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Artificial Intelligence ECG as a Novel Screening Tool to Detect a Newly Abnormal Left Ventricular Ejection Fraction After Anthracycline-Based Cancer Therapy. Eur J Prev Cardiol 2023:zwad348. [PMID: 37943680 DOI: 10.1093/eurjpc/zwad348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 10/15/2023] [Accepted: 11/02/2023] [Indexed: 11/12/2023]
Abstract
AIM Cardiotoxicity is a serious side effect of anthracycline treatment, most commonly manifesting as a reduction in left ventricular ejection fraction (LVEF). Early recognition and treatment have been advocated, but robust, convenient and cost-effective alternatives to cardiac imaging are missing. Recent developments in artificial intelligence (AI) techniques applied to electrocardiograms (ECGs) may fill this gap, but no study so far has demonstrated its merit for the detection of an abnormal LVEF after anthracycline therapy. METHODS Single center consecutive cohort study of all breast cancer patients with ECG and transthoracic echocardiography (TTE) evaluation before and after (neo)adjuvant anthracycline chemotherapy. Patients with HER-2-directed therapy, metastatic disease, second primary malignancy or pre-existing cardiovascular disease were excluded from the analyses as were patients with LVEF decline for reasons other than anthracycline-induced cardiotoxicity. Primary readout was the diagnostic performance of AI-ECG by area under the curve (AUC) for LVEFs <50%. RESULTS Of 989 consecutive female breast cancer patients, 22 developed a decline in LVEF attributed to anthracycline therapy over a follow-up time of 9.83 ± 4.2 years. After exclusion of patients who did not have an ECGs within 90 days of a TTE, 20 cases and 683 controls remained. The AI-ECG model detected an LVEF <50% and ≤35% after anthracycline therapy with an AUC of 0.93 and 0.94, respectively. CONCLUSIONS These data support the use of AI-ECG for cardiotoxicity screening after anthracycline-based chemotherapy. This technology could serve as a gatekeeper to more costly cardiac imaging and could enable patients to monitor themselves over long periods of time.
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ASSESSING THE ASSOCIATION BETWEEN AREA DEPRIVATION INDEX AND LEFT VENTRICULAR SYSTOLIC DYSFUNCTION PROBABILITY AS DETERMINATE BY AN ARTIFICIAL INTELLIGENCE-ENABLED ELECTROCARDIOGRAM ALGORITHM. J Am Coll Cardiol 2023. [DOI: 10.1016/s0735-1097(23)02250-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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PREDICTIVE VALUE OF ARTIFICIAL INTELLIGENCE-ENABLED ELECTROCARDIOGRAPHY IN PATIENTS WITH ACUTE CORONARY SYNDROME. J Am Coll Cardiol 2023. [DOI: 10.1016/s0735-1097(23)01780-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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PROSPECTIVE VALIDATION OF A 12-LEAD ECG BASED ARTIFICIAL INTELLIGENCE MODEL FOR DETECTION OF LOW EJECTION FRACTION AMONG YOUNG WOMEN. J Am Coll Cardiol 2023. [DOI: 10.1016/s0735-1097(23)02729-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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EFFECTIVENESS OF AN ARTIFICIAL INTELLIGENCE-ENHANCED DIGITAL STETHOSCOPE TO SCREEN FOR CARDIOMYOPATHY AMONG YOUNG WOMEN: A PROSPECTIVE OBSERVATIONAL STUDY. J Am Coll Cardiol 2023. [DOI: 10.1016/s0735-1097(23)02593-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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ARTIFICIAL INTELLIGENCE ALGORITHM FOR THE DETECTION OF ATRIAL SEPTAL DEFECT USING ELECTROCARDIOGRAM. J Am Coll Cardiol 2023. [DOI: 10.1016/s0735-1097(23)02798-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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STRUCTURAL, FUNCTIONAL, AND HEMODYNAMIC CORRELATES OF ARTIFICIAL INTELLIGENCE-ENABLED ELECTROCARDIOGRAM IN AS. J Am Coll Cardiol 2023. [DOI: 10.1016/s0735-1097(23)02386-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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REAL-WORLD PERFORMANCE AND VALIDATION OF THE ARTIFICIAL INTELLIGENCE ENHANCED ELECTROCARDIOGRAM FOR THE DETECTION OF AMYLOIDOSIS. J Am Coll Cardiol 2023. [DOI: 10.1016/s0735-1097(23)02849-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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RISK FACTORS FOR POSTOPERATIVE ATRIAL FIBRILLATION FOLLOWING CORONARY ARTERY BYPASS SURGERY. J Am Coll Cardiol 2023. [DOI: 10.1016/s0735-1097(23)00699-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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PO-661-01 SMART CLOTHING FOR THE DETECTION OF CARDIAC ELECTRICAL ACTIVITY AND ARRHYTHMIAS. Heart Rhythm 2022. [DOI: 10.1016/j.hrthm.2022.03.325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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AN ARTIFICIAL INTELLIGENCE-ENABLED ECG FOR ASSESSING THE RISK OF LEFT VENTRICULAR DYSFUNCTION AMONG CANCER PATIENTS RECEIVING CHEMOTHERAPY. J Am Coll Cardiol 2022. [DOI: 10.1016/s0735-1097(22)02923-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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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] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Detecting cardiomyopathies in pregnancy and the postpartum period using ECG. Eur Heart J 2021. [DOI: 10.1093/eurheartj/ehab724.3062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Background
Cardiovascular disease (CVD) has been identified as a major threat to maternal health in the US and UK with cardiomyopathy being one of the most common acquired CVD in the pregnant and postpartum period. Diagnosing cardiomyopathy in pregnancy is challenging due to an overlap of cardiovascular symptoms with normal pregnancy symptoms.
Purpose
The purpose of this study was to evaluate the effectiveness of an ECG based deep learning model in identifying cardiomyopathy among pregnant and postpartum women.
Methods
We utilized an ECG based deep learning model to detect cardiomyopathy in a cohort of pregnant or postpartum women seen at multiple hospital sites. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, and specificity. We compared the diagnostic probabilities of the deep learning model with natriuretic peptides and a multivariable model consisting of demographic and clinical parameters.
Results
1,807 women were included. 7%, 10% and 13% had LVEF ≤35%, <45% and <50% respectively. The ECG based deep learning model identified cardiomyopathy with an AUC of 0.92 for left ventricular ejection fraction (LVEF) ≤35%, 0.89 for LVEF <45% and 0.87 for LVEF <50%. For LVEF ≤35%, AUC was higher in Black (0.95) and Hispanic (0.98) women compared to white (0.91). Natriuretic peptides and the multivariable model had AUCs of 0.85 and 0.72 respectively.
Conclusions
A deep learning model effectively identifies cardiomyopathy in pregnant or postpartum women, outperforms natriuretic peptides and traditional clinical parameters with the potential to become a powerful initial screening tool for cardiomyopathy in the obstetric care setting.
Funding Acknowledgement
Type of funding sources: Other. Main funding source(s): This study was made possible using resources supported by the Mayo Clinic Women's Health Research Center and the Mayo Clinic Building Interdisciplinary Research Careers in Women's Health (BIRCWH) Program funded by the National Institutes of Health (NIH), grant number K12 HD065987. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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Machine learning aids clinical decision making in patients presenting with angina and non-obstructive coronary artery disease. Eur Heart J 2021. [DOI: 10.1093/eurheartj/ehab724.1162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Background
Coronary microvascular dysfunction (CMD) is associated with an increased risk of cardiovascular morbidity and mortality. The current gold-standard comprehensive assessment of CMD is through a limited-access invasive catheterization lab procedure.
Purposes
We aimed to develop a point-of-care tool to assist clinical guidance in patients presenting with chest pain and/or an abnormal functional stress test and with non-obstructive coronary artery disease (NOCAD) based on demographic data and resting ECG waveforms.
Methods
This study included 1,893 NOCAD patients (<50% angiographic stenosis) who underwent CMD evaluation as well as a standard ECG up to 1 year prior. Microvascular endothelial-independent dysfunction was defined by abnormal coronary flow reserve (CFR) <2.5 in response to intracoronary adenosine administration. Microvascular endothelial dysfunction was defined by a maximal percent increase in coronary blood flow (%ΔCBF) ≤50% in response to intracoronary acetylcholine infusion. We trained algorithms to distinguish between the following outcomes: CFR ≤2.5, ΔCBF (%) ≤50, and the combination of both. Two classes of algorithms were trained, one depending on ECG waveforms as input, and another using tabular style clinical data. The optimal classification threshold was determined by maximizing Youden's J.
Results
Mean age was 51±12 years and 66% were females (n=1,257). AUC values ranged from 0.49 to 0.67 for all the outcomes. The best performance in our analysis was for the outcome CFR ≤2.5 via logistic regression on tabular variables. AUC and accuracy were 0.67 and 60%; while sensitivity and NPV were 70% and 85%. Specificity and PPV were 56% and 0.35%. When decreasing the threshold from the “optimal” one (24%) to 15%, sensitivity and NPV increased to 92% and 90% respectively, while specificity and PPV decreased to 25% and 29% respectively (Figure 2).
Conclusion
An AI-enabled algorithm may be able to assist clinical guidance by ruling out CMD in patients presenting with chest pain and/or an abnormal functional stress test. This algorithm needs to be prospectively validated in different cohorts.
Funding Acknowledgement
Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): The James Nutter Family & Maria Long Family Fellowship in Cardiovascular Research and Mayo Clinic Figure 1Figure 2
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Abstract
Abstract
Background
An artificial intelligence (AI) algorithm detecting age from 12-lead ECG has been suggested to signal “physiological age” of the individual. Importantly, increased physiological age gauged by an increased difference between ECG-age and chronological age has been associated with higher risk of cardiac events in non-transplant population.
Purpose
We sought to investigate the validity of the AI-derived ECG-age algorithm in patients who underwent heart transplantation and its relationship to major adverse cardiovascular events (MACE).
Methods
A total of 489 consecutive patients who had undergone heart transplantation in our institution between 1994 and 2018 were studied. AI-ECG age was calculated by a previously-trained artificial intelligence (AI) algorithm using a 12-lead ECG per patient. ECGs used in the training process of the algorithm were excluded. The average of the ECG-ages within one year before and one year after heart transplantation was used to represent pre- and post-transplant ECG-ages. MACE was defined as any incidence of revascularization, re-transplantation, and death.
Results
Pre-transplant ECG-age (mean 63±10 years) correlated significantly with recipient chronological age (mean 50±13 years, r=0.57, p<0.0001), but this correlation between recipient and ECG-ages was weakened after transplantation (mean post-transplant ECG age of 55±10 years, r=0.34, p<0.0001). Interestingly, post-transplant ECG-age correlated significantly with donor age (mean ECG age of 55±10 years vs. mean donor age of 32±13 years, r=0.42, p<0.0001). During a median (IQR) follow-up of 9 (5, 14) years, 251 patients had MACE. Mean change in ECG age after transplantation compared to before was −8.8±12.7 years. Patients who had an increase in ECG-age after compared to before transplantation showed increased risk of MACE (HR: 1.53 [1.16, 2.01], p=0.002), independent of recipient and donor ages (adjusted HR: 1.68 [1.26, 2.25], p=0.001); whereas there were no significant differences in risk of MACE in patients who were transplanted with an older donor heart (HR: 1.07 [0.77, 1.50], p=0.66). In a Kaplan Meier survival analysis, those with increased ECG-age after transplantation had significantly lower MACE-free survival compared to those with decreased ECG-age. (Log-rank P=0.002; Wilcoxon P=0.001) (Figure)
Conclusion
Post-transplant ECG-age correlates more faithfully with the donor's than the recipient's chronological age, suggesting that ECG-age more closely reflects cardiac age than the patient age. Furthermore, ECG-age derived cardiac aging after transplantation is associated with higher risk of MACE.
Funding Acknowledgement
Type of funding sources: None.
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B-PO01-080 ARTIFICIAL INTELLIGENCE-ENABLED ASSESSMENT OF THE HEART RATE CORRECTED QT INTERVAL USING A MOBILE ELECTROCARDIOGRAM DEVICE IN CHILDREN AND ADOLESCENTS. Heart Rhythm 2021. [DOI: 10.1016/j.hrthm.2021.06.225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Abstract
This collaborative statement from the Digital Health Committee of the Heart Rhythm Society provides everyday clinical scenarios in which wearables may be utilized by patients for cardiovascular health and arrhythmia management. We describe herein the spectrum of wearables that are commercially available for patients, and their benefits, shortcomings and areas for technological improvement. Although wearables for rhythm diagnosis and management have not been examined in large randomized clinical trials, undoubtedly the usage of wearables has quickly escalated in clinical practice. This document is the first of a planned series in which we will update information on wearables as they are revised and released to consumers.
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B-PO01-098 REAL-LIFE PERFORMANCE, LONG-TERM ROBUSTNESS, AND ABSENCE OF RACE BIAS IN THE ARTIFICIAL INTELLIGENCE ENHANCED ELECTROCARDIOGRAM FOR THE DETECTION OF LEFT VENTRICULAR SYSTOLIC DYSFUNCTION. Heart Rhythm 2021. [DOI: 10.1016/j.hrthm.2021.06.242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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ARTIFICIAL-INTELLIGENCE ENHANCED SCREENING FOR CARDIAC AMYLOIDOSIS BY ELECTROCARDIOGRAPHY. J Am Coll Cardiol 2021. [DOI: 10.1016/s0735-1097(21)01886-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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DEEP LEARNING ENABLED ELECTROCARDIOGRAPHIC PREDICTION OF COMPUTER TOMOGRAPHY-BASED HIGH CORONARY CALCIUM SCORE (CAC). J Am Coll Cardiol 2021. [DOI: 10.1016/s0735-1097(21)04624-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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DETECTION OF HYPERTROPHIC CARDIOMYOPATHY BY ARTIFICIAL INTELLIGENCE-ENABLED ELECTROCARDIOGRAPHY IN CHILDREN AND ADOLESCENTS. J Am Coll Cardiol 2021. [DOI: 10.1016/s0735-1097(21)04601-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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ARTIFICIAL INTELLIGENCE HELPS IDENTIFY PATIENTS WITH GRAVES' DISEASE AT RISK FOR ATRIAL FIBRILLATION. J Am Coll Cardiol 2021. [DOI: 10.1016/s0735-1097(21)01678-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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VALIDATION OF AN ARTIFICIAL INTELLIGENCE ELECTROCARDIOGRAM BASED ALGORITHM FOR THE DETECTION OF LEFT VENTRICULAR SYSTOLIC DYSFUNCTION IN SUBJECTS WITH CHAGAS DISEASE. J Am Coll Cardiol 2021. [DOI: 10.1016/s0735-1097(21)04608-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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UNDERSTANDING SPECTRUM BIAS IN ALGORITHMS DERIVED BY ARTIFICIAL INTELLIGENCE A CASE STUDY IN DETECTING AORTIC STENOSIS USING ELECTROCARDIOGRAMS. J Am Coll Cardiol 2021. [DOI: 10.1016/s0735-1097(21)04595-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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AI ENHANCED ECG ENABLED RAPID NON-INVASIVE EXCLUSION OF SEVERE ACUTE RESPIRATORY SYNDROME CORONAVIRUS 2 (SARS-COV-2) INFECTION. J Am Coll Cardiol 2021. [PMCID: PMC8091410 DOI: 10.1016/s0735-1097(21)04525-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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ARTIFICIAL INTELLIGENCE-ENHANCED ECG IDENTIFICATION OF PREVIOUSLY UNRECOGNIZED CARDIOVASCULAR DISEASES. J Am Coll Cardiol 2021. [DOI: 10.1016/s0735-1097(21)04399-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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An artificial intelligence-enabled analysis of ECG changes after androgen deprivation therapy (ADT) for prostate cancer. J Clin Oncol 2020. [DOI: 10.1200/jco.2020.38.15_suppl.e17535] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
e17535 Background: Androgen Deprivation Therapy (ADT) is commonly used to treat prostate cancer (PC), but has been associated with cardiac morbidity and mortality. The exact mechanisms of this association are unclear. We sought to use an artificial intelligence (AI) enabled algorithm to identify ECG changes in PC patients who received ADT compared to PC patients who did not receive ADT. Methods: From 1,000,000 ECGs performed on 210,414 patients between 1993 and 2017 at our institution, a convolutional neural network was developed to detect predictive signatures for cardiac pathologies. During this process, the ability to predict “estimated sex” of the patient was developed, with output values ranging from 0 (female) to 1 (male). We applied this algorithm to 8,619 ECGs performed on 1,057 men age 75 or younger treated with radiation for high-risk or recurrent PC at our institution, and compared estimated sex after receiving ADT (n = 1,065) to ADT-naive ECGs (n = 7,554). We correlated ECG-identified estimated sex with serum testosterone levels using Spearman rank correlation. Results: Patients who had received ADT had a mean (SD) estimated sex value of 0.81 (0.26) compared to 0.92 (0.17) for those who did not (p < 0.001). Difference between estimated sex in post-ADT ECGs and ADT-naive ECGs remained significant across age groups (Table). Decreased serum total testosterone correlated with decreased estimated sex values in men receiving ADT (R = .57, p < 0.001). Conclusions: ADT for prostate cancer is associated with changes in AI-identified ECG parameters, including lower estimated male sex after receiving ADT. Lower ECG male sex estimate was associated with decreased serum testosterone. In this study, we provide preliminary proof of concept for a potential non-invasive means of monitoring treatment effect and physiologic change using ECGs. [Table: see text]
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TRASTUZUMAB CARDIOTOXICITY SURVEILLANCE BY ARTIFICIAL INTELLIGENCE-AUGMENTED ELECTROCARDIOGRAPHY IN A MULTI SITE STUDY. J Am Coll Cardiol 2020. [DOI: 10.1016/s0735-1097(20)31543-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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MACHINE LEARNING ALGORITHMS TO PREDICT 10-YEAR ATHEROSCLEROTIC CARDIOVASCULAR RISK IN A CONTEMPORARY, COMMUNITY-BASED HISTORICAL COHORT. J Am Coll Cardiol 2020. [DOI: 10.1016/s0735-1097(20)32654-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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DETECTION OF AORTIC STENOSIS USING AN ARTIFICIAL INTELLIGENCE-ENABLED ELECTROCARDIOGRAM. J Am Coll Cardiol 2020. [DOI: 10.1016/s0735-1097(20)32742-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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APPLICATION OF ARTIFICIAL INTELLIGENCE-ENABLED ELECTROCARDIOGRAPHY IN FAMILIAL DILATED CARDIOMYOPATHY. J Am Coll Cardiol 2020. [DOI: 10.1016/s0735-1097(20)34098-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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PREDICTION OF THE HEART RATE CORRECTED QT INTERVAL (QTC) FROM A NOVEL, MULTILEAD SMARTPHONE-ENABLED ECG USING A DEEP NEURAL NETWORK. J Am Coll Cardiol 2019. [DOI: 10.1016/s0735-1097(19)30976-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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