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Osoro L, Zylla MM, Braunschweig F, Leyva F, Figueras J, Pürerfellner H, Merino JL, Casado-Arroyo R, Boriani G. Challenging the status quo: a scoping review of value-based care models in cardiology and electrophysiology. Europace 2024; 26:euae210. [PMID: 39158601 PMCID: PMC11393573 DOI: 10.1093/europace/euae210] [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: 06/09/2024] [Revised: 07/18/2024] [Accepted: 08/01/2024] [Indexed: 08/20/2024] Open
Abstract
AIMS The accomplishment of value-based healthcare (VBHC) models could save up to $1 trillion per year for healthcare systems worldwide while improving patients' wellbeing and experience. Nevertheless, its adoption and development are challenging. This review aims to provide an overview of current literature pertaining to the implementation of VBHC models used in cardiology, with a focus on cardiac electrophysiology. METHODS AND RESULTS This scoping review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis for Scoping Reviews. The records included in this publication were relevant documents published in PubMed, Mendeley, and ScienceDirect. The search criteria were publications about VBHC in the field of cardiology and electrophysiology published between 2006 and 2023. The implementation of VBHC models in cardiology and electrophysiology is still in its infant stages. There is a clear need to modify the current organizational structure in order to establish cross-functional teams with the patient at the centre of care. The adoption of new reimbursement schemes is crucial to moving this process forward. The implementation of technologies for data analysis and patient management, among others, poses challenges to the change process. CONCLUSION New VBHC models have the potential to improve the care process and patient experience while optimizing the costs. The implementation of this model has been insufficient mainly because it requires substantial changes in the existing infrastructures and local organization, the need to track adherence to guidelines, and the evaluation of the quality of life improvement and patient satisfaction, among others.
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Affiliation(s)
- Lucia Osoro
- Department of Cardiology, H.U.B.-Hôpital Erasme, Université Libre de Bruxelles, Rte de Lennik 808, 1070 Bruxelles, Belgium
| | - Maura M Zylla
- Department of Cardiology, HCR (Heidelberg Center for Heart Rhythm Disorders), Medical University Hospital, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany
- mHealth and Health Economics and PROM Committee of EHRA (European Heart Rhythm Association), Rue de la Loi 34/6th Floor B, 1040 Bruxelles, Belgium
| | - Frieder Braunschweig
- mHealth and Health Economics and PROM Committee of EHRA (European Heart Rhythm Association), Rue de la Loi 34/6th Floor B, 1040 Bruxelles, Belgium
- Department of Medicine, Solna Karolinska Institutet and ME Cardiology, Karolinska University Hospital, Norrbacka S1:02, Eugeniavagen 27, Stockholm 171 77, Sweden
| | - Francisco Leyva
- mHealth and Health Economics and PROM Committee of EHRA (European Heart Rhythm Association), Rue de la Loi 34/6th Floor B, 1040 Bruxelles, Belgium
- Department of Cardiology, Aston Medical Research Institute, Aston Medical School, Aston University, Aston Triangle, Birmingham B4 7ET, UK
| | - Josep Figueras
- European Observatory of Health Systems and Policies, Place Victor Horta 40/30 Eurostation, 1060 Brussels, Belgium
| | - Helmut Pürerfellner
- mHealth and Health Economics and PROM Committee of EHRA (European Heart Rhythm Association), Rue de la Loi 34/6th Floor B, 1040 Bruxelles, Belgium
- Ordensklinikum Linz Elisabethinen, Interne II/Kardiologie und Interne Intensivmedizin, Fadingerstraße 1, 4020 Linz, Austria
| | - Josè Luis Merino
- mHealth and Health Economics and PROM Committee of EHRA (European Heart Rhythm Association), Rue de la Loi 34/6th Floor B, 1040 Bruxelles, Belgium
- Arrhythmia-Robotic Electrophysiology Unit, La Paz University Hospital, IdiPAZ, Universidad Autónoma, Madrid, Spain
| | - Ruben Casado-Arroyo
- Department of Cardiology, H.U.B.-Hôpital Erasme, Université Libre de Bruxelles, Rte de Lennik 808, 1070 Bruxelles, Belgium
- mHealth and Health Economics and PROM Committee of EHRA (European Heart Rhythm Association), Rue de la Loi 34/6th Floor B, 1040 Bruxelles, Belgium
| | - Giuseppe Boriani
- mHealth and Health Economics and PROM Committee of EHRA (European Heart Rhythm Association), Rue de la Loi 34/6th Floor B, 1040 Bruxelles, Belgium
- Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, Via del Pozzo, 71, Modena 41124, Italy
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Riaz Gondal MU, Atta Mehdi H, Khenhrani RR, Kumari N, Ali MF, Kumar S, Faraz M, Malik J. Role of Machine Learning and Artificial Intelligence in Arrhythmias and Electrophysiology. Cardiol Rev 2024:00045415-990000000-00270. [PMID: 38761137 DOI: 10.1097/crd.0000000000000715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/20/2024]
Abstract
Machine learning (ML), a subset of artificial intelligence (AI) centered on machines learning from extensive datasets, stands at the forefront of a technological revolution shaping various facets of society. Cardiovascular medicine has emerged as a key domain for ML applications, with considerable efforts to integrate these innovations into routine clinical practice. Within cardiac electrophysiology, ML applications, especially in the automated interpretation of electrocardiograms, have garnered substantial attention in existing literature. However, less recognized are the diverse applications of ML in cardiac electrophysiology and arrhythmias, spanning basic science research on arrhythmia mechanisms, both experimental and computational, as well as contributions to enhanced techniques for mapping cardiac electrical function and translational research related to arrhythmia management. This comprehensive review delves into various ML applications within the scope of this journal, organized into 3 parts. The first section provides a fundamental understanding of general ML principles and methodologies, serving as a foundational resource for readers interested in exploring ML applications in arrhythmia research. The second part offers an in-depth review of studies in arrhythmia and electrophysiology that leverage ML methodologies, showcasing the broad potential of ML approaches. Each subject is thoroughly outlined, accompanied by a review of notable ML research advancements. Finally, the review delves into the primary challenges and future perspectives surrounding ML-driven cardiac electrophysiology and arrhythmias research.
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Affiliation(s)
| | - Hassan Atta Mehdi
- Department of Medicine, Jinnah Postgraduate Medical Centre, Karachi, Pakistan
| | - Raja Ram Khenhrani
- Department of Medicine, Internal Medicine Fellow, Shaheed Mohtarma Benazir Bhutto Medical College and Lyari General Hospital, Karachi, Pakistan
| | - Neha Kumari
- Department of Medicine, Jinnah Postgraduate Medical Centre, Karachi, Pakistan
| | - Muhammad Faizan Ali
- Department of Medicine, Jinnah Postgraduate Medical Centre, Karachi, Pakistan
| | - Sooraj Kumar
- Department of Medicine, Jinnah Sindh Medical University, Karachi, Pakistan; and
| | - Maria Faraz
- Department of Cardiovascular Medicine, Cardiovascular Analytics Group, Rawalpindi, Pakistan
| | - Jahanzeb Malik
- Department of Cardiovascular Medicine, Cardiovascular Analytics Group, Rawalpindi, Pakistan
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Volis I, Postnikov M, Reiner-Benaim A, Hellman Y, Marcusohn E. Effect of angiotensin receptor neprilysin inhibitor on physical activity in patients with heart failure with reduced ejection fraction, monitored by implantable electronic device home monitoring. J Cardiovasc Med (Hagerstown) 2024; 25:193-199. [PMID: 38251452 DOI: 10.2459/jcm.0000000000001595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
AIMS Angiotensin receptor neprilysin inhibitor (ARNI) therapy is a cornerstone in the treatment of heart failure with reduced ejection fraction (HFrEF), with significant improvement in mortality as well as morbidity and quality of life. However, maximal ARNI doses often result in hypotension. Recent studies with 'real world' experience suggest that lower doses of ARNI are as effective as higher doses.In order to evaluate the symptomatic effect of low-dose ARNI in HFrEF patients, we analyzed physical activity data obtained via home monitoring of patients with cardiac implantable electronic devices (CIEDs). METHODS We retrospectively analyzed physical activity data obtained from HFrEF patients with CIED-active home monitoring during the years 2021-2022. Patients with ARNI therapy were further divided into subgroups according to the administered dose. Low-dose ARNI included doses of up to 24/26 mg sacubitril/valsartan daily. Intermediate dose and high dose included doses of 72/78-120/130 mg/day, and 144/156-194/206 mg/day, respectively. RESULTS A total of 122 patients had home monitoring-compatible CIEDs and HFrEF during the study period. Sixty-four of these patients were treated with ARNI. Administration of low-dose ARNI resulted in a 20% increase in daily activity when compared with patients without ARNI treatment ( P = 0.038). Change in physical activity of patients in the intermediate-dose and high-dose groups was not significant. Younger patients, patients with cardiac resynchronization therapy, and patients without diabetes mellitus were more physically active. CONCLUSION Low-dose ARNI had a beneficial effect on physical activity in HFrEF patients. MH via CIED provided real-life objective data for patients' follow-up.
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Affiliation(s)
| | - Maria Postnikov
- Department of Internal Medicine "B", Rambam Healthcare Campus, Haifa, Israel
| | - Anat Reiner-Benaim
- Department of Epidemiology, Biostatistics, and Community Health Sciences, School of Public Health, Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva
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Brown G, Conway S, Ahmad M, Adegbie D, Patel N, Myneni V, Alradhawi M, Kumar N, Obaid DR, Pimenta D, Bray JJH. Role of artificial intelligence in defibrillators: a narrative review. Open Heart 2022; 9:openhrt-2022-001976. [PMID: 35790317 PMCID: PMC9258481 DOI: 10.1136/openhrt-2022-001976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 05/17/2022] [Indexed: 02/01/2023] Open
Abstract
Automated external defibrillators (AEDs) and implantable cardioverter defibrillators (ICDs) are used to treat life-threatening arrhythmias. AEDs and ICDs use shock advice algorithms to classify ECG tracings as shockable or non-shockable rhythms in clinical practice. Machine learning algorithms have recently been assessed for shock decision classification with increasing accuracy. Outside of rhythm classification alone, they have been evaluated in diagnosis of causes of cardiac arrest, prediction of success of defibrillation and rhythm classification without the need to interrupt cardiopulmonary resuscitation. This review explores the many applications of machine learning in AEDs and ICDs. While these technologies are exciting areas of research, there remain limitations to their widespread use including high processing power, cost and the ‘black-box’ phenomenon.
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Affiliation(s)
- Grace Brown
- Cardiology Department, Royal Free Hospital, London, UK
| | - Samuel Conway
- Cardiology Department, Royal Free Hospital, London, UK
| | - Mahmood Ahmad
- Medical Sciences, University College London, London, UK
| | - Divine Adegbie
- Cardiology Department, East and North Hertfordshire NHS Trust, Stevenage, Hertfordshire, UK
| | - Nishil Patel
- Cardiology Department, North Middlesex University Hospital, London, UK
| | | | | | - Niraj Kumar
- Institute of Cardiovascular Science, University College London, London, UK.,Cardiology Department, Barts Health NHS Trust, London, UK
| | - Daniel R Obaid
- Institute of Life Sciences, Swansea University, Swansea, UK
| | - Dominic Pimenta
- Cardiology Department, Richmond Research Institute, London, UK
| | - Jonathan J H Bray
- Cardiff University College of Biomedical and Life Sciences, Cardiff, UK
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Chung CT, Bazoukis G, Lee S, Liu Y, Liu T, Letsas KP, Armoundas AA, Tse G. Machine learning techniques for arrhythmic risk stratification: a review of the literature. INTERNATIONAL JOURNAL OF ARRHYTHMIA 2022; 23. [PMID: 35449883 PMCID: PMC9020640 DOI: 10.1186/s42444-022-00062-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Ventricular arrhythmias (VAs) and sudden cardiac death (SCD) are significant adverse events that affect the morbidity and mortality of both the general population and patients with predisposing cardiovascular risk factors. Currently, conventional disease-specific scores are used for risk stratification purposes. However, these risk scores have several limitations, including variations among validation cohorts, the inclusion of a limited number of predictors while omitting important variables, as well as hidden relationships between predictors. Machine learning (ML) techniques are based on algorithms that describe intervariable relationships. Recent studies have implemented ML techniques to construct models for the prediction of fatal VAs. However, the application of ML study findings is limited by the absence of established frameworks for its implementation, in addition to clinicians’ unfamiliarity with ML techniques. This review, therefore, aims to provide an accessible and easy-to-understand summary of the existing evidence about the use of ML techniques in the prediction of VAs. Our findings suggest that ML algorithms improve arrhythmic prediction performance in different clinical settings. However, it should be emphasized that prospective studies comparing ML algorithms to conventional risk models are needed while a regulatory framework is required prior to their implementation in clinical practice.
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Nedios S, Iliodromitis K, Kowalewski C, Bollmann A, Hindricks G, Dagres N, Bogossian H. Big Data in electrophysiology. Herzschrittmacherther Elektrophysiol 2022; 33:26-33. [PMID: 35137276 DOI: 10.1007/s00399-022-00837-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Accepted: 01/07/2022] [Indexed: 06/14/2023]
Abstract
The quantity of data produced and captured in medicine today is unprecedented. Technological improvements and automation have expanded the traditional statistical methods and enabled the analysis of Big Data. This has permitted the discovery of new associations with a granularity that was previously hidden to human eyes. In the first part of this review, the authors would like to provide an overview of basic Machine Learning (ML) principles and techniques in order to better understand their application in recent publications about cardiac arrhythmias. In the second part, ML-enabled advances in disease detection and diagnosis, outcome prediction, and novel disease characterization in topics like electrocardiography, atrial fibrillation, ventricular arrhythmias, and cardiac devices are presented. Finally, the limitations and challenges of applying ML in clinical practice, such as validation, replication, generalizability, and regulatory issues, are discussed. More carefully designed studies and collaborations are needed for ML to become feasible, trustworthy, accurate, and reproducible and to reach its full potential for patient-oriented precision medicine.
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Affiliation(s)
- Sotirios Nedios
- Department of Electrophysiology, Heart Center Leipzig at the University of Leipzig, Leipzig, Germany.
- Rhythmologie, Herzzentrum Leipzig, Universität Leipzig, Strümpellstr. 39, 04289, Leipzig, Germany.
| | - Konstantinos Iliodromitis
- Department of Cardiology and Rhythmology, Ev. Krankenhaus Hagen, Hagen, Germany
- Department of Cardiology, University Witten/Herdecke, Witten, Germany
| | - Christopher Kowalewski
- Department of Electrophysiology, Heart Center Leipzig at the University of Leipzig, Leipzig, Germany
| | - Andreas Bollmann
- Department of Electrophysiology, Heart Center Leipzig at the University of Leipzig, Leipzig, Germany
| | - Gerhard Hindricks
- Department of Electrophysiology, Heart Center Leipzig at the University of Leipzig, Leipzig, Germany
| | - Nikolaos Dagres
- Department of Electrophysiology, Heart Center Leipzig at the University of Leipzig, Leipzig, Germany
| | - Harilaos Bogossian
- Department of Cardiology and Rhythmology, Ev. Krankenhaus Hagen, Hagen, Germany
- Department of Cardiology, University Witten/Herdecke, Witten, Germany
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7
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Kolk MZ, Frodi DM, Andersen TO, Langford J, Diederichsen SZ, Svendsen JH, Tan HL, Knops RE, Tjong FV. Accelerometer-assessed physical behavior and the association with clinical outcomes in implantable cardioverter-defibrillator recipients: A systematic review. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2022; 3:46-55. [PMID: 35265934 PMCID: PMC8890329 DOI: 10.1016/j.cvdhj.2021.11.006] [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: 11/29/2022] Open
Abstract
Background Current implantable cardioverter-defibrillator (ICD) devices are equipped with a device-embedded accelerometer capable of capturing physical activity (PA). In contrast, wearable accelerometer-based methods enable the measurement of physical behavior (PB) that encompasses not only PA but also sleep behavior, sedentary time, and rest-activity patterns. Objective This systematic review evaluates accelerometer-based methods used in patients carrying an ICD or at high risk of sudden cardiac death. Methods Papers were identified via the OVID MEDLINE and OVID EMBASE databases. PB could be assessed using a wearable accelerometer or an embedded accelerometer in the ICD. Results A total of 52 papers were deemed appropriate for this review. Out of these studies, 30 examined device-embedded accelerometry (189,811 patients), 19 examined wearable accelerometry (1601 patients), and 3 validated wearable accelerometry against device-embedded accelerometry (106 patients). The main findings were that a low level of PA after implantation of the ICD and a decline in PA were both associated with an increased risk of mortality, heart failure hospitalization, and appropriate ICD shock. Second, PA was affected by cardiac factors (eg, onset of atrial fibrillation, ICD shocks) and noncardiac factors (eg, seasonal differences, societal factors). Conclusion This review demonstrated the potential of accelerometer-measured PA as a marker of clinical deterioration and ventricular arrhythmias. Notwithstanding that the evidence of PB assessed using wearable accelerometry was limited, there seems to be potential for accelerometers to improve early warning systems and facilitate preventative and proactive strategies.
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Affiliation(s)
- Maarten Z.H. Kolk
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, Academic Medical Center, Amsterdam, the Netherlands
| | - Diana M. Frodi
- Department of Cardiology, Copenhagen University Hospital – Rigshospitalet, Copenhagen, Denmark
| | - Tariq O. Andersen
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
- Vital Beats, Copenhagen, Denmark
| | - Joss Langford
- Activinsights, Cambridgeshire, United Kingdom
- College of Life and Environmental Sciences, University of Exeter, Exeter, United Kingdom
| | - Soeren Z. Diederichsen
- Department of Cardiology, Copenhagen University Hospital – Rigshospitalet, Copenhagen, Denmark
| | - Jesper H. Svendsen
- Department of Cardiology, Copenhagen University Hospital – Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Hanno L. Tan
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, Academic Medical Center, Amsterdam, the Netherlands
- Netherlands Heart Institute, Utrecht, the Netherlands
| | - Reinoud E. Knops
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, Academic Medical Center, Amsterdam, the Netherlands
| | - Fleur V.Y. Tjong
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, Academic Medical Center, Amsterdam, the Netherlands
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Abstract
Machine learning (ML), a branch of artificial intelligence, where machines learn from big data, is at the crest of a technological wave of change sweeping society. Cardiovascular medicine is at the forefront of many ML applications, and there is a significant effort to bring them into mainstream clinical practice. In the field of cardiac electrophysiology, ML applications have also seen a rapid growth and popularity, particularly the use of ML in the automatic interpretation of ECGs, which has been extensively covered in the literature. Much lesser known are the other aspects of ML application in cardiac electrophysiology and arrhythmias, such as those in basic science research on arrhythmia mechanisms, both experimental and computational; in the development of better techniques for mapping of cardiac electrical function; and in translational research related to arrhythmia management. In the current review, we examine comprehensively such ML applications as they match the scope of this journal. The current review is organized in 3 parts. The first provides an overview of general ML principles and methodologies that will afford readers of the necessary information on the subject, serving as the foundation for inviting further ML applications in arrhythmia research. The basic information we provide can serve as a guide on how one might design and conduct an ML study. The second part is a review of arrhythmia and electrophysiology studies in which ML has been utilized, highlighting the broad potential of ML approaches. For each subject, we outline comprehensively the general topics, while reviewing some of the research advances utilizing ML under the subject. Finally, we discuss the main challenges and the perspectives for ML-driven cardiac electrophysiology and arrhythmia research.
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Affiliation(s)
- Natalia A. Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, USA 21218
- Alliance for Cardiovascular Diagnosis and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, USA 21218
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 733 North Broadway, Baltimore, MD, USA 21205
| | - Dan M. Popescu
- Alliance for Cardiovascular Diagnosis and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, USA 21218
- Department of Applied Mathematics and Statistics, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, USA 21218
| | - Julie K. Shade
- Department of Biomedical Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, USA 21218
- Alliance for Cardiovascular Diagnosis and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, USA 21218
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Biswas M, Levy A, Weber R, Tarakji K, Chung M, Noseworthy PA, Newton-Cheh C, Rosenberg MA. Multicenter Analysis of Dosing Protocols for Sotalol Initiation. J Cardiovasc Pharmacol Ther 2019; 25:212-218. [PMID: 31707834 DOI: 10.1177/1074248419887710] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Sotalol, a Vaughan-Williams Class III antiarrhythmic medication, is used to manage atrial arrhythmias. Due to its QT-prolonging effect and subsequent increased risk of torsade de pointes, many centers admit patients during the initial dosing period. Despite its widespread use, little information is available regarding dosing protocols during this period. In this multicenter investigation, dosing protocols in patients initiating sotalol therapy were examined to identify predictors of successful sotalol initiation. Over a 4-year period, patients admitted to 5 hospitals in the United States for inpatient telemetry monitoring during initiation for nonresearch purposes were enrolled. A 3-day course of 5 of 6 doses of sotalol was considered successful completion of the loading protocol. Of the 213 enrolled patients, over 90% were successfully discharged on sotalol. Significant bradycardia, ineffectiveness, and excessive QT prolongation were reasons for failed completion. Absence of a dose adjustment was a strong predictor of successful initiation (odds ratio: 6.6, 95% confidence interval: 1.3-32.7, P = .02). Hypertension, use of a calcium channel blocker, use of a separate β-blocker, and presence of a pacemaker were predictors of dose adjustments. Marginal structural models (ie, inverse probability weighting based on probability of a dose adjustment) verified that these factors also predicted successful initiation via preventing any dose adjustment and suggests that considering these factors may result in a higher likelihood of successful initiation in future investigations. In conclusion, we found that the majority of patients admitted for sotalol initiation are successfully discharged on the medication. The study findings suggest that factors predicting need for dose adjustment can be used to identify patients who could undergo outpatient initiation. Prospective studies are needed to verify this approach.
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Affiliation(s)
- Minakshi Biswas
- Division of Cardiology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Andrew Levy
- Division of Cardiology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Rachel Weber
- Division of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA
| | - Khaldoun Tarakji
- Center for Atrial Fibrillation, Section of Cardiac Pacing and Electrophysiology, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Mina Chung
- Center for Atrial Fibrillation, Section of Cardiac Pacing and Electrophysiology, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Peter A Noseworthy
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | - Christopher Newton-Cheh
- Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael A Rosenberg
- Division of Cardiology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.,Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
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