1
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Zheng YQ, Huang HH, Chen SX, Xu XE, Li ZM, Li YH, Chen SZ, Luo WX, Guo Y, Liu W, Li EM, Xu LY. Discovery and validation of combined biomarkers for the diagnosis of esophageal intraepithelial neoplasia and esophageal squamous cell carcinoma. J Proteomics 2024; 304:105233. [PMID: 38925350 DOI: 10.1016/j.jprot.2024.105233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 06/19/2024] [Accepted: 06/22/2024] [Indexed: 06/28/2024]
Abstract
Early diagnosis and intervention of esophageal squamous cell carcinoma (ESCC) can improve the prognosis. The purpose of this study was to identify biomarkers for ESCC and esophageal precancerous lesions (intraepithelial neoplasia, IEN). Based on the proteomic and genomic data of esophageal tissue including previously reported data, up-regulated proteins with copy number amplification in esophageal cancer were screened as candidate biomarkers. Five proteins, including KDM2A, RAD9A, ECT2, CYHR1 and TONSL, were confirmed by immunohistochemistry on ESCC and normal esophagus (NE). Then, we investigated the expression of 5 proteins in 236 participants (60 NEs, 93 IENs and 83 ESCCs) which were randomly divided into training set and test set. When distinguishing ESCC from NE, the area under curve (AUC) of the multiprotein model was 0.940 in the training set, while the lowest AUC of a protein was 0.735. In the test set, the results were similar. When distinguishing ESCC from IEN or distinguishing IEN from NE, the diagnostic efficiency of the multi-protein models were also improved compared with that of single protein. Our findings suggest that combined detection of KDM2A, RAD9A, ECT2, CYHR1 and TONSL can be used as potential biomarkers for the early diagnosis of ESCC and precancerous lesion development prediction. SIGNIFICANCE: Candidate biomarkers including KDM2A, RAD9A, ECT2, CYHR1 and TONSL screened by integrating genomic and proteomic data from the esophagus can be used as potential biomarkers for the early diagnosis of esophageal squamous cell carcinoma and precancerous lesion development prediction.
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Affiliation(s)
- Ya-Qi Zheng
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Institute of Oncologic Pathology, Cancer Research Center, Shantou University Medical College, Shantou, Guangdong 515041, China; Department of Pathology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510000, China
| | - Hai-Hua Huang
- Department of Pathology, Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong 515041, China
| | - Shu-Xian Chen
- Department of Digestive Endoscopy, First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong 515041, China
| | - Xiu-E Xu
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Institute of Oncologic Pathology, Cancer Research Center, Shantou University Medical College, Shantou, Guangdong 515041, China
| | - Zhi-Mao Li
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Institute of Oncologic Pathology, Cancer Research Center, Shantou University Medical College, Shantou, Guangdong 515041, China
| | - Yue-Hong Li
- Department of Digestive Endoscopy, First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong 515041, China
| | - Su-Zuan Chen
- Department of Digestive Endoscopy, First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong 515041, China
| | - Wen-Xiong Luo
- Department of Endoscopy, Shantou Central Hospital, Shantou, Guangdong 515041, China
| | - Yi Guo
- Department of Endoscopy, Cancer Hospital of Shantou University Medical College, Shantou, Guangdong 515041, China
| | - Wei Liu
- College of Science, Heilongjiang Institute of Technology, Harbin, Heilongjiang 150000, China
| | - En-Min Li
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou, Guangdong 515041, China.
| | - Li-Yan Xu
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Institute of Oncologic Pathology, Cancer Research Center, Shantou University Medical College, Shantou, Guangdong 515041, China.
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2
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Oikonomou EK, Khera R. Designing medical artificial intelligence systems for global use: focus on interoperability, scalability, and accessibility. Hellenic J Cardiol 2024:S1109-9666(24)00158-1. [PMID: 39025234 DOI: 10.1016/j.hjc.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 06/21/2024] [Accepted: 07/02/2024] [Indexed: 07/20/2024] Open
Abstract
Advances in artificial intelligence (AI) and machine learning systems promise faster, more efficient and more personalized care. While many of these models are built on the premise of improving access to the timely screening, diagnosis, and treatment of cardiovascular disease, their validity and accessibility across diverse and international cohorts remains unknown. In this mini-review article, we summarize key obstacles in the effort to design AI systems that will be scalable, accessible, and accurate across distinct geographical and temporal settings. We discuss representativeness, interoperability, quality assurance and the importance of vendor-agnostic data types that will be immediately available to end-users across the globe. These topics illustrate how the timely integration of these principles into AI development is crucial to maximizing the global benefits of AI in cardiology.
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Affiliation(s)
- Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA; Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA; Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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3
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Khera R, Oikonomou EK, Nadkarni GN, Morley JR, Wiens J, Butte AJ, Topol EJ. Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice: JACC State-of-the-Art Review. J Am Coll Cardiol 2024; 84:97-114. [PMID: 38925729 DOI: 10.1016/j.jacc.2024.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 05/03/2024] [Accepted: 05/07/2024] [Indexed: 06/28/2024]
Abstract
Artificial intelligence (AI) has the potential to transform every facet of cardiovascular practice and research. The exponential rise in technology powered by AI is defining new frontiers in cardiovascular care, with innovations that span novel diagnostic modalities, new digital native biomarkers of disease, and high-performing tools evaluating care quality and prognosticating clinical outcomes. These digital innovations promise expanded access to cardiovascular screening and monitoring, especially among those without access to high-quality, specialized care historically. Moreover, AI is propelling biological and clinical discoveries that will make future cardiovascular care more personalized, precise, and effective. The review brings together these diverse AI innovations, highlighting developments in multimodal cardiovascular AI across clinical practice and biomedical discovery, and envisioning this new future backed by contemporary science and emerging discoveries. Finally, we define the critical path and the safeguards essential to realizing this AI-enabled future that helps achieve optimal cardiovascular health and outcomes for all.
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Affiliation(s)
- Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA; Center for Outcomes Research and Evaluation (CORE), New Haven, Connecticut, USA; Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut, USA; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.
| | - Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Girish N Nadkarni
- The Samuel Bronfman Department of Medicine, Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jessica R Morley
- Digital Ethics Center, Yale University, New Haven, Connecticut, USA
| | - Jenna Wiens
- Electrical Engineering and Computer Science, Computer Science and Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Atul J Butte
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, USA; Center for Data-Driven Insights and Innovation, University of California Health, Oakland, California, USA
| | - Eric J Topol
- Molecular Medicine, Scripps Research Translational Institute, Scripps Research, La Jolla, California, USA
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4
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Elias P, Jain SS, Poterucha T, Randazzo M, Lopez Jimenez F, Khera R, Perez M, Ouyang D, Pirruccello J, Salerno M, Einstein AJ, Avram R, Tison GH, Nadkarni G, Natarajan V, Pierson E, Beecy A, Kumaraiah D, Haggerty C, Avari Silva JN, Maddox TM. Artificial Intelligence for Cardiovascular Care-Part 1: Advances: JACC Review Topic of the Week. J Am Coll Cardiol 2024; 83:2472-2486. [PMID: 38593946 DOI: 10.1016/j.jacc.2024.03.400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 03/14/2024] [Indexed: 04/11/2024]
Abstract
Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on automating measurements, enhancing image quality, and detecting diseases using novel methods. Applications span wearables, electrocardiograms, echocardiography, angiography, genetics, and more. AI models detect diseases from electrocardiograms at accuracy not previously achieved by technology or human experts, including reduced ejection fraction, valvular heart disease, and other cardiomyopathies. However, AI's unique characteristics necessitate rigorous validation by addressing training methods, real-world efficacy, equity concerns, and long-term reliability. Despite an exponentially growing number of studies in cardiovascular AI, trials showing improvement in outcomes remain lacking. A number are currently underway. Embracing this rapidly evolving technology while setting a high evaluation benchmark will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.
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Affiliation(s)
- Pierre Elias
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA; Department of Biomedical Informatics Columbia University Irving Medical Center, New York, New York, USA
| | - Sneha S Jain
- Division of Cardiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - Timothy Poterucha
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Michael Randazzo
- Division of Cardiology, University of Chicago Medical Center, Chicago, Illinois, USA
| | | | - Rohan Khera
- Division of Cardiology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Marco Perez
- Division of Cardiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - David Ouyang
- Division of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - James Pirruccello
- Division of Cardiology, University of California-San Francisco, San Francisco, California, USA
| | - Michael Salerno
- Division of Cardiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - Andrew J Einstein
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Robert Avram
- Division of Cardiology, Montreal Heart Institute, Montreal, Quebec, Canada
| | - Geoffrey H Tison
- Division of Cardiology, University of California-San Francisco, San Francisco, California, USA
| | - Girish Nadkarni
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Emma Pierson
- Department of Computer Science, Cornell Tech, New York, New York, USA
| | - Ashley Beecy
- NewYork-Presbyterian Health System, New York, New York, USA; Division of Cardiology, Weill Cornell Medical College, New York, New York, USA
| | - Deepa Kumaraiah
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA; NewYork-Presbyterian Health System, New York, New York, USA
| | - Chris Haggerty
- Department of Biomedical Informatics Columbia University Irving Medical Center, New York, New York, USA; NewYork-Presbyterian Health System, New York, New York, USA
| | - Jennifer N Avari Silva
- Division of Cardiology, Washington University School of Medicine, St Louis, Missouri, USA
| | - Thomas M Maddox
- Division of Cardiology, Washington University School of Medicine, St Louis, Missouri, USA.
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5
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Ose B, Sattar Z, Gupta A, Toquica C, Harvey C, Noheria A. Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review. Curr Cardiol Rep 2024; 26:561-580. [PMID: 38753291 DOI: 10.1007/s11886-024-02062-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/17/2024] [Indexed: 06/26/2024]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) is transforming electrocardiography (ECG) interpretation. AI diagnostics can reach beyond human capabilities, facilitate automated access to nuanced ECG interpretation, and expand the scope of cardiovascular screening in the population. AI can be applied to the standard 12-lead resting ECG and single-lead ECGs in external monitors, implantable devices, and direct-to-consumer smart devices. We summarize the current state of the literature on AI-ECG. RECENT FINDINGS Rhythm classification was the first application of AI-ECG. Subsequently, AI-ECG models have been developed for screening structural heart disease including hypertrophic cardiomyopathy, cardiac amyloidosis, aortic stenosis, pulmonary hypertension, and left ventricular systolic dysfunction. Further, AI models can predict future events like development of systolic heart failure and atrial fibrillation. AI-ECG exhibits potential in acute cardiac events and non-cardiac applications, including acute pulmonary embolism, electrolyte abnormalities, monitoring drugs therapy, sleep apnea, and predicting all-cause mortality. Many AI models in the domain of cardiac monitors and smart watches have received Food and Drug Administration (FDA) clearance for rhythm classification, while others for identification of cardiac amyloidosis, pulmonary hypertension and left ventricular dysfunction have received breakthrough device designation. As AI-ECG models continue to be developed, in addition to regulatory oversight and monetization challenges, thoughtful clinical implementation to streamline workflows, avoiding information overload and overwhelming of healthcare systems with false positive results is necessary. Research to demonstrate and validate improvement in healthcare efficiency and improved patient outcomes would be required before widespread adoption of any AI-ECG model.
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Affiliation(s)
- Benjamin Ose
- The University of Kansas School of Medicine, Kansas City, KS, USA
| | - Zeeshan Sattar
- Division of General and Hospital Medicine, The University of Kansas Medical Center, Kansas City, KS, USA
| | - Amulya Gupta
- Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, USA
- Program for AI & Research in Cardiovascular Medicine (PARC), The University of Kansas Medical Center, Kansas City, KS, USA
| | | | - Chris Harvey
- Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, USA
- Program for AI & Research in Cardiovascular Medicine (PARC), The University of Kansas Medical Center, Kansas City, KS, USA
| | - Amit Noheria
- Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, USA.
- Program for AI & Research in Cardiovascular Medicine (PARC), The University of Kansas Medical Center, Kansas City, KS, USA.
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6
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Kenyon CR, Pietri MP, Rosenthal JL, Arsanjani R, Ayoub C. Artificial Intelligence Assists in the Early Identification of Cardiac Amyloidosis. J Pers Med 2024; 14:559. [PMID: 38929780 PMCID: PMC11205191 DOI: 10.3390/jpm14060559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 05/18/2024] [Accepted: 05/21/2024] [Indexed: 06/28/2024] Open
Abstract
A 69-year-old female presented with symptomatic atrial fibrillation. Cardiac amyloidosis was suspected due to an artificial intelligence clinical tool applied to the presenting electrocardiogram predicting a high probability for amyloidosis, and the subsequent unexpected finding of left atrial appendage thrombus reinforced this clinical suspicion. This facilitated an early diagnosis by the biopsy of AL cardiac amyloidosis and the prompt initiation of targeted therapy. This case highlights the utilization of an AI clinical tool and its impact on clinical care, particularly for the early detection of a rare and difficult to diagnose condition where early therapy is critical.
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Affiliation(s)
| | | | | | | | - Chadi Ayoub
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA; (C.R.K.); (M.P.P.); (J.L.R.); (R.A.)
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7
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Mangold KE, Carter RE, Siontis KC, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Friedman PA, Attia ZI. Unlocking the potential of artificial intelligence in electrocardiogram biometrics: age-related changes, anomaly detection, and data authenticity in mobile health platforms. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:314-323. [PMID: 38774362 PMCID: PMC11104462 DOI: 10.1093/ehjdh/ztae024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 03/05/2024] [Accepted: 03/12/2024] [Indexed: 05/24/2024]
Abstract
Aims Mobile devices such as smartphones and watches can now record single-lead electrocardiograms (ECGs), making wearables a potential screening tool for cardiac and wellness monitoring outside of healthcare settings. Because friends and family often share their smart phones and devices, confirmation that a sample is from a given patient is important before it is added to the electronic health record. Methods and results We sought to determine whether the application of Siamese neural network would permit the diagnostic ECG sample to serve as both a medical test and biometric identifier. When using similarity scores to discriminate whether a pair of ECGs came from the same patient or different patients, inputs of single-lead and 12-lead medians produced an area under the curve of 0.94 and 0.97, respectively. Conclusion The similar performance of the single-lead and 12-lead configurations underscores the potential use of mobile devices to monitor cardiac health.
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Affiliation(s)
- Kathryn E Mangold
- Department of Cardiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA
| | - Rickey E Carter
- Department of Quantitative Health Sciences, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, USA
| | | | - Peter A Noseworthy
- Department of Cardiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA
| | | | - Samuel J Asirvatham
- Department of Cardiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA
| | - Paul A Friedman
- Department of Cardiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA
| | - Zachi I Attia
- Department of Cardiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA
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8
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Pereyra Pietri M, Farina JM, Mahmoud AK, Scalia IG, Galasso F, Killian ME, Suppah M, Kenyon CR, Koepke LM, Padang R, Chao CJ, Sweeney JP, Fortuin FD, Eleid MF, Sell-Dottin KA, Steidley DE, Scott LR, Fonseca R, Lopez-Jimenez F, Attia ZI, Dispenzieri A, Grogan M, Rosenthal JL, Arsanjani R, Ayoub C. The prognostic value of artificial intelligence to predict cardiac amyloidosis in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:295-302. [PMID: 38774378 PMCID: PMC11104461 DOI: 10.1093/ehjdh/ztae022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/15/2024] [Accepted: 03/05/2024] [Indexed: 05/24/2024]
Abstract
Aims Cardiac amyloidosis (CA) is common in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). Cardiac amyloidosis has poor outcomes, and its assessment in all TAVR patients is costly and challenging. Electrocardiogram (ECG) artificial intelligence (AI) algorithms that screen for CA may be useful to identify at-risk patients. Methods and results In this retrospective analysis of our institutional National Cardiovascular Disease Registry (NCDR)-TAVR database, patients undergoing TAVR between January 2012 and December 2018 were included. Pre-TAVR CA probability was analysed by an ECG AI predictive model, with >50% risk defined as high probability for CA. Univariable and propensity score covariate adjustment analyses using Cox regression were performed to compare clinical outcomes between patients with high CA probability vs. those with low probability at 1-year follow-up after TAVR. Of 1426 patients who underwent TAVR (mean age 81.0 ± 8.5 years, 57.6% male), 349 (24.4%) had high CA probability on pre-procedure ECG. Only 17 (1.2%) had a clinical diagnosis of CA. After multivariable adjustment, high probability of CA by ECG AI algorithm was significantly associated with increased all-cause mortality [hazard ratio (HR) 1.40, 95% confidence interval (CI) 1.01-1.96, P = 0.046] and higher rates of major adverse cardiovascular events (transient ischaemic attack (TIA)/stroke, myocardial infarction, and heart failure hospitalizations] (HR 1.36, 95% CI 1.01-1.82, P = 0.041), driven primarily by heart failure hospitalizations (HR 1.58, 95% CI 1.13-2.20, P = 0.008) at 1-year follow-up. There were no significant differences in TIA/stroke or myocardial infarction. Conclusion Artificial intelligence applied to pre-TAVR ECGs identifies a subgroup at higher risk of clinical events. These targeted patients may benefit from further diagnostic evaluation for CA.
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Affiliation(s)
- Milagros Pereyra Pietri
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Juan M Farina
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Ahmed K Mahmoud
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Isabel G Scalia
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Francesca Galasso
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Michael E Killian
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Mustafa Suppah
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Courtney R Kenyon
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Laura M Koepke
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Ratnasari Padang
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Chieh-Ju Chao
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - John P Sweeney
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - F David Fortuin
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Mackram F Eleid
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - David E Steidley
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Luis R Scott
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Rafael Fonseca
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | | | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Martha Grogan
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Julie L Rosenthal
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Reza Arsanjani
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Chadi Ayoub
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
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Martini N, Sinigiani G, De Michieli L, Mussinelli R, Perazzolo Marra M, Iliceto S, Zorzi A, Perlini S, Corrado D, Cipriani A. Electrocardiographic features and rhythm disorders in cardiac amyloidosis. Trends Cardiovasc Med 2024; 34:257-264. [PMID: 36841466 DOI: 10.1016/j.tcm.2023.02.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 02/16/2023] [Accepted: 02/18/2023] [Indexed: 02/27/2023]
Abstract
Cardiac amyloidosis (CA) is an infiltrative cardiomyopathy caused by extracellular deposition of amyloid fibrils, mainly derived from transthyretin, either wild-type or hereditary variants, or immunoglobulin light chains misfolding. It is characterized by an increased left ventricular (LV) mass and diastolic dysfunction, which can lead to heart failure with preserved ejection fraction and/or conduction disturbances. The diagnosis is based on invasive pathology demonstration of amyloid deposits, or non-invasive criteria using advanced cardiovascular imaging techniques. Nevertheless, 12-lead electrocardiogram (ECG) remains of crucial importance in the assessment of patients with CA, since they can manifest peculiar features such as low QRS voltages, in discordance with the LV hypertrophy, but also pseudo-infarction patterns, sinus node dysfunction, atrioventricular blocks, premature supraventricular and ventricular beats, which support the presence of a myocardial disease. Great awareness of these common ECG characteristics of CA is needed to increase diagnostic performance and improve patient's outcome. In the present review, we discuss the current role of the ECG in the diagnosis and management of CA, focusing on the most common ECG abnormalities and rhythm disorders.
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Affiliation(s)
- Nicolò Martini
- Department of Cardio-Thoraco-Vascular Sciences and Public Health, University of Padua, Via Giustiniani, 2, Padua 35128, Italy
| | - Giulio Sinigiani
- Department of Cardio-Thoraco-Vascular Sciences and Public Health, University of Padua, Via Giustiniani, 2, Padua 35128, Italy
| | - Laura De Michieli
- Department of Cardio-Thoraco-Vascular Sciences and Public Health, University of Padua, Via Giustiniani, 2, Padua 35128, Italy
| | - Roberta Mussinelli
- Amyloidosis Research and Treatment Center, IRCCS Policlinico San Matteo Foundation, University of Pavia, Pavia, Italy
| | - Martina Perazzolo Marra
- Department of Cardio-Thoraco-Vascular Sciences and Public Health, University of Padua, Via Giustiniani, 2, Padua 35128, Italy
| | - Sabino Iliceto
- Department of Cardio-Thoraco-Vascular Sciences and Public Health, University of Padua, Via Giustiniani, 2, Padua 35128, Italy
| | - Alessandro Zorzi
- Department of Cardio-Thoraco-Vascular Sciences and Public Health, University of Padua, Via Giustiniani, 2, Padua 35128, Italy
| | - Stefano Perlini
- Amyloidosis Research and Treatment Center, IRCCS Policlinico San Matteo Foundation, University of Pavia, Pavia, Italy; Emergency Medicine, Vascular and Metabolic Disease Unit, Department of Internal Medicine, IRCCS Policlinico San Matteo Foundation, University of Pavia, Pavia, Italy
| | - Domenico Corrado
- Department of Cardio-Thoraco-Vascular Sciences and Public Health, University of Padua, Via Giustiniani, 2, Padua 35128, Italy
| | - Alberto Cipriani
- Department of Cardio-Thoraco-Vascular Sciences and Public Health, University of Padua, Via Giustiniani, 2, Padua 35128, Italy.
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10
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Pieroni M, Namdar M, Olivotto I, Desnick RJ. Anderson-Fabry disease management: role of the cardiologist. Eur Heart J 2024; 45:1395-1409. [PMID: 38486361 DOI: 10.1093/eurheartj/ehae148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 02/04/2024] [Accepted: 02/27/2024] [Indexed: 04/22/2024] Open
Abstract
Anderson-Fabry disease (AFD) is a lysosomal storage disorder characterized by glycolipid accumulation in cardiac cells, associated with a peculiar form of hypertrophic cardiomyopathy (HCM). Up to 1% of patients with a diagnosis of HCM indeed have AFD. With the availability of targeted therapies for sarcomeric HCM and its genocopies, a timely differential diagnosis is essential. Specifically, the therapeutic landscape for AFD is rapidly evolving and offers increasingly effective, disease-modifying treatment options. However, diagnosing AFD may be difficult, particularly in the non-classic phenotype with prominent or isolated cardiac involvement and no systemic red flags. For many AFD patients, the clinical journey from initial clinical manifestations to diagnosis and appropriate treatment remains challenging, due to late recognition or utter neglect. Consequently, late initiation of treatment results in an exacerbation of cardiac involvement, representing the main cause of morbidity and mortality, irrespective of gender. Optimal management of AFD patients requires a dedicated multidisciplinary team, in which the cardiologist plays a decisive role, ranging from the differential diagnosis to the prevention of complications and the evaluation of timing for disease-specific therapies. The present review aims to redefine the role of cardiologists across the main decision nodes in contemporary AFD clinical care and drug discovery.
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Affiliation(s)
- Maurizio Pieroni
- Cardiovascular Department, San Donato Hospital, Via Pietro Nenni 22, 52100 Arezzo, Italy
| | - Mehdi Namdar
- Cardiology Division, Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - Iacopo Olivotto
- Cardiomyopathy Unit, Careggi Hospital and Meyer Children's Hospital IRCCS, Florence, Italy
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Robert J Desnick
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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11
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Kamel MA, Abbas MT, Kanaan CN, Awad KA, Baba Ali N, Scalia IG, Farina JM, Pereyra M, Mahmoud AK, Steidley DE, Rosenthal JL, Ayoub C, Arsanjani R. How Artificial Intelligence Can Enhance the Diagnosis of Cardiac Amyloidosis: A Review of Recent Advances and Challenges. J Cardiovasc Dev Dis 2024; 11:118. [PMID: 38667736 PMCID: PMC11050851 DOI: 10.3390/jcdd11040118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/09/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
Cardiac amyloidosis (CA) is an underdiagnosed form of infiltrative cardiomyopathy caused by abnormal amyloid fibrils deposited extracellularly in the myocardium and cardiac structures. There can be high variability in its clinical manifestations, and diagnosing CA requires expertise and often thorough evaluation; as such, the diagnosis of CA can be challenging and is often delayed. The application of artificial intelligence (AI) to different diagnostic modalities is rapidly expanding and transforming cardiovascular medicine. Advanced AI methods such as deep-learning convolutional neural networks (CNNs) may enhance the diagnostic process for CA by identifying patients at higher risk and potentially expediting the diagnosis of CA. In this review, we summarize the current state of AI applications to different diagnostic modalities used for the evaluation of CA, including their diagnostic and prognostic potential, and current challenges and limitations.
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Affiliation(s)
- Moaz A. Kamel
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA
| | | | | | - Kamal A. Awad
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Nima Baba Ali
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Isabel G. Scalia
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Juan M. Farina
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Milagros Pereyra
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Ahmed K. Mahmoud
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA
| | - D. Eric Steidley
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Julie L. Rosenthal
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Chadi Ayoub
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA
- Division of Cardiovascular Imaging, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Reza Arsanjani
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA
- Division of Cardiovascular Imaging, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
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12
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Armoundas AA, Narayan SM, Arnett DK, Spector-Bagdady K, Bennett DA, Celi LA, Friedman PA, Gollob MH, Hall JL, Kwitek AE, Lett E, Menon BK, Sheehan KA, Al-Zaiti SS. Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association. Circulation 2024; 149:e1028-e1050. [PMID: 38415358 PMCID: PMC11042786 DOI: 10.1161/cir.0000000000001201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.
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13
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Chao CJ, Jeong J, Arsanjani R, Kim K, Tsai YL, Yu WC, Farina JM, Mahmoud AK, Ayoub C, Grogan M, Kane GC, Banerjee I, Oh JK. Echocardiography-Based Deep Learning Model to Differentiate Constrictive Pericarditis and Restrictive Cardiomyopathy. JACC Cardiovasc Imaging 2024; 17:349-360. [PMID: 37943236 DOI: 10.1016/j.jcmg.2023.09.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 09/07/2023] [Accepted: 09/25/2023] [Indexed: 11/10/2023]
Abstract
BACKGROUND Constrictive pericarditis (CP) is an uncommon but reversible cause of diastolic heart failure if appropriately identified and treated. However, its diagnosis remains a challenge for clinicians. Artificial intelligence may enhance the identification of CP. OBJECTIVES The authors proposed a deep learning approach based on transthoracic echocardiography to differentiate CP from restrictive cardiomyopathy. METHODS Patients with a confirmed diagnosis of CP and cardiac amyloidosis (CA) (as the representative disease of restrictive cardiomyopathy) at Mayo Clinic Rochester from January 2003 to December 2021 were identified to extract baseline demographics. The apical 4-chamber view from transthoracic echocardiography studies was used as input data. The patients were split into a 60:20:20 ratio for training, validation, and held-out test sets of the ResNet50 deep learning model. The model performance (differentiating CP and CA) was evaluated in the test set with the area under the curve. GradCAM was used for model interpretation. RESULTS A total of 381 patients were identified, including 184 (48.3%) CP, and 197 (51.7%) CA cases. The mean age was 68.7 ± 11.4 years, and 72.8% were male. ResNet50 had a performance with an area under the curve of 0.97 to differentiate the 2-class classification task (CP vs CA). The GradCAM heatmap showed activation around the ventricular septal area. CONCLUSIONS With a standard apical 4-chamber view, our artificial intelligence model provides a platform to facilitate the detection of CP, allowing for improved workflow efficiency and prompt referral for more advanced evaluation and intervention of CP.
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Affiliation(s)
- Chieh-Ju Chao
- Mayo Clinic Rochester, Rochester, Minnesota, USA; Mayo Clinic Arizona, Scottsdale, Arizona, USA
| | - Jiwoong Jeong
- Mayo Clinic Arizona, Scottsdale, Arizona, USA; Arizona State University, Tempe, Arizona, USA
| | | | - Kihong Kim
- Mayo Clinic Rochester, Rochester, Minnesota, USA
| | - Yi-Lin Tsai
- Taipei Veterans General Hospital, Taipei, Taiwan
| | - Wen-Chung Yu
- Taipei Veterans General Hospital, Taipei, Taiwan
| | | | | | - Chadi Ayoub
- Mayo Clinic Arizona, Scottsdale, Arizona, USA
| | | | | | - Imon Banerjee
- Mayo Clinic Arizona, Scottsdale, Arizona, USA; Arizona State University, Tempe, Arizona, USA
| | - Jae K Oh
- Mayo Clinic Rochester, Rochester, Minnesota, USA.
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14
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Pencovich N, Smith BH, Attia ZI, Jimenez FL, Bentall AJ, Schinstock CA, Khamash HA, Jadlowiec CC, Jarmi T, Mao SA, Park WD, Diwan TS, Friedman PA, Stegall MD. Electrocardiography-based Artificial Intelligence Algorithms Aid in Prediction of Long-term Mortality After Kidney Transplantation. Transplantation 2024:00007890-990000000-00715. [PMID: 38557657 DOI: 10.1097/tp.0000000000005023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
BACKGROUND Predicting long-term mortality postkidney transplantation (KT) using baseline clinical data presents significant challenges. This study aims to evaluate the predictive power of artificial intelligence (AI)-enabled analysis of preoperative electrocardiograms (ECGs) in forecasting long-term mortality following KT. METHODS We analyzed preoperative ECGs from KT recipients at three Mayo Clinic sites (Minnesota, Florida, and Arizona) between January 1, 2006, and July 30, 2021. The study involved 6 validated AI algorithms, each trained to predict future development of atrial fibrillation, aortic stenosis, low ejection fraction, hypertrophic cardiomyopathy, amyloid heart disease, and biological age. These algorithms' outputs based on a single preoperative ECG were correlated with patient mortality data. RESULTS Among 6504 KT recipients included in the study, 1764 (27.1%) died within a median follow-up of 5.7 y (interquartile range: 3.00-9.29 y). All AI-ECG algorithms were independently associated with long-term all-cause mortality (P < 0.001). Notably, few patients had a clinical cardiac diagnosis at the time of transplant, indicating that AI-ECG scores were predictive even in asymptomatic patients. When adjusted for multiple clinical factors such as recipient age, diabetes, and pretransplant dialysis, AI algorithms for atrial fibrillation and aortic stenosis remained independently associated with long-term mortality. These algorithms also improved the C-statistic for predicting overall (C = 0.74) and cardiac-related deaths (C = 0.751). CONCLUSIONS The findings suggest that AI-enabled preoperative ECG analysis can be a valuable tool in predicting long-term mortality following KT and could aid in identifying patients who may benefit from enhanced cardiac monitoring because of increased risk.
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Affiliation(s)
- Niv Pencovich
- Departments of Surgery and Immunology, William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, MN
- Department of General Surgery and Transplantation, Sheba Medical Center, Tel Hashomer, Tel-Aviv University, Tel-Aviv, Israel
| | - Byron H Smith
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | | | - Andrew J Bentall
- Departments of Surgery and Immunology, William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, MN
| | - Carrie A Schinstock
- Departments of Surgery and Immunology, William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, MN
| | | | | | - Tambi Jarmi
- Department of Transplant, Mayo Clinic Florida, Jacksonville, FL
| | - Shennen A Mao
- Division of Transplant Surgery, Department of Surgery, Mayo Clinic, Phoenix, AZ
| | - Walter D Park
- Departments of Surgery and Immunology, William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, MN
| | - Tayyab S Diwan
- Departments of Surgery and Immunology, William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, MN
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Mark D Stegall
- Departments of Surgery and Immunology, William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, MN
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15
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Baqal O, Habib EA, Hasabo EA, Galasso F, Barry T, Arsanjani R, Sweeney JP, Noseworthy P, David Fortuin F. Artificial intelligence-enabled electrocardiogram (AI-ECG) does not predict atrial fibrillation following patent foramen ovale closure. IJC HEART & VASCULATURE 2024; 51:101361. [PMID: 38379633 PMCID: PMC10877678 DOI: 10.1016/j.ijcha.2024.101361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 02/06/2024] [Accepted: 02/08/2024] [Indexed: 02/22/2024]
Abstract
Background Atrial fibrillation (AF) is a known complication following patent foramen ovale (PFO) closure. AI-enabled ECG (AI-ECG) acquired during normal sinus rhythm has been shown to identify individuals with AF by noting high-risk ECG features invisible to the human eye. We sought to characterize the value of AI-ECG in predicting AF development following PFO closure and investigate key clinical and procedural characteristics possibly associated with post-procedural AF. Methods We performed a retrospective analysis of patients who underwent PFO closure at our hospital from January 2011 to December 2022. We recorded the probability (%) of AF using the Mayo Clinic AI-ECG dashboard from pre- and post-procedure ECGs. The cut-off point of ≥ 11 %, which was found to optimally balance sensitivity and specificity in the original derivation paper (the Youden index) was used to label an AI-ECG "positive" for AF. Pre-procedural transesophageal echocardiography (TEE) and pre- and post-procedure transcranial doppler (TCD) data was also recorded. Results Out of 93 patients, 49 (53 %) were male, mean age was 55 ± 15 years with mean post-procedure follow up of 29 ± 3 months. Indication for PFO closure in 69 (74 %) patients was for secondary prevention of transient ischemic attack (TIA) and/or stroke. Twenty patients (22 %) developed paroxysmal AF post-procedure, with the majority within the first month post-procedure (15 patients, 75 %). Patients who developed AF were not significantly more likely to have a positive post-procedure AI-ECG than those who did not develop AF (30 % AF vs 27 % no AF, p = 0.8).Based on the PFO-Associated Stroke Causal Likelihood (PASCAL) classification, patients who had PFO closure for secondary prevention of TIA and/or stroke in the "possible" group were significantly more likely to develop AF than patients in "probable" and "unlikely" groups (p = 0.034). AF-developing patients were more likely to have post-procedure implantable loop recorder (ILR) (55 % vs 9.6 %, p < 0.001), and longer duration of ILR monitoring (121 vs 92.5 weeks, p = 0.035). There were no significant differences in TCD and TEE characteristics, device type, or device size between those who developed AF vs those who did not. Conclusions In this small, retrospective study, AI-ECG did not accurately distinguish patients who developed AF post-PFO closure from those who did not. Although AI-ECG has emerged as a valuable tool for risk prediction of AF, extrapolation of its performance to procedural settings such as PFO closure requires further investigation.
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Affiliation(s)
- Omar Baqal
- Department of Internal Medicine, Mayo Clinic, Phoenix, AZ, USA
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ, USA
| | - Eiad A. Habib
- Department of Internal Medicine, Mayo Clinic, Phoenix, AZ, USA
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ, USA
| | - Elfatih A. Hasabo
- CORRIB Research Centre for Advanced Imaging and Core Laboratory, Clinical Science Institute, University of Galway, Galway, Ireland
- Discipline of Cardiology, Saolta Healthcare Group, Health Service Executive, Galway University Hospital, Galway, Ireland
| | - Francesca Galasso
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ, USA
| | - Timothy Barry
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ, USA
| | - Reza Arsanjani
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ, USA
| | - John P. Sweeney
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ, USA
| | - Peter Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - F. David Fortuin
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ, USA
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16
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Muzammil MA, Javid S, Afridi AK, Siddineni R, Shahabi M, Haseeb M, Fariha FNU, Kumar S, Zaveri S, Nashwan AJ. Artificial intelligence-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseases. J Electrocardiol 2024; 83:30-40. [PMID: 38301492 DOI: 10.1016/j.jelectrocard.2024.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/28/2023] [Accepted: 01/22/2024] [Indexed: 02/03/2024]
Abstract
Electrocardiography (ECG), improved by artificial intelligence (AI), has become a potential technique for the precise diagnosis and treatment of cardiovascular disorders. The conventional ECG is a frequently used, inexpensive, and easily accessible test that offers important information about the physiological and anatomical state of the heart. However, the ECG can be interpreted differently by humans depending on the interpreter's level of training and experience, which could make diagnosis more difficult. Using AI, especially deep learning convolutional neural networks (CNNs), to look at single, continuous, and intermittent ECG leads that has led to fully automated AI models that can interpret the ECG like a human, possibly more accurately and consistently. These AI algorithms are effective non-invasive biomarkers for cardiovascular illnesses because they can identify subtle patterns and signals in the ECG that may not be readily apparent to human interpreters. The use of AI in ECG analysis has several benefits, including the quick and precise detection of problems like arrhythmias, silent cardiac illnesses, and left ventricular failure. It has the potential to help doctors with interpretation, diagnosis, risk assessment, and illness management. Aside from that, AI-enhanced ECGs have been demonstrated to boost the identification of heart failure and other cardiovascular disorders, particularly in emergency department settings, allowing for quicker and more precise treatment options. The use of AI in cardiology, however, has several limitations and obstacles, despite its potential. The effective implementation of AI-powered ECG analysis is limited by issues such as systematic bias. Biases based on age, gender, and race result from unbalanced datasets. A model's performance is impacted when diverse demographics are inadequately represented. Potentially disregarded age-related ECG variations may result from skewed age data in training sets. ECG patterns are affected by physiological differences between the sexes; a dataset that is inclined toward one sex may compromise the accuracy of the others. Genetic variations influence ECG readings, so racial diversity in datasets is significant. Furthermore, issues such as inadequate generalization, regulatory barriers, and interpretability concerns contribute to deployment difficulties. The lack of robustness in models when applied to disparate populations frequently hinders their practical applicability. The exhaustive validation required by regulatory requirements causes a delay in deployment. Difficult models that are not interpretable erode the confidence of clinicians. Diverse dataset curation, bias mitigation strategies, continuous validation across populations, and collaborative efforts for regulatory approval are essential for the successful deployment of AI ECG in clinical settings and must be undertaken to address these issues. To guarantee a safe and successful deployment in clinical practice, the use of AI in cardiology must be done with a thorough understanding of the algorithms and their limits. In summary, AI-enhanced electrocardiography has enormous potential to improve the management of cardiovascular illness by delivering precise and timely diagnostic insights, aiding clinicians, and enhancing patient outcomes. Further study and development are required to fully realize AI's promise for improving cardiology practices and patient care as technology continues to advance.
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Affiliation(s)
| | - Saman Javid
- CMH Kharian Medical College, Gujrat, Pakistan
| | | | | | | | | | - F N U Fariha
- Dow University of Health Sciences, Karachi, Pakistan
| | - Satesh Kumar
- Shaheed Mohtarma Benazir Bhutto Medical College, Karachi, Pakistan
| | - Sahil Zaveri
- Department of Medicine, SUNY Downstate Health Sciences University, New York, USA; Cardiovascular Research Program, VA New York Harbor Healthcare System, New York, USA
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17
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Raileanu G, de Jong JSSG. Electrocardiogram Interpretation Using Artificial Intelligence: Diagnosis of Cardiac and Extracardiac Pathologic Conditions. How Far Has Machine Learning Reached? Curr Probl Cardiol 2024; 49:102097. [PMID: 37739276 DOI: 10.1016/j.cpcardiol.2023.102097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 09/18/2023] [Indexed: 09/24/2023]
Abstract
Artificial intelligence (AI) is already widely used in different fields of medicine, making possible the integration of the paraclinical exams with the clinical findings in patients, for a more accurate and rapid diagnosis and treatment decision. The electrocardiogram remains one of the most important, fastest, cheapest, and noninvasive methods of diagnosis in cardiology, despite the rapid development and progression of the technology. Even if studied a long time ago, it still has a lot of less understood features that, with a better understanding, can give more clues to a correct and prompt diagnosis in a short time. The use of AI in the interpretation of the ECG improved the accuracy and the time to diagnosis in different cardiovascular diseases, and more than this, explaining the decision to make AI diagnosis improved the human understanding of the different features of the ECG that might be considered for a more accurate diagnosis. The purpose of this article is to provide an overview of the most recently published articles about the use of AI in ECG interpretation.
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Affiliation(s)
- Gabriela Raileanu
- Department of Cardiology, Onze Lieve Vrouwe Gasthuis, Amsterdam, The Netherlands.
| | - Jonas S S G de Jong
- Department of Cardiology, Onze Lieve Vrouwe Gasthuis, Amsterdam, The Netherlands
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18
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Melo L, Ciconte G, Christy A, Vicedomini G, Anastasia L, Pappone C, Grant E. Deep learning unmasks the ECG signature of Brugada syndrome. PNAS NEXUS 2023; 2:pgad327. [PMID: 37937270 PMCID: PMC10627411 DOI: 10.1093/pnasnexus/pgad327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 09/29/2023] [Indexed: 11/09/2023]
Abstract
One in 10 cases of sudden cardiac death strikes without warning as the result of an inherited arrhythmic cardiomyopathy, such as Brugada Syndrome (BrS). Normal physiological variations often obscure visible signs of this and related life-threatening channelopathies in conventional electrocardiograms (ECGs). Sodium channel blockers can reveal previously hidden diagnostic ECG features, however, their use carries the risk of life-threatening proarrhythmic side effects. The absence of a nonintrusive test places a grossly underestimated fraction of the population at risk of SCD. Here, we present a machine-learning algorithm that extracts, aligns, and classifies ECG waveforms for the presence of BrS. This protocol, which succeeds without the use of a sodium channel blocker (88.4% accuracy, 0.934 AUC in validation), can aid clinicians in identifying the presence of this potentially life-threatening heart disease.
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Affiliation(s)
- Luke Melo
- Department of Chemistry, University of British Columbia, Vancouver, BC V6T 1Z1, Canada
| | - Giuseppe Ciconte
- Arrhythmia and Electrophysiology Center, IRCCS Policlinico San Donato, Milan 20097, Italy
| | - Ashton Christy
- Department of Chemistry, University of British Columbia, Vancouver, BC V6T 1Z1, Canada
| | - Gabriele Vicedomini
- Arrhythmia and Electrophysiology Center, IRCCS Policlinico San Donato, Milan 20097, Italy
| | - Luigi Anastasia
- Stem Cell Laboratory for Tissue Engineering, Università Vita-Salute San Raffaele, Milan 20132, Italy
| | - Carlo Pappone
- Arrhythmia and Electrophysiology Center, IRCCS Policlinico San Donato, Milan 20097, Italy
- Department of Cardiology, Università Vita-Salute San Raffaele, Milan 20132, Italy
| | - Edward Grant
- Department of Chemistry, University of British Columbia, Vancouver, BC V6T 1Z1, Canada
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19
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Siontis KC, Suárez AB, Sehrawat O, Ackerman MJ, Attia ZI, Friedman PA, Noseworthy PA, Maanja M. Saliency maps provide insights into artificial intelligence-based electrocardiography models for detecting hypertrophic cardiomyopathy. J Electrocardiol 2023; 81:286-291. [PMID: 37599145 DOI: 10.1016/j.jelectrocard.2023.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 06/13/2023] [Accepted: 07/04/2023] [Indexed: 08/22/2023]
Abstract
INTRODUCTION A 12‑lead electrocardiography (ECG)-based convolutional neural network (CNN) model can detect hypertrophic cardiomyopathy (HCM). However, since these models do not rely on discrete measurements as inputs, it is not apparent what drives their performance. We hypothesized that saliency maps could be used to visually identify ECG segments that contribute to a CNN's robust classification of HCM. METHODS We derived a new one‑lead (lead I) CNN model based on median beats using the same methodology and cohort used for the original 12‑lead CNN model (3047 patients with HCM, and 63,926 sex- and age-matched non-HCM controls). One‑lead, median-beat saliency maps were generated and visually evaluated in an independent cohort of 100 patients with a diagnosis of HCM and a high artificial intelligence (AI)-ECG-HCM probability score to determine which ECG segments contributed to the model's detection of HCM. RESULTS The one‑lead, median-beat CNN had an AUC of 0.90 (95% CI 0.89-0.92) for HCM detection, similar to the original 12‑lead ECG model. In the independent HCM cohort (n = 100), saliency maps highlighted the ST-T segment in 92 ECGs, the atrial depolarization segment in 12 ECGs, and the QRS complex in 5 ECGs. CONCLUSIONS Saliency maps of a one‑lead, median-beat-based CNN model identified perturbations in ventricular repolarization as the main region of interest in detecting HCM.
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Affiliation(s)
| | | | - Ojasav Sehrawat
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Maren Maanja
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA; Department of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, Stockholm, Sweden.
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20
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Harmon DM, Mangold K, Baez Suarez A, Scott CG, Murphree DH, Malik A, Attia ZI, Lopez-Jimenez F, Friedman PA, Dispenzieri A, Grogan M. Postdevelopment Performance and Validation of the Artificial Intelligence-Enhanced Electrocardiogram for Detection of Cardiac Amyloidosis. JACC. ADVANCES 2023; 2:100612. [PMID: 38638999 PMCID: PMC11025724 DOI: 10.1016/j.jacadv.2023.100612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 05/29/2023] [Accepted: 07/07/2023] [Indexed: 04/20/2024]
Abstract
BACKGROUND We have previously applied artificial intelligence (AI) to an electrocardiogram (ECG) to detect cardiac amyloidosis (CA). OBJECTIVES In this validation study, the authors observe the postdevelopment performance of the AI-enhanced ECG to detect CA with respect to multiple potential confounders. METHODS Amyloid patients diagnosed after algorithm development (June 2019-January 2022) with a 12-lead ECG were identified (n = 440) and were required to have CA. A 15:1 age- and sex-matched control group was identified (n = 6,600). Area under the receiver operating characteristic (AUC) was determined for the cohort and subgroups. RESULTS The average age was 70.4 ± 10.3 years, 25.0% were female, and most patients were White (91.3%). In this validation, the AI-ECG for amyloidosis had an AUC of 0.84 (95% CI: 0.82-0.86) for the overall cohort and between amyloid subtypes, which is a slight decrease from the original study (AUC 0.91). White, Black, and patients of "other" races had similar algorithm performance (AUC >0.81) with a decreased performance for Hispanic patients (AUC 0.66). Algorithm performance shift over time was not observed. Low ECG voltage and infarct pattern exhibited high AUC (>0.90), while left ventricular hypertrophy and left bundle branch block demonstrated lesser performance (AUC 0.75 and 0.76, respectively). CONCLUSIONS The AI-ECG for the detection of CA maintained an overall strong performance with respect to patient age, sex, race, and amyloid subtype. Lower performance was noted in left bundle branch block, left ventricular hypertrophy, and ethnically diverse populations emphasizing the need for subgroup-specific validation efforts.
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Affiliation(s)
- David M. Harmon
- Department of Internal Medicine, Mayo Clinic School of Graduate Medical Education, Rochester, Minnesota, USA
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | - Kathryn Mangold
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | - Abraham Baez Suarez
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | - Christopher G. Scott
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Dennis H. Murphree
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Awais Malik
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | - Zachi I. Attia
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Francisco Lopez-Jimenez
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | - Paul A. Friedman
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | - Angela Dispenzieri
- Division of Hematology, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Martha Grogan
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
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21
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Sau A, Ng FS. --The emerging role of artificial intelligence enabled electrocardiograms in healthcare. BMJ MEDICINE 2023; 2:e000193. [PMID: 37841970 PMCID: PMC10568120 DOI: 10.1136/bmjmed-2022-000193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 06/01/2023] [Indexed: 10/17/2023]
Affiliation(s)
- Arunashis Sau
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - Fu Siong Ng
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
- Department of Cardiology, Chelsea and Westminster Hospital NHS Foundation Trust, London, UK
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22
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Martínez-Sellés M, Marina-Breysse M. Current and Future Use of Artificial Intelligence in Electrocardiography. J Cardiovasc Dev Dis 2023; 10:jcdd10040175. [PMID: 37103054 PMCID: PMC10145690 DOI: 10.3390/jcdd10040175] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 04/14/2023] [Accepted: 04/16/2023] [Indexed: 04/28/2023] Open
Abstract
Artificial intelligence (AI) is increasingly used in electrocardiography (ECG) to assist in diagnosis, stratification, and management. AI algorithms can help clinicians in the following areas: (1) interpretation and detection of arrhythmias, ST-segment changes, QT prolongation, and other ECG abnormalities; (2) risk prediction integrated with or without clinical variables (to predict arrhythmias, sudden cardiac death, stroke, and other cardiovascular events); (3) monitoring ECG signals from cardiac implantable electronic devices and wearable devices in real time and alerting clinicians or patients when significant changes occur according to timing, duration, and situation; (4) signal processing, improving ECG quality and accuracy by removing noise/artifacts/interference, and extracting features not visible to the human eye (heart rate variability, beat-to-beat intervals, wavelet transforms, sample-level resolution, etc.); (5) therapy guidance, assisting in patient selection, optimizing treatments, improving symptom-to-treatment times, and cost effectiveness (earlier activation of code infarction in patients with ST-segment elevation, predicting the response to antiarrhythmic drugs or cardiac implantable devices therapies, reducing the risk of cardiac toxicity, etc.); (6) facilitating the integration of ECG data with other modalities (imaging, genomics, proteomics, biomarkers, etc.). In the future, AI is expected to play an increasingly important role in ECG diagnosis and management, as more data become available and more sophisticated algorithms are developed.
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Affiliation(s)
- Manuel Martínez-Sellés
- Cardiology Department, Hospital General Universitario Gregorio Marañón, Calle Doctor Esquerdo, 46, 28007 Madrid, Spain
- Centro de Investigación Biomédica en Red-Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III, 28029 Madrid, Spain
- Facultad de Ciencias de la Salud, Universidad Europea, Villaviciosa de Odón, 28670 Madrid, Spain
- Facultad de Medicina, Universidad Complutense, 28040 Madrid, Spain
| | - Manuel Marina-Breysse
- Centro de Investigación Biomédica en Red-Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III, 28029 Madrid, Spain
- IDOVEN Research, 28013 Madrid, Spain
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Myocardial Pathophysiology Area, 28029 Madrid, Spain
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23
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Jimenez-Zepeda V, Bril V, Lemieux-Blanchard E, Royal V, McCurdy A, Schwartz D, Davis MK. A Comprehensive Multidisciplinary Diagnostic Algorithm for the Early and Efficient Detection of Amyloidosis. CLINICAL LYMPHOMA, MYELOMA & LEUKEMIA 2023; 23:194-202. [PMID: 36653205 DOI: 10.1016/j.clml.2022.12.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 12/16/2022] [Accepted: 12/21/2022] [Indexed: 12/25/2022]
Abstract
Amyloidosis is a rare protein misfolding disease caused by the accumulation of amyloid fibrils in various tissues and organs. There are different subtypes of amyloidosis, with light chain (AL) amyloidosis being the most common. Amyloidosis is notoriously difficult to diagnose because it is clinically heterogeneous, no single test is diagnostic for the disease, and diagnosis typically involves multiple specialists. Here, we propose an integrated, multidisciplinary algorithm for efficiently diagnosing amyloidosis. Drawing on research from several medical disciplines, we have combined clinical decisions and best practices into a comprehensive algorithm to facilitate the early detection of amyloidosis. Currently, many patients are diagnosed more than 6 months after symptom onset, yet early diagnosis is the major predictor of survival. Our algorithm aims to shorten the time to diagnosis with efficient sequencing of tests and minimizing uninformative investigations. We also recommend typing and staging of confirmed amyloidosis to guide treatment. By reducing time to diagnosis, our algorithm could lead to earlier and more targeted treatment, ultimately improving prognosis and survival.
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Affiliation(s)
- Victor Jimenez-Zepeda
- Department of Hematology, University of Calgary and Arnie Charbonneau Cancer Institute, Calgary, Alberta, Canada.
| | - Vera Bril
- Division of Neurology, Department of Medicine, University of Toronto and University Health Network, Toranto, Ontario, Canada
| | - Emilie Lemieux-Blanchard
- Department of Hematology, Service d'hématologie-oncologie du Centre hospitalier de l'Université de Montréal and Centre de recherche du CHUM, Montreal, Quebec, Canada
| | - Virginie Royal
- Department of Pathology, Hôpital Maisonneuve-Rosemont, Université de Montreal, Montreal, Quebec, Canada
| | - Arleigh McCurdy
- Division of Hematology, The Ottawa Hospital and University of Ottawa, Ottawa, Ontario, Canada
| | - Daniel Schwartz
- Faculty of Medicine, Division of Nephrology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Margot K Davis
- Division of Cardiology, University of British Columbia, Vancouver, British Columbia, Canada
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24
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Attia ZI, Friedman PA. Explainable AI for ECG-based prediction of cardiac resynchronization therapy outcomes: learning from machine learning? Eur Heart J 2023; 44:693-695. [PMID: 36546617 DOI: 10.1093/eurheartj/ehac733] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Affiliation(s)
- Zachi I Attia
- Mayo Clinic Minnesota, Cardiovascular Diseases, 200 1st st SW, Rochester, MN 55901, USA
| | - Paul A Friedman
- Mayo Clinic Minnesota, Cardiovascular Diseases, 200 1st st SW, Rochester, MN 55901, USA
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25
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Denysyuk HV, Pinto RJ, Silva PM, Duarte RP, Marinho FA, Pimenta L, Gouveia AJ, Gonçalves NJ, Coelho PJ, Zdravevski E, Lameski P, Leithardt V, Garcia NM, Pires IM. Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review. Heliyon 2023; 9:e13601. [PMID: 36852052 PMCID: PMC9958295 DOI: 10.1016/j.heliyon.2023.e13601] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 01/31/2023] [Accepted: 02/05/2023] [Indexed: 02/12/2023] Open
Abstract
The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient's autonomy.
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Key Words
- AI, Artificial Intelligence
- BNN, Binarized Neural Network
- CNN, Concolutional Neural Networks
- Cardiovascular diseases
- DL, Deep Learning
- DNN, Deep Neural Networks
- Diagnosis
- ECG sensors
- ECG, Electrocardiography
- GAN, Generative Adversarial Networks
- GMM, Gaussian Mixture Model
- GNB, Gaussian Naive bayes
- GRU, Gated Recurrent Unit
- LASSO, Least Absolute Shrinkage and Selection Operator
- LDA, Linear Discriminant Analysis
- LR, Linear Regression
- LSTM, Long Short-Term Memory
- ML, Machine Learning
- MLP, Multiplayer Perceptron
- MLR, Multiple Linear Regression
- NLP, Natural Language Processing
- POAF, Postoperative Atrial Fibrillation
- RF, Random Forest
- RNN, Recurrent Neural Network
- SHAP, SHapley Additive exPlanations
- SVM, Support Vector Machine
- Systematic review
- WHO, World Health Organization
- kNN, k-nearest neighbors
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Affiliation(s)
| | - Rui João Pinto
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Pedro Miguel Silva
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Rui Pedro Duarte
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Francisco Alexandre Marinho
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Luís Pimenta
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - António Jorge Gouveia
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Norberto Jorge Gonçalves
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Paulo Jorge Coelho
- Polytechnic of Leiria, Leiria, Portugal
- Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra), Coimbra, Portugal
| | - Eftim Zdravevski
- Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, Macedonia
| | - Petre Lameski
- Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, Macedonia
| | - Valderi Leithardt
- VALORIZA, Research Center for Endogenous Resources Valorization, Instituto Politécnico de Portalegre, 7300-555 Portalegre, Portugal
- COPELABS, Universidade Lusófona de Humanidades e Tecnologias, Lisboa, Portugal
| | - Nuno M. Garcia
- Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
| | - Ivan Miguel Pires
- Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
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26
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Caponetti AG, Accietto A, Saturi G, Ponziani A, Sguazzotti M, Massa P, Giovannetti A, Ditaranto R, Parisi V, Leone O, Guaraldi P, Cortelli P, Gagliardi C, Longhi S, Galiè N, Biagini E. Screening approaches to cardiac amyloidosis in different clinical settings: Current practice and future perspectives. Front Cardiovasc Med 2023; 10:1146725. [PMID: 36970351 PMCID: PMC10033591 DOI: 10.3389/fcvm.2023.1146725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 02/20/2023] [Indexed: 03/29/2023] Open
Abstract
Cardiac amyloidosis is a serious and progressive infiltrative disease caused by the deposition of amyloid fibrils in the heart. In the last years, a significant increase in the diagnosis rate has been observed owing to a greater awareness of its broad clinical presentation. Cardiac amyloidosis is frequently associated to specific clinical and instrumental features, so called "red flags", and it appears to occur more commonly in particular clinical settings such as multidistrict orthopedic conditions, aortic valve stenosis, heart failure with preserved or mildly reduced ejection fraction, arrhythmias, plasma cell disorders. Multimodality approach and new developed techniques such PET fluorine tracers or artificial intelligence may contribute to strike up extensive screening programs for an early recognition of the disease.
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Affiliation(s)
- Angelo Giuseppe Caponetti
- Cardiology Unit, Cardiac Thoracic and Vascular Department, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
| | - Antonella Accietto
- Cardiology Unit, Cardiac Thoracic and Vascular Department, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
| | - Giulia Saturi
- Cardiology Unit, Cardiac Thoracic and Vascular Department, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
| | - Alberto Ponziani
- Cardiology Unit, Cardiac Thoracic and Vascular Department, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
| | - Maurizio Sguazzotti
- Cardiology Unit, Cardiac Thoracic and Vascular Department, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
| | - Paolo Massa
- Cardiology Unit, Cardiac Thoracic and Vascular Department, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
| | - Alessandro Giovannetti
- Cardiology Unit, Cardiac Thoracic and Vascular Department, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
| | - Raffaello Ditaranto
- Cardiology Unit, Cardiac Thoracic and Vascular Department, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
| | - Vanda Parisi
- Cardiology Unit, Cardiac Thoracic and Vascular Department, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
| | - Ornella Leone
- Department of Pathology, Cardiovascular and Cardiac Transplant Pathology Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Pietro Guaraldi
- IRCCS Istituto Delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Pietro Cortelli
- IRCCS Istituto Delle Scienze Neurologiche di Bologna, Bologna, Italy
- Department of Biomedical and NeuroMotor Sciences (DiBiNeM), Alma Mater Studiorum-University of Bologna, Bologna, Italy
| | - Christian Gagliardi
- Cardiology Unit, Cardiac Thoracic and Vascular Department, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
- European Reference Network for Rare, Low Prevalence and Complex Diseases of the Heart-ERN GUARD-Heart, Bologna, Italy
| | - Simone Longhi
- Cardiology Unit, Cardiac Thoracic and Vascular Department, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
- European Reference Network for Rare, Low Prevalence and Complex Diseases of the Heart-ERN GUARD-Heart, Bologna, Italy
| | - Nazzareno Galiè
- Cardiology Unit, Cardiac Thoracic and Vascular Department, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
| | - Elena Biagini
- Cardiology Unit, Cardiac Thoracic and Vascular Department, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
- European Reference Network for Rare, Low Prevalence and Complex Diseases of the Heart-ERN GUARD-Heart, Bologna, Italy
- Correspondence: Elena Biagini
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27
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Zanwar S, Gertz MA, Muchtar E. Immunoglobulin Light Chain Amyloidosis: Diagnosis and Risk Assessment. J Natl Compr Canc Netw 2023; 21:83-90. [PMID: 36630897 PMCID: PMC10164359 DOI: 10.6004/jnccn.2022.7077] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 09/22/2022] [Indexed: 01/13/2023]
Abstract
Immunoglobulin light chain (AL) amyloidosis is a clonal plasma cell disorder with multiple clinical presentations. The diagnosis of AL amyloidosis requires a high index of suspicion, making a delay in diagnosis common, which contributes to the high early mortality seen in this disease. Establishing the diagnosis of AL amyloidosis requires the demonstration of tissue deposition of amyloid fibrils. A bone marrow biopsy and fat pad aspirate performed concurrently have a high sensitivity for the diagnosis of AL amyloidosis and negate the need for organ biopsies in most patients. An accurate diagnosis requires amyloid typing via additional testing, including tissue mass spectrometry. Prognostication for AL amyloidosis is largely driven by the organs impacted. Cardiac involvement represents the single most important prognostic marker, and the existing staging systems are driven by cardiac biomarkers. Apart from organ involvement, plasma cell percentage on the bone marrow biopsy, specific fluorescence in situ hybridization findings, age at diagnosis, and performance status are important prognostic markers. This review elaborates on the diagnostic testing and prognostication for patients with newly diagnosed AL amyloidosis.
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Affiliation(s)
- Saurabh Zanwar
- Division of Hematology, Mayo Clinic, Rochester, Minnesota
| | - Morie A Gertz
- Division of Hematology, Mayo Clinic, Rochester, Minnesota
| | - Eli Muchtar
- Division of Hematology, Mayo Clinic, Rochester, Minnesota
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28
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Attia ZI, Harmon DM, Dugan J, Manka L, Lopez-Jimenez F, Lerman A, Siontis KC, Noseworthy PA, Yao X, Klavetter EW, Halamka JD, Asirvatham SJ, Khan R, Carter RE, Leibovich BC, Friedman PA. Prospective evaluation of smartwatch-enabled detection of left ventricular dysfunction. Nat Med 2022; 28:2497-2503. [PMID: 36376461 PMCID: PMC9805528 DOI: 10.1038/s41591-022-02053-1] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 09/23/2022] [Indexed: 11/16/2022]
Abstract
Although artificial intelligence (AI) algorithms have been shown to be capable of identifying cardiac dysfunction, defined as ejection fraction (EF) ≤ 40%, from 12-lead electrocardiograms (ECGs), identification of cardiac dysfunction using the single-lead ECG of a smartwatch has yet to be tested. In the present study, a prospective study in which patients of Mayo Clinic were invited by email to download a Mayo Clinic iPhone application that sends watch ECGs to a secure data platform, we examined patient engagement with the study app and the diagnostic utility of the ECGs. We digitally enrolled 2,454 unique patients (mean age 53 ± 15 years, 56% female) from 46 US states and 11 countries, who sent 125,610 ECGs to the data platform between August 2021 and February 2022; 421 participants had at least one watch-classified sinus rhythm ECG within 30 d of an echocardiogram, of whom 16 (3.8%) had an EF ≤ 40%. The AI algorithm detected patients with low EF with an area under the curve of 0.885 (95% confidence interval 0.823-0.946) and 0.881 (0.815-0.947), using the mean prediction within a 30-d window or the closest ECG relative to the echocardiogram that determined the EF, respectively. These findings indicate that consumer watch ECGs, acquired in nonclinical environments, can be used to identify patients with cardiac dysfunction, a potentially life-threatening and often asymptomatic condition.
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Affiliation(s)
- Zachi I. Attia
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - David M. Harmon
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN, USA.,Department of Internal Medicine, Mayo Clinic School of Graduate Medical Education, Rochester, MN, USA
| | - Jennifer Dugan
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Lukas Manka
- Center for Digital Health, Mayo Clinic, Rochester, MN, USA
| | | | - Amir Lerman
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN, USA
| | | | - Peter A. Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Xiaoxi Yao
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN, USA.,Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | - Eric W. Klavetter
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN, USA
| | | | - Samuel J. Asirvatham
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Rita Khan
- Center for Digital Health, Mayo Clinic, Rochester, MN, USA
| | - Rickey E. Carter
- Department of Quantitative Health Sciences, Jacksonville, FL, USA
| | - Bradley C. Leibovich
- Center for Digital Health, Mayo Clinic, Rochester, MN, USA.,Department of Urology, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Paul A. Friedman
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN, USA.,Correspondence and requests for materials should be addressed to Paul A. Friedman.,
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29
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Fatunde OA, Fonseca R, Rosenthal JL. Raise the Flag. JACC CardioOncol 2022; 4:471-473. [DOI: 10.1016/j.jaccao.2022.10.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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30
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Davies DR, Redfield MM, Scott CG, Minamisawa M, Grogan M, Dispenzieri A, Chareonthaitawee P, Shah AM, Shah SJ, Wehbe RM, Solomon SD, Reddy YNV, Borlaug BA, AbouEzzeddine OF. A Simple Score to Identify Increased Risk of Transthyretin Amyloid Cardiomyopathy in Heart Failure With Preserved Ejection Fraction. JAMA Cardiol 2022; 7:1036-1044. [PMID: 36069809 PMCID: PMC9453635 DOI: 10.1001/jamacardio.2022.1781] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 05/09/2022] [Indexed: 11/14/2022]
Abstract
Importance Transthyretin amyloid cardiomyopathy (ATTR-CM) is a form of heart failure (HF) with preserved ejection fraction (HFpEF). Technetium Tc 99m pyrophosphate scintigraphy (PYP) enables ATTR-CM diagnosis. It is unclear which patients with HFpEF have sufficient risk of ATTR-CM to warrant PYP. Objective To derive and validate a simple ATTR-CM score to predict increased risk of ATTR-CM in patients with HFpEF. Design, Setting, and Participants Retrospective cohort study of 666 patients with HF (ejection fraction ≥ 40%) and suspected ATTR-CM referred for PYP at Mayo Clinic, Rochester, Minnesota, from May 10, 2013, through August 31, 2020. These data were analyzed September 2020 through December 2020. A logistic regression model predictive of ATTR-CM was derived and converted to a point-based ATTR-CM risk score. The score was further validated in a community ATTR-CM epidemiology study of older patients with HFpEF with increased left ventricular wall thickness ([WT] ≥ 12 mm) and in an external (Northwestern University, Chicago, Illinois) HFpEF cohort referred for PYP. Race was self-reported by the participants. In all cohorts, both case patients and control patients were definitively ascertained by PYP scanning and specialist evaluation. Main Outcomes and Measures Performance of the derived ATTR-CM score in all cohorts (referral validation, community validation, and external validation) and prevalence of a high-risk ATTR-CM score in 4 multinational HFpEF clinical trials. Results Participant cohorts included were referral derivation (n = 416; 13 participants [3%] were Black and 380 participants [94%] were White; ATTR-CM prevalence = 45%), referral validation (n = 250; 12 participants [5%]were Black and 228 participants [93%] were White; ATTR-CM prevalence = 48% ), community validation (n = 286; 5 participants [2%] were Black and 275 participants [96%] were White; ATTR-CM prevalence = 6% ), and external validation (n = 66; 23 participants [37%] were Black and 36 participants [58%] were White; ATTR-CM prevalence = 39%). Score variables included age, male sex, hypertension diagnosis, relative WT more than 0.57, posterior WT of 12 mm or more, and ejection fraction less than 60% (score range -1 to 10). Discrimination (area under the receiver operating characteristic curve [AUC] 0.89; 95% CI, 0.86-0.92; P < .001) and calibration (Hosmer-Lemeshow; χ2 = 4.6; P = .46) were strong. Discrimination (AUC ≥ 0.84; P < .001 for all) and calibration (Hosmer-Lemeshow χ2 = 2.8; P = .84; Hosmer-Lemeshow χ2 = 4.4; P = .35; Hosmer-Lemeshow χ2 = 2.5; P = .78 in referral, community, and external validation cohorts, respectively) were maintained in all validation cohorts. Precision-recall curves and predictive value vs prevalence plots indicated clinically useful classification performance for a score of 6 or more (positive predictive value ≥25%) in clinically relevant ATTR-CM prevalence (≥10% of patients with HFpEF) scenarios. In the HFpEF clinical trials, 11% to 35% of male and 0% to 6% of female patients had a high-risk (≥6) ATTR-CM score. Conclusions and Relevance A simple 6 variable clinical score may be used to guide use of PYP and increase recognition of ATTR-CM among patients with HFpEF in the community. Further validation in larger and more diverse populations is needed.
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Affiliation(s)
- Daniel R. Davies
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | | | - Christopher G. Scott
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Masatoshi Minamisawa
- Cardiovascular Division, Brigham and Women’s Hospital, Boston, Massachusetts
- Department of Cardiovascular Medicine, Shinshu University Hospital, Matsumoto, Nagano, Japan
| | - Martha Grogan
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - Angela Dispenzieri
- Division of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | | | - Amil M. Shah
- Cardiovascular Division, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Sanjiv J. Shah
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Ramsey M. Wehbe
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Scott D. Solomon
- Cardiovascular Division, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Yogesh N. V. Reddy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - Barry A. Borlaug
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
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31
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Klein CJ, Ozcan I, Attia ZI, Cohen-Shelly M, Lerman A, Medina-Inojosa JR, Lopez-Jimenez F, Friedman PA, Milone M, Shelly S. Electrocardiogram-Artificial Intelligence and Immune-Mediated Necrotizing Myopathy: Predicting Left Ventricular Dysfunction and Clinical Outcomes. Mayo Clin Proc Innov Qual Outcomes 2022; 6:450-457. [PMID: 36147867 PMCID: PMC9485848 DOI: 10.1016/j.mayocpiqo.2022.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Objective To characterize the utility of an existing electrocardiogram (ECG)-artificial intelligence (AI) algorithm of left ventricular dysfunction (LVD) in immune-mediated necrotizing myopathy (IMNM). Patients and Methods A retrospective cohort observational study was conducted within our tertiary-care neuromuscular clinic for patients with IMNM meeting European Neuromuscular Centre diagnostic criteria (January 1, 2000, to December 31, 2020). A validated AI algorithm using 12-lead standard ECGs to detect LVD was applied. The output was presented as a percent probability of LVD. Electrocardiograms before and while on immunotherapy were reviewed. The LVD-predicted probability scores were compared with echocardiograms, immunotherapy treatment response, and mortality. Results The ECG-AI algorithm had acceptable accuracy in LVD prediction in 74% (68 of 89) of patients with IMNM with available echocardiograms (discrimination threshold, 0.74; 95% CI, 0.6-0.87). This translates into a sensitivity of 80.0% and specificity of 62.8% to detect LVD. Best cutoff probability prediction was 7 times more likely to have LVD (odds ratio, 6.75; 95% CI, 2.11-21.51; P=.001). Early detection occurred in 18% (16 of 89) of patients who initially had normal echocardiograms and were without cardiorespiratory symptoms, of which 6 subsequently advanced to LVD cardiorespiratory failure. The LVD probability scores improved for patients on immunotherapy (median slope, −3.96; R = −0.12; P=.002). Mortality risk was 7 times greater with abnormal LVD probability scores (hazard ratio, 7.33; 95% CI, 1.63-32.88; P=.009). Conclusion In IMNM, an AI-ECG algorithm assists detection of LVD, enhancing the decision to advance to echocardiogram testing, while also informing on mortality risk, which is important in the decision of immunotherapy escalation and monitoring.
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Key Words
- AI, artificial intelligence
- ASCVD, atherosclerotic cardiovascular disease
- AUC, area under the curve
- CK, creatine kinase
- CNN, convolutional neural network
- ECG, electrocardiogram
- IIM, idiopathic immune-mediated myopathy
- IMNM, immune-mediated necrotizing myopathy
- LVD, left ventricular dysfunction
- MRI, magnetic resonance imaging
- OR, odds ratio
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Affiliation(s)
- Christopher J Klein
- Department of Neurology, Mayo Clinic, Rochester, MN.,Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Ilke Ozcan
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Michal Cohen-Shelly
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN.,Sami Sagol AI Hub, ARC, Sheba Medical Center, Israel
| | - Amir Lerman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | | | | | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | | | - Shahar Shelly
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN.,Department of Neurology, Rambam Health Care Campus, Haifa, Israel.,Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
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32
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Abstract
Artificial intelligence (AI) and machine learning (ML) have significantly impacted the field of cardiovascular medicine, especially cardiac electrophysiology (EP), on multiple fronts. The goal of this review is to familiarize readers with the field of AI and ML and their emerging role in EP. The current review is divided into 3 sections. In the first section, we discuss the definitions and basics of AI, ML, and big data. In the second section, we discuss their application to EP in the context of detection, prediction, and management of arrhythmias. Finally, we discuss the regulatory issues, challenges, and future directions of AI in EP.
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33
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Haq IU, Chhatwal K, Sanaka K, Xu B. Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects. Vasc Health Risk Manag 2022; 18:517-528. [PMID: 35855754 PMCID: PMC9288176 DOI: 10.2147/vhrm.s279337] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 07/07/2022] [Indexed: 11/23/2022] Open
Abstract
Cardiovascular disease (CVD) represents a significant and increasing burden on healthcare systems. Artificial intelligence (AI) is a rapidly evolving transdisciplinary field employing machine learning (ML) techniques, which aim to simulate human intuition to offer cost-effective and scalable solutions to better manage CVD. ML algorithms are increasingly being developed and applied in various facets of cardiovascular medicine, including and not limited to heart failure, electrophysiology, valvular heart disease and coronary artery disease. Within heart failure, AI algorithms can augment diagnostic capabilities and clinical decision-making through automated cardiac measurements. Occult cardiac disease is increasingly being identified using ML from diagnostic data. Improved diagnostic and prognostic capabilities using ML algorithms are enhancing clinical care of patients with valvular heart disease and coronary artery disease. The growth of AI techniques is not without inherent challenges, most important of which is the need for greater external validation through multicenter, prospective clinical trials.
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Affiliation(s)
- Ikram U Haq
- Department of Internal Medicine, Mayo Clinic, Rochester, MN, 55905, USA
| | | | | | - Bo Xu
- Section of Cardiovascular Imaging, Robert and Suzanne Tomsich Department of Cardiovascular Medicine, Sydell and Arnold Miller Family Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
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34
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Madan N, Lucas J, Akhter N, Collier P, Cheng F, Guha A, Zhang L, Sharma A, Hamid A, Ndiokho I, Wen E, Garster NC, Scherrer-Crosbie M, Brown SA. Artificial intelligence and imaging: Opportunities in cardio-oncology. AMERICAN HEART JOURNAL PLUS : CARDIOLOGY RESEARCH AND PRACTICE 2022; 15:100126. [PMID: 35693323 PMCID: PMC9187287 DOI: 10.1016/j.ahjo.2022.100126] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/20/2022] [Accepted: 03/21/2022] [Indexed: 12/29/2022]
Abstract
Cardiovascular disease is a leading cause of death in cancer survivors. It is critical to apply new predictive and early diagnostic methods in this population, as this can potentially inform cardiovascular treatment and surveillance decision-making. We discuss the application of artificial intelligence (AI) technologies to cardiovascular imaging in cardio-oncology, with a particular emphasis on prevention and targeted treatment of a variety of cardiovascular conditions in cancer patients. Recently, the use of AI-augmented cardiac imaging in cardio-oncology is gaining traction. A large proportion of cardio-oncology patients are screened and followed using left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS), currently obtained using echocardiography. This use will continue to increase with new cardiotoxic cancer treatments. AI is being tested to increase precision, throughput, and accuracy of LVEF and GLS, guide point-of-care image acquisition, and integrate imaging and clinical data to optimize the prediction and detection of cardiac dysfunction. The application of AI to cardiovascular magnetic resonance imaging (CMR), computed tomography (CT; especially coronary artery calcium or CAC scans), single proton emission computed tomography (SPECT) and positron emission tomography (PET) imaging acquisition is also in early stages of analysis for prediction and assessment of cardiac tumors and cardiovascular adverse events in patients treated for childhood or adult cancer. The opportunities for application of AI in cardio-oncology imaging are promising, and if availed, will improve clinical practice and benefit patient care.
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Affiliation(s)
- Nidhi Madan
- Division of Cardiology, Rush University Medical Center, Chicago, IL, USA
| | | | - Nausheen Akhter
- Division of Cardiology, Northwestern University, Chicago, IL, USA
| | - Patrick Collier
- Robert and Suzanne Tomsich Department of Cardiovascular Medicine, Sydell and Arnold Miller Family Heart and Vascular Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Avirup Guha
- Harrington Heart and Vascular Institute, Cleveland, OH, USA
| | - Lili Zhang
- Cardio-Oncology Program, Division of Cardiology, Department of Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Abhinav Sharma
- Division of Cardiovascular Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - Imeh Ndiokho
- Medical College of Wisconsin, Milwaukee, WI, USA
| | - Ethan Wen
- Medical College of Wisconsin, Milwaukee, WI, USA
| | - Noelle C. Garster
- Division of Cardiovascular Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - Sherry-Ann Brown
- Cardio-Oncology Program, Division of Cardiovascular Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
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35
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Adedinsewo DA, Pollak AW, Phillips SD, Smith TL, Svatikova A, Hayes SN, Mulvagh SL, Norris C, Roger VL, Noseworthy PA, Yao X, Carter RE. Cardiovascular Disease Screening in Women: Leveraging Artificial Intelligence and Digital Tools. Circ Res 2022; 130:673-690. [PMID: 35175849 PMCID: PMC8889564 DOI: 10.1161/circresaha.121.319876] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Cardiovascular disease remains the leading cause of death in women. Given accumulating evidence on sex- and gender-based differences in cardiovascular disease development and outcomes, the need for more effective approaches to screening for risk factors and phenotypes in women is ever urgent. Public health surveillance and health care delivery systems now continuously generate massive amounts of data that could be leveraged to enable both screening of cardiovascular risk and implementation of tailored preventive interventions across a woman's life span. However, health care providers, clinical guidelines committees, and health policy experts are not yet sufficiently equipped to optimize the collection of data on women, use or interpret these data, or develop approaches to targeting interventions. Therefore, we provide a broad overview of the key opportunities for cardiovascular screening in women while highlighting the potential applications of artificial intelligence along with digital technologies and tools.
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Affiliation(s)
- Demilade A. Adedinsewo
- Department of Cardiovascular Medicine (D.A.A., A.W.P., S.D.P.), Mayo Clinic, Jacksonville, FL
| | - Amy W. Pollak
- Department of Cardiovascular Medicine (D.A.A., A.W.P., S.D.P.), Mayo Clinic, Jacksonville, FL
| | - Sabrina D. Phillips
- Department of Cardiovascular Medicine (D.A.A., A.W.P., S.D.P.), Mayo Clinic, Jacksonville, FL
| | - Taryn L. Smith
- Division of General Internal Medicine (T.L.S.), Mayo Clinic, Jacksonville, FL
| | - Anna Svatikova
- Department of Cardiovascular Diseases (A.S.), Mayo Clinic, Phoenix, AZ
| | - Sharonne N. Hayes
- Department of Cardiovascular Medicine (S.N.H., S.L.M., V.L.R., P.A.N.), Mayo Clinic, Rochester, MN
| | - Sharon L. Mulvagh
- Department of Cardiovascular Medicine (S.N.H., S.L.M., V.L.R., P.A.N.), Mayo Clinic, Rochester, MN
- Division of Cardiology, Dalhousie University, Halifax, Nova Scotia, Canada (S.L.M.)
| | - Colleen Norris
- Cardiovascular Health and Stroke Strategic Clinical Network, Edmonton, Canada (C.N.)
| | - Veronique L. Roger
- Department of Cardiovascular Medicine (S.N.H., S.L.M., V.L.R., P.A.N.), Mayo Clinic, Rochester, MN
- Department of Quantitative Health Sciences (V.L.R.), Mayo Clinic, Rochester, MN
- Epidemiology and Community Health Branch, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD (V.L.R.)
| | - Peter A. Noseworthy
- Department of Cardiovascular Medicine (S.N.H., S.L.M., V.L.R., P.A.N.), Mayo Clinic, Rochester, MN
| | - Xiaoxi Yao
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery (X.Y.), Mayo Clinic, Rochester, MN
| | - Rickey E. Carter
- Department of Quantitative Health Sciences (R.E.C.), Mayo Clinic, Jacksonville, FL
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36
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Vardas PE, Asselbergs FW, van Smeden M, Friedman P. The year in cardiovascular medicine 2021: digital health and innovation. Eur Heart J 2022; 43:271-279. [PMID: 34974610 DOI: 10.1093/eurheartj/ehab874] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/15/2021] [Accepted: 11/23/2021] [Indexed: 12/15/2022] Open
Abstract
This article presents some of the most important developments in the field of digital medicine that have appeared over the last 12 months and are related to cardiovascular medicine. The article consists of three main sections, as follows: (i) artificial intelligence-enabled cardiovascular diagnostic tools, techniques, and methodologies, (ii) big data and prognostic models for cardiovascular risk protection, and (iii) wearable devices in cardiovascular risk assessment, cardiovascular disease prevention, diagnosis, and management. To conclude the article, the authors present a brief further prospective on this new domain, highlighting existing gaps that are specifically related to artificial intelligence technologies, such as explainability, cost-effectiveness, and, of course, the importance of proper regulatory oversight for each clinical implementation.
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Affiliation(s)
- Panos E Vardas
- Heart Sector, Hygeia Hospitals Group, HHG, 5, Erithrou Stavrou, Marousi, Athens 15123, Greece.,European Heart Agency, ESC, Brussels, Belgium
| | - Folkert W Asselbergs
- Department of Cardiology, Division of Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.,Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Paul Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
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37
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Herrmann J, López-Fernández T, Lyon AR. Year in cardiovascular medicine: cardio-oncology 2020-21. Eur Heart J 2022:ehab891. [PMID: 34974609 DOI: 10.1093/eurheartj/ehab891] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 12/16/2021] [Indexed: 12/27/2022] Open
Affiliation(s)
- Joerg Herrmann
- Department of Cardiovascular Diseases, Mayo Clinic, 200 First Street SW, Rochester, MN 55902, USA
| | - Teresa López-Fernández
- Division of Cardiology, Cardiac Imaging and Cardio-Oncology Unit, La Paz University Hospital, IdiPAZ Research Institute, Paseo de la Castellana 261, 28046 Madrid, Spain
| | - Alexander R Lyon
- Cardio-Oncology Service, Royal Brompton Hospital and Imperial College, London, UK
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38
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Martyn T, Rubio AC, Estep JD, Hanna M. Opportunities for Earlier Diagnosis and Treatment of Cardiac Amyloidosis. Methodist Debakey Cardiovasc J 2022; 18:27-39. [PMID: 36561083 PMCID: PMC9733170 DOI: 10.14797/mdcvj.1163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 10/07/2022] [Indexed: 12/12/2022] Open
Abstract
Despite the rapid expansion of noninvasive (nonbiopsy) diagnosis, contemporary patients with cardiac amyloidosis too often present with advanced features of disease, such as diminished quality of life, elevated natriuretic peptides, and advanced heart failure. Therapeutics for transthyretin cardiomyopathy (ATTR-CM) are most effective when administered before significant symptoms of cardiac dysfunction manifest, making early identification of affected individuals of paramount importance. Community engagement and ensuring that a broad range of clinicians have working knowledge of how to screen for ATTR-CM in everyday practice will be an important step in moving disease identification further upstream. However, reliance on the appropriate and timely diagnosis by individual clinicians may continue to underperform. This review highlights how targeted screening of special populations may facilitate earlier diagnosis. Systems of care that operationalize screening of high-risk subpopulations and prospective validation of novel approaches to ATTR-CM identification are needed.
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Affiliation(s)
- Trejeeve Martyn
- Robert and Suzanne Tomsich Department of Cardiovascular Medicine, George and Linda Kaufman Center for Heart Failure and Recovery, Cleveland Clinic, Cleveland, Ohio, US,Amyloidosis Center, Cleveland Clinic, Cleveland, Ohio, US
| | - Andres Carmona Rubio
- Robert and Suzanne Tomsich Department of Cardiovascular Medicine, George and Linda Kaufman Center for Heart Failure and Recovery, Cleveland Clinic, Cleveland, Ohio, US,Amyloidosis Center, Cleveland Clinic, Cleveland, Ohio, US
| | - Jerry D. Estep
- Amyloidosis Center, Cleveland Clinic, Cleveland, Ohio, US,Department of Cardiovascular Medicine, Cleveland Clinic Florida, Weston, Florida, US
| | - Mazen Hanna
- Robert and Suzanne Tomsich Department of Cardiovascular Medicine, George and Linda Kaufman Center for Heart Failure and Recovery, Cleveland Clinic, Cleveland, Ohio, US,Amyloidosis Center, Cleveland Clinic, Cleveland, Ohio, US
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39
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Lee S, Chu Y, Ryu J, Park YJ, Yang S, Koh SB. Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis. Yonsei Med J 2022; 63:S93-S107. [PMID: 35040610 PMCID: PMC8790582 DOI: 10.3349/ymj.2022.63.s93] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 10/27/2021] [Accepted: 10/31/2021] [Indexed: 11/27/2022] Open
Abstract
PURPOSE Several artificial intelligence (AI) models for the detection and prediction of cardiovascular-related diseases, including arrhythmias, diabetes, and sleep apnea, have been reported. This systematic review and meta-analysis aimed to identify AI models developed for or applicable to wearable and mobile devices for diverse cardiovascular-related diseases. MATERIALS AND METHODS The searched databases included Medline, Embase, and Cochrane Library. For AI models for atrial fibrillation (AF) detection, a meta-analysis of diagnostic accuracy was performed to summarize sensitivity and specificity. RESULTS A total of 102 studies were included in the qualitative review. There were AI models for the detection of arrythmia (n=62), followed by sleep apnea (n=11), peripheral vascular diseases (n=6), diabetes mellitus (n=5), hyper/hypotension (n=5), valvular heart disease (n=4), heart failure (n=3), myocardial infarction and cardiac arrest (n=2), and others (n=4). For quantitative analysis of 26 studies reporting AI models for AF detection, meta-analyzed sensitivity was 94.80% and specificity was 96.96%. Deep neural networks showed superior performance [meta-analyzed area under receiver operating characteristics curve (AUROC) of 0.981] compared to conventional machine learning algorithms (meta-analyzed AUROC of 0.961). However, AI models tested with proprietary dataset (meta-analyzed AUROC of 0.972) or data acquired from wearable devices (meta-analyzed AUROC of 0.977) showed inferior performance than those with public dataset (meta-analyzed AUROC of 0.986) or data from in-hospital devices (meta-analyzed AUROC of 0.983). CONCLUSION This review found that AI models for diverse cardiovascular-related diseases are being developed, and that they are gradually developing into a form that is suitable for wearable and mobile devices.
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Affiliation(s)
- Solam Lee
- Department of Preventive Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea
- Department of Dermatology, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Yuseong Chu
- Department of Biomedical Engineering, Yonsei University, Wonju, Korea
| | - Jiseung Ryu
- Department of Biomedical Engineering, Yonsei University, Wonju, Korea
| | - Young Jun Park
- Division of Cardiology, Department of Internal Medicine, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Sejung Yang
- Department of Biomedical Engineering, Yonsei University, Wonju, Korea.
| | - Sang Baek Koh
- Department of Preventive Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea.
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40
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Noseworthy PA, Siontis KC. Pushing the Limits of the ECG: Can We Assess Biventricular Function Without Imaging? JACC Cardiovasc Imaging 2021; 15:411-412. [PMID: 34656479 DOI: 10.1016/j.jcmg.2021.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 09/02/2021] [Indexed: 11/18/2022]
Affiliation(s)
- Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
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