><h3>Objectives</h3><div>Our goal was to automatically extract data elements used in the Mayo Clinic PCI models from EHR to enable point of care risk assessment.</div></div><div><h3>Methods</h3><div>Using the Mayo Clinic PCI registry, variables in the Mayo Clinic PCI risk score were trained and tested in an EHR to identify in-hospital death, stroke, bleeding, acute kidney injury (AKI) in patients who underwent PCI from 2016 to 2024. Least absolute shrinkage and selection operator regression was utilized to train (data building) and test (assessing performance) prediction models and to estimate effect sizes that were weighted and integrated into a scoring system.</div></div><div><h3>Results</h3><div>Death, stroke, bleeding, AKI occurred in 157 (1.8%), 43 (0.5%), 157 (1.8%), and 682 (7.6%), respectively. The C-statistics (95% CI) from the training and testing data sets were 0.83 (95% CI: 0.80-0.86) and 0.84 (95% CI: 0.78-0.89); 0.76 (95% CI: 0.65-0.84) and 0.77 (95% CI: 0.65-0.86); 0.80 (95% CI: 0.75-0.83) and 0.75 (95% CI: 0.68-0.81); and 0.82 (95% CI: 0.80-0.84) and 0.80 (95% CI: 0.77-0.84) for in-hospital death, stroke, bleeding, and AKI, respectively. Bootstrap analysis indicated that the models were not overfit to the available data set. The probabilities estimated from the models matched the observed data well, as indicated by the calibration curve slope and intercept and across subgroups, including women, acute coronary syndrome, cardiogenic shock, and diabetes mellitus.</div></div><div><h3>Conclusions</h3><div>Real-time, automated, point of care PCI risk assessment is feasible in an EHR environment.</div></div>","PeriodicalId":73527,"journal":{"name":"JACC advances","volume":"4 7","pages":"Article 101865"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Pharmacist Telehealth Comanagement for Heart Failure","authors":"D. Steven Fox MD ,&nbsp;Nadine Zawadski PhD ,&nbsp;Kimberly Buss MD ,&nbsp;Angela Leahy PhamD ,&nbsp;Q. Laura Zhang PharmD ,&nbsp;Yu Christine Chan BS Pharm","doi":"10.1016/j.jacadv.2025.101906","DOIUrl":"10.1016/j.jacadv.2025.101906","url":null,"abstract":"<div><h3>Background</h3><div>Heart failure with reduced ejection fraction (HFrEF) imposes high morbidity and mortality burdens. Outcomes improve significantly with guideline-directed medical therapy (GDMT), but patients infrequently achieve target regimens in practice.</div></div><div><h3>Objectives</h3><div>The purpose of this study was to determine the effectiveness of telehealth-delivered pharmacist comanagement for patients with HFrEF vs usual care to: 1) achieve goal GDMT therapy; and 2) reduce health care utilization.</div></div><div><h3>Methods</h3><div>This nonrandomized controlled study, spanning 2022 to 2023, analyzed a health care delivery improvement project at an integrated health care network. In-network Medicare recipients with a HFrEF diagnosis (based on chart review) were divided into those covered by the network’s risk-sharing agreement (intervention group) vs otherwise similar (comparison group) patients. A difference-in-difference analysis with inverse propensity weighting adjusted for observable risk factors. Intervention patients received medication reconciliation, new drug initiation, dose adjustments, and safety monitoring by program pharmacists via telehealth. Main outcome measures were hospitalizations and achievement of target GDMT therapy.</div></div><div><h3>Results</h3><div>There were 190 intervention and 277 comparison group patients. The relative risk of cardiac hospitalization in the intervention group (vs comparison group) was 0.26 (95% CI: 0.08-0.86; <em>P</em> = 0.026), with an adjusted absolute risk reduction of 14.2 hospitalizations per 100 patient-years. In the intervention group, the ORs for achieving 3+ and 4 GDMT classes (vs comparison) were 2.73 (95% CI: 1.91-3.87; <em>P</em> &lt; 0.001) and 2.27 (95% CI: 1.29-4.01; <em>P</em> = 0.005), respectively. The adjusted absolute increase in patients on 3+ and 4 GDMT classes were 23% and 21%, respectively.</div></div><div><h3>Conclusions</h3><div>A dedicated pharmacist comanagement telehealth program for patients with HFrEF proved effective at improving GDMT use and reducing cardiac hospitalizations.</div></div>","PeriodicalId":73527,"journal":{"name":"JACC advances","volume":"4 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictors of Length-of-Stay Among Transcatheter Aortic Valve Replacement Patients Using a Supervised Machine Learning Algorithm","authors":"Gregory L. Judson MD ,&nbsp;Jeff Luck PhD ,&nbsp;Skye Lawrence BA ,&nbsp;Rakan Khaki MPH ,&nbsp;Harsh Agrawal MD ,&nbsp;Krishan Soni MD ,&nbsp;Kirsten Tolstrup MD ,&nbsp;Vijayadithyan Jaganathan MD ,&nbsp;Vaikom S. Mahadevan MD","doi":"10.1016/j.jacadv.2025.101902","DOIUrl":"10.1016/j.jacadv.2025.101902","url":null,"abstract":"<div><h3>Background</h3><div>Length of stay following transcatheter aortic valve replacement (TAVR) continues to improve, but significant gaps remain in predicting the length of stay following TAVR.</div></div><div><h3>Objectives</h3><div>This study aimed to develop a novel machine learning (ML) algorithm that would facilitate the understanding of the predictors of early and late hospital discharge in patients who have undergone TAVR.</div></div><div><h3>Methods</h3><div>Using the Biome data set, 9,172 outpatient TAVR procedures were analyzed from 21 centers between 2017 and 2021 across the United States. Supervised random forest ML algorithms were developed to identify variables involved in short length of stay (SLOS) (length of stay &lt;36 hours) and long length of stay (LLOS) (length of stay ≥72 hours) in a 70% sample of the Biome data set. The models were then tested on the remaining 30% of the data set and results compared to standard multivariable models in predicting LOS.</div></div><div><h3>Results</h3><div>Twenty and 22 variables were identified and included as important predictors for the SLOS and LLOS multivariable models, respectively. The predictive power of both the SLOS (sensitivity 0.81, specificity 0.70, area under the curve [AUC] 0.82) and LLOS (sensitivity 0.45, specificity 0.94, AUC 0.85) ML models were more robust than the standard multivariable model (SLOS AUC 0.65, LLOS AUC 0.65). Our study uncovered several previously unreported predictors for length of stay following TAVR, such as procedural duration, postprocedure physical therapy, and procedure day of the week.</div></div><div><h3>Conclusions</h3><div>ML algorithms may have an important role in identifying novel predictors of short and prolonged length of stay following TAVR. These efforts may facilitate targeted quality improvement programs to decrease length of stay post-TAVR.</div></div>","PeriodicalId":73527,"journal":{"name":"JACC advances","volume":"4 8","pages":"Article 101902"},"PeriodicalIF":0.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence for Detection of Prognostically Significant Left Ventricular Dysfunction From Echocardiography","authors":"David Playford MBBS, PhD ,&nbsp;Simon Stewart PhD, DMSc ,&nbsp;Andrew Watts PhD ,&nbsp;Dean Kezurer BPhil(Hons) ,&nbsp;Yih-Kai Chan PhD ,&nbsp;Geoff Strange PhD","doi":"10.1016/j.jacadv.2025.101891","DOIUrl":"10.1016/j.jacadv.2025.101891","url":null,"abstract":"<div><h3>Background</h3><div>Identification of left ventricular (LV) dysfunction following echocardiographic investigations remains problematic, particularly when the ejection fraction (EF) is preserved.</div></div><div><h3>Objectives</h3><div>The authors examined the operational characteristics of artificial intelligence LV dysfunction (AI-LVD) identification from routinely obtained echocardiographic measurements.</div></div><div><h3>Methods</h3><div>Following initial training in 126,136 (imputation cohort) and 254,735 (training cohort) cases from the National Echo Database of Australia, the AI-LVD was tested in 81,509 cases (last echo January 1, 2000-May 21, 2019) with no mitral valve intervention or pacemaker. This cohort comprised 41,796 men (51.3%) aged 62.3 ± 17.1 years and 39,713 women aged 63.2 ± 18.4 years, in whom 4,490 (5.5%), 3,734 (4.6%), and 59,297 (72.7%) had reduced, mildly reduced, and preserved EF, while 13,988 (17.2%) had no recorded EF and 39,940 (45.2%) had “indeterminate” filling pressures.</div></div><div><h3>Results</h3><div>Overall, the AI-LVD generated a (sex-specific) output in decile distributions consistent with increasingly higher levels of LV dysfunction and mortality—actual 5-year mortality rising from 5.7% to 66.3% and 2.3% to 64.2% in men and women, respectively. The prognostic capacity of the AI-LVD persisted in preserved EF, when adjusting for age, year of echo, and missing echo parameters—with adjusted hazard for all-cause mortality during 1,541 (812-2,682) days follow-up 4.93-fold (95% CI: 4.35-5.59) and 7.11-fold (95% CI: 5.85-8.64) higher in the highest vs lowest decile group in men and women, respectively.</div></div><div><h3>Conclusions</h3><div>A new AI-LVD algorithm using only echocardiographic measurements can reliably identify prognostically important LV dysfunction, including in preserved EF, even when key reporting parameters are missing. The AI-LVD can be used in real-time during routine echocardiography reporting.</div></div>","PeriodicalId":73527,"journal":{"name":"JACC advances","volume":"4 7","pages":"Article 101891"},"PeriodicalIF":0.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}