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1,148 results on '"Noseworthy, Peter A."'

1. Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial

Author

Adedinsewo, Demilade A., Morales-Lara, Andrea Carolina, Afolabi, Bosede B., Kushimo, Oyewole A., Mbakwem, Amam C., Ibiyemi, Kehinde F., Ogunmodede, James Ayodele, Raji, Hadijat Olaide, Ringim, Sadiq H., Habib, Abdullahi A., Hamza, Sabiu M., Ogah, Okechukwu S., Obajimi, Gbolahan, Saanu, Olugbenga Oluseun, Jagun, Olusoji E., Inofomoh, Francisca O., Adeolu, Temitope, Karaye, Kamilu M., Gaya, Sule A., Alfa, Isiaka, Yohanna, Cynthia, Venkatachalam, K. L., Dugan, Jennifer, Yao, Xiaoxi, Sledge, Hanna J., Johnson, Patrick W., Wieczorek, Mikolaj A., Attia, Zachi I., Phillips, Sabrina D., Yamani, Mohamad H., Tobah, Yvonne Butler, Rose, Carl H., Sharpe, Emily E., Lopez-Jimenez, Francisco, Friedman, Paul A., Noseworthy, Peter A., and Carter, Rickey E.

Published
2024
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4. Patient-Level Artificial Intelligence–Enhanced Electrocardiography in Hypertrophic Cardiomyopathy Longitudinal Treatment and Clinical Biomarker Correlations

Author

Siontis, Konstantinos C, Abreau, Sean, Attia, Zachi I, Barrios, Joshua P, Dewland, Thomas A, Agarwal, Priyanka, Balasubramanyam, Aarthi, Li, Yunfan, Lester, Steven J, Masri, Ahmad, Wang, Andrew, Sehnert, Amy J, Edelberg, Jay M, Abraham, Theodore P, Friedman, Paul A, Olgin, Jeffrey E, Noseworthy, Peter A, and Tison, Geoffrey H

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Heart Disease, Prevention, Clinical Research, Cardiovascular, artificial intelligence, electrocardiogram, hypertrophic cardiomyopathy, machine learning, mavacamten
Abstract

BackgroundArtificial intelligence (AI) applied to 12-lead electrocardiographs (ECGs) can detect hypertrophic cardiomyopathy (HCM).ObjectivesThe purpose of this study was to determine if AI-enhanced ECG (AI-ECG) can track longitudinal therapeutic response and changes in cardiac structure, function, or hemodynamics in obstructive HCM during mavacamten treatment.MethodsWe applied 2 independently developed AI-ECG algorithms (University of California-San Francisco and Mayo Clinic) to serial ECGs (n = 216) from the phase 2 PIONEER-OLE trial of mavacamten for symptomatic obstructive HCM (n = 13 patients, mean age 57.8 years, 69.2% male). Control ECGs from 2,600 age- and sex-matched individuals without HCM were obtained. AI-ECG output was correlated longitudinally to echocardiographic and laboratory metrics of mavacamten treatment response.ResultsIn the validation cohorts, both algorithms exhibited similar performance for HCM diagnosis, and exhibited mean HCM score decreases during mavacamten treatment: patient-level score reduction ranged from approximately 0.80 to 0.45 for Mayo and 0.70 to 0.35 for USCF algorithms; 11 of 13 patients demonstrated absolute score reduction from start to end of follow-up for both algorithms. HCM scores were significantly associated with other HCM-relevant parameters, including left ventricular outflow tract gradient at rest, postexercise, and with Valsalva, and NT-proBNP level, independent of age and sex (all P < 0.01). For both algorithms, the strongest longitudinal correlation was between AI-ECG HCM score and left ventricular outflow tract gradient postexercise (slope estimate: University of California-San Francisco 0.70 [95% CI: 0.45-0.96], P < 0.0001; Mayo 0.40 [95% CI: 0.11-0.68], P = 0.007).ConclusionsAI-ECG analysis longitudinally correlated with changes in echocardiographic and laboratory markers during mavacamten treatment in obstructive HCM. These results provide early evidence for a potential paradigm for monitoring HCM therapeutic response.

Published
2023

5. Research Opportunities in the Treatment of Mitral Valve Prolapse: JACC Expert Panel.

Author

Delling, Francesca N, Noseworthy, Peter A, Adams, David H, Basso, Cristina, Borger, Michael, Bouatia-Naji, Nabila, Elmariah, Sammy, Evans, Frank, Gerstenfeld, Edward, Hung, Judy, Le Tourneau, Thierry, Lewis, John, Miller, Marc A, Norris, Russell A, Padala, Muralidhar, Perazzolo-Marra, Martina, Shah, Dipan J, Weinsaft, Jonathan W, Enriquez-Sarano, Maurice, and Levine, Robert A

Subjects
Humans, Death, Sudden, Cardiac, Mitral Valve Prolapse, cardiac magnetic resonance imaging, echocardiography, mitral regurgitation, mitral valve prolapse, sudden cardiac death, Heart Disease, Clinical Research, Cardiovascular, 2.1 Biological and endogenous factors, Aetiology, Cardiorespiratory Medicine and Haematology, Public Health and Health Services, Cardiovascular System & Hematology
Abstract

In light of the adverse prognosis related to severe mitral regurgitation, heart failure, or sudden cardiac death in a subset of patients with mitral valve prolapse (MVP), identifying those at higher risk is key. For the first time in decades, researchers have the means to rapidly advance discovery in the field of MVP thanks to state-of-the-art imaging techniques, novel omics methodologies, and the potential for large-scale collaborations using web-based platforms. The National Heart, Lung, and Blood Institute recently initiated a webinar-based workshop to identify contemporary research opportunities in the treatment of MVP. This report summarizes 3 specific areas in the treatment of MVP that were the focus of the workshop: 1) improving management of degenerative mitral regurgitation and associated left ventricular systolic dysfunction; 2) preventing sudden cardiac death in MVP; and 3) understanding the mechanisms and progression of MVP through genetic studies and small and large animal models, with the potential of developing medical therapies.

Published
2022

6. Cost-Effectiveness of Artificial Intelligence-Enabled Electrocardiograms for Early Detection of Low Ejection Fraction: A Secondary Analysis of the Electrocardiogram Artificial Intelligence-Guided Screening for Low Ejection Fraction Trial

Author

Thao, Viengneesee, Zhu, Ye, Tseng, Andrew S., Inselman, Jonathan W., Borah, Bijan J., McCoy, Rozalina G., Attia, Zachi I., Lopez-Jimenez, Francisco, Pellikka, Patricia A., Rushlow, David R., Friedman, Paul A., Noseworthy, Peter A., and Yao, Xiaoxi

Published
2024
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9. Artificial intelligence enabled interpretation of ECG images to predict hematopoietic cell transplantation toxicity

Author

Shaffer, Brian C., Brown, Samantha, Chinapen, Stephanie, Mangold, Kathryn E., Lahoud, Oscar, Lopez-Jimenez, Francisco, Schaffer, Wendy, Liu, Jennifer, Giralt, Sergio, Devlin, Sean, Shah, Gunjan, Scordo, Michael, Papadopoulos, Esperanza, Landau, Heather, Usmani, Saad, Perales, Miguel-Angel, Friedman, Paul A., Gersh, Bernard J., Attia, Itzhak Zachi, Noseworthy, Peter A., and Kosmidou, Ioanna

Published
2024
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11. Artificial intelligence–based screening for cardiomyopathy in an obstetric population: A pilot study

Author

Adedinsewo, Demilade, Morales-Lara, Andrea Carolina, Hardway, Heather, Johnson, Patrick, Young, Kathleen A., Garzon-Siatoya, Wendy Tatiana, Butler Tobah, Yvonne S., Rose, Carl H., Burnette, David, Seccombe, Kendra, Fussell, Mia, Phillips, Sabrina, Lopez-Jimenez, Francisco, Attia, Zachi I., Friedman, Paul A., Carter, Rickey E., and Noseworthy, Peter A.

Published
2024
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13. Heart Watch Study: protocol for a pragmatic randomised controlled trial

Author

Dhruva, Sanket S, Shah, Nilay D, Vemulapalli, Sreekanth, Deshmukh, Abhishek, Beatty, Alexis L, Gamble, Ginger M, Freeman, James V, Hummel, James P, Piccini, Jonathan P, Akar, Joseph G, Ervin, Keondae, Arges, Kristine L, Emanuel, Lindsay, Noseworthy, Peter A, Hu, Tiffany, Bartlett, Victoria, and Ross, Joseph S

Subjects
Health Services and Systems, Biomedical and Clinical Sciences, Clinical Sciences, Health Sciences, Prevention, Clinical Trials and Supportive Activities, Patient Safety, Heart Disease, Cardiovascular, Clinical Research, Health Services, Management of diseases and conditions, 7.3 Management and decision making, Good Health and Well Being, Atrial Fibrillation, Atrial Flutter, Electric Countershock, Humans, Multicenter Studies as Topic, Pragmatic Clinical Trials as Topic, Prospective Studies, Quality of Life, Randomized Controlled Trials as Topic, adult cardiology, pacing & electrophysiology, telemedicine, Public Health and Health Services, Other Medical and Health Sciences, Biomedical and clinical sciences, Health sciences, Psychology
Abstract

IntroductionPersonal digital devices that provide health information, such as the Apple Watch, have developed an increasing array of cardiopulmonary tracking features which have received regulatory clearance and are directly marketed to consumers. Despite their widespread and increasing use, data about the impact of personal digital device use on patient-reported outcomes and healthcare utilisation are sparse. Among a population of patients with atrial fibrillation and/or atrial flutter undergoing cardioversion, our primary aim is to determine the impact of the heart rate measurement, irregular rhythm notification, and ECG features of the Apple Watch on quality of life and healthcare utilisation.Methods and analysisWe are conducting a prospective, open-label multicentre pragmatic randomised clinical trial, leveraging a unique patient-centred health data sharing platform for enrolment and follow-up. A total of 150 patients undergoing cardioversion for atrial fibrillation or atrial flutter will be randomised 1:1 to receive the Apple Watch Series 6 or Withings Move at the time of cardioversion. The primary outcome is the difference in the Atrial Fibrillation Effect on QualiTy-of-life global score at 6 months postcardioversion. Secondary outcomes include inpatient and outpatient healthcare utilisation. Additional secondary outcomes include a comparison of the Apple Watch ECG and pulse oximeter features with gold-standard data obtained in routine clinical care settings.Ethics and disseminationThe Institutional Review Boards at Yale University, Mayo Clinic, and Duke University Health System have approved the trial protocol. This trial will provide important data to policymakers, clinicians and patients about the impact of the heart rate, irregular rhythm notification, and ECG features of widely used personal digital devices on patient quality of life and healthcare utilisation. Findings will be disseminated to study participants, at professional society meetings and in peer-reviewed journals.Trial registration numberNCT04468321.

Published
2021

16. Real-World Data Analysis of Implantable Cardioverter Defibrillator (ICD) in Patients with Hypertrophic Cardiomyopathy (HCM)

Author

Moon, Sungrim, Wen, Andrew, Scott, Christopher G., Ackerman, Michael J., Geske, Jeffrey B., Noseworthy, Peter A., Ommen, Steve R, Shellum, Jane L, Liu, Hongfang, and Nishimura, Rick A.

Subjects
Physics - Medical Physics
Abstract

Background: One of the common causes of sudden cardiac death (SCD) in young people is hypertrophic cardiomyopathy (HCM) and the primary prevention of SCD is with an implantable cardioverter defibrillators (ICD). Concerning the incidence of appropriate ICD therapy and the complications associated with ICD implantation and discharge, patients with implanted ICDs are closely monitored and interrogation reports are generated from clinical consultations. Methods: In this study, we compared the performance of structured device data and unstructured interrogation reports for extracting information of ICD therapy and heart rhythm. We sampled 687 reports with a gold standard generated through manual chart review. A rule-based natural language processing (NLP) system was developed using 480 reports and the information in the corresponding device data was aggregated for the task. We compared the performance of the NLP system with information aggregated from structured device data using the remaining 207 reports. Results: The rule-based NLP system achieved F-measure of 0.92 and 0.98 for ICD therapy and heart rhythm while the performance of aggregating device data was significantly lower with F-measure of 0.78 and 0.45 respectively. Limitations of using only structured device data include no differentiation of real events from management events, data availability, and disparate perspectives of vendor and data granularity while using interrogation reports needs to overcome non-representative keyword/pattern and contextual errors. Conclusions: Extracting phenotyping information from data generated in real-world requires the incorporation of medical knowledge. It is essential to analyze, compare, and harmonize multiple data sources for real-world evidence generation.

Published
2020

18. Patient similarity and other artificial intelligence machine learning algorithms in clinical decision aid for shared decision-making in the Prevention of Cardiovascular Toxicity (PACT): a feasibility trial design

Author

Brown, Sherry-Ann, Chung, Brian Y., Doshi, Krishna, Hamid, Abdulaziz, Pederson, Erin, Maddula, Ragasnehith, Hanna, Allen, Choudhuri, Indrajit, Sparapani, Rodney, Bagheri Mohamadi Pour, Mehri, Zhang, Jun, Kothari, Anai N., Collier, Patrick, Caraballo, Pedro, Noseworthy, Peter, and Arruda-Olson, Adelaide

Published
2023
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19. RApid Throughput Screening for Asymptomatic COVID-19 Infection With an Electrocardiogram: A Prospective Observational Study

Author

Adedinsewo, Demilade, Dugan, Jennifer, Johnson, Patrick W., Douglass, Erika J., Morales-Lara, Andrea Carolina, Parkulo, Mark A., Ting, Henry H., Cooper, Leslie T., Scott, Luis R., Valverde, Arturo M., Padmanabhan, Deepak, Peters, Nicholas S., Bachtiger, Patrik, Kelshiker, Mihir, Fernandez-Aviles, Francisco, Atienza, Felipe, Glotzer, Taya V., Lahiri, Marc K., Dominic, Paari, Attia, Zachi I., Kapa, Suraj, Noseworthy, Peter A., Pereira, Naveen L., Cruz, Jessica, Berbari, Elie F., Carter, Rickey E., and Friedman, Paul A.

Published
2023
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22. Team principles for successful interdisciplinary research teams

Author

Brown, Sherry-Ann, Sparapani, Rodney, Osinski, Kristen, Zhang, Jun, Blessing, Jeffrey, Cheng, Feixiong, Hamid, Abdulaziz, MohamadiPour, Mehri Bagheri, Lal, Jessica Castrillon, Kothari, Anai N., Caraballo, Pedro, Noseworthy, Peter, Johnson, Roger H., Hansen, Kathryn, Sun, Louise Y., Crotty, Bradley, Cheng, Yee Chung, Echefu, Gift, Doshi, Krishna, and Olson, Jessica

Published
2023
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31. Abstract 17189: Association Between an Artificial Intelligence-Enabled ECG Algorithm for Detection of Atrial Fibrillation While in Sinus Rhythm, and Hospitalization for Recurrent Ischemic Stroke in the Community

Author

Sheffeh, Mohammad Ali, Medina-Inojosa, Jose R, Brown, Robert D, Klaas, James P, Koriesh, Ahmed, Medina-Inojosa, Betsy, Mangold, Kathryn, Estrada Magana, Andres, Rabinstein, Alejandro A, Noseworthy, Peter A, Friedman, Paul, Attia, Zachi, and Lopez-Jimenez, Francisco

Published
2023
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36. Abstract 14425: Independent Predictors of ECG Interpretation Proficiency in Healthcare Professionals

Author

Kashou, Anthony H, Noseworthy, Peter A, Beckman, Thomas, Anavekar, Nandan S, Cullen, Michael W, Sandefur, Benjamin, Boswell, Christopher L, Angstman, Kurt, Shapiro, Brian P, Wiley, Brandon, Kates, Andrew, Huneycutt, david, Braisted, Andrew, Kerwin, Scott, Beard, John, Young, Brian, Rowlandson, Ian, Baranchuk, Adrian, OʼBrien, Kevin, Knohl, Stephen, and May, Adam

Published
2023
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37. Abstract 14370: Educate: A Randomized Controlled Trial of Education Curriculum Assessment for Teaching Electrocardiography

Author

Kashou, Anthony H, Noseworthy, Peter A, Beckman, Thomas, Anavekar, Nandan S, Cullen, Michael W, Angstman, Kurt, Sandefur, Benjamin, Shapiro, Brian P, Wiley, Brandon, Kates, Andrew, Sadhu, Justin, Thakker, Prashanth, Huneycutt, david, Braisted, Andrew, Smith, Stephen W, Baranchuk, Adrian, Grauer, Ken, OʼBrien, Kevin, Kaul, Viren, Gambhir, Harvir, Knohl, Stephen, Restrepo, Daniel, and May, Adam

Published
2023
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38. Abstract 13990: ECG Interpretation Proficiency of Healthcare Professionals

Author

Kashou, Anthony H, Noseworthy, Peter A, Beckman, Thomas, Anavekar, Nandan S, Cullen, Michael W, Angstman, Kurt B, Sandefur, Benjamin J, Shapiro, Brian P, Wiley, Brandon W, Kates, Andrew, Huneycutt, david, Braisted, Andrew, Smith, Stephen W, Baranchuk, Adrian, Grauer, Ken, OʼBrien, Kevin, Kaul, Viren, Gambhir, Harvir S, Knohl, Stephen J, Albert, David E, KLIGFIELD, Paul, Macfarlane, Peter W, and May, Adam

Published
2023
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40. Emerging role of artificial intelligence in cardiac electrophysiology

Author

Kabra, Rajesh, Israni, Sharat, Vijay, Bharat, Baru, Chaitanya, Mendu, Raghuveer, Fellman, Mark, Sridhar, Arun, Mason, Pamela, Cheung, Jim W., DiBiase, Luigi, Mahapatra, Srijoy, Kalifa, Jerome, Lubitz, Steven A., Noseworthy, Peter A., Navara, Rachita, McManus, David D., Cohen, Mitchell, Chung, Mina K., Trayanova, Natalia, Gopinathannair, Rakesh, and Lakkireddy, Dhanunjaya

Published
2022
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45. Artificial Intelligence–Enabled Electrocardiogram for Atrial Fibrillation Identifies Cognitive Decline Risk and Cerebral Infarcts

Author

Weil, Erika L., Noseworthy, Peter A., Lopez, Camden L., Rabinstein, Alejandro A., Friedman, Paul A., Attia, Zachi I., Yao, Xiaoxi, Siontis, Konstantinos C., Kremers, Walter K., Christopoulos, Georgios, Mielke, Michelle M., Vemuri, Prashanthi, Jack, Clifford R., Jr., Gersh, Bernard J., Machulda, Mary M., Knopman, David S., Petersen, Ronald C., and Graff-Radford, Jonathan

Published
2022
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