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Dzikowicz DJ. A Scoping Review of Varying Mobile Electrocardiographic Devices. Biol Res Nurs 2024; 26:303-314. [PMID: 38029286 DOI: 10.1177/10998004231216923] [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] [Indexed: 12/01/2023]
Abstract
The electrocardiogram (ECG) can now be measured using mobile devices. Mobile ECG devices, which are defined as devices capable of recording and transmitting non-standard ECGs, offer numerous advantages such as cost-effectiveness and being user-friendly. Mobile ECG can also extend recording lengths (e.g., 2 days, 14 days), which is necessary to capture important intermittent events (e.g., cardiac arrhythmias) and evaluate prognostic risk markers (e.g., prolonged corrected QT (QTc) interval). Some mobile ECG devices can even connect to broadband networks allowing patients to remotely transmit their ECG to a clinician. This article systematically examines different mobile ECG devices used in prior studies and provides a detailed assessment of five diverse yet commonly used mobile ECG devices: AliveCor KardiaMobile; AliveCor KardiaMobile 6L; iRhythm ZioPatch; Apple Smartwatch ECG; and CardioSecur System. These mobile ECG devices are diverse in the number of leads measured and the duration of monitoring. Similar to their diversity, there has been a wide range of clinical applications of mobile ECG devices. Despite significant progress, questions regarding data quality, and clinican and patient acceptance and compliance persist.
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Affiliation(s)
- Dillon J Dzikowicz
- University of Rochester School of Nursing, Rochester, NY, USA
- Clinical Cardiovascular Research Center, University of Rochester, Rochester, NY, USA
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Chaudhari GR, Mayfield JJ, Barrios JP, Abreau S, Avram R, Olgin JE, Tison GH. Deep learning augmented ECG analysis to identify biomarker-defined myocardial injury. Sci Rep 2023; 13:3364. [PMID: 36849487 PMCID: PMC9969952 DOI: 10.1038/s41598-023-29989-9] [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: 03/23/2022] [Accepted: 02/14/2023] [Indexed: 03/01/2023] Open
Abstract
Chest pain is a common clinical complaint for which myocardial injury is the primary concern and is associated with significant morbidity and mortality. To aid providers' decision-making, we aimed to analyze the electrocardiogram (ECG) using a deep convolutional neural network (CNN) to predict serum troponin I (TnI) from ECGs. We developed a CNN using 64,728 ECGs from 32,479 patients who underwent ECG within 2 h prior to a serum TnI laboratory result at the University of California, San Francisco (UCSF). In our primary analysis, we classified patients into groups of TnI < 0.02 or ≥ 0.02 µg/L using 12-lead ECGs. This was repeated with an alternative threshold of 1.0 µg/L and with single-lead ECG inputs. We also performed multiclass prediction for a set of serum troponin ranges. Finally, we tested the CNN in a cohort of patients selected for coronary angiography, including 3038 ECGs from 672 patients. Cohort patients were 49.0% female, 42.8% white, and 59.3% (19,283) never had a positive TnI value (≥ 0.02 µg/L). CNNs accurately predicted elevated TnI, both at a threshold of 0.02 µg/L (AUC = 0.783, 95% CI 0.780-0.786) and at a threshold of 1.0 µg/L (AUC = 0.802, 0.795-0.809). Models using single-lead ECG data achieved significantly lower accuracy, with AUCs ranging from 0.740 to 0.773 with variation by lead. Accuracy of the multi-class model was lower for intermediate TnI value-ranges. Our models performed similarly on the cohort of patients who underwent coronary angiography. Biomarker-defined myocardial injury can be predicted by CNNs from 12-lead and single-lead ECGs.
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Affiliation(s)
- Gunvant R. Chaudhari
- grid.266102.10000 0001 2297 6811Department of Medicine, University of California, 555 Mission Bay Blvd South Box 3120, San Francisco, CA 94158 USA
| | - Jacob J. Mayfield
- grid.266102.10000 0001 2297 6811Department of Medicine, University of California, 555 Mission Bay Blvd South Box 3120, San Francisco, CA 94158 USA ,grid.34477.330000000122986657Division of Cardiology, University of Washington, Seattle, USA
| | - Joshua P. Barrios
- grid.266102.10000 0001 2297 6811Division of Cardiology, University of California, San Francisco, USA ,grid.266102.10000 0001 2297 6811Cardiovascular Research Institute, University of California, San Francisco, USA
| | - Sean Abreau
- grid.266102.10000 0001 2297 6811Division of Cardiology, University of California, San Francisco, USA ,grid.266102.10000 0001 2297 6811Cardiovascular Research Institute, University of California, San Francisco, USA
| | - Robert Avram
- grid.266102.10000 0001 2297 6811Department of Medicine, University of California, 555 Mission Bay Blvd South Box 3120, San Francisco, CA 94158 USA ,grid.266102.10000 0001 2297 6811Division of Cardiology, University of California, San Francisco, USA
| | - Jeffrey E. Olgin
- grid.266102.10000 0001 2297 6811Department of Medicine, University of California, 555 Mission Bay Blvd South Box 3120, San Francisco, CA 94158 USA ,grid.266102.10000 0001 2297 6811Division of Cardiology, University of California, San Francisco, USA ,grid.266102.10000 0001 2297 6811Cardiovascular Research Institute, University of California, San Francisco, USA
| | - Geoffrey H. Tison
- grid.266102.10000 0001 2297 6811Department of Medicine, University of California, 555 Mission Bay Blvd South Box 3120, San Francisco, CA 94158 USA ,grid.266102.10000 0001 2297 6811Division of Cardiology, University of California, San Francisco, USA ,grid.266102.10000 0001 2297 6811Cardiovascular Research Institute, University of California, San Francisco, USA ,grid.266102.10000 0001 2297 6811Bakar Institute of Computational Health Sciences, University of California, San Francisco, USA
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3
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Chiu IM, Cheng JY, Chen TY, Wang YM, Cheng CY, Kung CT, Cheng FJ, Yau FFF, Lin CHR. Using Deep Transfer Learning to Detect Hyperkalemia From Ambulatory Electrocardiogram Monitors in Intensive Care Units: Personalized Medicine Approach. J Med Internet Res 2022; 24:e41163. [PMID: 36469396 DOI: 10.2196/41163] [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: 07/17/2022] [Revised: 11/07/2022] [Accepted: 11/17/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Hyperkalemia is a critical condition, especially in intensive care units. So far, there have been no accurate and noninvasive methods for recognizing hyperkalemia events on ambulatory electrocardiogram monitors. OBJECTIVE This study aimed to improve the accuracy of hyperkalemia predictions from ambulatory electrocardiogram (ECG) monitors using a personalized transfer learning method; this would be done by training a generic model and refining it with personal data. METHODS This retrospective cohort study used open source data from the Waveform Database Matched Subset of the Medical Information Mart From Intensive Care III (MIMIC-III). We included patients with multiple serum potassium test results and matched ECG data from the MIMIC-III database. A 1D convolutional neural network-based deep learning model was first developed to predict hyperkalemia in a generic population. Once the model achieved a state-of-the-art performance, it was used in an active transfer learning process to perform patient-adaptive heartbeat classification tasks. RESULTS The results show that by acquiring data from each new patient, the personalized model can improve the accuracy of hyperkalemia detection significantly, from an average of 0.604 (SD 0.211) to 0.980 (SD 0.078), when compared with the generic model. Moreover, the area under the receiver operating characteristic curve level improved from 0.729 (SD 0.240) to 0.945 (SD 0.094). CONCLUSIONS By using the deep transfer learning method, we were able to build a clinical standard model for hyperkalemia detection using ambulatory ECG monitors. These findings could potentially be extended to applications that continuously monitor one's ECGs for early alerts of hyperkalemia and help avoid unnecessary blood tests.
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Affiliation(s)
- I-Min Chiu
- Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung City, Taiwan.,Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung City, Taiwan
| | - Jhu-Yin Cheng
- Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung City, Taiwan
| | - Tien-Yu Chen
- Division of Cardiology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung City, Taiwan
| | - Yi-Min Wang
- Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung City, Taiwan
| | - Chi-Yung Cheng
- Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung City, Taiwan.,Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung City, Taiwan
| | - Chia-Te Kung
- Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung City, Taiwan
| | - Fu-Jen Cheng
- Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung City, Taiwan
| | - Fei-Fei Flora Yau
- Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung City, Taiwan
| | - Chun-Hung Richard Lin
- Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung City, Taiwan
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Hutter T, Collings TS, Kostova G, Karet Frankl FE. Point-of-care and self-testing for potassium: recent advances. SENSORS & DIAGNOSTICS 2022; 1:614-626. [PMID: 35923773 PMCID: PMC9280758 DOI: 10.1039/d2sd00062h] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 05/27/2022] [Indexed: 01/12/2023]
Abstract
Potassium is an important bodily electrolyte which is kept within tight limits in health. Many medical conditions as well as commonly-used drugs either raise or lower blood potassium levels, which can be dangerous or even fatal. For at-risk patients, frequent monitoring of potassium can improve safety and lifestyle, but conventional venous blood draws are inconvenient, don't provide a timely result and may be inaccurate. This review summarises current solutions and recent developments in point-of-care and self-testing potassium measurement technologies, which include devices for measurement of potassium in venous blood, devices for home blood collection and remote measurement, devices for rapid home measurement of potassium, wearable sensors for potassium in interstitial fluid, in sweat, in urine, as well as non-invasive potassium detection. We discuss the practical and clinical applicability of these technologies and provide future outlooks.
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Affiliation(s)
- Tanya Hutter
- Materials Science and Engineering Program & Texas Materials Institute, The University of Texas at Austin USA
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Bukhari HA, Sánchez C, Srinivasan S, Palmieri F, Potse M, Laguna P, Pueyo E. Estimation of potassium levels in hemodialysis patients by T wave nonlinear dynamics and morphology markers. Comput Biol Med 2022; 143:105304. [PMID: 35168084 DOI: 10.1016/j.compbiomed.2022.105304] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 02/01/2022] [Accepted: 02/05/2022] [Indexed: 12/11/2022]
Abstract
OBJECTIVE Noninvasive screening of hypo- and hyperkalemia can prevent fatal arrhythmia in end-stage renal disease (ESRD) patients, but current methods for monitoring of serum potassium (K+) have important limitations. We investigated changes in nonlinear dynamics and morphology of the T wave in the electrocardiogram (ECG) of ESRD patients during hemodialysis (HD), assessing their relationship with K+ and designing a K+ estimator. METHODS ECG recordings from twenty-nine ESRD patients undergoing HD were processed. T waves in 2-min windows were extracted at each hour during an HD session as well as at 48 h after HD start. T wave nonlinear dynamics were characterized by two indices related to the maximum Lyapunov exponent (λt, λwt) and a divergence-related index (η). Morphological variability in the T wave was evaluated by three time warping-based indices (dw, reflecting morphological variability in the time domain, and da and daNL, in the amplitude domain). K+was measured from blood samples extracted during and after HD. Stage-specific and patient-specific K+ estimators were built based on the quantified indices and leave-one-out cross-validation was performed separately for each of the estimators. RESULTS The analyzed indices showed high inter-individual variability in their relationship with K+. Nevertheless, all of them had higher values at the HD start and 48 h after it, corresponding to the highest K+. The indices η and dw were the most strongly correlated with K+ (median Pearson correlation coefficient of 0.78 and 0.83, respectively) and were used in univariable and multivariable linear K+ estimators. Agreement between actual and estimated K+ was confirmed, with averaged errors over patients and time points being 0.000 ± 0.875 mM and 0.046 ± 0.690 mM for stage-specific and patient-specific multivariable K+ estimators, respectively. CONCLUSION ECG descriptors of T wave nonlinear dynamics and morphological variability allow noninvasive monitoring of K+ in ESRD patients. SIGNIFICANCE ECG markers have the potential to be used for hypo- and hyperkalemia screening in ESRD patients.
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Affiliation(s)
- Hassaan A Bukhari
- BSICoS group, I3A Institute, University of Zaragoza, IIS Aragón, Zaragoza, Spain; CIBER en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza, Spain; Carmen team, Inria Bordeaux - Sud-Ouest, Talence, France; University of Bordeaux, IMB, UMR 5251, Talence, France.
| | - Carlos Sánchez
- BSICoS group, I3A Institute, University of Zaragoza, IIS Aragón, Zaragoza, Spain; CIBER en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza, Spain
| | - Sabarathinam Srinivasan
- BSICoS group, I3A Institute, University of Zaragoza, IIS Aragón, Zaragoza, Spain; CIBER en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza, Spain
| | - Flavio Palmieri
- CIBER en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza, Spain; Centre de Recerca en Enginyeria Biomèdica, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Mark Potse
- Carmen team, Inria Bordeaux - Sud-Ouest, Talence, France; University of Bordeaux, IMB, UMR 5251, Talence, France
| | - Pablo Laguna
- BSICoS group, I3A Institute, University of Zaragoza, IIS Aragón, Zaragoza, Spain; CIBER en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza, Spain
| | - Esther Pueyo
- BSICoS group, I3A Institute, University of Zaragoza, IIS Aragón, Zaragoza, Spain; CIBER en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza, Spain
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Sehrawat O, Kashou AH, Noseworthy PA. Artificial Intelligence and Atrial Fibrillation. J Cardiovasc Electrophysiol 2022; 33:1932-1943. [PMID: 35258136 PMCID: PMC9717694 DOI: 10.1111/jce.15440] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 02/03/2022] [Accepted: 03/01/2022] [Indexed: 11/30/2022]
Abstract
In the context of atrial fibrillation (AF), traditional clinical practices have thus far fallen short in several domains such as identifying patients at risk of incident AF or patients with concomitant undetected paroxysmal AF. Novel approaches leveraging artificial intelligence have the potential to provide new tools to deal with some of these old problems. In this review we focus on the roles of artificial intelligence-enabled ECG pertaining to AF, potential roles of deep learning (DL) models in the context of current knowledge gaps, as well as limitations of these models. One key area where DL models can translate to better patient outcomes is through automated ECG interpretation. Further, we overview some of the challenges facing AF screening and the harms and benefits of screening. In this context, a unique model was developed to detect underlying hidden AF from sinus rhythm and is discussed in detail with its potential uses. Knowledge gaps also remain regarding the best ways to monitor patients with embolic stroke of undetermined source (ESUS) and who would benefit most from oral anticoagulation. The AI-enabled AF model is one potential way to tackle this complex problem as it could be used to identify a subset of high-risk ESUS patients likely to benefit from empirical oral anticoagulation. Role of DL models assessing AF burden from long duration ECG data is also discussed as a way of guiding management. There is a trend towards the use of consumer-grade wristbands and watches to detect AF from photoplethysmography data. However, ECG currently remains the gold standard to detect arrythmias including AF. Lastly, role of adequate external validation of the models and clinical trials to study true performance is discussed. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Ojasav Sehrawat
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Anthony H Kashou
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
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Bodington R, Kassianides X, Bhandari S. Point-of-care testing technologies for the home in chronic kidney disease: a narrative review. Clin Kidney J 2021; 14:2316-2331. [PMID: 34751234 PMCID: PMC8083235 DOI: 10.1093/ckj/sfab080] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Indexed: 01/09/2023] Open
Abstract
Point-of-care testing (POCT) performed by the patient at home, paired with eHealth technologies, offers a wealth of opportunities to develop individualized, empowering clinical pathways. The non-dialysis-dependent chronic kidney disease (CKD) patient who is at risk of or may already be suffering from a number of the associated complications of CKD represents an ideal patient group for the development of such initiatives. The current coronavirus disease 2019 pandemic and drive towards shielding vulnerable individuals have further highlighted the need for home testing pathways. In this narrative review we outline the evidence supporting remote patient management and the various technologies in use in the POCT setting. We then review the devices currently available for use in the home by patients in five key areas of renal medicine: anaemia, biochemical, blood pressure (BP), anticoagulation and diabetes monitoring. Currently there are few devices and little evidence to support the use of home POCT in CKD. While home testing in BP, anticoagulation and diabetes monitoring is relatively well developed, the fields of anaemia and biochemical POCT are still in their infancy. However, patients' attitudes towards eHealth and home POCT are consistently positive and physicians also find this care highly acceptable. The regulatory and translational challenges involved in the development of new home-based care pathways are significant. Pragmatic and adaptable trials of a hybrid effectiveness-implementation design, as well as continued technological POCT device advancement, are required to deliver these innovative new pathways that our patients desire and deserve.
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Affiliation(s)
- Richard Bodington
- Sheffield Kidney Institute, Northern General Hospital, Sheffield, UK
| | | | - Sunil Bhandari
- Department of Renal Research, Hull Royal Infirmary, Hull, UK
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Ahmad A, Shelly-Cohen M, Corban MT, Murphree Jr DH, Toya T, Sara JD, Ozcan I, Lerman LO, Friedman PA, Attia ZI, Lerman A. Machine learning aids clinical decision-making in patients presenting with angina and non-obstructive coronary artery disease. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2021; 2:597-605. [PMID: 36713103 PMCID: PMC9707870 DOI: 10.1093/ehjdh/ztab084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 09/14/2021] [Indexed: 02/01/2023]
Abstract
Aims The current gold standard comprehensive assessment of coronary microvascular dysfunction (CMD) is through a limited-access invasive catheterization lab procedure. We aimed to develop a point-of-care tool to assist clinical guidance in patients presenting with chest pain and/or an abnormal cardiac functional stress test and with non-obstructive coronary artery disease (NOCAD). Methods and results This study included 1893 NOCAD patients (<50% angiographic stenosis) who underwent CMD evaluation as well as an electrocardiogram (ECG) up to 1-year prior. Endothelial-independent CMD was defined by coronary flow reserve (CFR) ≤2.5 in response to intracoronary adenosine. Endothelial-dependent CMD was defined by a maximal percent increase in coronary blood flow (%ΔCBF) ≤50% in response to intracoronary acetylcholine infusion. We trained algorithms to distinguish between the following outcomes: CFR ≤2.5, %ΔCBF ≤50, and the combination of both. Two classes of algorithms were trained, one depending on ECG waveforms as input, and another using tabular clinical data. Mean age was 51 ± 12 years and 66% were females (n = 1257). Area under the curve values ranged from 0.49 to 0.67 for all the outcomes. The best performance in our analysis was for the outcome CFR ≤2.5 with clinical variables. Area under the curve and accuracy were 0.67% and 60%. When decreasing the threshold of positivity, sensitivity and negative predictive value increased to 92% and 90%, respectively, while specificity and positive predictive value decreased to 25% and 29%, respectively. Conclusion An artificial intelligence-enabled algorithm may be able to assist clinical guidance by ruling out CMD in patients presenting with chest pain and/or an abnormal functional stress test. This algorithm needs to be prospectively validated in different cohorts.
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Affiliation(s)
- Ali Ahmad
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55902, USA
| | - Michal Shelly-Cohen
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55902, USA
| | - Michel T Corban
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55902, USA
| | - Dennis H Murphree Jr
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55902, USA
| | - Takumi Toya
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55902, USA,Department of Medicine, Division of Cardiology, National Defense Medical College, Tokorozawa, Saitama, Japan
| | - Jaskanwal D Sara
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55902, USA
| | - Ilke Ozcan
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55902, USA
| | - Lilach O Lerman
- Department of Medicine, Division of Nephrology and Hypertension, Mayo Clinic, 200 First Street SW, Rochester, MN 55902, USA
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55902, USA
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55902, USA
| | - Amir Lerman
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55902, USA,Corresponding author. Tel: +1 507 255 4152, Fax: +1 507 255 7798,
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Kashou AH, Medina-Inojosa JR, Noseworthy PA, Rodeheffer RJ, Lopez-Jimenez F, Attia IZ, Kapa S, Scott CG, Lee AT, Friedman PA, McKie PM. Artificial Intelligence-Augmented Electrocardiogram Detection of Left Ventricular Systolic Dysfunction in the General Population. Mayo Clin Proc 2021; 96:2576-2586. [PMID: 34120755 PMCID: PMC9904428 DOI: 10.1016/j.mayocp.2021.02.029] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 02/08/2021] [Accepted: 02/11/2021] [Indexed: 01/19/2023]
Abstract
OBJECTIVE To validate an artificial intelligence-augmented electrocardiogram (AI-ECG) algorithm for the detection of preclinical left ventricular systolic dysfunction (LVSD) in a large community-based cohort. METHODS We identified a randomly selected community-based cohort of 2041 subjects age 45 years or older in Olmsted County, Minnesota. All participants underwent a study echocardiogram and ECG. We first assessed the performance of the AI-ECG to identify LVSD (ejection fraction ≤40%). After excluding participants with clinical heart failure, we further assessed the AI-ECG to detect preclinical LVSD among all patients (n=1996) and in a high-risk subgroup (n=1348). Next we modelled an imputed screening program for preclinical LVSD detection where a positive AI-ECG triggered an echocardiogram. Finally, we assessed the ability of the AI-ECG to predict future LVSD. Participants were enrolled between January 1, 1997, and September 30, 2000; and LVSD surveillance was performed for 10 years after enrollment. RESULTS For detection of LVSD in the total population (prevalence, 2.0%), the area under the receiver operating curve for AI-ECG was 0.97 (sensitivity, 90%; specificity, 92%); in the high-risk subgroup (prevalence 2.7%), the area under the curve was 0.97 (sensitivity, 92%; specificity, 93%). In an imputed screening program, identification of one preclinical LSVD case would require 88.3 AI-ECGs and 8.7 echocardiograms in the total population and 65.7 AI-ECGs and 5.5 echocardiograms in the high-risk subgroup. The unadjusted hazard ratio for a positive AI-ECG for incident LVSD over 10 years was 2.31 (95% CI, 1.32 to 4.05; P=.004). CONCLUSION Artificial intelligence-augmented ECG can identify preclinical LVSD in the community and warrants further study as a screening tool for preclinical LVSD.
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Affiliation(s)
| | | | | | | | | | | | - Suraj Kapa
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | | | | | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Paul M McKie
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN.
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Development and validation of a deep learning model to screen hypokalemia from electrocardiogram in emergency patients. Chin Med J (Engl) 2021; 134:2333-2339. [PMID: 34483253 PMCID: PMC8509898 DOI: 10.1097/cm9.0000000000001650] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
Background: A deep learning model (DLM) that enables non-invasive hypokalemia screening from an electrocardiogram (ECG) may improve the detection of this life-threatening condition. This study aimed to develop and evaluate the performance of a DLM for the detection of hypokalemia from the ECGs of emergency patients. Methods: We used a total of 9908 ECG data from emergency patients who were admitted at the Second Affiliated Hospital of Nanchang University, Jiangxi, China, from September 2017 to October 2020. The DLM was trained using 12 ECG leads (lead I, II, III, aVR, aVL, aVF, and V1–6) to detect patients with serum potassium concentrations <3.5 mmol/L and was validated using retrospective data from the Jiangling branch of the Second Affiliated Hospital of Nanchang University. The blood draw was completed within 10 min before and after the ECG examination, and there was no new or ongoing infusion during this period. Results: We used 6904 ECGs and 1726 ECGs as development and internal validation data sets, respectively. In addition, 1278 ECGs from the Jiangling branch of the Second Affiliated Hospital of Nanchang University were used as external validation data sets. Using 12 ECG leads (leads I, II, III, aVR, aVL, aVF, and V1–6), the area under the receiver operating characteristic curve (AUC) of the DLM was 0.80 (95% confidence interval [CI]: 0.77–0.82) for the internal validation data set. Using an optimal operating point yielded a sensitivity of 71.4% and a specificity of 77.1%. Using the same 12 ECG leads, the external validation data set resulted in an AUC for the DLM of 0.77 (95% CI: 0.75–0.79). Using an optimal operating point yielded a sensitivity of 70.0% and a specificity of 69.1%. Conclusions: In this study, using 12 ECG leads, a DLM detected hypokalemia in emergency patients with an AUC of 0.77 to 0.80. Artificial intelligence could be used to analyze an ECG to quickly screen for hypokalemia.
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Książczyk M, Dębska-Kozłowska A, Warchoł I, Lubiński A. Enhancing Healthcare Access-Smartphone Apps in Arrhythmia Screening: Viewpoint. JMIR Mhealth Uhealth 2021; 9:e23425. [PMID: 34448723 PMCID: PMC8433858 DOI: 10.2196/23425] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 01/04/2021] [Accepted: 07/28/2021] [Indexed: 01/23/2023] Open
Abstract
Atrial fibrillation is the most commonly reported arrhythmia and, if undiagnosed or untreated, may lead to thromboembolic events. It is therefore desirable to provide screening to patients in order to detect atrial arrhythmias. Specific mobile apps and accessory devices, such as smartphones and smartwatches, may play a significant role in monitoring heart rhythm in populations at high risk of arrhythmia. These apps are becoming increasingly common among patients and professionals as a part of mobile health. The rapid development of mobile health solutions may revolutionize approaches to arrhythmia screening. In this viewpoint paper, we assess the availability of smartphone and smartwatch apps and evaluate their efficacy for monitoring heart rhythm and arrhythmia detection. The findings obtained so far suggest they are on the right track to improving the efficacy of early detection of atrial fibrillation, thus lowering the risk of stroke and reducing the economic burden placed on public health.
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Affiliation(s)
- Marcin Książczyk
- Department of Interventional Cardiology and Cardiac Arrhythmias, Medical University of Lodz, Łódź, Poland.,Department of Noninvasive Cardiology, Medical University of Lodz, Łódź, Poland
| | - Agnieszka Dębska-Kozłowska
- Department of Interventional Cardiology and Cardiac Arrhythmias, Medical University of Lodz, Łódź, Poland
| | - Izabela Warchoł
- Department of Interventional Cardiology and Cardiac Arrhythmias, Medical University of Lodz, Łódź, Poland
| | - Andrzej Lubiński
- Department of Interventional Cardiology and Cardiac Arrhythmias, Medical University of Lodz, Łódź, Poland
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12
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Cadamuro J. Rise of the Machines: The Inevitable Evolution of Medicine and Medical Laboratories Intertwining with Artificial Intelligence-A Narrative Review. Diagnostics (Basel) 2021; 11:1399. [PMID: 34441333 PMCID: PMC8392825 DOI: 10.3390/diagnostics11081399] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 07/05/2021] [Accepted: 07/21/2021] [Indexed: 01/04/2023] Open
Abstract
Laboratory medicine has evolved from a mainly manual profession, providing few selected test results to a highly automated and standardized medical discipline, generating millions of test results per year. As the next inevitable evolutional step, artificial intelligence (AI) algorithms will need to assist us in structuring and making sense of the masses of diagnostic data collected today. Such systems will be able to connect clinical and diagnostic data and to provide valuable suggestions in diagnosis, prognosis or therapeutic options. They will merge the often so separated worlds of the laboratory and the clinics. When used correctly, it will be a tool, capable of freeing the physicians time so that he/she can refocus on the patient. In this narrative review I therefore aim to provide an overview of what AI is, what applications currently are available in healthcare and in laboratory medicine in particular. I will discuss the challenges and pitfalls of applying AI algorithms and I will elaborate on the question if healthcare workers will be replaced by such systems in the near future.
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Affiliation(s)
- Janne Cadamuro
- Department of Laboratory Medicine, Paracelsus Medical University, A-5020 Salzburg, Austria
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13
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Palmieri F, Gomis P, Ruiz JE, Ferreira D, Martín-Yebra A, Pueyo E, Martínez JP, Ramírez J, Laguna P. ECG-based monitoring of blood potassium concentration: Periodic versus principal component as lead transformation for biomarker robustness. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102719] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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14
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Attia IZ, Tseng AS, Benavente ED, Medina-Inojosa JR, Clark TG, Malyutina S, Kapa S, Schirmer H, Kudryavtsev AV, Noseworthy PA, Carter RE, Ryabikov A, Perel P, Friedman PA, Leon DA, Lopez-Jimenez F. External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction. Int J Cardiol 2021; 329:130-135. [PMID: 33400971 PMCID: PMC7955278 DOI: 10.1016/j.ijcard.2020.12.065] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 11/24/2020] [Accepted: 12/18/2020] [Indexed: 12/16/2022]
Abstract
OBJECTIVE To validate a novel artificial-intelligence electrocardiogram algorithm (AI-ECG) to detect left ventricular systolic dysfunction (LVSD) in an external population. BACKGROUND LVSD, even when asymptomatic, confers increased morbidity and mortality. We recently derived AI-ECG to detect LVSD using ECGs based on a large sample of patients treated at the Mayo Clinic. METHODS We performed an external validation study with subjects from the Know Your Heart Study, a cross-sectional study of adults aged 35-69 years residing in two cities in Russia, who had undergone both ECG and transthoracic echocardiography. LVSD was defined as left ventricular ejection fraction ≤ 35%. We assessed the performance of the AI-ECG to identify LVSD in this distinct patient population. RESULTS Among 4277 subjects in this external population-based validation study, 0.6% had LVSD (compared to 7.8% of the original clinical derivation study). The overall performance of the AI-ECG to detect LVSD was robust with an area under the receiver operating curve of 0.82. When using the LVSD probability cut-off of 0.256 from the original derivation study, the sensitivity, specificity, and accuracy in this population were 26.9%, 97.4%, 97.0%, respectively. Other probability cut-offs were analysed for different sensitivity values. CONCLUSIONS The AI-ECG detected LVSD with robust test performance in a population that was very different from that used to develop the algorithm. Population-specific cut-offs may be necessary for clinical implementation. Differences in population characteristics, ECG and echocardiographic data quality may affect test performance.
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Affiliation(s)
| | - Andrew S Tseng
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Ernest Diez Benavente
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK; Department of Experimental Cardiology, University Medical Center Utrecht, Netherlands
| | | | - Taane G Clark
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Sofia Malyutina
- Novosibirsk State Medical University, Russian Ministry of Health, Novosibirsk 630091, Russia; Research Institute of Internal and Preventive Medicine, Branch of Institute of Cytology and Genetics, Siberian Branch of the Russion Academy of Sciences, Novosibirsk 630090, Russia
| | - Suraj Kapa
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Henrik Schirmer
- Department of Community Medicine, UiT The Arctic University of Norway, Tromsø 9037, Norway; Institute for Clinical Medicine, University of Oslo, Campus Ahus, Lørenskog PB 1000 1478, Norway; Department of Cardiology, Akershus University Hospital, 1478 Nordbyhagen, Oslo, Norway
| | - Alexander V Kudryavtsev
- Northern State Medical University, Arkhangelsk 163000, Russia; Department of Community Medicine, UiT The Arctic University of Norway, Tromsø 9037, Norway
| | | | - Rickey E Carter
- Department of Health Sciences Research, Mayo Clinic, Jacksonville, FL, USA
| | - Andrew Ryabikov
- Novosibirsk State Medical University, Russian Ministry of Health, Novosibirsk 630091, Russia; Research Institute of Internal and Preventive Medicine, Branch of Institute of Cytology and Genetics, Siberian Branch of the Russion Academy of Sciences, Novosibirsk 630090, Russia
| | - Pablo Perel
- Department of Non-communicable Diseases Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - David A Leon
- Department of Community Medicine, UiT The Arctic University of Norway, Tromsø 9037, Norway; Department of Non-communicable Diseases Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK; International Laboratory for Population and Health, National Research University, Higher School of Economics, Moscow, Russia
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15
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Siontis KC, Noseworthy PA, Attia ZI, Friedman PA. Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nat Rev Cardiol 2021; 18:465-478. [PMID: 33526938 PMCID: PMC7848866 DOI: 10.1038/s41569-020-00503-2] [Citation(s) in RCA: 199] [Impact Index Per Article: 66.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/21/2020] [Indexed: 01/31/2023]
Abstract
The application of artificial intelligence (AI) to the electrocardiogram (ECG), a ubiquitous and standardized test, is an example of the ongoing transformative effect of AI on cardiovascular medicine. Although the ECG has long offered valuable insights into cardiac and non-cardiac health and disease, its interpretation requires considerable human expertise. Advanced AI methods, such as deep-learning convolutional neural networks, have enabled rapid, human-like interpretation of the ECG, while signals and patterns largely unrecognizable to human interpreters can be detected by multilayer AI networks with precision, making the ECG a powerful, non-invasive biomarker. Large sets of digital ECGs linked to rich clinical data have been used to develop AI models for the detection of left ventricular dysfunction, silent (previously undocumented and asymptomatic) atrial fibrillation and hypertrophic cardiomyopathy, as well as the determination of a person's age, sex and race, among other phenotypes. The clinical and population-level implications of AI-based ECG phenotyping continue to emerge, particularly with the rapid rise in the availability of mobile and wearable ECG technologies. In this Review, we summarize the current and future state of the AI-enhanced ECG in the detection of cardiovascular disease in at-risk populations, discuss its implications for clinical decision-making in patients with cardiovascular disease and critically appraise potential limitations and unknowns.
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Affiliation(s)
- Konstantinos C. Siontis
- grid.66875.3a0000 0004 0459 167XDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN USA
| | - Peter A. Noseworthy
- grid.66875.3a0000 0004 0459 167XDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN USA
| | - Zachi I. Attia
- grid.66875.3a0000 0004 0459 167XDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN USA
| | - Paul A. Friedman
- grid.66875.3a0000 0004 0459 167XDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN USA
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16
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Pilia N, Severi S, Raimann JG, Genovesi S, Dössel O, Kotanko P, Corsi C, Loewe A. Quantification and classification of potassium and calcium disorders with the electrocardiogram: What do clinical studies, modeling, and reconstruction tell us? APL Bioeng 2020; 4:041501. [PMID: 33062908 PMCID: PMC7532940 DOI: 10.1063/5.0018504] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 09/13/2020] [Indexed: 11/14/2022] Open
Abstract
Diseases caused by alterations of ionic concentrations are frequently observed challenges and play an important role in clinical practice. The clinically established method for the diagnosis of electrolyte concentration imbalance is blood tests. A rapid and non-invasive point-of-care method is yet needed. The electrocardiogram (ECG) could meet this need and becomes an established diagnostic tool allowing home monitoring of the electrolyte concentration also by wearable devices. In this review, we present the current state of potassium and calcium concentration monitoring using the ECG and summarize results from previous work. Selected clinical studies are presented, supporting or questioning the use of the ECG for the monitoring of electrolyte concentration imbalances. Differences in the findings from automatic monitoring studies are discussed, and current studies utilizing machine learning are presented demonstrating the potential of the deep learning approach. Furthermore, we demonstrate the potential of computational modeling approaches to gain insight into the mechanisms of relevant clinical findings and as a tool to obtain synthetic data for methodical improvements in monitoring approaches.
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Affiliation(s)
- N Pilia
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
| | - S Severi
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi," University of Bologna, 47522 Cesena, Italy
| | - J G Raimann
- Renal Research Institute, New York, New York 10065, USA
| | - S Genovesi
- Department of Medicine and Surgery, University of Milan-Bicocca, 20100 Milan, Italy
| | - O Dössel
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
| | | | - C Corsi
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi," University of Bologna, 47522 Cesena, Italy
| | - A Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
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17
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Attia ZI, Kapa S, Noseworthy PA, Lopez-Jimenez F, Friedman PA. Artificial Intelligence ECG to Detect Left Ventricular Dysfunction in COVID-19: A Case Series. Mayo Clin Proc 2020; 95:2464-2466. [PMID: 33153634 PMCID: PMC7501873 DOI: 10.1016/j.mayocp.2020.09.020] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 08/28/2020] [Accepted: 09/16/2020] [Indexed: 01/18/2023]
Abstract
Coronavirus disease 2019 (COVID-19) can result in deterioration of cardiac function, which is associated with high mortality. A simple point-of-care diagnostic test to screen for ventricular dysfunction would be clinically useful to guide management. We sought to review the clinical experience with an artificial intelligence electrocardiogram (AI ECG) to screen for ventricular dysfunction in patients with documented COVID-19. We examined all patients in the Mayo Clinic system who underwent clinically indicated electrocardiography and echocardiography within 2 weeks following a positive COVID-19 test and had permitted use of their data for research were included. Of the 27 patients who met the inclusion criteria, one had a history of normal ventricular function who developed COVID-19 myocarditis with rapid clinical decline. The initial AI ECG in this patient indicated normal ventricular function. Repeat AI ECG showed a probability of ejection fraction (EF) less than or equal to 40% of 90.2%, corroborated with an echocardiographic EF of 35%. One other patient had a pre-existing EF less than or equal to 40%, accurately detected by the algorithm before and after COVID-19 diagnosis, and another was found to have a low EF by AI ECG and echocardiography with the COVID-19 diagnosis. The area under the curve for detection of EF less than or equal to 40% was 0.95. This case series suggests that the AI ECG, previously shown to detect ventricular dysfunction in a large general population, may be useful as a screening tool for the detection of cardiac dysfunction in patients with COVID-19.
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Affiliation(s)
- Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Suraj Kapa
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | | | | | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN.
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18
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Galloway CD, Valys AV, Shreibati JB, Treiman DL, Petterson FL, Gundotra VP, Albert DE, Attia ZI, Carter RE, Asirvatham SJ, Ackerman MJ, Noseworthy PA, Dillon JJ, Friedman PA. Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram. JAMA Cardiol 2020; 4:428-436. [PMID: 30942845 DOI: 10.1001/jamacardio.2019.0640] [Citation(s) in RCA: 147] [Impact Index Per Article: 36.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Importance For patients with chronic kidney disease (CKD), hyperkalemia is common, associated with fatal arrhythmias, and often asymptomatic, while guideline-directed monitoring of serum potassium is underused. A deep-learning model that enables noninvasive hyperkalemia screening from the electrocardiogram (ECG) may improve detection of this life-threatening condition. Objective To evaluate the performance of a deep-learning model in detection of hyperkalemia from the ECG in patients with CKD. Design, Setting, and Participants A deep convolutional neural network (DNN) was trained using 1 576 581 ECGs from 449 380 patients seen at Mayo Clinic, Rochester, Minnesota, from 1994 to 2017. The DNN was trained using 2 (leads I and II) or 4 (leads I, II, V3, and V5) ECG leads to detect serum potassium levels of 5.5 mEq/L or less (to convert to millimoles per liter, multiply by 1) and was validated using retrospective data from the Mayo Clinic in Minnesota, Florida, and Arizona. The validation included 61 965 patients with stage 3 or greater CKD. Each patient had a serum potassium count drawn within 4 hours after their ECG was recorded. Data were analyzed between April 12, 2018, and June 25, 2018. Exposures Use of a deep-learning model. Main Outcomes and Measures Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity, with serum potassium level as the reference standard. The model was evaluated at 2 operating points, 1 for equal specificity and sensitivity and another for high (90%) sensitivity. Results Of the total 1 638 546 ECGs, 908 000 (55%) were from men. The prevalence of hyperkalemia in the 3 validation data sets ranged from 2.6% (n = 1282 of 50 099; Minnesota) to 4.8% (n = 287 of 6011; Florida). Using ECG leads I and II, the AUC of the deep-learning model was 0.883 (95% CI, 0.873-0.893) for Minnesota, 0.860 (95% CI, 0.837-0.883) for Florida, and 0.853 (95% CI, 0.830-0.877) for Arizona. Using a 90% sensitivity operating point, the sensitivity was 90.2% (95% CI, 88.4%-91.7%) and specificity was 63.2% (95% CI, 62.7%-63.6%) for Minnesota; the sensitivity was 91.3% (95% CI, 87.4%-94.3%) and specificity was 54.7% (95% CI, 53.4%-56.0%) for Florida; and the sensitivity was 88.9% (95% CI, 84.5%-92.4%) and specificity was 55.0% (95% CI, 53.7%-56.3%) for Arizona. Conclusions and Relevance In this study, using only 2 ECG leads, a deep-learning model detected hyperkalemia in patients with renal disease with an AUC of 0.853 to 0.883. The application of artificial intelligence to the ECG may enable screening for hyperkalemia. Prospective studies are warranted.
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Affiliation(s)
| | | | | | | | | | | | | | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - Rickey E Carter
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.,Department of Health Sciences Research, Mayo Clinic, Jacksonville, Florida
| | | | - Michael J Ackerman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - John J Dillon
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
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19
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Abstract
PURPOSE OF REVIEW Hypertension (HTN) and chronic kidney disease (CKD) are significant problems. With recent advances in technologies, biosensors have shown a great potential to provide better home monitoring in hypertension (HTN), medication compliance, diagnostic device for kidney disease, CKD/end-stage renal disease (ESRD) care, and post kidney transplant management. RECENT FINDINGS Multiple devices/biosensors have been developed related to HTN, kidney function including real-time glomerular filtration rate, CKD/end-stage renal disease, and transplant care. In recent advances in wearable biosensors, point of care monitoring system could provide more integrated care to the patients via telenephrology. SUMMARY This review focuses on the recent advances in biosensors which may be useful for HTN and nephrology. We will discuss future potential clinical implication of these biosensors.
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20
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Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, Carter RE, Yao X, Rabinstein AA, Erickson BJ, Kapa S, Friedman PA. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet 2019; 394:861-867. [PMID: 31378392 DOI: 10.1016/s0140-6736(19)31721-0] [Citation(s) in RCA: 594] [Impact Index Per Article: 118.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Revised: 06/10/2019] [Accepted: 06/13/2019] [Indexed: 12/14/2022]
Abstract
BACKGROUND Atrial fibrillation is frequently asymptomatic and thus underdetected but is associated with stroke, heart failure, and death. Existing screening methods require prolonged monitoring and are limited by cost and low yield. We aimed to develop a rapid, inexpensive, point-of-care means of identifying patients with atrial fibrillation using machine learning. METHODS We developed an artificial intelligence (AI)-enabled electrocardiograph (ECG) using a convolutional neural network to detect the electrocardiographic signature of atrial fibrillation present during normal sinus rhythm using standard 10-second, 12-lead ECGs. We included all patients aged 18 years or older with at least one digital, normal sinus rhythm, standard 10-second, 12-lead ECG acquired in the supine position at the Mayo Clinic ECG laboratory between Dec 31, 1993, and July 21, 2017, with rhythm labels validated by trained personnel under cardiologist supervision. We classified patients with at least one ECG with a rhythm of atrial fibrillation or atrial flutter as positive for atrial fibrillation. We allocated ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. We calculated the area under the curve (AUC) of the receiver operatoring characteristic curve for the internal validation dataset to select a probability threshold, which we applied to the testing dataset. We evaluated model performance on the testing dataset by calculating the AUC and the accuracy, sensitivity, specificity, and F1 score with two-sided 95% CIs. FINDINGS We included 180 922 patients with 649 931 normal sinus rhythm ECGs for analysis: 454 789 ECGs recorded from 126 526 patients in the training dataset, 64 340 ECGs from 18 116 patients in the internal validation dataset, and 130 802 ECGs from 36 280 patients in the testing dataset. 3051 (8·4%) patients in the testing dataset had verified atrial fibrillation before the normal sinus rhythm ECG tested by the model. A single AI-enabled ECG identified atrial fibrillation with an AUC of 0·87 (95% CI 0·86-0·88), sensitivity of 79·0% (77·5-80·4), specificity of 79·5% (79·0-79·9), F1 score of 39·2% (38·1-40·3), and overall accuracy of 79·4% (79·0-79·9). Including all ECGs acquired during the first month of each patient's window of interest (ie, the study start date or 31 days before the first recorded atrial fibrillation ECG) increased the AUC to 0·90 (0·90-0·91), sensitivity to 82·3% (80·9-83·6), specificity to 83·4% (83·0-83·8), F1 score to 45·4% (44·2-46·5), and overall accuracy to 83·3% (83·0-83·7). INTERPRETATION An AI-enabled ECG acquired during normal sinus rhythm permits identification at point of care of individuals with atrial fibrillation. FUNDING None.
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Affiliation(s)
- Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | | | | | | | - Bernard J Gersh
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Rickey E Carter
- Department of Health Sciences Research, Mayo Clinic, Jacksonville, FL, USA
| | - Xiaoxi Yao
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | | | | | - Suraj Kapa
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
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21
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Dzobo K, Adotey S, Thomford NE, Dzobo W. Integrating Artificial and Human Intelligence: A Partnership for Responsible Innovation in Biomedical Engineering and Medicine. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2019; 24:247-263. [PMID: 31313972 DOI: 10.1089/omi.2019.0038] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Historically, the term "artificial intelligence" dates to 1956 when it was first used in a conference at Dartmouth College in the US. Since then, the development of artificial intelligence has in part been shaped by the field of neuroscience. By understanding the human brain, scientists have attempted to build new intelligent machines capable of performing complex tasks akin to humans. Indeed, future research into artificial intelligence will continue to benefit from the study of the human brain. While the development of artificial intelligence algorithms has been fast paced, the actual use of most artificial intelligence (AI) algorithms in biomedical engineering and clinical practice is still markedly below its conceivably broader potentials. This is partly because for any algorithm to be incorporated into existing workflows it has to stand the test of scientific validation, clinical and personal utility, application context, and is equitable as well. In this context, there is much to be gained by combining AI and human intelligence (HI). Harnessing Big Data, computing power and storage capacities, and addressing societal issues emergent from algorithm applications, demand deploying HI in tandem with AI. Very few countries, even economically developed states, lack adequate and critical governance frames to best understand and steer the AI innovation trajectories in health care. Drug discovery and translational pharmaceutical research stand to gain from AI technology provided they are also informed by HI. In this expert review, we analyze the ways in which AI applications are likely to traverse the continuum of life from birth to death, and encompassing not only humans but also all animal, plant, and other living organisms that are increasingly touched by AI. Examples of AI applications include digital health, diagnosis of diseases in newborns, remote monitoring of health by smart devices, real-time Big Data analytics for prompt diagnosis of heart attacks, and facial analysis software with consequences on civil liberties. While we underscore the need for integration of AI and HI, we note that AI technology does not have to replace medical specialists or scientists and rather, is in need of such expert HI. Altogether, AI and HI offer synergy for responsible innovation and veritable prospects for improving health care from prevention to diagnosis to therapeutics while unintended consequences of automation emergent from AI and algorithms should be borne in mind on scientific cultures, work force, and society at large.
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Affiliation(s)
- Kevin Dzobo
- International Centre for Genetic Engineering and Biotechnology (ICGEB), Cape Town Component, Wernher and Beit Building (South), UCT Medical Campus, Anzio Road, Observatory 7925, Cape Town, South Africa.,Division of Medical Biochemistry and Institute of Infectious Disease and Molecular Medicine, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Sampson Adotey
- International Development Innovation Network, D-Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Nicholas E Thomford
- Pharmacogenetics Research Group, Division of Human Genetics, Department of Pathology and Institute of Infectious Diseases and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Observatory 7925, Cape Town, South Africa
| | - Witness Dzobo
- Pathology and Immunology Department, University Hospital Southampton, Mail Point B, Tremona Road, Southampton, UK.,University of Portsmouth, Faculty of Science, St Michael's Building, White Swan Road, Portsmouth, UK
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22
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Attia ZI, Kapa S, Yao X, Lopez‐Jimenez F, Mohan TL, Pellikka PA, Carter RE, Shah ND, Friedman PA, Noseworthy PA. Prospective validation of a deep learning electrocardiogram algorithm for the detection of left ventricular systolic dysfunction. J Cardiovasc Electrophysiol 2019; 30:668-674. [DOI: 10.1111/jce.13889] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Revised: 01/22/2019] [Accepted: 01/23/2019] [Indexed: 01/22/2023]
Affiliation(s)
- Zachi I. Attia
- Department of Cardiovascular MedicineMayo ClinicRochester Minnesota
| | - Suraj Kapa
- Department of Cardiovascular MedicineMayo ClinicRochester Minnesota
| | - Xiaoxi Yao
- Department of Health Sciences Research, Division of Health Care Policy and ResearchMayo ClinicRochester Minnesota
- Robert D. and Patricia E. Kern Center for the Science of Health Care DeliveryMayo ClinicRochester Minnesota
| | | | - Tarun L. Mohan
- Department of Cardiovascular MedicineMayo ClinicRochester Minnesota
| | | | - Rickey E. Carter
- Division of Biomedical Statistics and Informatics, Health Sciences ResearchMayo Clinic College of MedicineJacksonville Florida
| | - Nilay D. Shah
- Department of Health Sciences Research, Division of Health Care Policy and ResearchMayo ClinicRochester Minnesota
- Robert D. and Patricia E. Kern Center for the Science of Health Care DeliveryMayo ClinicRochester Minnesota
| | - Paul A. Friedman
- Department of Cardiovascular MedicineMayo ClinicRochester Minnesota
| | - Peter A. Noseworthy
- Department of Cardiovascular MedicineMayo ClinicRochester Minnesota
- Robert D. and Patricia E. Kern Center for the Science of Health Care DeliveryMayo ClinicRochester Minnesota
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23
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Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, Pellikka PA, Enriquez-Sarano M, Noseworthy PA, Munger TM, Asirvatham SJ, Scott CG, Carter RE, Friedman PA. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med 2019; 25:70-74. [PMID: 30617318 DOI: 10.1038/s41591-018-0240-2] [Citation(s) in RCA: 526] [Impact Index Per Article: 105.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Accepted: 10/01/2018] [Indexed: 01/10/2023]
Abstract
Asymptomatic left ventricular dysfunction (ALVD) is present in 3-6% of the general population, is associated with reduced quality of life and longevity, and is treatable when found1-4. An inexpensive, noninvasive screening tool for ALVD in the doctor's office is not available. We tested the hypothesis that application of artificial intelligence (AI) to the electrocardiogram (ECG), a routine method of measuring the heart's electrical activity, could identify ALVD. Using paired 12-lead ECG and echocardiogram data, including the left ventricular ejection fraction (a measure of contractile function), from 44,959 patients at the Mayo Clinic, we trained a convolutional neural network to identify patients with ventricular dysfunction, defined as ejection fraction ≤35%, using the ECG data alone. When tested on an independent set of 52,870 patients, the network model yielded values for the area under the curve, sensitivity, specificity, and accuracy of 0.93, 86.3%, 85.7%, and 85.7%, respectively. In patients without ventricular dysfunction, those with a positive AI screen were at 4 times the risk (hazard ratio, 4.1; 95% confidence interval, 3.3 to 5.0) of developing future ventricular dysfunction compared with those with a negative screen. Application of AI to the ECG-a ubiquitous, low-cost test-permits the ECG to serve as a powerful screening tool in asymptomatic individuals to identify ALVD.
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Affiliation(s)
- Zachi I Attia
- Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Suraj Kapa
- Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Paul M McKie
- Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Gaurav Satam
- Business Development, Mayo Clinic, Rochester, MN, USA
| | | | | | | | | | | | | | - Rickey E Carter
- Health Sciences Research, Mayo Clinic, Jacksonville, FL, USA
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