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Kubler MM, Kashou AH, Anavekar NS. 55-Year-Old Woman With Acute Progressive Dyspnea. Mayo Clin Proc 2024; 99:480-485. [PMID: 38323943 DOI: 10.1016/j.mayocp.2023.06.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 06/29/2023] [Accepted: 06/30/2023] [Indexed: 02/08/2024]
Affiliation(s)
- Manfred M Kubler
- Resident in Internal Medicine, Mayo Clinic School of Graduate Medical Education, Rochester, MN
| | - Anthony H Kashou
- Resident in Cardiovascular Medicine, Mayo Clinic School of Graduate Medical Education, Rochester, MN
| | - Nandan S Anavekar
- Advisor to residents and Consultant in Cardiovascular Medicine, Mayo Clinic, Rochester, MN.
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2
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Kashou AH, Noseworthy PA, Beckman TJ, Anavekar NS, Cullen MW, Angstman KB, Sandefur BJ, Shapiro BP, Wiley BW, Kates AM, Sadhu J, Thakker P, Huneycutt D, Braisted A, Smith SW, Baranchuk A, Grauer K, O'Brien K, Kaul V, Gambhir HS, Knohl SJ, Restrepo D, May AM. EDUCATE: An international, randomized controlled trial for teaching electrocardiography. Curr Probl Cardiol 2024; 49:102409. [PMID: 38232918 PMCID: PMC10922800 DOI: 10.1016/j.cpcardiol.2024.102409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Accepted: 01/14/2024] [Indexed: 01/19/2024]
Abstract
INTRODUCTION Despite the critical role of electrocardiograms (ECGs) in patient care, evident gaps exist in ECG interpretation competency among healthcare professionals across various medical disciplines and training levels. Currently, no practical, evidence-based, and easily accessible ECG learning solution is available for healthcare professionals. The aim of this study was to assess the effectiveness of web-based, learner-directed interventions in improving ECG interpretation skills in a diverse group of healthcare professionals. METHODS In an international, prospective, randomized controlled trial, 1206 healthcare professionals from various disciplines and training levels were enrolled. They underwent a pre-intervention test featuring 30 12-lead ECGs with common urgent and non-urgent findings. Participants were randomly assigned to four groups: (i) practice ECG interpretation question bank (question bank), (ii) lecture-based learning resource (lectures), (iii) hybrid question- and lecture-based learning resource (hybrid), or (iv) no ECG learning resources (control). After four months, a post-intervention test was administered. The primary outcome was the overall change in ECG interpretation performance, with secondary outcomes including changes in interpretation time, self-reported confidence, and accuracy for specific ECG findings. Both unadjusted and adjusted scores were used for performance assessment. RESULTS Among 1206 participants, 863 (72 %) completed the trial. Following the intervention, the question bank, lectures, and hybrid intervention groups each exhibited significant improvements, with average unadjusted score increases of 11.4 % (95 % CI, 9.1 to 13.7; P<0.01), 9.8 % (95 % CI, 7.8 to 11.9; P<0.01), and 11.0 % (95 % CI, 9.2 to 12.9; P<0.01), respectively. In contrast, the control group demonstrated a non-significant improvement of 0.8 % (95 % CI, -1.2 to 2.8; P=0.54). While no differences were observed among intervention groups, all outperformed the control group significantly (P<0.01). Intervention groups also excelled in adjusted scores, confidence, and proficiency for specific ECG findings. CONCLUSION Web-based, self-directed interventions markedly enhanced ECG interpretation skills across a diverse range of healthcare professionals, providing an accessible and evidence-based solution.
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Affiliation(s)
- Anthony H Kashou
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
| | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Thomas J Beckman
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Nandan S Anavekar
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Michael W Cullen
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Kurt B Angstman
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Benjamin J Sandefur
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | | | - Brandon W Wiley
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Andrew M Kates
- Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Justin Sadhu
- Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Prashanth Thakker
- Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | | | | | - Stephen W Smith
- Hennepin County Medical Center and University of Minnesota, Minneapolis, MN, USA
| | | | - Ken Grauer
- University of Florida, Gainesville, FL, USA
| | | | - Viren Kaul
- SUNY Upstate Medical University, Syracuse, NY, USA
| | | | | | - Daniel Restrepo
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Adam M May
- Washington University School of Medicine in St. Louis, St. Louis, MO, USA
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3
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Kashou AH, Noseworthy PA, Beckman TJ, Anavekar NS, Cullen MW, Boswell CL, Angstman KB, Sandefur BJ, Shapiro BP, Wiley BW, Kates AM, Huneycutt D, Braisted A, Kerwin S, Young B, Rowlandson I, Beard JW, Baranchuk A, O'Brien K, Knohl SJ, May AM. Predictors of ECG Interpretation Proficiency in Healthcare Professionals. Curr Probl Cardiol 2023; 48:102011. [PMID: 37544624 PMCID: PMC10838348 DOI: 10.1016/j.cpcardiol.2023.102011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 08/01/2023] [Indexed: 08/08/2023]
Abstract
Accurate ECG interpretation is vital, but variations in skills exist among healthcare professionals. This study aims to identify factors contributing to ECG interpretation proficiency. Survey data and ECG interpretation test scores from participants in the EDUCATE Trial were analyzed to identify predictors of performance for 30 sequential 12-lead ECGs. Nonmodifiable factors (being a physician, clinical experience, patient care impact) and modifiable factors (weekly interpretation volume, training hours, expert supervision frequency) were analyzed. Bivariate and multivariate analyses were used to generate a Comprehensive Model (incorporating all factors) and Actionable Model (incorporating modifiable factors only). Among 1206 participants analyzed, there were 72 (6.0%) primary care physicians, 146 (12.1%) cardiology fellows-in-training, 353 (29.3%) resident physicians, 182 (15.1%) medical students, 84 (7.0%) advanced practice providers, 120 (9.9%) nurses, and 249 (20.7%) allied health professionals. Among them, 571 (47.3%) were physicians and 453 (37.6%) were nonphysicians. The average test score was 56.4% ± 17.2%. Bivariate analysis demonstrated significant associations between test scores and >10 weekly ECG interpretations, being a physician, >5 training hours, patient care impact, and expert supervision but not clinical experience. In the Comprehensive Model, independent associations were found with weekly interpretation volume (9.9 score increase; 95% CI, 7.9-11.8; P < 0.001), being a physician (9.0 score increase; 95% CI, 7.2-10.8; P < 0.001), and training hours (5.7 score increase; 95% CI, 3.7-7.6; P < 0.001). In the Actionable Model, scores were independently associated with weekly interpretation volume (12.0 score increase; 95% CI, 10.0-14.0; P < 0.001) and training hours (4.7 score increase; 95% CI, 2.6-6.7; P < 0.001). The Comprehensive and Actionable Models explained 18.7% and 12.3% of the variance in test scores, respectively. Predictors of ECG interpretation proficiency include nonmodifiable factors like physician status and modifiable factors such as training hours and weekly ECG interpretation volume.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Brandon W Wiley
- Keck School of Medicine, University of Southern California, Los Angeles CA
| | - Andrew M Kates
- Washington University School of Medicine in St. Louis, St. Louis, MO
| | | | | | | | | | | | | | | | | | | | - Adam M May
- Washington University School of Medicine in St. Louis, St. Louis, MO
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Kashou AH, Noseworthy PA, Beckman TJ, Anavekar NS, Cullen MW, Angstman KB, Sandefur BJ, Shapiro BP, Wiley BW, Kates AM, Huneycutt D, Braisted A, Manoukian SV, Kerwin S, Young B, Rowlandson I, Beard JW, Baranchuk A, O'Brien K, Knohl SJ, May AM. Impact of Computer-Interpreted ECGs on the Accuracy of Healthcare Professionals. Curr Probl Cardiol 2023; 48:101989. [PMID: 37482286 PMCID: PMC10800643 DOI: 10.1016/j.cpcardiol.2023.101989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 07/19/2023] [Indexed: 07/25/2023]
Abstract
The interpretation of electrocardiograms (ECGs) involves a dynamic interplay between computerized ECG interpretation (CEI) software and human overread. However, the impact of computer ECG interpretation on the performance of healthcare professionals remains largely unexplored. The aim of this study was to evaluate the interpretation proficiency of various medical professional groups, with and without access to the CEI report. Healthcare professionals from diverse disciplines, training levels, and countries sequentially interpreted 60 standard 12-lead ECGs, demonstrating both urgent and nonurgent findings. The interpretation process consisted of 2 phases. In the first phase, participants interpreted 30 ECGs with clinical statements. In the second phase, the same 30 ECGs and clinical statements were randomized and accompanied by a CEI report. Diagnostic performance was evaluated based on interpretation accuracy, time per ECG (in seconds [s]), and self-reported confidence (rated 0 [not confident], 1 [somewhat confident], or 2 [confident]). A total of 892 participants from various medical professional groups participated in the study. This cohort included 44 (4.9%) primary care physicians, 123 (13.8%) cardiology fellows-in-training, 259 (29.0%) resident physicians, 137 (15.4%) medical students, 56 (6.3%) advanced practice providers, 82 (9.2%) nurses, and 191 (21.4%) allied health professionals. The inclusion of the CEI was associated with a significant improvement in interpretation accuracy by 15.1% (95% confidence interval, 14.3-16.0; P < 0.001), decrease in interpretation time by 52 s (-56 to -48; P < 0.001), and increase in confidence by 0.06 (0.03-0.09; P = 0.003). Improvement in interpretation accuracy was seen across all professional subgroups, including primary care physicians by 12.9% (9.4-16.3; P = 0.003), cardiology fellows-in-training by 10.9% (9.1-12.7; P < 0.001), resident physicians by 14.4% (13.0-15.8; P < 0.001), medical students by 19.9% (16.8-23.0; P < 0.001), advanced practice providers by 17.1% (13.3-21.0; P < 0.001), nurses by 16.2% (13.4-18.9; P < 0.001), allied health professionals by 15% (13.4-16.6; P < 0.001), physicians by 13.2% (12.2-14.3; P < 0.001), and nonphysicians by 15.6% (14.3-17.0; P < 0.001).CEI integration improves ECG interpretation accuracy, efficiency, and confidence among healthcare professionals.
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Affiliation(s)
- Anthony H Kashou
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN.
| | | | - Thomas J Beckman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | | | - Michael W Cullen
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Kurt B Angstman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | | | | | - Brandon W Wiley
- Keck School of Medicine, University of Southern California, Los Angeles CA
| | - Andrew M Kates
- Washington University School of Medicine in St. Louis, St. Louis, MO
| | | | | | | | | | | | | | | | | | | | | | - Adam M May
- Washington University School of Medicine in St. Louis, St. Louis, MO
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Kashou AH, LoCoco S, Gardner MR, Webb J, Jentzer JC, Noseworthy PA, DeSimone CV, Deshmukh AJ, Asirvatham SJ, May AM. Mayo Clinic VT calculator: A practical tool for accurate wide complex tachycardia differentiation. Ann Noninvasive Electrocardiol 2023; 28:e13085. [PMID: 37670480 PMCID: PMC10646384 DOI: 10.1111/anec.13085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 07/25/2023] [Accepted: 08/14/2023] [Indexed: 09/07/2023] Open
Abstract
The discrimination of ventricular tachycardia (VT) versus supraventricular wide complex tachycardia (SWCT) via 12-lead electrocardiogram (ECG) is crucial for achieving appropriate, high-quality, and cost-effective care in patients presenting with wide QRS complex tachycardia (WCT). Decades of rigorous research have brought forth an expanding arsenal of applicable manual algorithm methods for differentiating WCTs. However, these algorithms are limited by their heavy reliance on the ECG interpreter for their proper execution. Herein, we introduce the Mayo Clinic ventricular tachycardia calculator (MC-VTcalc) as a novel generalizable, accurate, and easy-to-use means to estimate VT probability independent of ECG interpreter competency. The MC-VTcalc, through the use of web-based and mobile device platforms, only requires the entry of computerized measurements (i.e., QRS duration, QRS axis, and T-wave axis) that are routinely displayed on standard 12-lead ECG recordings.
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Affiliation(s)
- Anthony H. Kashou
- Department of Cardiovascular MedicineMayo ClinicRochesterMinnesotaUSA
| | - Sarah LoCoco
- Department of MedicineWashington University School of MedicineSt. LouisMissouriUSA
| | | | - Jocelyn Webb
- Mayo Clinic Center for Digital HealthMayo ClinicRochesterMinnesotaUSA
| | - Jacob C. Jentzer
- Department of Cardiovascular MedicineMayo ClinicRochesterMinnesotaUSA
| | | | | | | | | | - Adam M. May
- Cardiovascular DivisionWashington University School of MedicineSt. LouisMissouriUSA
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LoCoco S, Kashou AH, Noseworthy PA, Cooper DH, Ghadban R, May AM. The emergence and destiny of automated methods to differentiate wide QRS complex tachycardias. J Electrocardiol 2023; 81:44-50. [PMID: 37517201 DOI: 10.1016/j.jelectrocard.2023.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/10/2023] [Accepted: 07/17/2023] [Indexed: 08/01/2023]
Abstract
Accurate differentiation of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) using non-invasive methods such as 12‑lead electrocardiogram (ECG) interpretation is crucial in clinical practice. Recent studies have demonstrated the potential for automated approaches utilizing computerized ECG interpretation software to achieve accurate WCT differentiation. In this review, we provide a comprehensive analysis of contemporary automated methods for VT and SWCT differentiation. Our objectives include: (i) presenting a general overview of the emergence of automated WCT differentiation methods, (ii) examining the role of machine learning techniques in automated WCT differentiation, (iii) reviewing the electrophysiology concepts leveraged existing automated algorithms, (iv) discussing recently developed automated WCT differentiation solutions, and (v) considering future directions that will enable the successful integration of automated methods into computerized ECG interpretation platforms.
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Affiliation(s)
- Sarah LoCoco
- Department of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, 660 S. Euclid Ave, CB 8086, St. Louis, MO 63110, United States of America.
| | - Anthony H Kashou
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America
| | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America
| | - Daniel H Cooper
- Department of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, 660 S. Euclid Ave, CB 8086, St. Louis, MO 63110, United States of America
| | - Rugheed Ghadban
- Department of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, 660 S. Euclid Ave, CB 8086, St. Louis, MO 63110, United States of America
| | - Adam M May
- Department of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, 660 S. Euclid Ave, CB 8086, St. Louis, MO 63110, United States of America
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7
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Kashou AH, Noseworthy PA, Beckman TJ, Anavekar NS, Angstman KB, Cullen MW, Sandefur BJ, Friedman PA, Shapiro BP, Wiley BW, Kates AM, Braisted A, Huneycutt D, Baranchuk A, Beard JW, Kerwin S, Young B, Rowlandson I, Knohl SJ, O'Brien K, May AM. Exploring Factors Influencing ECG Interpretation Proficiency of Medical Professionals. Curr Probl Cardiol 2023; 48:101865. [PMID: 37321283 DOI: 10.1016/j.cpcardiol.2023.101865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 06/04/2023] [Indexed: 06/17/2023]
Abstract
The electrocardiogram (ECG) is a crucial diagnostic tool in medicine with concerns about its interpretation proficiency across various medical disciplines. Our study aimed to explore potential causes of these issues and identify areas requiring improvement. A survey was conducted among medical professionals to understand their experiences with ECG interpretation and education. A total of 2515 participants from diverse medical backgrounds were surveyed. A total of 1989 (79%) participants reported ECG interpretation as part of their practice. However, 45% expressed discomfort with independent interpretation. A significant 73% received less than 5 hours of ECG-specific education, with 45% reporting no education at all. Also, 87% reported limited or no expert supervision. Nearly all medical professionals (2461, 98%) expressed a desire for more ECG education. These findings were consistent across all groups and did not vary between primary care physicians, cardiology FIT, resident physicians, medical students, APPs, nurses, physicians, and nonphysicians. This study reveals substantial deficiencies in ECG interpretation training, supervision, and confidence among medical professionals, despite a strong interest in increased ECG education.
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Affiliation(s)
- Anthony H Kashou
- Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
| | | | | | | | | | | | | | - Paul A Friedman
- Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Brian P Shapiro
- Cardiovascular Medicine, Mayo Clinic, Jacksonville, Florida, USA
| | - Brandon W Wiley
- Cardiovascular Medicine, Keck School of Medicine, University of Southern California, Los Angeles California, USA
| | - Andrew M Kates
- Cardiovascular Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Andrew Braisted
- Cardiovascular Medicine, HCA Healthcare, Nashville, Tennessee, USA
| | - David Huneycutt
- Cardiovascular Medicine, HCA Healthcare, Nashville, Tennessee, USA
| | - Adrian Baranchuk
- Cardiovascular Medicine, Queen's University, Kingston, Ontario, Canada
| | | | | | | | | | - Stephen J Knohl
- Internal Medicine, SUNY Upstate Medical University, Syracuse, New York, USA
| | - Kevin O'Brien
- Internal Medicine, University of South Florida, Tampa, Florida, USA
| | - Adam M May
- Cardiovascular Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
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Kashou AH, Noseworthy PA, Beckman TJ, Anavekar NS, Cullen MW, Angstman KB, Sandefur BJ, Shapiro BP, Wiley BW, Kates AM, Huneycutt D, Braisted A, Smith SW, Baranchuk A, Grauer K, O'Brien K, Kaul V, Gambhir HS, Knohl SJ, Albert D, Kligfield PD, Macfarlane PW, Drew BJ, May AM. ECG Interpretation Proficiency of Healthcare Professionals. Curr Probl Cardiol 2023; 48:101924. [PMID: 37394202 DOI: 10.1016/j.cpcardiol.2023.101924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 06/27/2023] [Indexed: 07/04/2023]
Abstract
ECG interpretation is essential in modern medicine, yet achieving and maintaining competency can be challenging for healthcare professionals. Quantifying proficiency gaps can inform educational interventions for addressing these challenges. Medical professionals from diverse disciplines and training levels interpreted 30 12-lead ECGs with common urgent and nonurgent findings. Average accuracy (percentage of correctly identified findings), interpretation time per ECG, and self-reported confidence (rated on a scale of 0 [not confident], 1 [somewhat confident], or 2 [confident]) were evaluated. Among the 1206 participants, there were 72 (6%) primary care physicians (PCPs), 146 (12%) cardiology fellows-in-training (FITs), 353 (29%) resident physicians, 182 (15%) medical students, 84 (7%) advanced practice providers (APPs), 120 (10%) nurses, and 249 (21%) allied health professionals (AHPs). Overall, participants achieved an average overall accuracy of 56.4% ± 17.2%, interpretation time of 142 ± 67 seconds, and confidence of 0.83 ± 0.53. Cardiology FITs demonstrated superior performance across all metrics. PCPs had a higher accuracy compared to nurses and APPs (58.1% vs 46.8% and 50.6%; P < 0.01), but a lower accuracy than resident physicians (58.1% vs 59.7%; P < 0.01). AHPs outperformed nurses and APPs in every metric and showed comparable performance to resident physicians and PCPs. Our findings highlight significant gaps in the ECG interpretation proficiency among healthcare professionals.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Brandon W Wiley
- Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Andrew M Kates
- Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | | | | | - Stephen W Smith
- Hennepin County Medical Center and University of Minnesota, Minneapolis, Minnesota
| | | | - Ken Grauer
- University of Florida, Gainesville, Florida
| | | | - Viren Kaul
- SUNY Upstate Medical University, Syracuse, New York
| | | | | | | | - Paul D Kligfield
- New York-Presbyterian/Weill Cornell Medical Center, New York, New York
| | - Peter W Macfarlane
- Electrocardiology Core Lab, New Lister Building, Royal Infirmary, Scotland, UK
| | | | - Adam M May
- Washington University School of Medicine in St. Louis, St. Louis, Missouri
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Kashou AH, Noseworthy PA, Beckman TJ, Anavekar NS, Cullen MW, Angstman KB, Sandefur BJ, Thompson CA, Halvorsen AJ, Shapiro BP, Wiley BW, Kates AM, Cosco D, Sadhu JS, Thakker PD, Huneycutt D, Braisted A, Smith SW, Baranchuk A, Grauer K, O'Brien K, Kaul V, Gambhir HS, Knohl SJ, Restrepo D, Kligfield PD, Macfarlane PW, Drew BJ, May AM. Education curriculum assessment for teaching electrocardiography: Rationale and design for the prospective, international, randomized controlled EDUCATE trial. J Electrocardiol 2023; 80:166-173. [PMID: 37467573 DOI: 10.1016/j.jelectrocard.2023.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 06/20/2023] [Accepted: 07/11/2023] [Indexed: 07/21/2023]
Abstract
BACKGROUND Electrocardiogram (ECG) interpretation training is a fundamental component of medical education across disciplines. However, the skill of interpreting ECGs is not universal among medical graduates, and numerous barriers and challenges exist in medical training and clinical practice. An evidence-based and widely accessible learning solution is needed. DESIGN The EDUcation Curriculum Assessment for Teaching Electrocardiography (EDUCATE) Trial is a prospective, international, investigator-initiated, open-label, randomized controlled trial designed to determine the efficacy of self-directed and active-learning approaches of a web-based educational platform for improving ECG interpretation proficiency. Target enrollment is 1000 medical professionals from a variety of medical disciplines and training levels. Participants will complete a pre-intervention baseline survey and an ECG interpretation proficiency test. After completion, participants will be randomized into one of four groups in a 1:1:1:1 fashion: (i) an online, question-based learning resource, (ii) an online, lecture-based learning resource, (iii) an online, hybrid question- and lecture-based learning resource, or (iv) a control group with no ECG learning resources. The primary endpoint will be the change in overall ECG interpretation performance according to pre- and post-intervention tests, and it will be measured within and compared between medical professional groups. Secondary endpoints will include changes in ECG interpretation time, self-reported confidence, and interpretation accuracy for specific ECG findings. CONCLUSIONS The EDUCATE Trial is a pioneering initiative aiming to establish a practical, widely available, evidence-based solution to enhance ECG interpretation proficiency among medical professionals. Through its innovative study design, it tackles the currently unaddressed challenges of ECG interpretation education in the modern era. The trial seeks to pinpoint performance gaps across medical professions, compare the effectiveness of different web-based ECG content delivery methods, and create initial evidence for competency-based standards. If successful, the EDUCATE Trial will represent a significant stride towards data-driven solutions for improving ECG interpretation skills in the medical community.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Brandon W Wiley
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Andrew M Kates
- Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Dominique Cosco
- Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Justin S Sadhu
- Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | | | | | | | - Stephen W Smith
- Hennepin County Medical Center and University of Minnesota, Minneapolis, MN, USA
| | | | - Ken Grauer
- University of Florida, Gainesville, FL, USA
| | | | - Viren Kaul
- SUNY Upstate Medical University, Syracuse, NY, USA
| | | | | | - Daniel Restrepo
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Paul D Kligfield
- New York-Presbyterian/Weill Cornell Medical Center, New York, NY, USA
| | | | | | - Adam M May
- Washington University School of Medicine in St. Louis, St. Louis, MO, USA
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Anderson HR, Borgen AC, Christnacht R, Ng J, Weller JG, Davison HN, Noseworthy PA, Olson R, O'Laughlin D, Disrud L, Kashou AH. Stats on the desats: alarm fatigue and the implications for patient safety. BMJ Open Qual 2023; 12:e002262. [PMID: 37474134 PMCID: PMC10357676 DOI: 10.1136/bmjoq-2023-002262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 07/07/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND Physiological monitoring systems, like Masimo, used during inpatient hospitalisation, offer a non-invasive approach to capture critical vital signs data. These systems trigger alarms when measurements deviate from preset parameters. However, often non-urgent or potentially false alarms contribute to 'alarm fatigue,' a form of sensory overload that can have adverse effects on both patients and healthcare staff. The Joint Commission, in 2021, announced a target to mitigate alarm fatigue-related fatalities through improved alarm management. Yet, no established guidelines are presently available. This study aims to address alarm fatigue at the Mayo Clinic to safeguard patient safety, curb staff burnout and improve the sensitivity of oxygen saturation monitoring to promptly detect emergencies. METHODS A quality improvement project was conducted to combat minimise the false alarm burden, with data collected 2 months prior to intervention commencement. The project's goal was to decrease the total alarm value by 20% from 55%-85% to 35%-75% within 2 months, leveraging quality improvement methodologies. INTERVENTIONS February to April 2021, we implemented a two-pronged intervention: (1) instituting a protocol to evaluate patients' continuous monitoring needs and discontinuing it when appropriate, and (2) introducing educational signage for patients and Mayo Clinic staff on monitoring best practices. RESULTS Baseline averages of red alarms (158.6), manual snoozes (37.8) and self-resolves (120.7); the first postintervention phase showed reductions in red alarms (125.5), manual snoozes (17.8) and self-resolves (107.8). Second postintervention phase recorded 138 red alarms, 13 manual snoozes and 125 self-resolves. Baseline comparison demonstrated an average of 16.92% reduction of alarms among both interventions (p value: 0.25). CONCLUSION Simple interventions like education and communication techniques proved instrumental in lessening the alarm burden for patients and staff. The findings underscore the practical use and efficacy of these methods in any healthcare setting, thus contributing to mitigating the prevalent issue of alarm fatigue.
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Affiliation(s)
| | - Alex C Borgen
- Physician Assistant Program, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Jenny Ng
- Physician Assistant Program, Mayo Clinic, Rochester, Minnesota, USA
| | - Joel G Weller
- Physician Assistant Program, Mayo Clinic, Rochester, Minnesota, USA
| | | | | | - Rachel Olson
- Center for Learning Innovation, University of Minnesota System, Minneapolis, Minnesota, USA
| | | | - Levi Disrud
- Cardiovascular Research, Mayo Clinic, Rochester, Minnesota, USA
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Jentzer JC, Noseworthy PA, Kashou AH, May AM, Chrispin J, Kabra R, Arps K, Blumer V, Tisdale JE, Solomon MA. Multidisciplinary Critical Care Management of Electrical Storm: JACC State-of-the-Art Review. J Am Coll Cardiol 2023; 81:2189-2206. [PMID: 37257955 PMCID: PMC10683004 DOI: 10.1016/j.jacc.2023.03.424] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 03/14/2023] [Indexed: 06/02/2023]
Abstract
Electrical storm (ES) reflects life-threatening cardiac electrical instability with 3 or more ventricular arrhythmia episodes within 24 hours. Identification of underlying arrhythmogenic cardiac substrate and reversible triggers is essential, as is interrogation and programming of an implantable cardioverter-defibrillator, if present. Medical management includes antiarrhythmic drugs, beta-adrenergic blockade, sedation, and hemodynamic support. The initial intensity of these interventions should be matched to the severity of ES using a stepped-care algorithm involving escalating treatments for higher-risk presentations or recurrent ventricular arrhythmias. Many patients with ES are considered for catheter ablation, which may require the use of temporary mechanical circulatory support. Outcomes after ES are poor, including frequent ES recurrences and deaths caused by progressive heart failure and other cardiac causes. A multidisciplinary collaborative approach to the management of ES is crucial, and evaluation for heart transplantation or palliative care is often appropriate, even for patients who survive the initial episode.
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Affiliation(s)
- Jacob C Jentzer
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
| | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Anthony H Kashou
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Adam M May
- Cardiovascular Division, Washington University School of Medicine, St Louis, Missouri, USA
| | - Jonathan Chrispin
- Clinical Cardiac Electrophysiology, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Rajesh Kabra
- Kansas City Heart Rhythm Institute, Overland Park, Kansas, USA
| | - Kelly Arps
- Cardiac Electrophysiology Section, Division of Cardiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Vanessa Blumer
- Department of Cardiology, Cleveland Clinic, Cleveland, Ohio, USA
| | - James E Tisdale
- College of Pharmacy, Purdue University, West Lafayette, Indiana, USA; School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - Michael A Solomon
- Critical Care Medicine Department, National Institutes of Health Clinical Center, National Institutes of Health, Bethesda, Maryland, USA; Cardiovascular Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
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Sehrawat O, Kashou AH, Van Houten HK, Cohen K, Joe Henk H, Gersh BJ, Abraham NS, Graff-Radford J, Friedman PA, Siontis KC, Noseworthy PA, Yao X. Contemporary trends and barriers to oral anticoagulation therapy in Non-valvular atrial fibrillation during DOAC predominant era. Int J Cardiol Heart Vasc 2023; 46:101212. [PMID: 37168417 PMCID: PMC10164915 DOI: 10.1016/j.ijcha.2023.101212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 04/11/2023] [Accepted: 04/18/2023] [Indexed: 05/13/2023]
Abstract
There is a need to reassess contemporary oral anticoagulation (OAC) trends and barriers against guideline directed therapy in the United States. Most previous studies were performed before major guideline changes recommended direct oral anticoagulant (DOAC) use over warfarin or have otherwise lacked patient level data. Data on overuse of OAC in low-risk group is also limited. To address these knowledge gaps, we performed a nationwide analysis to analyze current trends. This is a retrospective cohort study assessing non-valvular AF identified using a large United States de-identified administrative claims database, including commercial and Medicare Advantage enrollees. Prescription fills were assessed within a 90-day follow-up from the patient's index AF encounter between January 1, 2016, and December 31, 2020. Among the 339,197 AF patients, 4.4%, 8.0%, and 87.6% were in the low-, moderate-, and high-risk groups (according to CHA2DS2-VASc score). An over (29.6%) and under (52.2%) utilization of OAC was reported in low- and high-risk AF patients. A considerably high frequency for warfarin use was also noted among high-risk group patients taking OAC (33.1%). The results suggest that anticoagulation use for stroke prevention in the United States is still comparable to the pre-DOAC era studies. About half of newly diagnosed high-risk non-valvular AF patients remain unprotected against stroke risk. Several predictors of OAC and DOAC use were also identified. Our findings may identify a population at risk of complications due to under- or over-treatment and highlight the need for future quality improvement efforts.
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Affiliation(s)
- Ojasav Sehrawat
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States
| | - Anthony H. Kashou
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States
| | - Holly K. Van Houten
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States
| | - Ken Cohen
- Optum Center for Research and Innovation, Minnetonka, MN, United States
| | - Henry Joe Henk
- UnitedHealthcare, 9700 Health Care Lane, Minnetonka, MN 55343, USA
| | - Bernard J. Gersh
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States
| | - Neena S. Abraham
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States
- Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Scottsdale, AZ, United States
| | | | - Paul A. Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States
| | | | - Peter A. Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States
- Corresponding author at: Department of Cardiovascular Medicine Mayo Clinic, 200 First Street SW |, Rochester, MN 55905, United States.
| | - Xiaoxi Yao
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States
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Kashou AH, May AM, Noseworthy PA. Comparison of two artificial intelligence-augmented ECG approaches: Machine learning and deep learning. J Electrocardiol 2023; 79:75-80. [PMID: 36989954 DOI: 10.1016/j.jelectrocard.2023.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 02/24/2023] [Accepted: 03/10/2023] [Indexed: 03/17/2023]
Abstract
BACKGROUND Artificial intelligence-augmented ECG (AI-ECG) refers to the application of novel AI solutions for complex ECG interpretation tasks. A broad variety of AI-ECG approaches exist, each having differing advantages and limitations relating to their creation and application. PURPOSE To provide illustrative comparison of two general AI-ECG modeling approaches: machine learning (ML) and deep learning (DL). METHOD COMPARISON Two AI-ECG algorithms were developed to carry out two separate tasks using ML and DL, respectively. ML modeling techniques were used to create algorithms designed for automatic wide QRS complex tachycardia differentiation into ventricular tachycardia and supraventricular tachycardia. A DL algorithm was formulated for the task of comprehensive 12‑lead ECG interpretation. First, we describe the ML models for WCT differentiation, which rely upon expert domain knowledge to identify and formulate ECG features (e.g., percent monophasic time-voltage area [PMonoTVA]) that enable strong diagnostic performance. Second, we describe the DL method for comprehensive 12‑lead ECG interpretation, which relies upon the independent recognition and analysis of a virtually incalculable number of ECG features from a vast collection of standard 12‑lead ECGs. CONCLUSION We have showcased two different AI-ECG methods, namely ML and DL respectively. In doing so, we highlighted the strengths and weaknesses of each approach. It is essential for investigators to understand these differences when attempting to create and apply novel AI-ECG solutions.
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Affiliation(s)
- Anthony H Kashou
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America.
| | - Adam M May
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, United States of America.
| | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America.
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Kashou AH, LoCoco S, Shaikh PA, Katbamna BB, Sehrawat O, Cooper DH, Sodhi SS, Cuculich PS, Gleva MJ, Deych E, Zhou R, Liu L, Deshmukh AJ, Asirvatham SJ, Noseworthy PA, DeSimone CV, May AM. Computerized electrocardiogram data transformation enables effective algorithmic differentiation of wide QRS complex tachycardias. Ann Noninvasive Electrocardiol 2022; 28:e13018. [PMID: 36409204 PMCID: PMC9833371 DOI: 10.1111/anec.13018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 10/16/2022] [Accepted: 10/19/2022] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Accurate automated wide QRS complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) can be accomplished using calculations derived from computerized electrocardiogram (ECG) data of paired WCT and baseline ECGs. OBJECTIVE Develop and trial novel WCT differentiation approaches for patients with and without a corresponding baseline ECG. METHODS We developed and trialed WCT differentiation models comprised of novel and previously described parameters derived from WCT and baseline ECG data. In Part 1, a derivation cohort was used to evaluate five different classification models: logistic regression (LR), artificial neural network (ANN), Random Forests [RF], support vector machine (SVM), and ensemble learning (EL). In Part 2, a separate validation cohort was used to prospectively evaluate the performance of two LR models using parameters generated from the WCT ECG alone (Solo Model) and paired WCT and baseline ECGs (Paired Model). RESULTS Of the 421 patients of the derivation cohort (Part 1), a favorable area under the receiver operating characteristic curve (AUC) by all modeling subtypes: LR (0.96), ANN (0.96), RF (0.96), SVM (0.96), and EL (0.97). Of the 235 patients of the validation cohort (Part 2), the Solo Model and Paired Model achieved a favorable AUC for 103 patients with (Solo Model 0.87; Paired Model 0.95) and 132 patients without (Solo Model 0.84; Paired Model 0.95) a corroborating electrophysiology procedure or intracardiac device recording. CONCLUSION Accurate WCT differentiation may be accomplished using computerized data of (i) the WCT ECG alone and (ii) paired WCT and baseline ECGs.
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Affiliation(s)
- Anthony H. Kashou
- Department of Cardiovascular MedicineMayo ClinicMinnesotaRochesterUSA
| | - Sarah LoCoco
- Department of MedicineWashington University School of MedicineMissouriSt. LouisUSA
| | - Preet A. Shaikh
- Department of Medicine, Division of Cardiovascular DiseasesWashington University School of MedicineMissouriSt. LouisUSA
| | - Bhavesh B. Katbamna
- Department of MedicineWashington University School of MedicineMissouriSt. LouisUSA
| | - Ojasav Sehrawat
- Department of Cardiovascular MedicineMayo ClinicMinnesotaRochesterUSA
| | - Daniel H. Cooper
- Department of Medicine, Division of Cardiovascular DiseasesWashington University School of MedicineMissouriSt. LouisUSA
| | - Sandeep S. Sodhi
- Department of Medicine, Division of Cardiovascular DiseasesWashington University School of MedicineMissouriSt. LouisUSA
| | - Phillip S. Cuculich
- Department of Medicine, Division of Cardiovascular DiseasesWashington University School of MedicineMissouriSt. LouisUSA
| | - Marye J. Gleva
- Department of Medicine, Division of Cardiovascular DiseasesWashington University School of MedicineMissouriSt. LouisUSA
| | - Elena Deych
- Division of BiostatisticsWashington University School of MedicineMissouriSt. LouisUSA
| | - Ruiwen Zhou
- Division of BiostatisticsWashington University School of MedicineMissouriSt. LouisUSA
| | - Lei Liu
- Division of BiostatisticsWashington University School of MedicineMissouriSt. LouisUSA
| | | | | | | | | | - Adam M. May
- Department of Medicine, Division of Cardiovascular DiseasesWashington University School of MedicineMissouriSt. LouisUSA
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Katbamna B, Kashou AH, Shaikh P, Lococo S, Cooper D, Cuculich P, Asirvatham S, Noseworthy P, Desimone C, May A. Transformation of computerized electrocardiogram data into novel means to differentiate wide complex tachycardias. Eur Heart J 2022. [DOI: 10.1093/eurheartj/ehac544.394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Background
Accurate automated wide QRS complex tachycardia (WCT) discrimination between ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) can be accomplished using novel calculations derived from computerized electrocardiogram (ECG) data from paired WCT and baseline ECGs.
Purpose
Our aim was to develop and trial novel WCT discrimination approaches for WCT patients with and without a corresponding baseline ECG. Central to this analysis was the creation and use of a novel parameter (i.e., percent monophasic time-voltage area [PMonoTVA] [%]) that may be derived from computerized ECG measurements present on the WCT ECG alone.
Methods
In a two-part study, we derived and tested WCT differentiation models comprised of novel and previously established parameters formulated from computerized data of paired WCT and baseline ECGs. In Part 1, novel and established parameters generated from WCT and baseline ECG data were used to derive, validate, and compare five different binary classification models: (i) logistic regression [LR], (ii) artificial neural network [ANN], (iii) Random Forests [RF], (iv) support vector machine [SVM], and (v) ensemble learning (EL). In Part 2, two unique LR models were derived, validated, and compared using parameters generated from computerized data of the (i) WCT ECG alone (i.e., Solo Model) and (ii) paired WCT and baseline ECGs (i.e., Paired Model).
Results
In Part 1, among 103 patients with VT or SWCT diagnoses established from corroborating electrophysiology studies or intra-cardiac device recordings, favorable diagnostic performance was achieved by all modeling technique subtypes: LR (area under the receiver operating characteristic curve [AUC] 0.95), ANN (AUC 0.91), RF (AUC 0.97), SVM (AUC 0.98), and EL (AUC 0.97). In Part 2, among 235 patients with a VT or SWCT diagnosis established with (Gold Standard cohort) or without (Non-Gold Standard cohort) a corroborating electrophysiology procedure or intra-cardiac device recording, favorable diagnostic performance was achieved by the Solo Model (AUC 0.86) and Paired Model (AUC 0.95) (Table).
Conclusion
Accurate WCT discrimination may be accomplished using novel parameters derived from computerized data of the WCT ECG alone and paired WCT and baseline ECGs.
Funding Acknowledgement
Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Institute of Health
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Affiliation(s)
- B Katbamna
- Washington University School of Medicine, Department of Medicine, Division of Cardiovascular Diseases , St Louis , United States of America
| | - A H Kashou
- Mayo Clinic, Department of Cardiovascular Medicine , Rochester , United States of America
| | - P Shaikh
- Washington University School of Medicine, Department of Medicine, Division of Cardiovascular Diseases , St Louis , United States of America
| | - S Lococo
- Washington University School of Medicine, Department of Medicine, Division of Cardiovascular Diseases , St Louis , United States of America
| | - D Cooper
- Washington University School of Medicine, Department of Medicine, Division of Cardiovascular Diseases , St Louis , United States of America
| | - P Cuculich
- Washington University School of Medicine, Department of Medicine, Division of Cardiovascular Diseases , St Louis , United States of America
| | - S Asirvatham
- Mayo Clinic, Department of Cardiovascular Medicine , Rochester , United States of America
| | - P Noseworthy
- Mayo Clinic, Department of Cardiovascular Medicine , Rochester , United States of America
| | - C Desimone
- Mayo Clinic, Department of Cardiovascular Medicine , Rochester , United States of America
| | - A May
- Washington University School of Medicine, Department of Medicine, Division of Cardiovascular Diseases , St Louis , United States of America
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Kashou AH, Adedinsewo DA, Siontis KC, Noseworthy PA. Artificial Intelligence-Enabled ECG: Physiologic and Pathophysiologic Insights and Implications. Compr Physiol 2022; 12:3417-3424. [PMID: 35766831 PMCID: PMC9795459 DOI: 10.1002/cphy.c210001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Advancements in machine learning and computing methods have given new life and great excitement to one of the most essential diagnostic tools to date-the electrocardiogram (ECG). The application of artificial intelligence-enabled ECG (AI-ECG) has resulted in the ability to identify electrocardiographic signatures of conventional and unique variables and pathologies, giving way to tremendous clinical potential. However, what these AI-ECG models are detecting that the human eye is missing remains unclear. In this article, we highlight some of the recent developments in the field and their potential clinical implications, while also attempting to shed light on the physiologic and pathophysiologic features that enable these models to have such high diagnostic yield. © 2022 American Physiological Society. Compr Physiol 12:3417-3424, 2022.
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Affiliation(s)
- Anthony H Kashou
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | | | | | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
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Rafie N, Jentzer JC, Noseworthy PA, Kashou AH. Mortality Prediction in Cardiac Intensive Care Unit Patients: A Systematic Review of Existing and Artificial Intelligence Augmented Approaches. Front Artif Intell 2022; 5:876007. [PMID: 35711617 PMCID: PMC9193583 DOI: 10.3389/frai.2022.876007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 05/06/2022] [Indexed: 11/13/2022] Open
Abstract
The medical complexity and high acuity of patients in the cardiac intensive care unit make for a unique patient population with high morbidity and mortality. While there are many tools for predictions of mortality in other settings, there is a lack of robust mortality prediction tools for cardiac intensive care unit patients. The ongoing advances in artificial intelligence and machine learning also pose a potential asset to the advancement of mortality prediction. Artificial intelligence algorithms have been developed for application of electrocardiogram interpretation with promising accuracy and clinical application. Additionally, artificial intelligence algorithms applied to electrocardiogram interpretation have been developed to predict various variables such as structural heart disease, left ventricular systolic dysfunction, and atrial fibrillation. These variables can be used and applied to new mortality prediction models that are dynamic with the changes in the patient's clinical course and may lead to more accurate and reliable mortality prediction. The application of artificial intelligence to mortality prediction will fill the gaps left by current mortality prediction tools.
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Affiliation(s)
- Nikita Rafie
- Department of Medicine, Mayo Clinic, Rochester, MN, United States
- *Correspondence: Nikita Rafie
| | - Jacob C. Jentzer
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, United States
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States
| | - Peter A. Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States
| | - Anthony H. Kashou
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States
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Kashou AH, Mulpuru SK, Deshmukh AJ, Ko WY, Attia ZI, Carter RE, Friedman PA, Noseworthy PA. An artificial intelligence-enabled ECG algorithm for comprehensive ECG interpretation: Can it pass the 'Turing test'? Cardiovasc Digit Health J 2022; 2:164-170. [PMID: 35265905 PMCID: PMC8890338 DOI: 10.1016/j.cvdhj.2021.04.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Objective To develop an artificial intelligence (AI)–enabled electrocardiogram (ECG) algorithm capable of comprehensive, human-like ECG interpretation and compare its diagnostic performance against conventional ECG interpretation methods. Methods We developed a novel AI-enabled ECG (AI-ECG) algorithm capable of complete 12-lead ECG interpretation. It was trained on nearly 2.5 million standard 12-lead ECGs from over 720,000 adult patients obtained at the Mayo Clinic ECG laboratory between 2007 and 2017. We then compared the need for human over-reading edits of the reports generated by the Marquette 12SL automated computer program, AI-ECG algorithm, and final clinical interpretations on 500 randomly selected ECGs from 500 patients. In a blinded fashion, 3 cardiac electrophysiologists adjudicated each interpretation as (1) ideal (ie, no changes needed), (2) acceptable (ie, minor edits needed), or (3) unacceptable (ie, major edits needed). Results Cardiologists determined that on average 202 (13.5%), 123 (8.2%), and 90 (6.0%) of the interpretations required major edits from the computer program, AI-ECG algorithm, and final clinical interpretations, respectively. They considered 958 (63.9%), 1058 (70.5%), and 1118 (74.5%) interpretations as ideal from the computer program, AI-ECG algorithm, and final clinical interpretations, respectively. They considered 340 (22.7%), 319 (21.3%), and 292 (19.5%) interpretations as acceptable from the computer program, AI-ECG algorithm, and final clinical interpretations, respectively. Conclusion An AI-ECG algorithm outperforms an existing standard automated computer program and better approximates expert over-read for comprehensive 12-lead ECG interpretation.
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Affiliation(s)
- Anthony H. Kashou
- Department of Medicine, Mayo Clinic, Rochester, Minnesota
- Address reprint requests and correspondence: Dr Anthony H. Kashou, Department of Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905.
| | - Siva K. Mulpuru
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | | | - Wei-Yin Ko
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - Zachi I. Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - Rickey E. Carter
- Department of Health Sciences Research, Mayo Clinic, Jacksonville, Florida
| | - Paul A. Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
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Sehrawat O, Kashou AH, Noseworthy PA. Artificial Intelligence and Atrial Fibrillation. J Cardiovasc Electrophysiol 2022; 33:1932-1943. [PMID: 35258136 PMCID: PMC9717694 DOI: 10.1111/jce.15440] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 02/03/2022] [Accepted: 03/01/2022] [Indexed: 11/30/2022]
Abstract
In the context of atrial fibrillation (AF), traditional clinical practices have thus far fallen short in several domains such as identifying patients at risk of incident AF or patients with concomitant undetected paroxysmal AF. Novel approaches leveraging artificial intelligence have the potential to provide new tools to deal with some of these old problems. In this review we focus on the roles of artificial intelligence-enabled ECG pertaining to AF, potential roles of deep learning (DL) models in the context of current knowledge gaps, as well as limitations of these models. One key area where DL models can translate to better patient outcomes is through automated ECG interpretation. Further, we overview some of the challenges facing AF screening and the harms and benefits of screening. In this context, a unique model was developed to detect underlying hidden AF from sinus rhythm and is discussed in detail with its potential uses. Knowledge gaps also remain regarding the best ways to monitor patients with embolic stroke of undetermined source (ESUS) and who would benefit most from oral anticoagulation. The AI-enabled AF model is one potential way to tackle this complex problem as it could be used to identify a subset of high-risk ESUS patients likely to benefit from empirical oral anticoagulation. Role of DL models assessing AF burden from long duration ECG data is also discussed as a way of guiding management. There is a trend towards the use of consumer-grade wristbands and watches to detect AF from photoplethysmography data. However, ECG currently remains the gold standard to detect arrythmias including AF. Lastly, role of adequate external validation of the models and clinical trials to study true performance is discussed. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Ojasav Sehrawat
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Anthony H Kashou
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
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Kashou AH, Noseworthy PA. Electrocardiographic biosignals to predict atrial fibrillation: Are we there yet? J Electrocardiol 2022; 70:37-38. [PMID: 34871963 PMCID: PMC8919434 DOI: 10.1016/j.jelectrocard.2021.11.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 11/20/2021] [Accepted: 11/21/2021] [Indexed: 01/03/2023]
Abstract
The prevalence of atrial fibrillation (AF) continues to grow in an aging population, and its impact on both patients and the health care system has has made it a global burden. There are limited available options to detect individuals at risk of AF that may benefit from prevention and treatment strategies. The ECG may be an effective tool do so. In this work, we discuss the latest work by Hayiroğlu and colleagues related to this work and the use of novel ECG prediction tools to identify individuals individuals that could benefit from early and proactive screening, surveillance, and management strategies.
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Affiliation(s)
- Anthony H. Kashou
- Corresponding author at: Mayo Clinic, Department of Cardiovascular Diseases, 200 First Street SW, Rochester, MN 55905, USA,
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Kashou AH, Noseworthy PA, Jentzer JC, Rafie N, Roy AR, Abraham HM, Sang PD, Kronzer EK, Inglis SS, Rezkalla JA, Julakanti RR, Saric P, Asirvatham SJ, Deshmukh AJ, DeSimone CV, May AM. Wide complex tachycardia discrimination tool improves physicians' diagnostic accuracy. J Electrocardiol 2022; 74:32-39. [PMID: 35933848 PMCID: PMC9799284 DOI: 10.1016/j.jelectrocard.2022.07.070] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 07/07/2022] [Accepted: 07/23/2022] [Indexed: 12/31/2022]
Abstract
BACKGROUND Timely and accurate discrimination of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular WCT (SWCT) is critically important. Previously we developed and validated an automated VT Prediction Model that provides a VT probability estimate using the paired WCT and baseline 12-lead ECGs. Whether this model improves physicians' diagnostic accuracy has not been evaluated. OBJECTIVE We sought to determine whether the VT Prediction Model improves physicians' WCT differentiation accuracy. METHODS Over four consecutive days, nine physicians independently interpreted fifty WCT ECGs (25 VTs and 25 SWCTs confirmed by electrophysiological study) as either VT or SWCT. Day 1 used the WCT ECG only, Day 2 used the WCT and baseline ECG, Day 3 used the WCT ECG and the VT Prediction Model's estimation of VT probability, and Day 4 used the WCT ECG, baseline ECG, and the VT Prediction Model's estimation of VT probability. RESULTS Inclusion of the VT Prediction Model data increased diagnostic accuracy versus the WCT ECG alone (Day 3: 84.2% vs. Day 1: 68.7%, p 0.009) and WCT and baseline ECGs together (Day 3: 84.2% vs. Day 2: 76.4%, p 0.003). There was no further improvement of accuracy with addition of the baseline ECG comparison to the VT Prediction Model (Day 3: 84.2% vs. Day 4: 84.0%, p 0.928). Overall sensitivity (Day 3: 78.2% vs. Day 1: 67.6%, p 0.005) and specificity (Day 3: 90.2% vs. Day 1: 69.8%, p 0.016) for VT were superior after the addition of the VT Prediction Model. CONCLUSION The VT Prediction Model improves physician ECG diagnostic accuracy for discriminating WCTs.
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Affiliation(s)
- Anthony H. Kashou
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | | | - Jacob C. Jentzer
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota,Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - Nikita Rafie
- Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | | | | | - Philip D. Sang
- Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - Ellen K. Kronzer
- Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - Sara S. Inglis
- Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - Joshua A. Rezkalla
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | | | - Petar Saric
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | | | | | | | - Adam M. May
- Department of Medicine, Washington University School of Medicine, St. Louis, Missouri
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22
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Kashou AH, Noseworthy PA, Lopez-Jimenez F, Attia ZI, Kapa S, Friedman PA, Jentzer JC. Corrigendum to "The effect of cardiac rhythm on artificial intelligence-enabled ECG evaluation of left ventricular ejection fraction prediction in cardiac intensive care unit patients" Int J Cardiol. 2021 Sep 15;339:54-55. Int J Cardiol 2021; 348:125. [PMID: 34890765 DOI: 10.1016/j.ijcard.2021.11.090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- A H Kashou
- Department of Internal Medicine, Mayo Clinic, Rochester, MN, United States of America
| | - P A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States of America
| | - F Lopez-Jimenez
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America
| | - Z I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America
| | - S Kapa
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America
| | - P A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America
| | - J C Jentzer
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States of America; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN, United States of America.
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23
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Abstract
Atrial fibrillation (AF) is one of the most common cardiac arrhythmias. Implantable and wearable cardiac devices have enabled the detection of asymptomatic AF episodes-termed subclinical AF (SCAF). SCAF, the prevalence of which is likely significantly underestimated, is associated with increased cardiovascular and all-cause mortality and a significant stroke risk. Recent advances in machine learning, namely artificial intelligence-enabled ECG (AI-ECG), have enabled identification of patients at higher likelihood of SCAF. Leveraging the capabilities of AI-ECG algorithms to drive screening protocols could eventually allow for earlier detection and treatment and help reduce the burden associated with AF. Expected final online publication date for the Annual Review of Medicine, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Anthony H Kashou
- Department of Medicine, Mayo Clinic, Rochester, Minnesota 55905, USA
| | - Demilade A Adedinsewo
- Department of Cardiovascular Medicine, Mayo Clinic, Jacksonville, Florida 32224, USA
| | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota 55905, USA;
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24
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Kashou AH, Medina-Inojosa JR, Noseworthy PA, Rodeheffer RJ, Lopez-Jimenez F, Attia IZ, Kapa S, Scott CG, Lee AT, Friedman PA, McKie PM. Artificial Intelligence-Augmented Electrocardiogram Detection of Left Ventricular Systolic Dysfunction in the General Population. Mayo Clin Proc 2021; 96:2576-2586. [PMID: 34120755 PMCID: PMC9904428 DOI: 10.1016/j.mayocp.2021.02.029] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 02/08/2021] [Accepted: 02/11/2021] [Indexed: 01/19/2023]
Abstract
OBJECTIVE To validate an artificial intelligence-augmented electrocardiogram (AI-ECG) algorithm for the detection of preclinical left ventricular systolic dysfunction (LVSD) in a large community-based cohort. METHODS We identified a randomly selected community-based cohort of 2041 subjects age 45 years or older in Olmsted County, Minnesota. All participants underwent a study echocardiogram and ECG. We first assessed the performance of the AI-ECG to identify LVSD (ejection fraction ≤40%). After excluding participants with clinical heart failure, we further assessed the AI-ECG to detect preclinical LVSD among all patients (n=1996) and in a high-risk subgroup (n=1348). Next we modelled an imputed screening program for preclinical LVSD detection where a positive AI-ECG triggered an echocardiogram. Finally, we assessed the ability of the AI-ECG to predict future LVSD. Participants were enrolled between January 1, 1997, and September 30, 2000; and LVSD surveillance was performed for 10 years after enrollment. RESULTS For detection of LVSD in the total population (prevalence, 2.0%), the area under the receiver operating curve for AI-ECG was 0.97 (sensitivity, 90%; specificity, 92%); in the high-risk subgroup (prevalence 2.7%), the area under the curve was 0.97 (sensitivity, 92%; specificity, 93%). In an imputed screening program, identification of one preclinical LSVD case would require 88.3 AI-ECGs and 8.7 echocardiograms in the total population and 65.7 AI-ECGs and 5.5 echocardiograms in the high-risk subgroup. The unadjusted hazard ratio for a positive AI-ECG for incident LVSD over 10 years was 2.31 (95% CI, 1.32 to 4.05; P=.004). CONCLUSION Artificial intelligence-augmented ECG can identify preclinical LVSD in the community and warrants further study as a screening tool for preclinical LVSD.
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Affiliation(s)
| | | | | | | | | | | | - Suraj Kapa
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | | | | | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Paul M McKie
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN.
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25
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Kashou AH, LoCoco S, McGill TD, Evenson CM, Deshmukh AJ, Hodge DO, Cooper DH, Sodhi SS, Cuculich PS, Asirvatham SJ, Noseworthy PA, DeSimone CV, May AM. Automatic wide complex tachycardia differentiation using mathematically synthesized vectorcardiogram signals. Ann Noninvasive Electrocardiol 2021; 27:e12890. [PMID: 34562325 PMCID: PMC8739609 DOI: 10.1111/anec.12890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 08/21/2021] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Automated wide complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) may be accomplished using novel calculations that quantify the extent of mean electrical vector changes between the WCT and baseline electrocardiogram (ECG). At present, it is unknown whether quantifying mean electrical vector changes within three orthogonal vectorcardiogram (VCG) leads (X, Y, and Z leads) can improve automated VT and SWCT classification. METHODS A derivation cohort of paired WCT and baseline ECGs was used to derive five logistic regression models: (i) one novel WCT differentiation model (i.e., VCG Model), (ii) three previously developed WCT differentiation models (i.e., WCT Formula, VT Prediction Model, and WCT Formula II), and (iii) one "all-inclusive" model (i.e., Hybrid Model). A separate validation cohort of paired WCT and baseline ECGs was used to trial and compare each model's performance. RESULTS The VCG Model, composed of WCT QRS duration, baseline QRS duration, absolute change in QRS duration, X-lead QRS amplitude change, Y-lead QRS amplitude change, and Z-lead QRS amplitude change, demonstrated effective WCT differentiation (area under the curve [AUC] 0.94) for the derivation cohort. For the validation cohort, the diagnostic performance of the VCG Model (AUC 0.94) was similar to that achieved by the WCT Formula (AUC 0.95), VT Prediction Model (AUC 0.91), WCT Formula II (AUC 0.94), and Hybrid Model (AUC 0.95). CONCLUSION Custom calculations derived from mathematically synthesized VCG signals may be used to formulate an effective means to differentiate WCTs automatically.
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Affiliation(s)
| | - Sarah LoCoco
- Division of Cardiovascular Diseases, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Trevon D McGill
- Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Christopher M Evenson
- Division of Cardiovascular Diseases, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Abhishek J Deshmukh
- Division of Cardiovascular Diseases, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - David O Hodge
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Daniel H Cooper
- Division of Cardiovascular Diseases, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Sandeep S Sodhi
- Division of Cardiovascular Diseases, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Phillip S Cuculich
- Division of Cardiovascular Diseases, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Samuel J Asirvatham
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota, USA
| | - Peter A Noseworthy
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Adam M May
- Division of Cardiovascular Diseases, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
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26
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Rabinstein AA, Yost MD, Faust L, Kashou AH, Latif OS, Graff-Radford J, Attia IZ, Yao X, Noseworthy PA, Friedman PA. Artificial Intelligence-Enabled ECG to Identify Silent Atrial Fibrillation in Embolic Stroke of Unknown Source. J Stroke Cerebrovasc Dis 2021; 30:105998. [PMID: 34303963 DOI: 10.1016/j.jstrokecerebrovasdis.2021.105998] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 06/18/2021] [Accepted: 07/05/2021] [Indexed: 11/28/2022] Open
Abstract
OBJECTIVES Embolic strokes of unknown source (ESUS) are common and often suspected to be caused by unrecognized paroxysmal atrial fibrillation (AF). An AI-enabled ECG (AI-ECG) during sinus rhythm has been shown to identify patients with unrecognized AF. We pursued this study to determine if the AI-ECG model differentiates between patients with ESUS and those with known causes of stroke, and to evaluate whether the AF prediction by AI-ECG among patients with ESUS was associated with the results of prolonged ambulatory cardiac rhythm monitoring. MATERIALS AND METHODS We reviewed consecutive patients admitted with acute ischemic stroke to a comprehensive stroke center between January 2018 and August 2019 and employed the TOAST classification to categorize the mechanisms of ischemia. Use and results of ambulatory cardiac rhythm monitoring after discharge were gathered. We ran the AI-ECG model to obtain AF probabilities from all ECGs acquired during the hospitalization and compared those probabilities in patients with ESUS versus those with known stroke causes (apart from AF), and between patients with and without AF detected by ambulatory cardiac rhythm monitoring. RESULTS The study cohort had 930 patients, including 263 patients (28.3%) with known AF or AF diagnosed during the index hospitalization and 265 cases (28.5%) categorized as ESUS. Ambulatory cardiac rhythm monitoring was performed in 226 (85.3%) patients with ESUS. AF probability by AI-ECG was not associated with ESUS. However, among patients with ESUS, the probability of AF by AI-ECG was associated with a higher likelihood of AF detection by ambulatory monitoring (P = 0.004). A probability of AF by AI-ECG greater than 0.20 was associated with AF detection by ambulatory cardiac rhythm monitoring with an OR of 5.47 (95% CI 1.51-22.51). CONCLUSIONS AI-ECG may help guide the use of prolonged ambulatory cardiac rhythm monitoring in patients with ESUS to identify those who might benefit from anticoagulation.
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Affiliation(s)
| | - Micah D Yost
- Neurology, Mayo Clinic, 200 First Street SW, Mayo W8B, Rochester, MN 55905, USA.
| | - Louis Faust
- Health Science Research, Mayo Clinic, Rochester, MN 55905, USA; Internal Medicine, Mayo Clinic, Rochester, MN 55905, USA.
| | | | - Omar S Latif
- Internal Medicine, Mayo Clinic, Rochester, MN 55905, USA.
| | | | | | - Xiaoxi Yao
- Health Science Research, Mayo Clinic, Rochester, MN 55905, USA.
| | | | - Paul A Friedman
- Cardiovascular Diseases, Mayo Clinic, Rochester, MN 55905, USA.
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27
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Kashou AH, Noseworthy PA, Lopez-Jimenez F, Attia ZI, Kapa S, Friedman PA, Jentzer JC. The effect of cardiac rhythm on artificial intelligence-enabled ECG evaluation of left ventricular ejection fraction prediction in cardiac intensive care unit patients. Int J Cardiol 2021; 339:54-55. [PMID: 34242690 DOI: 10.1016/j.ijcard.2021.07.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 05/28/2021] [Accepted: 07/02/2021] [Indexed: 11/18/2022]
Abstract
The presence of left ventricular systolic dysfunction (LVSD) alters clinical management and prognosis in most acute and chronic cardiovascular conditions. While transthoracic echocardiography (TTE) remains the most common diagnostic tool to screen for LVSD, it is operator-dependent, time-consuming, effort-intensive, and relatively expensive. Recent work has demonstrated the ability of an artificial intelligence-augment ECG (AI-ECG) model to accurately predict LVSD in critical intensive care unit (CICU) patients. We demonstrate that the AI-ECG algorithm can maintain its performance in these patients with and without AF despite their clinical differences. An AI-ECG algorithm can serve as a non-invasive, inexpensive, and rapid screening tool for early detection of LVSD in resource-limited settings, and potentially expedite clinical decision making and guideline-directed therapies in the acute care setting.
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Affiliation(s)
- Anthony H Kashou
- Department of Internal Medicine, Mayo Clinic, Rochester, MN, United States of America
| | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States of America
| | | | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America
| | - Suraj Kapa
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America
| | - Jacob C Jentzer
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN, United States of America.
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28
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Evenson CM, Kashou AH, LoCoco S, DeSimone CV, Deshmukh AJ, Cuculich PS, Noseworthy PA, May AM. Conceptual and literature basis for wide complex tachycardia and baseline ECG comparison. J Electrocardiol 2021; 65:50-54. [PMID: 33503517 DOI: 10.1016/j.jelectrocard.2021.01.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/08/2021] [Accepted: 01/11/2021] [Indexed: 11/29/2022]
Abstract
Accurate wide QRS complex tachycardia (WCT) differentiation into either ventricular tachycardia or supraventricular wide complex tachycardia using 12‑lead electrocardiogram (ECG) interpretation is essential for diagnostic, therapeutic, and prognostic reasons. There is an ever-expanding variety of WCT differentiation methods and criteria available to clinicians. However, only a few make use of the diagnostic value of comparing the ECG during WCT to that of the patient's baseline ECG. Therefore, we highlight the conceptual rationale and scientific literature supporting the diagnostic value of WCT and baseline ECG comparison.
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Affiliation(s)
- Christopher M Evenson
- Department of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, USA
| | | | - Sarah LoCoco
- Department of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, USA
| | | | | | - Phillip S Cuculich
- Department of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, USA
| | | | - Adam M May
- Department of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, USA.
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29
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Jentzer JC, Kashou AH, Attia ZI, Lopez-Jimenez F, Kapa S, Friedman PA, Noseworthy PA. Left ventricular systolic dysfunction identification using artificial intelligence-augmented electrocardiogram in cardiac intensive care unit patients. Int J Cardiol 2020; 326:114-123. [PMID: 33152415 DOI: 10.1016/j.ijcard.2020.10.074] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 10/09/2020] [Accepted: 10/26/2020] [Indexed: 01/08/2023]
Abstract
BACKGROUND An artificial intelligence-augmented electrocardiogram (AI-ECG) can identify left ventricular systolic dysfunction (LVSD). We examined the accuracy of AI ECG for identification of LVSD (defined as LVEF ≤40% by transthoracic echocardiogram [TTE]) in cardiac intensive care unit (CICU) patients. METHOD We included unique Mayo Clinic CICU patients admitted from 2007 to 2018 who underwent AI-ECG and TTE within 7 days, at least one of which was during hospitalization. Discrimination of the AI-ECG for LVSD was determined using receiver-operator characteristic curve (AUC) values. RESULTS We included 5680 patients with a mean age of 68 ± 15 years (37% females). Acute coronary syndrome (ACS) was present in 55%. LVSD was present in 34% of patients (mean LVEF 48 ± 16%). The AI-ECG had an AUC of 0.83 (95% confidence interval 0.82-0.84) for discrimination of LVSD. Using the optimal cut-off, the AI-ECG had 73%, specificity 78%, negative predictive value 85% and overall accuracy 76% for LVSD. AUC values were higher for patients aged <70 years (0.85 versus 0.80), males (0.84 versus 0.79), patients without ACS (0.86 versus 0.80), and patients who did not undergo revascularization (0.84 versus 0.80). CONCLUSIONS The AI-ECG algorithm had very good discrimination for LVSD in this critically-ill CICU cohort with a high prevalence of LVSD. Performance was better in younger male patients and those without ACS, highlighting those CICU patients in whom screening for LVSD using AI ECG may be more effective. The AI-ECG might potentially be useful for identification of LVSD in resource-limited settings when TTE is unavailable.
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Affiliation(s)
- Jacob C Jentzer
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN, United States of America; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States of America.
| | - Anthony H Kashou
- Department of Internal Medicine, Mayo Clinic, Rochester, MN, United States of America.
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America.
| | - Francisco Lopez-Jimenez
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America.
| | - Suraj Kapa
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America.
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America.
| | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States of America.
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30
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Jentzer JC, Kashou AH, Lopez-Jimenez F, Attia ZI, Kapa S, Friedman PA, Noseworthy PA. Mortality risk stratification using artificial intelligence-augmented electrocardiogram in cardiac intensive care unit patients. Eur Heart J Acute Cardiovasc Care 2020; 10:532-541. [PMID: 33620440 DOI: 10.1093/ehjacc/zuaa021] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 09/01/2020] [Accepted: 09/04/2020] [Indexed: 01/07/2023]
Abstract
AIMS An artificial intelligence-augmented electrocardiogram (AI-ECG) algorithm can identify left ventricular systolic dysfunction (LVSD). We sought to determine whether this AI-ECG algorithm could stratify mortality risk in cardiac intensive care unit (CICU) patients, independent of the presence of LVSD by transthoracic echocardiography (TTE). METHODS AND RESULTS We included 11 266 unique Mayo Clinic CICU patients admitted from 2007 to 2018 who underwent AI-ECG after CICU admission. Left ventricular ejection fraction (LVEF) data were extracted for patients with a TTE during hospitalization. Hospital mortality was analysed using multivariable logistic regression. Mean age was 68 ± 15 years, including 37% females. Higher AI-ECG probability of LVSD remained associated with higher hospital mortality [adjusted odds ratio (OR) 1.05 per 0.1 higher, 95% confidence interval (CI) 1.02-1.08, P = 0.003] after adjustment for LVEF, which itself was inversely related with the risk of hospital mortality (adjusted OR 0.96 per 5% higher, 95% CI 0.93-0.99, P = 0.02). Patients with available LVEF data (n = 8242) were divided based on the presence of predicted (by AI-ECG) vs. observed (by TTE) LVSD (defined as LVEF ≤ 35%), using TTE as the gold standard. A stepwise increase in hospital mortality was observed for patients with a true negative, false positive, false negative, and true positive AI-ECG. CONCLUSION The AI-ECG prediction of LVSD is associated with hospital mortality in CICU patients, affording risk stratification in addition to that provided by echocardiographic LVEF. Our results emphasize the prognostic value of electrocardiographic patterns reflecting underlying myocardial disease that are recognized by the AI-ECG.
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Affiliation(s)
- Jacob C Jentzer
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.,Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Anthony H Kashou
- Department of Internal Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Francisco Lopez-Jimenez
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Suraj Kapa
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.,Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
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31
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Kashou AH, Evenson CM, Noseworthy PA, Muralidharan TR, DeSimone CV, Deshmukh AJ, Asirvatham SJ, May AM. Differentiating wide complex tachycardias: A historical perspective. Indian Heart J 2020; 73:7-13. [PMID: 33714412 PMCID: PMC7961210 DOI: 10.1016/j.ihj.2020.09.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 09/03/2020] [Accepted: 09/10/2020] [Indexed: 11/02/2022] Open
Abstract
One of the most critical and challenging skills is the distinction of wide complex tachycardias into ventricular tachycardia or supraventricular wide complex tachycardia. Prompt and accurate differentiation of wide complex tachycardias naturally influences short- and long-term management decisions and may directly affect patient outcomes. Currently, there are many useful electrocardiographic criteria and algorithms designed to distinguish ventricular tachycardia and supraventricular wide complex tachycardia accurately; however, no single approach guarantees diagnostic certainty. In this review, we offer an in-depth analysis of available methods to differentiate wide complex tachycardias by retrospectively examining its rich literature base - one that spans several decades.
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Affiliation(s)
| | - Christopher M Evenson
- Cardiovascular Division, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Thoddi R Muralidharan
- Department of Cardiology, Sri Ramachandra Medical Centre, Porur Chennai, Tamil Nadu, India
| | | | | | | | - Adam M May
- Cardiovascular Division, Washington University School of Medicine, St. Louis, MO, USA
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32
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Siontis KC, Gersh BJ, Weston SA, Jiang R, Kashou AH, Roger VL, Noseworthy PA, Chamberlain AM. Association of New-Onset Atrial Fibrillation After Noncardiac Surgery With Subsequent Stroke and Transient Ischemic Attack. JAMA 2020; 324:871-878. [PMID: 32870297 PMCID: PMC7489856 DOI: 10.1001/jama.2020.12518] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
IMPORTANCE Outcomes of postoperative atrial fibrillation (AF) after noncardiac surgery are not well defined. OBJECTIVE To determine the association of new-onset postoperative AF vs no AF after noncardiac surgery with risk of nonfatal and fatal outcomes. DESIGN, SETTING, AND PARTICIPANTS Retrospective cohort study in Olmsted County, Minnesota, involving 550 patients who had their first-ever documented AF within 30 days after undergoing a noncardiac surgery (postoperative AF) between 2000 and 2013. Of these patients, 452 were matched 1:1 on age, sex, year of surgery, and type of surgery to patients with noncardiac surgery who were not diagnosed with AF within 30 days following the surgery (no AF). The last date of follow-up was December 31, 2018. EXPOSURES Postoperative AF vs no AF after noncardiac surgery. MAIN OUTCOMES AND MEASURES The primary outcome was ischemic stroke or transient ischemic attack (TIA). Secondary outcomes included subsequent documented AF, all-cause mortality, and cardiovascular mortality. RESULTS The median age of the 452 matched patients was 75 years (IQR, 67-82 years) and 51.8% of patients were men. Patients with postoperative AF had significantly higher CHA2DS2-VASc scores than those in the no AF group (median, 4 [IQR, 2-5] vs 3 [IQR, 2-5]; P < .001). Over a median follow-up of 5.4 years (IQR, 1.4-9.2 years), there were 71 ischemic strokes or TIAs, 266 subsequent documented AF episodes, and 571 deaths, of which 172 were cardiovascular related. Patients with postoperative AF exhibited a statistically significantly higher risk of ischemic stroke or TIA (incidence rate, 18.9 vs 10.0 per 1000 person-years; absolute risk difference [RD] at 5 years, 4.7%; 95% CI, 1.0%-8.4%; HR, 2.69; 95% CI, 1.35-5.37) compared with those with no AF. Patients with postoperative AF had statistically significantly higher risks of subsequent documented AF (incidence rate 136.4 vs 21.6 per 1000 person-years; absolute RD at 5 years, 39.3%; 95% CI, 33.6%-45.0%; HR, 7.94; 95% CI, 4.85-12.98), and all-cause death (incidence rate, 133.2 vs 86.8 per 1000 person-years; absolute RD at 5 years, 9.4%; 95% CI, 4.9%-13.7%; HR, 1.66; 95% CI, 1.32-2.09). No significant difference in the risk of cardiovascular death was observed for patients with and without postoperative AF (incidence rate, 42.5 vs 25.0 per 1000 person-years; absolute RD at 5 years, 6.2%; 95% CI, 2.2%-10.4%; HR, 1.51; 95% CI, 0.97-2.34). CONCLUSIONS AND RELEVANCE Among patients undergoing noncardiac surgery, new-onset postoperative AF compared with no AF was associated with a significant increased risk of stroke or TIA. However, the implications of these findings for the management of postoperative AF, such as the need for anticoagulation therapy, require investigation in randomized trials.
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Affiliation(s)
| | - Bernard J. Gersh
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - Susan A. Weston
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Ruoxiang Jiang
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | | | - Véronique L. Roger
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Peter A. Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota
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Kashou AH, Ko WY, Attia ZI, Cohen MS, Friedman PA, Noseworthy PA. A comprehensive artificial intelligence–enabled electrocardiogram interpretation program. Cardiovascular Digital Health Journal 2020; 1:62-70. [PMID: 35265877 PMCID: PMC8890098 DOI: 10.1016/j.cvdhj.2020.08.005] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Background Automated computerized electrocardiogram (ECG) interpretation algorithms are designed to enhance physician ECG interpretation, minimize medical error, and expedite clinical workflow. However, the performance of current computer algorithms is notoriously inconsistent. We aimed to develop and validate an artificial intelligence–enabled ECG (AI-ECG) algorithm capable of comprehensive 12-lead ECG interpretation with accuracy comparable to practicing cardiologists. Methods We developed an AI-ECG algorithm using a convolutional neural network as a multilabel classifier capable of assessing 66 discrete, structured diagnostic ECG codes using the cardiologist’s final annotation as the gold-standard interpretation. We included 2,499,522 ECGs from 720,978 patients ≥18 years of age with a standard 12-lead ECG obtained at the Mayo Clinic ECG laboratory between 1993 and 2017. The total sample was randomly divided into training (n = 1,749,654), validation (n = 249,951), and testing (n = 499,917) datasets with a similar distribution of codes. We compared the AI-ECG algorithm’s performance to the cardiologist’s interpretation in the testing dataset using receiver operating characteristic (ROC) and precision recall (PR) curves. Results The model performed well for various rhythm, conduction, ischemia, waveform morphology, and secondary diagnoses codes with an area under the ROC curve of ≥0.98 for 62 of the 66 codes. PR metrics were used to assess model performance accounting for category imbalance and demonstrated a sensitivity ≥95% for all codes. Conclusions An AI-ECG algorithm demonstrates high diagnostic performance in comparison to reference cardiologist interpretation of a standard 12-lead ECG. The use of AI-ECG reading tools may permit scalability as ECG acquisition becomes more ubiquitous.
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Affiliation(s)
| | - Wei-Yin Ko
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - Zachi I. Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - Michal S. Cohen
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - Paul A. Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - Peter A. Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
- Address reprint requests and correspondence: Dr Peter A. Noseworthy, Department of Cardiovascular Diseases, Electrophysiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905.
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Adedinsewo D, Carter RE, Attia Z, Johnson P, Kashou AH, Dugan JL, Albus M, Sheele JM, Bellolio F, Friedman PA, Lopez-Jimenez F, Noseworthy PA. Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea. Circ Arrhythm Electrophysiol 2020; 13:e008437. [PMID: 32986471 DOI: 10.1161/circep.120.008437] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
BACKGROUND Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The reasons for dyspnea are often multifactorial. A focused physical evaluation and diagnostic testing can lack sensitivity and specificity. The objective of this study was to assess the accuracy of an artificial intelligence-enabled ECG to identify patients presenting with dyspnea who have left ventricular systolic dysfunction (LVSD). METHODS We retrospectively applied a validated artificial intelligence-enabled ECG algorithm for the identification of LVSD (defined as LV ejection fraction ≤35%) to a cohort of patients aged ≥18 years who were evaluated in the ED at a Mayo Clinic site with dyspnea. Patients were included if they had at least one standard 12-lead ECG acquired on the date of the ED visit and an echocardiogram performed within 30 days of presentation. Patients with prior LVSD were excluded. We assessed the model performance using area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity. RESULTS A total of 1606 patients were included. Median time from ECG to echocardiogram was 1 day (Q1: 1, Q3: 2). The artificial intelligence-enabled ECG algorithm identified LVSD with an area under the receiver operating characteristic curve of 0.89 (95% CI, 0.86-0.91) and accuracy of 85.9%. Sensitivity, specificity, negative predictive value, and positive predictive value were 74%, 87%, 97%, and 40%, respectively. To identify an ejection fraction <50%, the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were 0.85 (95% CI, 0.83-0.88), 86%, 63%, and 91%, respectively. NT-proBNP (N-terminal pro-B-type natriuretic peptide) alone at a cutoff of >800 identified LVSD with an area under the receiver operating characteristic curve of 0.80 (95% CI, 0.76-0.84). CONCLUSIONS The ECG is an inexpensive, ubiquitous, painless test which can be quickly obtained in the ED. It effectively identifies LVSD in selected patients presenting to the ED with dyspnea when analyzed with artificial intelligence and outperforms NT-proBNP. Graphic Abstract: A graphic abstract is available for this article.
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Affiliation(s)
| | - Rickey E Carter
- Department of Health Sciences Research (R.E.C., P.J.), Mayo Clinic, Jacksonville, FL
| | - Zachi Attia
- Division of Cardiovascular Medicine (Z.A., J.L.D., P.A.F., F.L.-J., P.A.N.), Mayo Clinic, Rochester, MN
| | - Patrick Johnson
- Department of Health Sciences Research (R.E.C., P.J.), Mayo Clinic, Jacksonville, FL
| | | | - Jennifer L Dugan
- Division of Cardiovascular Medicine (Z.A., J.L.D., P.A.F., F.L.-J., P.A.N.), Mayo Clinic, Rochester, MN
| | - Michael Albus
- Department of Emergency Medicine (M.A., J.M.S.), Mayo Clinic, Jacksonville, FL
| | - Johnathan M Sheele
- Department of Emergency Medicine (M.A., J.M.S.), Mayo Clinic, Jacksonville, FL
| | | | - Paul A Friedman
- Division of Cardiovascular Medicine (Z.A., J.L.D., P.A.F., F.L.-J., P.A.N.), Mayo Clinic, Rochester, MN
| | - Francisco Lopez-Jimenez
- Division of Cardiovascular Medicine (Z.A., J.L.D., P.A.F., F.L.-J., P.A.N.), Mayo Clinic, Rochester, MN
| | - Peter A Noseworthy
- Division of Cardiovascular Medicine (Z.A., J.L.D., P.A.F., F.L.-J., P.A.N.), Mayo Clinic, Rochester, MN
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Kashou AH, DeSimone CV, Deshmukh AJ, McGill TD, Hodge DO, Carter R, Cooper DH, Cuculich PS, Noheria A, Asirvatham SJ, Noseworthy PA, May AM. The WCT Formula II: An effective means to automatically differentiate wide complex tachycardias. J Electrocardiol 2020; 61:121-129. [DOI: 10.1016/j.jelectrocard.2020.05.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 05/01/2020] [Accepted: 05/09/2020] [Indexed: 10/24/2022]
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Kashou AH, LoCoco S, Asirvatham SJ, May AM, Noseworthy PA. A lateral lead variant of the de Winter pattern due to left main stenosis and left anterior descending artery occlusion. J Electrocardiol 2020; 61:77-80. [DOI: 10.1016/j.jelectrocard.2020.06.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 05/21/2020] [Accepted: 06/03/2020] [Indexed: 02/06/2023]
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Kashou AH, DeSimone CV, Asirvatham SJ, Kapa S. Left atrial dissection as a trigger for recurrent atrial fibrillation. HeartRhythm Case Rep 2020; 6:329-333. [PMID: 32577388 PMCID: PMC7300347 DOI: 10.1016/j.hrcr.2020.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
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Abstract
The primary goal of the initial ECG evaluation of every wide complex tachycardia is to determine whether the tachyarrhythmia has a ventricular or supraventricular origin. The answer to this question drives immediate patient care decisions, ensuing clinical workup, and long‐term management strategies. Thus, the importance of arriving at the correct diagnosis cannot be understated and has naturally spurred rigorous research, which has brought forth an ever‐expanding abundance of manually applied and automated methods to differentiate wide complex tachycardias. In this review, we provide an in‐depth analysis of traditional and more contemporary methods to differentiate ventricular tachycardia and supraventricular wide complex tachycardia. In doing so, we: (1) review hallmark wide complex tachycardia differentiation criteria, (2) examine the conceptual and structural design of standard wide complex tachycardia differentiation methods, (3) discuss practical limitations of manually applied ECG interpretation approaches, and (4) highlight recently formulated methods designed to differentiate ventricular tachycardia and supraventricular wide complex tachycardia automatically.
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Affiliation(s)
| | | | | | | | | | - Adam M May
- Cardiovascular Division Washington University School of Medicine St. Louis MO
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Kashou AH, Attia IZ, Yao X, Friedman PA, Noseworthy PA. Artificial intelligence-enabled electrocardiogram: can we identify patients with unrecognized atrial fibrillation? Expert Review of Precision Medicine and Drug Development 2020. [DOI: 10.1080/23808993.2020.1735935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
| | | | - Xiaoxi Yao
- Robert D. And Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | - Paul A. Friedman
- Division of Cardiovascular Diseases, Mayo Clinic, Rochester MN, USA
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McGill TD, Kashou AH, Deshmukh AJ, LoCoco S, May AM, DeSimone CV. Wide complex tachycardia differentiation: An examination of traditional and contemporary approaches. J Electrocardiol 2020; 60:203-208. [DOI: 10.1016/j.jelectrocard.2020.04.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Revised: 04/03/2020] [Accepted: 04/11/2020] [Indexed: 10/24/2022]
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Kashou AH, DeSimone CV, Hodge DO, Carter R, Lin G, Asirvatham SJ, Noseworthy PA, Deshmukh AJ, May AM. The ventricular tachycardia prediction model: Derivation and validation data. Data Brief 2020; 30:105515. [PMID: 32382594 PMCID: PMC7200856 DOI: 10.1016/j.dib.2020.105515] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 03/10/2020] [Accepted: 03/24/2020] [Indexed: 11/23/2022] Open
Abstract
In a recent publication [1], we introduced and described a novel means (i.e. VT Prediction Model) to correctly categorize wide complex tachycardias (WCTs) into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) using routine measurements shown on electrocardiogram (ECG) paper recordings. In this article, we summarize data components relating to the derivation and validation of the VT Prediction Model.
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Affiliation(s)
| | | | - David O. Hodge
- Department of Department of Health Sciences Research, Mayo Clinic, United States
| | - Rickey Carter
- Department of Department of Health Sciences Research, Mayo Clinic, United States
| | - Grace Lin
- Department of Cardiovascular Diseases, Mayo Clinic, United States
| | | | | | | | - Adam M. May
- Department of Medicine, Division of Cardiovascular Diseases, Washington University in St. Louis, United States
- Corresponding author.
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Kashou AH, Rabinstein AA, Attia IZ, Asirvatham SJ, Gersh BJ, Friedman PA, Noseworthy PA. Recurrent cryptogenic stroke: A potential role for an artificial intelligence-enabled electrocardiogram? HeartRhythm Case Rep 2020; 6:202-205. [PMID: 32322497 PMCID: PMC7156980 DOI: 10.1016/j.hrcr.2019.12.013] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Affiliation(s)
| | | | - Itzhak Zachi Attia
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota
| | | | - Bernard J. Gersh
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota
| | - Paul A. Friedman
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota
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Kashou AH, Noseworthy PA. Artificial intelligence capable of detecting left ventricular hypertrophy: pushing the limits of the electrocardiogram? Europace 2020; 22:338-339. [PMID: 31898741 DOI: 10.1093/europace/euz349] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Anthony H Kashou
- Department of Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Peter A Noseworthy
- Division of Cardiovascular Diseases, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
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Kashou AH, May AM, Noseworthy PA. 85-Year-Old Man With Chest Pain. Mayo Clin Proc 2020; 95:e1-e6. [PMID: 31902434 DOI: 10.1016/j.mayocp.2019.06.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 06/03/2019] [Accepted: 06/05/2019] [Indexed: 11/22/2022]
Affiliation(s)
- Anthony H Kashou
- Resident in Internal Medicine, Mayo Clinic School of Graduate Medical Education, Rochester, MN
| | - Adam M May
- Resident in Cardiovascular Diseases, Mayo Clinic School of Graduate Medical Education, Rochester, MN
| | - Peter A Noseworthy
- Advisor to residents and Consultant in Cardiovascular Diseases, Mayo Clinic, Rochester, MN.
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May AM, DeSimone CV, Kashou AH, Sridhar H, Hodge DO, Carter R, Lin G, Asirvatham SJ, Noseworthy PA, Deshmukh AJ. The VT Prediction Model: A simplified means to differentiate wide complex tachycardias. J Cardiovasc Electrophysiol 2019; 31:185-195. [DOI: 10.1111/jce.14321] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 11/12/2019] [Accepted: 12/12/2019] [Indexed: 11/28/2022]
Affiliation(s)
- Adam M. May
- Division of Cardiovascular Disease, Department of MedicineWashington University in St. LouisSt. Louis Missouri
| | | | | | | | - David O. Hodge
- Department of Health Sciences ResearchMayo ClinicJacksonville Florida
| | - Rickey Carter
- Department of Health Sciences ResearchMayo ClinicJacksonville Florida
| | - Grace Lin
- Department of Cardiovascular DiseasesMayo ClinicRochester Minnesota
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Kashou AH, Noseworthy PA. Etripamil nasal spray: an investigational agent for the rapid termination of paroxysmal supraventricular tachycardia (SVT). Expert Opin Investig Drugs 2019; 29:1-4. [DOI: 10.1080/13543784.2020.1703180] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Braiteh N, Zgheib A, Kashou AH, Dimassi H, Ghanem G. Immediate and long-term results of percutaneous mitral commissurotomy: up to 15 years. Am J Cardiovasc Dis 2019; 9:34-41. [PMID: 31516761 PMCID: PMC6737353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 07/22/2019] [Indexed: 06/10/2023]
Abstract
PURPOSE To evaluate immediate and long-term clinical results of percutaneous mitral commissurotomy (PMC) in patients with severe mitral stenosis. METHODS In a retrospective study, data were included from 317 patients over 18 years of age (mean age 45) who had been treated for mitral stenosis between January 1993 and March 2015 with PMC using the Inoue balloon technique. Immediate results: Valvular function improved as evidenced by an increase in mitral valve area from 1.01 ± 0.24 cm2 to 2 ± 0.31 cm2 (P < 0.001) and a decrease in mean mitral gradient from 13.64 ± 6.03 mm Hg to 5.40 ± 2.49 mm Hg. Long-term follow-up: At 5-15 years (mean 10.2 years, Inter-quartile range 8.25), 105 (33.1%) of the 317 patients were available for follow-up, 95 living patients and 10 deceased. Of the deceased, average time from PMC to death was 8 years. Results were strongly significant showing that age at the time of PMC and surface area before the procedure were the best predictors of survival at 15 years follow-up, showing significance values of P = 0.022 and P = 0.001, respectively. CONCLUSIONS PMC using the Inoue balloon technique improves morbidity and long-term mortality rates in patients with severe mitral stenosis. Lower Wilkins score and NYHA class at baseline were not found to be significant predictors of mortality in older patients (age > 45). Overall, 65 (61.9%) had survived at 5-15 years follow-up without further cardiac intervention.
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Affiliation(s)
- Nabil Braiteh
- United Health Services Hospitals, Wilson Regional Medical Center, Department of Cardiology33-57 Harrison St, Johnson City, NY 13790, USA
| | - Ali Zgheib
- American University of Beirut, Department of CardiologyP.O. Box:11-0236, Riad-El-Sold Beirut 1107 2020, Beirut, Lebanon
| | - Anthony H Kashou
- Mayo Clinic, Department of Internal Medicine200 First St. SW, Rochester, MN 55905, USA
| | - Hani Dimassi
- Lebanese American University, School of PharmacyP.O. Box 36 Byblos, Lebanon
| | - Georges Ghanem
- Lebanese American University Medical Center-Rizk Hospital, Gilbert and Rose Marie Chagoury School of MedicineZahar Street, Achrafieh P.O. Box 11-3288, Beirut, Lebanon
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Kashou AH, May AM, DeSimone CV, Deshmukh AJ, Asirvatham SJ, Noseworthy PA. Diffuse ST-segment depression despite prior coronary bypass grafting: An electrocardiographic-angiographic correlation. J Electrocardiol 2019; 55:28-31. [PMID: 31078104 DOI: 10.1016/j.jelectrocard.2019.04.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 04/16/2019] [Accepted: 04/26/2019] [Indexed: 11/29/2022]
Abstract
The standard 12‑lead electrocardiogram (ECG) has become a mainstay diagnostic tool in patients suspected to have myocardial ischemia. The identification of hallmark electrocardiographic abnormalities, such as ST-segment deviation or serial T wave changes, not only helps identify the presence of myocardial ischemia but also may help localize myocardial territories with an ongoing injury. Widespread ST-segment depression is commonly attributed to diffuse subendocardial ischemia precipitated by severe multivessel or left main coronary artery disease. However, among patients with prior coronary revascularization, clear electrocardiographic-angiographic relationships responsible for widespread ST-segment depressions have not been well defined. We report a case in which diffuse ST-segment depression emerged from a patient with prior coronary artery bypass grafting. In this report, we examine the patient's presenting ECG pattern as to (1) establish causal inferences which align with the distribution of myocardial ischemia supported by angiography and (2) provide an accompanying analysis of the relevant scientific literature.
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Affiliation(s)
| | - Adam M May
- Department of Cardiovascular Diseases, Mayo Clinic, United States of America
| | | | - Abhishek J Deshmukh
- Department of Cardiovascular Diseases, Mayo Clinic, United States of America
| | - Samuel J Asirvatham
- Department of Cardiovascular Diseases, Mayo Clinic, United States of America
| | - Peter A Noseworthy
- Department of Cardiovascular Diseases, Mayo Clinic, United States of America
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May AM, DeSimone CV, Kashou AH, Hodge DO, Lin G, Kapa S, Asirvatham SJ, Deshmukh AJ, Noseworthy PA, Brady PA. The WCT Formula: A novel algorithm designed to automatically differentiate wide-complex tachycardias. J Electrocardiol 2019; 54:61-68. [DOI: 10.1016/j.jelectrocard.2019.02.008] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 02/07/2019] [Accepted: 02/21/2019] [Indexed: 11/16/2022]
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Kashou AH, Braiteh N, Kashou HE. Reversible atrioventricular block and the importance of close follow-up: Two cases of Lyme carditis. J Cardiol Cases 2018; 17:171-174. [PMID: 30279884 DOI: 10.1016/j.jccase.2018.01.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Revised: 12/19/2017] [Accepted: 01/09/2018] [Indexed: 11/30/2022] Open
Abstract
Lyme carditis is an uncommon presentation of the early-disseminated phase of Lyme disease, although it is recognizable and often curable. Because of its rarity, diagnosing Lyme carditis requires a high level of suspicion, especially when young patients in certain endemic areas present with symptoms of bradycardia and/or evidence of high-degree atrioventricular (AV) block. Temporary cardiac pacing along with antibiotic therapy has been shown to aid in the management of Lyme carditis until symptoms and conduction blocks have resolved. Herein, we report two cases of Lyme carditis-induced AV block that were successfully managed and reversed with temporary cardiac pacing and antibiotics. In order to monitor for any late sequela that may arise, we also recommend close follow-up for patients treated for Lyme carditis with high-degree AV block. <Learning objective: Lyme carditis manifests as a conduction system disease, predominantly involving the atrioventricular (AV) node. It can present without the classical signs of Lyme disease. It is critical to have a high suspicion of Lyme carditis in patients who present with symptoms of bradycardia or high-degree AV block in high prevalence areas. Early initiation of antibiotics, along with external temporary pacing, dramatically improves mortality rates. Close follow-up is important in patients that develop high-degree AV block.>.
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Affiliation(s)
| | - Nabil Braiteh
- United Health Services Hospitals, Wilson Regional Medical Center, Department of Internal Medicine, Johnson City, NY, USA
| | - Hisham E Kashou
- United Health Services Hospitals, Wilson Regional Medical Center, Department of Cardiology, 30 Harrison St #250, Johnson City, NY, USA
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