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Baykaner T, Noseworthy PA, Wan EY. Implementing AI in clinical practice: Practical insights on integrating AI through digital dashboards and beyond. Heart Rhythm 2024:S1547-5271(24)03109-6. [PMID: 39207349 DOI: 10.1016/j.hrthm.2024.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2024] [Accepted: 08/01/2024] [Indexed: 09/04/2024]
Affiliation(s)
| | - Peter A Noseworthy
- Division of Heart Rhythm Services, Department of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota
| | - Elaine Y Wan
- Division of Cardiology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York.
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van den Broek JLPM, Gottlieb LA, Vermeer JR, Overeem S, Dekker LRC. When the Clock Strikes A-fib. JACC Clin Electrophysiol 2024; 10:1916-1928. [PMID: 39093277 DOI: 10.1016/j.jacep.2024.05.035] [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: 01/31/2024] [Revised: 05/09/2024] [Accepted: 05/25/2024] [Indexed: 08/04/2024]
Abstract
Within the broad spectrum of atrial fibrillation (AF) symptomatology, there is a striking subset of patients with predominant or even solitary nocturnal onset of the arrhythmia. This review covers AF with nocturnal onset, with the aim of defining this distinctive subgroup among patients with AF. A periodicity analysis is provided showing a clear increased onset between 10:00 pm and 7:00 am. Multiple interacting mechanisms are discussed, such as circadian modulation of electrophysiological properties, vagal tone, and sleep disorders, as well as the potential interaction and synergism between these factors, to provide a better understanding of this clinical entity. Lastly, potential therapeutic targets for AF with nocturnal onset are addressed such as upstream therapy for underlying comorbidities, type of drug and timing of drug administration and pulmonary vein isolation, ablation of the ganglionated plexus, and autonomic nervous system modulation. Understanding the underlying AF mechanisms in the individual patient with nocturnal onset will contribute to patient-specific therapy.
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Affiliation(s)
- Johannes L P M van den Broek
- Department of Cardiology, Catharina Hospital Eindhoven, Eindhoven, the Netherlands; Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.
| | | | - Jasper R Vermeer
- Department of Cardiology, Catharina Hospital Eindhoven, Eindhoven, the Netherlands; Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Sebastiaan Overeem
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Kempenhaeghe Centre for Sleep Medicine, Heeze, the Netherlands
| | - Lukas R C Dekker
- Department of Cardiology, Catharina Hospital Eindhoven, Eindhoven, the Netherlands; Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
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3
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Liang H, Zhang H, Wang J, Shao X, Wu S, Lyu S, Xu W, Wang L, Tan J, Wang J, Yang Y. The Application of Artificial Intelligence in Atrial Fibrillation Patients: From Detection to Treatment. Rev Cardiovasc Med 2024; 25:257. [PMID: 39139434 PMCID: PMC11317345 DOI: 10.31083/j.rcm2507257] [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: 10/17/2023] [Revised: 01/16/2024] [Accepted: 01/26/2024] [Indexed: 08/15/2024] Open
Abstract
Atrial fibrillation (AF) is the most prevalent arrhythmia worldwide. Although the guidelines for AF have been updated in recent years, its gradual onset and associated risk of stroke pose challenges for both patients and cardiologists in real-world practice. Artificial intelligence (AI) is a powerful tool in image analysis, data processing, and for establishing models. It has been widely applied in various medical fields, including AF. In this review, we focus on the progress and knowledge gap regarding the use of AI in AF patients and highlight its potential throughout the entire cycle of AF management, from detection to drug treatment. More evidence is needed to demonstrate its ability to improve prognosis through high-quality randomized controlled trials.
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Affiliation(s)
- Hanyang Liang
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Han Zhang
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Juan Wang
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Xinghui Shao
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Shuang Wu
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Siqi Lyu
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Wei Xu
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Lulu Wang
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Jiangshan Tan
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Jingyang Wang
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Yanmin Yang
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
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Yalvaç HE, Babayiğit E, Görenek B. Is it effective SGLT2 inhibitors for every AF patients? Pacing Clin Electrophysiol 2024; 47:980. [PMID: 38683877 DOI: 10.1111/pace.14991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 04/09/2024] [Indexed: 05/02/2024]
Affiliation(s)
| | - Erdi Babayiğit
- Faculty of Medicine, Department of Cardiology, Eskişehir Osmangazi University, Eskisehir, Turkey
| | - Bülent Görenek
- Faculty of Medicine, Department of Cardiology, Eskişehir Osmangazi University, Eskisehir, Turkey
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Wang Y, Zhou M, Ding Y, Li X, Xie T, Zhou Z, Fu W, Shi Z. Unsupervised machine learning cluster analysis to identification EVAR patients clinical phenotypes based on radiomics. Vascular 2024:17085381241262575. [PMID: 38885967 DOI: 10.1177/17085381241262575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Abstract
OBJECTIVE This study used unsupervised machine learning (UML) cluster analysis to explore clinical phenotypes of endovascular aortic repair (EVAR) for abdominal aortic aneurysm (AAA) patients based on radiomics. METHOD We retrospectively reviewed 1785 patients with infra-renal AAA who underwent elective EVAR procedures between January 2010 and December 2020. Pyradiomics was used to extract the radiomics features. Statistical analysis was applied to determine the radiomics features that related to severe adverse events (SAEs) after EVAR. The selected features were used for UML cluster analysis in training set and validation in test set. Comparison of basic characteristics and radiomics features of different clusters. The Kaplan-Meier analysis was conducted to generate the cumulative incidence of freedom from SAEs rate. RESULT A total of 1180 patients were enrolled. During the follow-up, 353 patients experienced EVAR-related SAEs. In total, 1223 radiomics features were extracted from each patient, of which 23 radiomics features were finally preserved to identify different clinical phenotypes. 944 patients were allocated to the training set. Three clusters were identified in training set, in which patients had identical clinical characteristics and morphological features, while varied considerably of selected radiomics features. This encouraging performance was further approved in the test set. In addition, each cluster was well differentiated from other clusters and Kaplan-Meier analysis showed significant differences of freedom from SAEs rate between different clusters both in the training (p = .0216) and test sets (p = .0253). CONCLUSION Based on radiomics, UML cluster analysis can identify clinical phenotypes in EVAR patients with distinct long-term outcomes.
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Affiliation(s)
- Yonggang Wang
- Department of Vascular Surgery, The First Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Min Zhou
- Department of Vascular Surgery, Zhongshan Hospital, Institute of Vascular Surgery, Fudan University, National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Yong Ding
- Department of Vascular Surgery, Zhongshan Hospital, Institute of Vascular Surgery, Fudan University, National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Xu Li
- Department of Vascular Surgery, Zhongshan Hospital, Institute of Vascular Surgery, Fudan University, National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Tianchen Xie
- Department of Vascular Surgery, Zhongshan Hospital, Institute of Vascular Surgery, Fudan University, National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Zhenyu Zhou
- Department of Vascular Surgery, Zhongshan Hospital, Institute of Vascular Surgery, Fudan University, National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Weiguo Fu
- Department of Vascular Surgery, Zhongshan Hospital, Institute of Vascular Surgery, Fudan University, National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Zhenyu Shi
- Department of Vascular Surgery, Zhongshan Hospital, Institute of Vascular Surgery, Fudan University, National Clinical Research Center for Interventional Medicine, Shanghai, China
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黄 凤, 钟 玥, 张 然, 白 文, 李 娅, 龚 深, 陈 石, 朱 亭, 陈 一, 饶 莉. [Cluster Analysis and Ablation Success Rate in Atrial Fibrillation Patients Undergoing Catheter Ablation]. SICHUAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF SICHUAN UNIVERSITY. MEDICAL SCIENCE EDITION 2024; 55:687-692. [PMID: 38948279 PMCID: PMC11211785 DOI: 10.12182/20240560101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Indexed: 07/02/2024]
Abstract
Objective Atrial fibrillation (AF) is a disease of high heterogeneity, and the association between AF phenotypes and the outcome of different catheter ablation strategies remains unclear. Conventional classification of AF (e.g. according to duration, atrial size, and thromboembolism risk) fails to provide reference for the optimal stratification of the prognostic risks or to guide individualized treatment plan. In recent years, research on machine learning has found that cluster analysis, an unsupervised data-driven approach, can uncover the intrinsic structure of data and identify clusters of patients with pathophysiological similarity. It has been demonstrated that cluster analysis helps improve the characterization of AF phenotypes and provide valuable prognostic information. In our cohort of AF inpatients undergoing radiofrequency catheter ablation, we used unsupervised cluster analysis to identify patient subgroups, to compare them with previous studies, and to evaluate their association with different suitable ablation patterns and outcomes. Methods The participants were AF patients undergoing radiofrequency catheter ablation at West China Hospital between October 2015 and December 2017. All participants were aged 18 years or older. They underwent radiofrequency catheter ablation during their hospitalization. They completed the follow-up process under explicit informed consent. Patients with AF of a reversible cause, severe mitral stenosis or prosthetic heart valve, congenital heart disease, new-onset acute coronary syndrome within three months prior to the surgery, or a life expectancy less than 12 months were excluded according to the exclusion criteria. The cohort consisted of 1102 participants with paroxysmal or persistent/long-standing persistent AF. Data on 59 variables representing demographics, AF type, comorbidities, therapeutic history, vital signs, electrocardiographic and echocardiographic findings, and laboratory findings were collected. Overall, data for the variables were rarely missing (<5%), and multiple imputation was used for correction of missing data. Follow-up surveys were conducted through outpatient clinic visits or by telephone. Patients were scheduled for follow-up with 12-lead resting electrocardiography and 24-hours Holter monitoring at 3 months and 6 months after the ablation procedure. Early ablation success was defined as the absence of documented AF, atrial flutter, or atrial tachycardia >30 seconds at 6-month follow-up. Hierarchical clustering was performed on the 59 baseline variables. All characteristic variables were standardized to have a mean of zero and a standard deviation of one. Initially, each patient was regarded as a separate cluster, and the distance between these clusters was calculated. Then, the Ward minimum variance method of clustering was used to merge the pair of clusters with the minimum total variance. This process continued until all patients formed one whole cluster. The "NbClust" package in R software, capable of calculating various statistical indices, including pseudo t2 index, cubic clustering criterion, silhouette index etc, was applied to determine the optimal number of clusters. The most frequently chosen number of clusters by these indices was selected. A heatmap was generated to illustrate the clinical features of clusters, while a tree diagram was used to depict the clustering process and the heterogeneity among clusters. Ablation strategies were compared within each cluster regarding ablation efficacy. Results Five statistically driven clusters were identified: 1) the younger age cluster (n=404), characterized by the lowest prevalence of cardiovascular and cerebrovascular comorbidities but the highest prevalence of obstructive sleep apnea syndrome (14.4%); 2) a cluster of elderly adults with chronic diseases (n=438), the largest cluster, showing relatively higher rates of hypertension, diabetes, stroke, and chronic obstructive pulmonary disease; 3) a cluster with high prevalence of sinus node dysfunction (n=160), with patients showing the highest prevalence of sick sinus syndrome and pacemaker implantation; 4) the heart failure cluster (n=80), with the highest prevalence of heart failure (58.8%) and persistent/long-standing persistent AF (73.7%); 5) prior coronary artery revascularization cluster (n=20), with patients of the most advanced age (median: 69.0 years old) and predominantly male patients, all of whom had prior myocardial infarction and coronary artery revascularization. Patients in cluster 2 achieved higher early ablation success with pulmonary veins isolation alone compared to extensive ablation strategies (79.6% vs. 66.5%; odds ratio [OR]=1.97, 95% confidence interval [CI]: 1.28-3.03). Although extensive ablation strategies had a slightly higher success rate in the heart failure group, the difference was not statistically significant. Conclusions This study provided a unique classification of AF patients undergoing catheter ablation by cluster analysis. Age, chronic disease, sinus node dysfunction, heart failure and history of coronary artery revascularization contributed to the formation of the five clinically relevant subtypes. These subtypes showed differences in ablation success rates, highlighting the potential of cluster analysis in guiding individualized risk stratification and treatment decisions for AF patients.
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Affiliation(s)
- 凤誉 黄
- 四川大学华西医院 心内科 (成都 610041)Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - 玥 钟
- 四川大学华西医院 心内科 (成都 610041)Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - 然 张
- 四川大学华西医院 心内科 (成都 610041)Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - 文娟 白
- 四川大学华西医院 心内科 (成都 610041)Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - 娅姣 李
- 四川大学华西医院 心内科 (成都 610041)Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - 深圳 龚
- 四川大学华西医院 心内科 (成都 610041)Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - 石 陈
- 四川大学华西医院 心内科 (成都 610041)Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - 亭西 朱
- 四川大学华西医院 心内科 (成都 610041)Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - 一龙 陈
- 四川大学华西医院 心内科 (成都 610041)Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - 莉 饶
- 四川大学华西医院 心内科 (成都 610041)Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
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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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Jung M, Yang PS, Kim D, Sung JH, Jang E, Yu HT, Kim TH, Uhm JS, Pak HN, Lee MH, Joung B. Multimorbidity in atrial fibrillation for clinical implications using the Charlson Comorbidity Index. Int J Cardiol 2024; 398:131605. [PMID: 38000669 DOI: 10.1016/j.ijcard.2023.131605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 11/05/2023] [Accepted: 11/19/2023] [Indexed: 11/26/2023]
Abstract
BACKGROUND Predicting survival in atrial fibrillation (AF) patients with comorbidities is challenging. This study aimed to assess multimorbidity in AF patients using the Charlson Comorbidity Index (CCI) and its clinical implications. METHODS We analyzed 451,368 participants from the Korea National Health Insurance Service-Health Screening cohort (2002-2013) without prior AF diagnoses. Patients were categorized into new-onset AF and non-AF groups, with a high CCI defined as ≥4 points. Antithrombotic treatment and outcomes (all-cause death, stroke, major bleeding, and heart failure [HF] hospitalization) were evaluated over 9 years. RESULTS In total, 9.5% of the enrolled patients had high CCI. During follow-up, 12,241 patients developed new-onset AF. Among AF patients, antiplatelet drug use increased significantly in those with high CCI (adjusted odds ratio [OR] 1.05, 95%confidence interval [CI] 1.02-1.08, P < .001). However, anticoagulants were significantly less prescribed in patients with high CCI (OR 0.97, 95%CI 0.95-0.99, P = .012). Incidence of adverse events (all-cause death, stroke, major bleeding, HF hospitalization) progressively increased in this order: low CCI without AF, high CCI without AF, low CCI with AF, and high CCI with AF (all P < .001). Furthermore, high CCI with AF had a significantly higher risk compared to low CCI without AF (all-cause death, adjusted hazard ratio [aHR] 2.52, 95% CI 2.37-2.68, P < .001; stroke, aHR 1.43, 95% CI 1.29-1.58, P < .001; major bleeding, aHR 1.14, 95% CI 1.04-1.26, P = .007; HF hospitalization, aHR 4.75, 95% CI 4.03-5.59, P < .001). CONCLUSIONS High CCI predicted increased antiplatelet use and reduced oral anticoagulant prescription. AF was associated with higher risks of all-cause death, stroke, major bleeding, and HF hospitalization compared to high CCI.
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Affiliation(s)
- Moonki Jung
- Division of Cardiology, Department of Internal Medicine, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Pil-Sung Yang
- Department of Cardiology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea
| | - Daehoon Kim
- Division of Cardiology, Department of Internal Medicine, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jung-Hoon Sung
- Department of Cardiology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea
| | - Eunsun Jang
- Division of Cardiology, Department of Internal Medicine, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hee Tae Yu
- Division of Cardiology, Department of Internal Medicine, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Tae-Hoon Kim
- Division of Cardiology, Department of Internal Medicine, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jae-Sun Uhm
- Division of Cardiology, Department of Internal Medicine, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hui-Nam Pak
- Division of Cardiology, Department of Internal Medicine, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Moon-Hyoung Lee
- Division of Cardiology, Department of Internal Medicine, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Boyoung Joung
- Division of Cardiology, Department of Internal Medicine, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
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Lacki A, Martinez-Millana A. A Comparison of the Impact of Pharmacological Treatments on Cardioversion, Rate Control, and Mortality in Data-Driven Atrial Fibrillation Phenotypes in Critical Care. Bioengineering (Basel) 2024; 11:199. [PMID: 38534473 DOI: 10.3390/bioengineering11030199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 02/09/2024] [Accepted: 02/18/2024] [Indexed: 03/28/2024] Open
Abstract
Critical care physicians are commonly faced with patients exhibiting atrial fibrillation (AF), a cardiac arrhythmia with multifaceted origins. Recent investigations shed light on the heterogeneity among AF patients by uncovering unique AF phenotypes, characterized by differing treatment strategies and clinical outcomes. In this retrospective study encompassing 9401 AF patients in an intensive care cohort, we sought to identify differences in average treatment effects (ATEs) across different patient groups. We extract data from the MIMIC-III database, use hierarchical agglomerative clustering to identify patients' phenotypes, and assign them to treatment groups based on their initial drug administration during AF episodes. The treatment options examined included beta blockers (BBs), potassium channel blockers (PCBs), calcium channel blockers (CCBs), and magnesium sulfate (MgS). Utilizing multiple imputation and inverse probability of treatment weighting, we estimate ATEs related to rhythm control, rate control, and mortality, approximated as hourly and daily rates (%/h, %/d). Our analysis unveiled four distinctive AF phenotypes: (1) postoperative hypertensive, (2) non-cardiovascular mutlimorbid, (3) cardiovascular multimorbid, and (4) valvulopathy atrial dilation. PCBs showed the highest cardioversion rates across phenotypes, ranging from 11.6%/h (9.35-13.3) to 7.69%/h (5.80-9.22). While CCBs demonstrated the highest effectiveness in controlling ventricular rates within the overall patient cohort, PCBs and MgS outperformed them in specific phenotypes. PCBs exhibited the most favorable mortality outcomes overall, except for the non-cardiovascular multimorbid cluster, where BBs displayed a lower mortality rate of 1.33%/d [1.04-1.93] compared to PCBs' 1.68%/d [1.10-2.24]. The results of this study underscore the significant diversity in ATEs among individuals with AF and suggest that phenotype-based classification could be a valuable tool for physicians, providing personalized insights to inform clinical decision making.
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Affiliation(s)
- Alexander Lacki
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, 46022 Valencia, Spain
| | - Antonio Martinez-Millana
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, 46022 Valencia, Spain
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10
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Davogustto G, Zhao S, Li Y, Farber-Eger E, Lowery BD, Shaffer LL, Mosley JD, Shoemaker MB, Xu Y, Roden DM, Wells QS. Unbiased characterization of atrial fibrillation phenotypic architecture provides insight to genetic liability and clinically relevant outcomes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.13.24302788. [PMID: 38405916 PMCID: PMC10888988 DOI: 10.1101/2024.02.13.24302788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Background Atrial Fibrillation (AF) is a common and clinically heterogeneous arrythmia. Machine learning (ML) algorithms can define data-driven disease subtypes in an unbiased fashion, but whether the AF subgroups defined in this way align with underlying mechanisms, such as high polygenic liability to AF or inflammation, and associate with clinical outcomes is unclear. Methods We identified individuals with AF in a large biobank linked to electronic health records (EHR) and genome-wide genotyping. The phenotypic architecture in the AF cohort was defined using principal component analysis of 35 expertly curated and uncorrelated clinical features. We applied an unsupervised co-clustering machine learning algorithm to the 35 features to identify distinct phenotypic AF clusters. The clinical inflammatory status of the clusters was defined using measured biomarkers (CRP, ESR, WBC, Neutrophil %, Platelet count, RDW) within 6 months of first AF mention in the EHR. Polygenic risk scores (PRS) for AF and cytokine levels were used to assess genetic liability of clusters to AF and inflammation, respectively. Clinical outcomes were collected from EHR up to the last medical contact. Results The analysis included 23,271 subjects with AF, of which 6,023 had available genome-wide genotyping. The machine learning algorithm identified 3 phenotypic clusters that were distinguished by increasing prevalence of comorbidities, particularly renal dysfunction, and coronary artery disease. Polygenic liability to AF across clusters was highest in the low comorbidity cluster. Clinically measured inflammatory biomarkers were highest in the high comorbid cluster, while there was no difference between groups in genetically predicted levels of inflammatory biomarkers. Subgroup assignment was associated with multiple clinical outcomes including mortality, stroke, bleeding, and use of cardiac implantable electronic devices after AF diagnosis. Conclusion Patient subgroups identified by unsupervised clustering were distinguished by comorbidity burden and associated with risk of clinically important outcomes. Polygenic liability to AF across clusters was greatest in the low comorbidity subgroup. Clinical inflammation, as reflected by measured biomarkers, was lowest in the subgroup with lowest comorbidities. However, there were no differences in genetically predicted levels of inflammatory biomarkers, suggesting associations between AF and inflammation is driven by acquired comorbidities rather than genetic predisposition.
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Krittayaphong R, Treewaree S, Wongtheptien W, Kaewkumdee P, Lip GYH. Clinical phenotype classification to predict risk and optimize the management of patients with atrial fibrillation using the Atrial Fibrillation Better Care (ABC) pathway: a report from the COOL-AF registry. QJM 2024; 117:16-23. [PMID: 37788118 DOI: 10.1093/qjmed/hcad219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 09/11/2023] [Indexed: 10/05/2023] Open
Abstract
BACKGROUND Phenotypic classification is a method of grouping patients with similar phenotypes. AIM We aimed to use phenotype classification based on a clustering process for risk stratification of patients with non-valvular atrial fibrillation (AF) and second, to assess the benefit of the Atrial Fibrillation Better Care (ABC) pathway. METHODS Patients with AF were prospectively enrolled from 27 hospitals in Thailand from 2014 to 2017, and followed up every 6 months for 3 years. Cluster analysis was performed from 46 variables using the hierarchical clustering using the Ward minimum variance method. Outcomes were a composite of all-cause death, ischemic stroke/systemic embolism, acute myocardial infarction and heart failure. RESULTS A total of 3405 patients were enrolled (mean age 67.8 ± 11.3 years, 58.2% male). During the mean follow-up of 31.8 ± 8.7 months. Three clusters were identified: Cluster 1 had the highest risk followed by Cluster 3 and Cluster 2 with a hazard ratio (HR) and 95% confidence interval (CI) of composite outcomes of 2.78 (2.25, 3.43), P < 0.001 for Cluster 1 and 1.99 (1.63, 2.42), P < 0.001 for Cluster 3 compared with Cluster 2. Management according to the ABC pathway was associated with reductions in adverse clinical outcomes especially those who belonged to Clusters 1 and 3 with HR and 95%CI of the composite outcome of 0.54 (0.40, 073), P < 0.001 for Cluster 1 and 0.49 (0.38, 0.63), P < 0.001 for Cluster 3. CONCLUSION Phenotypic classification helps in risk stratification and prognostication. Compliance with the ABC pathway was associated with improved clinical outcomes.
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Affiliation(s)
- R Krittayaphong
- Division of Cardiology, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - S Treewaree
- Division of Cardiology, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - W Wongtheptien
- Department of Cardiology, Chiangrai Prachanukroh Hospital, Chiangrai, Thailand
| | - P Kaewkumdee
- Division of Cardiology, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - G Y H Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK
- Danish Center for Clinical Health Services Research, Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
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Wang Q, Ouyang H, Lv L, Gui L, Yang S, Hua P. Left main coronary artery morphological phenotypes and its hemodynamic properties. Biomed Eng Online 2024; 23:9. [PMID: 38254133 PMCID: PMC10804578 DOI: 10.1186/s12938-024-01205-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND Atherosclerosis may be linked to morphological defects that lead to variances in coronary artery hemodynamics. Few objective strategies exit at present for generalizing morphological phenotypes of coronary arteries in terms of hemodynamics. We used unsupervised clustering (UC) to classify the morphology of the left main coronary artery (LM) and looked at how hemodynamic distribution differed between phenotypes. METHODS In this study, 76 LMs were obtained from 76 patients. After LMs were reconstructed with coronary computed tomography angiography, centerlines were used to extract the geometric characteristics. Unsupervised clustering was carried out using these characteristics to identify distinct morphological phenotypes of LMs. The time-averaged wall shear stress (TAWSS) for each phenotype was investigated by means of computational fluid dynamics (CFD) analysis of the left coronary artery. RESULTS We identified four clusters (i.e., four phenotypes): Cluster 1 had a shorter stem and thinner branches (n = 26); Cluster 2 had a larger bifurcation angle (n = 10); Cluster 3 had an ostium at an angulation to the coronary sinus and a more curved stem, and thick branches (n = 10); and Cluster 4 had an ostium at an angulation to the coronary sinus and a flatter stem (n = 14). TAWSS features varied widely across phenotypes. Nodes with low TAWSS (L-TAWSS) were typically found around the branching points of the left anterior descending artery (LAD), particularly in Cluster 2. CONCLUSION Our findings demonstrated that UC is a powerful technique for morphologically classifying LMs. Different LM phenotypes exhibited distinct hemodynamic characteristics in certain regions. This morphological clustering method could aid in identifying people at high risk for developing coronary atherosclerosis, hence facilitating early intervention.
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Affiliation(s)
- Qi Wang
- Department of Cardio-Vascular Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yan Jiang West Road, Guangzhou, 510120, China
- Department of Cardiovascular Surgery, Qilu Hospital of Shandong University, Shandong University, Jinan, China
| | - Hua Ouyang
- Department of Cardio-Vascular Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yan Jiang West Road, Guangzhou, 510120, China
| | - Lei Lv
- Department of Cardio-Vascular Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yan Jiang West Road, Guangzhou, 510120, China
- Department of Cardiac and Vascular Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming Medical University, Kunming, China
| | - Long Gui
- Department of Cardio-Vascular Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yan Jiang West Road, Guangzhou, 510120, China
| | - Songran Yang
- Department of Biobank and Bioinformatics, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yan Jiang West Road, Guangzhou, 510120, China.
| | - Ping Hua
- Department of Cardio-Vascular Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yan Jiang West Road, Guangzhou, 510120, China.
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Saito Y, Omae Y, Nagashima K, Miyauchi K, Nishizaki Y, Miyazaki S, Hayashi H, Nojiri S, Daida H, Minamino T, Okumura Y. Phenotyping of atrial fibrillation with cluster analysis and external validation. Heart 2023; 109:1751-1758. [PMID: 37263768 DOI: 10.1136/heartjnl-2023-322447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 05/15/2023] [Indexed: 06/03/2023] Open
Abstract
OBJECTIVES Atrial fibrillation (AF) is a heterogeneous condition. We performed a cluster analysis in a cohort of patients with AF and assessed the prognostic implication of the identified cluster phenotypes. METHODS We used two multicentre, prospective, observational registries of AF: the SAKURA AF registry (Real World Survey of Atrial Fibrillation Patients Treated with Warfarin and Non-vitamin K Antagonist Oral Anticoagulants) (n=3055, derivation cohort) and the RAFFINE registry (Registry of Japanese Patients with Atrial Fibrillation Focused on anticoagulant therapy in New Era) (n=3852, validation cohort). Cluster analysis was performed by the K-prototype method with 14 clinical variables. The endpoints were all-cause mortality and composite cardiovascular events. RESULTS The analysis subclassified derivation cohort patients into five clusters. Cluster 1 (n=414, 13.6%) was characterised by younger men with a low prevalence of comorbidities; cluster 2 (n=1003, 32.8%) by a high prevalence of hypertension; cluster 3 (n=517, 16.9%) by older patients without hypertension; cluster 4 (n=652, 21.3%) by the oldest patients, who were mainly female and with a high prevalence of heart failure history; and cluster 5 (n=469, 15.3%) by older patients with high prevalence of diabetes and ischaemic heart disease. During follow-up, the risk of all-cause mortality and composite cardiovascular events increased across clusters (log-rank p<0.001, p<0.001). Similar results were found in the external validation cohort. CONCLUSIONS Machine learning-based cluster analysis identified five different phenotypes of AF with unique clinical characteristics and different clinical outcomes. The use of these phenotypes may help identify high-risk patients with AF.
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Affiliation(s)
- Yuki Saito
- Division of Cardiology, Department of Medicine, Nihon University School of Medicine, Tokyo, Japan
| | - Yuto Omae
- Department of Industrial Engineering and Management, College of Industrial Technology, Nihon University, Chiba, Japan
| | - Koichi Nagashima
- Division of Cardiology, Department of Medicine, Nihon University School of Medicine, Tokyo, Japan
| | - Katsumi Miyauchi
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yuji Nishizaki
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Sakiko Miyazaki
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Hidemori Hayashi
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Shuko Nojiri
- Medical Technology Innovation Center, Juntendo University, Tokyo, Japan
| | - Hiroyuki Daida
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Tohru Minamino
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yasuo Okumura
- Division of Cardiology, Department of Medicine, Nihon University School of Medicine, Tokyo, Japan
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Jiang C, Li M, Hu Y, Du X, Li X, He L, Lai Y, Chen T, Li Y, Guo X, Jiang C, Tang R, Sang C, Long D, Xie G, Dong J, Ma C. Identification of atrial fibrillation phenotypes at low risk of stroke in patients with CHA2DS2-VASc ≥2: Insight from the China-AF study. Pacing Clin Electrophysiol 2023; 46:1203-1211. [PMID: 37736697 DOI: 10.1111/pace.14829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 08/07/2023] [Accepted: 09/07/2023] [Indexed: 09/23/2023]
Abstract
OBJECTIVE Patients with atrial fibrillation (AF) are highly heterogeneous, and current risk stratification scores are only modestly good at predicting an individual's stroke risk. We aim to identify distinct AF clinical phenotypes with cluster analysis to optimize stroke prevention practices. METHODS From the prospective Chinese Atrial Fibrillation Registry cohort study, we included 4337 AF patients with CHA2 DS2 -VASc≥2 for males and 3 for females who were not treated with oral anticoagulation. We randomly split the patients into derivation and validation sets by a ratio of 7:3. In the derivation set, we used outcome-driven patient clustering with metric learning to group patients into clusters with different risk levels of ischemic stroke and systemic embolism, and identify clusters of patients with low risks. Then we tested the results in the validation set, using the clustering rules generated from the derivation set. Finally, the survival decision tree was applied as a sensitivity analysis to confirm the results. RESULTS Up to the follow-up of 1 year, 140 thromboembolic events (ischemic stroke or systemic embolism) occurred. After supervised metric learning from six variables involved in CHA2 DS2 -VASc scheme, we identified a cluster of patients (255/3035, 8.4%) at an annual thromboembolism risk of 0.8% in the derivation set. None of the patients in the low-risk cluster had prior thromboembolism, heart failure, diabetes, or age older than 70 years. After applying the regularities from metric learning on the validation set, we also identified a cluster of patients (137/1302, 10.5%) with an incident thromboembolism rate of 0.7%. Sensitivity analysis based on the survival decision tree approach selected a subgroup of patients with the same phenotypes as the metric-learning algorithm. CONCLUSIONS Cluster analysis identified a distinct clinical phenotype at low risk of stroke among high-risk [CHA2 DS2 -VASc≥2 (3 for females)] patients with AF. The use of the novel analytic approach has the potential to prevent a subset of AF patients from unnecessary anticoagulation and avoid the associated risk of major bleeding.
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Affiliation(s)
- Chao Jiang
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing, China
| | - Mingxiao Li
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing, China
| | - Yiying Hu
- Ping An Health Technology, Beijing, China
| | - Xin Du
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing, China
- Heart Health Research Center, Beijing, China
| | - Xiang Li
- Ping An Health Technology, Beijing, China
| | - Liu He
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing, China
| | - Yiwei Lai
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing, China
| | - Tiange Chen
- School of Public Health, Peking University Health Science Center, Beijing, China
| | - Yingxue Li
- Ping An Health Technology, Beijing, China
| | - Xueyuan Guo
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing, China
| | - Chenxi Jiang
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing, China
| | - Ribo Tang
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing, China
| | - Caihua Sang
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing, China
| | - Deyong Long
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing, China
| | | | - Jianzeng Dong
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing, China
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Changsheng Ma
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing, China
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Sison SDM, Lin KJ, Najafzadeh M, Ko D, Patorno E, Bessette LG, Zakoul H, Kim DH. Common non-cardiovascular multimorbidity groupings and clinical outcomes in older adults with major cardiovascular disease. J Am Geriatr Soc 2023; 71:3179-3188. [PMID: 37354026 PMCID: PMC10592495 DOI: 10.1111/jgs.18479] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 05/17/2023] [Accepted: 05/19/2023] [Indexed: 06/25/2023]
Abstract
BACKGROUND Among older adults, non-cardiovascular multimorbidity often coexists with cardiovascular disease (CVD) but their clinical significance is uncertain. We identified common non-cardiovascular comorbidity patterns and their association with clinical outcomes in Medicare fee-for-service beneficiaries with acute myocardial infarction (AMI), congestive heart failure (CHF), or atrial fibrillation (AF). METHODS Using 2015-2016 Medicare data, we took 1% random sample to create 3 cohorts of beneficiaries diagnosed with AMI (n = 24,808), CHF (n = 57,285), and AF (n = 36,277) prior to 1/1/2016. Within each cohort, we applied latent class analysis to classify beneficiaries based on 9 non-cardiovascular comorbidities (anemia, cancer, chronic kidney disease, chronic lung disease, dementia, depression, diabetes, hypothyroidism, and musculoskeletal disease). Mortality, cardiovascular and non-cardiovascular hospitalizations, and home time lost over a 1-year follow-up period were compared across non-cardiovascular multimorbidity classes. RESULTS Similar non-cardiovascular multimorbidity classes emerged from the 3 CVD cohorts: (1) minimal, (2) depression-lung, (3) chronic kidney disease (CKD)-diabetes, and (4) multi-system class. Across CVD cohorts, multi-system class had the highest risk of mortality (hazard ratio [HR], 2.7-3.9), cardiovascular hospitalization (HR, 1.6-3.3), non-cardiovascular hospitalization (HR, 3.1-7.2), and home time lost (rate ratio, 2.7-5.4). Among those with AMI, the CKD-diabetes class was more strongly associated with all the adverse outcomes than the depression-lung class. In CHF and AF, differences in risk between the depression-lung and CKD-diabetes classes varied per outcome; and the depression-lung and multi-system classes had double the rates of non-cardiovascular hospitalizations than cardiovascular hospitalizations. CONCLUSION Four non-cardiovascular multimorbidity patterns were found among Medicare beneficiaries with CHF, AMI, or AF. Compared to the minimal class, the multi-system, CKD-diabetes, and depression-lung classes were associated with worse outcomes. Identification of these classes offers insight into specific segments of the population that may benefit from more than the usual cardiovascular care.
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Affiliation(s)
- Stephanie Denise M. Sison
- Division of Gerontology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA
- Department of Internal Medicine, University of Massachusetts Chan Medical School, Worcester, MA
| | - Kueiyu Joshua Lin
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Boston, MA
| | - Mehdi Najafzadeh
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Boston, MA
| | - Darae Ko
- Section of Cardiovascular Medicine, Boston University School of Medicine, Boston, MA
| | - Elisabetta Patorno
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Boston, MA
| | - Lily G. Bessette
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Boston, MA
| | - Heidi Zakoul
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Boston, MA
| | - Dae Hyun Kim
- Division of Gerontology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Boston, MA
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Van Deutekom C, Geelhoed B, Van Munster BC, Bakker SJL, Gansevoort RT, Van Gelder IC, Rienstra M. Cardiovascular and renal multimorbidity increase risk of atrial fibrillation in the PREVEND cohort. Open Heart 2023; 10:e002315. [PMID: 37460268 DOI: 10.1136/openhrt-2023-002315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 06/30/2023] [Indexed: 07/20/2023] Open
Abstract
OBJECTIVE Atrial fibrillation (AF) is a condition that occurs in the presence of comorbidities. With the accumulation of comorbidities (multimorbidity), some combinations may more often occur together than others. Information on the impact of clustering of these on incident AF is sparse. We aimed to investigate clustering of cardiovascular and renal comorbidities and study the association between comorbidity clusters and incident AF. METHODS We used the community-based Prevention of Renal and Vascular ENd-stage Disease (PREVEND) cohort in which 8592 individuals participated. Latent class analysis was performed to assess clustering of 10 cardiovascular and renal comorbidities. RESULTS We excluded individuals with prior AF or missing ECG data, leaving 8265 individuals for analysis (mean age 48.9±12.6 years, 50.2% women). During 9.2±2.1 years of follow-up, 251 individuals (3.0%) developed AF. A model with three clusters was the optimal model, with one cluster being young (44.5±10.8 years) and healthy, carrying a low (1.0%) risk of incident AF; one cluster being older (63.0±8.4 years) and multimorbid, carrying a high (16.2%) risk of incident AF and a third middle-aged (57.0±11.3 years), obese and hypertensive cluster carrying an intermediate risk (5.9%) of incident AF. While the prevalence of the comorbidities differed between classes, no clear combination(s) of comorbidities was observed within the classes. CONCLUSIONS We identified three clusters of comorbidities in individuals in the community-based PREVEND cohort. The three clusters contained different amount of comorbidities carrying different risks of incident AF. However, there were no differences between the clusters regarding specific combination(s) of comorbidities.
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Affiliation(s)
- Colinda Van Deutekom
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Bastiaan Geelhoed
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Barbara C Van Munster
- Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Stephan J L Bakker
- Department of Nephrology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Ron T Gansevoort
- Department of Nephrology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Isabelle C Van Gelder
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Michiel Rienstra
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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Bisson A, M Fawzy A, Romiti GF, Proietti M, Angoulvant D, El-Bouri W, Y H Lip G, Fauchier L. Phenotypes and outcomes in non-anticoagulated patients with atrial fibrillation: An unsupervised cluster analysis. Arch Cardiovasc Dis 2023; 116:342-351. [PMID: 37422421 DOI: 10.1016/j.acvd.2023.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 06/01/2023] [Accepted: 06/05/2023] [Indexed: 07/10/2023]
Abstract
BACKGROUND Patients with atrial fibrillation are characterized by great clinical heterogeneity and complexity. The usual classifications may not adequately characterize this population. Data-driven cluster analysis reveals different possible patient classifications. AIMS To identify different clusters of patients with atrial fibrillation who share similar clinical phenotypes, and to evaluate the association between identified clusters and clinical outcomes, using cluster analysis. METHODS An agglomerative hierarchical cluster analysis was performed in non-anticoagulated patients from the Loire Valley Atrial Fibrillation cohort. Associations between clusters and a composite outcome comprising stroke/systemic embolism/death and all-cause death, stroke and major bleeding were evaluated using Cox regression analyses. RESULTS The study included 3434 non-anticoagulated patients with atrial fibrillation (mean age 70.3±17 years; 42.8% female). Three clusters were identified: cluster 1 was composed of younger patients, with a low prevalence of co-morbidities; cluster 2 included old patients with permanent atrial fibrillation, cardiac pathologies and a high burden of cardiovascular co-morbidities; cluster 3 identified old female patients with a high burden of cardiovascular co-morbidities. Compared with cluster 1, clusters 2 and 3 were independently associated with an increased risk of the composite outcome (hazard ratio 2.85, 95% confidence interval 1.32-6.16 and hazard ratio 1.52, 95% confidence interval 1.09-2.11, respectively) and all-cause death (hazard ratio 3.54, 95% confidence interval 1.49-8.43 and hazard ratio 1.88, 95% confidence interval 1.26-2.79, respectively). Cluster 3 was independently associated with an increased risk of major bleeding (hazard ratio 1.72, 95% confidence interval 1.06-2.78). CONCLUSION Cluster analysis identified three statistically driven groups of patients with atrial fibrillation, with distinct phenotype characteristics and associated with different risks for major clinical adverse events.
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Affiliation(s)
- Arnaud Bisson
- Service de cardiologie, centre hospitalier régional universitaire et faculté de médecine de Tours, 37000 Tours, France; Service de cardiologie, centre hospitalier régional universitaire d'Orléans, 45100 Orléans, France; EA4245, transplantation immunité inflammation, université de Tours, 37032 Tours, France; Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, L7 8TX Liverpool, United Kingdom.
| | - Ameenathul M Fawzy
- Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, L7 8TX Liverpool, United Kingdom
| | - Giulio Francesco Romiti
- Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, L7 8TX Liverpool, United Kingdom; Department of Translational and Precision Medicine, Sapienza - University of Rome, 00185 Rome, Italy
| | - Marco Proietti
- Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, L7 8TX Liverpool, United Kingdom; Department of Clinical Sciences and Community Health, University of Milan, 20122 Milan, Italy; IRCCS Istituti Clinici Scientifici Maugeri, 20138 Milan, Italy
| | - Denis Angoulvant
- Service de cardiologie, centre hospitalier régional universitaire et faculté de médecine de Tours, 37000 Tours, France; EA4245, transplantation immunité inflammation, université de Tours, 37032 Tours, France
| | - Wahbi El-Bouri
- Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, L7 8TX Liverpool, United Kingdom
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, L7 8TX Liverpool, United Kingdom; Department of Clinical Medicine, Aalborg University, 9000 Aalborg, Denmark
| | - Laurent Fauchier
- Service de cardiologie, centre hospitalier régional universitaire et faculté de médecine de Tours, 37000 Tours, France
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18
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Pallares Robles A, Ten Cate V, Lenz M, Schulz A, Prochaska JH, Rapp S, Koeck T, Leineweber K, Heitmeier S, Opitz CF, Held M, Espinola-Klein C, Lackner KJ, Münzel T, Konstantinides SV, Ten Cate-Hoek A, Ten Cate H, Wild PS. Unsupervised clustering of venous thromboembolism patients by clinical features at presentation identifies novel endotypes that improve prognostic stratification. Thromb Res 2023:S0049-3848(23)00124-X. [PMID: 37202285 DOI: 10.1016/j.thromres.2023.04.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 04/20/2023] [Accepted: 04/28/2023] [Indexed: 05/20/2023]
Abstract
BACKGROUND Individuals with acute venous thromboembolism (VTE) constitute a heterogeneous group of patients with diverse clinical characteristics and outcome. OBJECTIVES To identify endotypes of individuals with acute VTE based on clinical characteristics at presentation through unsupervised cluster analysis and to evaluate their molecular proteomic profile and clinical outcome. METHODS Data from 591 individuals from the Genotyping and Molecular phenotyping of Venous thromboembolism (GMP-VTE) project were explored. Hierarchical clustering was applied to 58 variables to define VTE endotypes. Clinical characteristics, three-year incidence of thromboembolic events or death, and acute-phase plasma proteomics were assessed. RESULTS Four endotypes were identified, exhibiting different patterns of clinical characteristics and clinical course. Endotype 1 (n = 300), comprising older individuals with comorbidities, had the highest incidence of thromboembolic events or death (HR [95 % CI]: 3.76 [1.96-7.19]), followed by endotype 4 (n = 127) (HR [95 % CI]: 2.55 [1.26-5.16]), characterised by men with history of VTE and provoking risk factors, and endotype 3 (n = 57) (HR [95 % CI]: 1.57 [0.63-3.87]), composed of young women with provoking risk factors, vs. reference endotype 2 (n = 107). The reference endotype was constituted by individuals diagnosed with PE without comorbidities, who had the lowest incidence of the investigated endpoint. Differentially expressed proteins associated with the endotypes were related to distinct biological processes, supporting differences in molecular pathophysiology. The endotypes had superior prognostic ability compared to existing risk stratifications such as provoked vs unprovoked VTE and D-dimer levels. CONCLUSION Four endotypes of VTE were identified by unsupervised phenotype-based clustering that diverge in clinical outcome and plasmatic protein signature. This approach might support the future development of individualized treatment in VTE.
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Affiliation(s)
- Alejandro Pallares Robles
- Clinical Epidemiology and Systems Medicine, Center for Thrombosis and Hemostasis (CTH), University Medical Center of the Johannes Gutenberg University Mainz, Germany; Departments of Internal Medicine and Biochemistry, Thrombosis Expertise Center, Maastricht University Medical Center and Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, the Netherlands
| | - Vincent Ten Cate
- Clinical Epidemiology and Systems Medicine, Center for Thrombosis and Hemostasis (CTH), University Medical Center of the Johannes Gutenberg University Mainz, Germany; Preventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | - Michael Lenz
- Preventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | - Andreas Schulz
- Preventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | - Jürgen H Prochaska
- Clinical Epidemiology and Systems Medicine, Center for Thrombosis and Hemostasis (CTH), University Medical Center of the Johannes Gutenberg University Mainz, Germany; Preventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany; German Center for Cardiovascular Research (DZHK), Partner Site Rhine Main, University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | - Steffen Rapp
- Preventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | - Thomas Koeck
- Preventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany; German Center for Cardiovascular Research (DZHK), Partner Site Rhine Main, University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | | | | | | | - Matthias Held
- Department of Internal Medicine, Medical Mission Hospital, Academic Teaching Hospital, Würzburg, Germany
| | - Christine Espinola-Klein
- Center for Cardiology-Cardiology I, University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | - Karl J Lackner
- Institute of Clinical Chemistry and Laboratory Medicine, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Thomas Münzel
- German Center for Cardiovascular Research (DZHK), Partner Site Rhine Main, University Medical Center of the Johannes Gutenberg University Mainz, Germany; Center for Cardiology-Cardiology I, University Medical Center of the Johannes Gutenberg University Mainz, Germany; Center for Thrombosis and Hemostasis (CTH), University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | - Stavros V Konstantinides
- Center for Thrombosis and Hemostasis (CTH), University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | - Arina Ten Cate-Hoek
- Departments of Internal Medicine and Biochemistry, Thrombosis Expertise Center, Maastricht University Medical Center and Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, the Netherlands
| | - Hugo Ten Cate
- Departments of Internal Medicine and Biochemistry, Thrombosis Expertise Center, Maastricht University Medical Center and Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, the Netherlands; Center for Thrombosis and Hemostasis (CTH), University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | - Philipp S Wild
- Clinical Epidemiology and Systems Medicine, Center for Thrombosis and Hemostasis (CTH), University Medical Center of the Johannes Gutenberg University Mainz, Germany; Preventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany; German Center for Cardiovascular Research (DZHK), Partner Site Rhine Main, University Medical Center of the Johannes Gutenberg University Mainz, Germany; Institute of molecular biology (IMB), Mainz, Germany.
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19
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Döring C, Richter U, Ulbrich S, Wunderlich C, Ebert M, Richter S, Linke A, Sveric KM. The Impact of Right Atrial Size to Predict Success of Direct Current Cardioversion in Patients With Persistent Atrial Fibrillation. Korean Circ J 2023; 53:331-343. [PMID: 37161747 PMCID: PMC10172272 DOI: 10.4070/kcj.2022.0291] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 01/29/2023] [Accepted: 02/08/2023] [Indexed: 05/11/2023] Open
Abstract
BACKGROUND AND OBJECTIVES The prognostic implication of right atrial (RA) and left atrial (LA) size for an immediate success of direct current cardioversion (DCCV) in atrial fibrillation (AF) remains unclear. This study aimed to compare RA and LA size for the prediction of DCCV success. METHODS Between 2012 and 2018, 734 consecutive outpatients were screened for our prospective registry. Each eligible patient received a medical history, blood analysis, and transthoracic echocardiography with a focus on indexed RA (iRA) area and LA volume (iLAV) prior to DCCV with up to three biphasic shocks (200-300-360 J) or additional administration of amiodarone or flecainide to restore sinus rhythm. RESULTS We enrolled 589 patients, and DCCV was in 89% (n=523) successful. Mean age was 68 ± 10 years, and 40% (n=234) had New York heart association class >II. A prevalence of the male sex (64%, n=376) and of persistent AF (86%, n=505) was observed. Although DCCV success was associated with female sex (odds ratio [OR], 1.88; 95% confidence interval [CI], 1.06-3.65), with absence of coronary heart disease and normal left ventricular function (OR, 2.24; 95% CI, 1.26-4.25), with short AF duration (OR, 1.93; 95% CI, 1.05-4.04) in univariable regression, only iRA area remained a stable and independent predictor of DCCV success (OR, 0.27; 95% CI, 0.12-0.69; area under the curve 0.71), but not iLAV size (OR, 1.16; 95% CI, 1.05-1.56) in multivariable analysis. CONCLUSIONS iRA area is superior to iLAV for the prediction of immediate DCCV success in AF.
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Affiliation(s)
- Christoph Döring
- Department for Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Dresden, Germany
| | - Utz Richter
- Department for Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Dresden, Germany
| | - Stefan Ulbrich
- Department for Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Dresden, Germany
| | - Carsten Wunderlich
- Department of Internal Medicine II, Helios Hospital Pirna, Pirna, Germany
| | - Micaela Ebert
- Department for Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Dresden, Germany
| | - Sergio Richter
- Department for Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Dresden, Germany
| | - Axel Linke
- Department for Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Dresden, Germany
| | - Krunoslav Michael Sveric
- Department for Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Dresden, Germany.
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20
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Palmu J, Börschel CS, Ortega-Alonso A, Markó L, Inouye M, Jousilahti P, Salido RA, Sanders K, Brennan C, Humphrey GC, Sanders JG, Gutmann F, Linz D, Salomaa V, Havulinna AS, Forslund SK, Knight R, Lahti L, Niiranen T, Schnabel RB. Gut microbiome and atrial fibrillation-results from a large population-based study. EBioMedicine 2023; 91:104583. [PMID: 37119735 PMCID: PMC10165189 DOI: 10.1016/j.ebiom.2023.104583] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 03/26/2023] [Accepted: 04/06/2023] [Indexed: 05/01/2023] Open
Abstract
BACKGROUND Atrial fibrillation (AF) is an important heart rhythm disorder in aging populations. The gut microbiome composition has been previously related to cardiovascular disease risk factors. Whether the gut microbial profile is also associated with the risk of AF remains unknown. METHODS We examined the associations of prevalent and incident AF with gut microbiota in the FINRISK 2002 study, a random population sample of 6763 individuals. We replicated our findings in an independent case-control cohort of 138 individuals in Hamburg, Germany. FINDINGS Multivariable-adjusted regression models revealed that prevalent AF (N = 116) was associated with nine microbial genera. Incident AF (N = 539) over a median follow-up of 15 years was associated with eight microbial genera with false discovery rate (FDR)-corrected P < 0.05. Both prevalent and incident AF were associated with the genera Enorma and Bifidobacterium (FDR-corrected P < 0.001). AF was not significantly associated with bacterial diversity measures. Seventy-five percent of top genera (Enorma, Paraprevotella, Odoribacter, Collinsella, Barnesiella, Alistipes) in Cox regression analyses showed a consistent direction of shifted abundance in an independent AF case-control cohort that was used for replication. INTERPRETATION Our findings establish the basis for the use of microbiome profiles in AF risk prediction. However, extensive research is still warranted before microbiome sequencing can be used for prevention and targeted treatment of AF. FUNDING This study was funded by European Research Council, German Ministry of Research and Education, Academy of Finland, Finnish Medical Foundation, and the Finnish Foundation for Cardiovascular Research, the Emil Aaltonen Foundation, and the Paavo Nurmi Foundation.
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Affiliation(s)
- Joonatan Palmu
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Turku, Finland; Department of Internal Medicine, Turku University Hospital and University of Turku, Finland
| | - Christin S Börschel
- Department of Cardiology, University Heart and Vascular Centre Hamburg-Eppendorf, Hamburg, Germany; German Centre for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Lübeck, Hamburg, Germany
| | - Alfredo Ortega-Alonso
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Turku, Finland; Neuroscience Center, University of Helsinki, Helsinki, Finland; Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
| | - Lajos Markó
- Experimental and Clinical Research Center, a Cooperation of Charité-Universitätsmedizin and the Max-Delbrück Center, Berlin, Germany; Max Delbrück Center for Molecular Medicine (MDC), Berlin, Germany; Charité - Universitätsmedizin Berlin, Berlin, Germany; DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Germany
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Pekka Jousilahti
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Turku, Finland
| | - Rodolfo A Salido
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Karenina Sanders
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Caitriona Brennan
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Gregory C Humphrey
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Jon G Sanders
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA; Cornell Institute for Host-Microbe Interaction and Disease, Cornell University, Ithaca, NY, USA
| | - Friederike Gutmann
- Experimental and Clinical Research Center, a Cooperation of Charité-Universitätsmedizin and the Max-Delbrück Center, Berlin, Germany; Max Delbrück Center for Molecular Medicine (MDC), Berlin, Germany; Charité - Universitätsmedizin Berlin, Berlin, Germany; DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Germany
| | - Dominik Linz
- Department of Cardiology, Maastricht University Medical Centre and Cardiovascular Research Institute Maastricht, Maastricht, the Netherlands; Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Centre for Heart Rhythm Disorders, Royal Adelaide Hospital, and University of Adelaide, Adelaide, Australia; Department of Cardiology, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Veikko Salomaa
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Turku, Finland
| | - Aki S Havulinna
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Turku, Finland; Institute for Molecular Medicine Finland, FIMM - HiLIFE, Helsinki, Finland
| | - Sofia K Forslund
- Experimental and Clinical Research Center, a Cooperation of Charité-Universitätsmedizin and the Max-Delbrück Center, Berlin, Germany; Max Delbrück Center for Molecular Medicine (MDC), Berlin, Germany; Charité - Universitätsmedizin Berlin, Berlin, Germany; DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Germany; Structural and Computational Biology, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Rob Knight
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA; Department of Computer Science & Engineering, University of California San Diego, La Jolla, CA, USA
| | - Leo Lahti
- Department of Computing, University of Turku, Turku, Finland
| | - Teemu Niiranen
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Turku, Finland; Department of Internal Medicine, Turku University Hospital and University of Turku, Finland
| | - Renate B Schnabel
- Department of Cardiology, University Heart and Vascular Centre Hamburg-Eppendorf, Hamburg, Germany; German Centre for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Lübeck, Hamburg, Germany.
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21
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de Winter MA, Uijl A, Büller HR, Carrier M, Cohen AT, Hansen JB, Kaasjager KHAH, Kakkar AK, Middeldorp S, Raskob GE, Sørensen HT, Wells PS, Nijkeuter M, Dorresteijn JAN. Redefining clinical venous thromboembolism phenotypes: a novel approach using latent class analysis. J Thromb Haemost 2023; 21:573-585. [PMID: 36696208 DOI: 10.1016/j.jtha.2022.11.013] [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: 06/02/2022] [Revised: 11/14/2022] [Accepted: 11/19/2022] [Indexed: 01/26/2023]
Abstract
BACKGROUND Patients with venous thromboembolism (VTE) are commonly classified by the presence or absence of provoking factors at the time of VTE to guide treatment decisions. This approach may not capture the heterogeneity of the disease and its prognosis. OBJECTIVES To evaluate clinically important novel phenotypic clusters among patients with VTE without cancer and to explore their association with anticoagulant treatment and clinical outcomes. METHODS Latent class analysis was performed with 18 baseline clinical variables in 3062 adult patients with VTE without active cancer participating in PREFER in VTE, a noninterventional disease registry. The derived latent classes were externally validated in a post hoc analysis of Hokusai-VTE (n = 6593), a randomized trial comparing edoxaban with warfarin. The associations between cluster membership and anticoagulant treatment, recurrent VTE, bleeding, and mortality after initial treatment were studied. RESULTS The following 5 clusters were identified: young men cluster (n = 1126, 37%), young women cluster (n = 215, 7%), older people cluster (n = 1106, 36%), comorbidity cluster (n = 447, 15%), and history of venous thromboembolism cluster (n = 168, 5%). Patient characteristics varied by age, sex, medical history, and treatment patterns. Consistent clusters were evident on external validation. In Cox proportional hazard models, recurrence risk was lower in the young women cluster (hazard ratio [HR], 0.27; 95% CI, 0.12-0.61) compared with the comorbidity cluster, after adjusting for extended anticoagulation. The risk of bleeding was lower in young men, young women, and older people clusters (HR, 0.50; 95% CI, 0.38-0.66; HR, 0.23; 95% CI, 0.11-0.46; and HR, 0.55; 95% CI 0.41-0.73, respectively). CONCLUSION The heterogeneity of VTE cases extends beyond the distinction between provoked and unprovoked VTE.
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Affiliation(s)
- Maria A de Winter
- Department of Acute Internal Medicine, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Alicia Uijl
- Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, the Netherlands; Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, the Netherlands; Division of Cardiology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Harry R Büller
- Department of Vascular Medicine, Amsterdam UMC, Amsterdam, the Netherlands
| | - Marc Carrier
- Department of Medicine, University of Ottawa, Ontario, Canada; The Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Alexander T Cohen
- Department of Haematological Medicine, Guys and St Thomas' Hospitals, King's College London, London, UK
| | - John-Bjarne Hansen
- Thrombosis Research Center, Department of Clinical Medicine, UiT - The Arctic University of Norway and University Hospital of North Norway, Tromsø, Norway
| | - Karin H A H Kaasjager
- Department of Acute Internal Medicine, University Medical Center Utrecht, Utrecht, the Netherlands
| | | | - Saskia Middeldorp
- Department of Internal Medicine & Radboud Institute of Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Gary E Raskob
- Hudson College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
| | - Henrik Toft Sørensen
- Department of Clinical Epidemiology, Aarhus University Hospital and Aarhus University, Aarhus, Denmark
| | - Philip S Wells
- Department of Medicine, University of Ottawa, Ontario, Canada; The Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Mathilde Nijkeuter
- Department of Acute Internal Medicine, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Jannick A N Dorresteijn
- Department of Vascular Medicine, University Medical Center Utrecht, Utrecht, the Netherlands.
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22
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Suzuki S, Yamashita T, Akao M, Atarashi H, Ikeda T, Okumura K, Koretsune Y, Shimizu W, Tsutsui H, Toyoda K, Hirayama A, Yasaka M, Yamaguchi T, Teramukai S, Kimura T, Morishima Y, Takita A, Inoue H. Clinical phenotypes of older adults with non-valvular atrial fibrillation not treated with oral anticoagulants by hierarchical cluster analysis in the ANAFIE Registry. PLoS One 2023; 18:e0280753. [PMID: 36753467 PMCID: PMC9907799 DOI: 10.1371/journal.pone.0280753] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 01/08/2023] [Indexed: 02/09/2023] Open
Abstract
BACKGROUND Although anticoagulants are indicated for many elderly patients with non-valvular atrial fibrillation (NVAF), some patients do not receive anticoagulant therapy, whose characteristics and outcomes are diverse. METHODS AND RESULTS In this sub-analysis of the All Nippon AF In the Elderly (ANAFIE) Registry, the phenotypes of patients who were not receiving anticoagulants at baseline were evaluated by cluster analysis using Ward's linkage hierarchical algorithm. Of 32,275 enrolled patients, 2445 (7.6%) were not receiving anticoagulants. Two clusters were identified: (1) elderly paroxysmal AF (PAF) patients with a high proportion of catheter ablation history (57%) and (2) very elderly patients with a high prevalence of previous major bleeding (43%). Respective mean ages were 80.9 and 84.2 years, mean CHA2DS2-VASc scores were 3.8 and 4.9, PAF prevalences were 100.0% and 31.4%, proportions of patients with catheter ablation history were 21.0% and 7.9%, and proportions of patients with a history of major bleeding were 4.0% and 10.8%. Annual incidence rates were 2.72% and 8.81% for all-cause death, 1.66% and 5.85% for major adverse cardiovascular or neurological events, 1.08% and 3.30% for stroke or systemic embolism, and 0.69% and 1.19% for major bleeding, respectively. CONCLUSIONS In this cohort of elderly NVAF patients from the ANAFIE Registry who were not receiving anticoagulants, over half had PAF with a high proportion of catheter ablation history and a low incidence of adverse outcomes; for them, non-prescription of anticoagulants may be partially understandable, but they should be carefully monitored regarding AF burden or atrial cardiomyopathy and be adequately anticoagulated when adverse findings are detected. The remaining were very elderly patients with a high prevalence of previous major bleeding and a high incidence of adverse outcomes; for them, non-prescription of anticoagulants is inappropriate because of the high thromboembolic risk. TRIAL REGISTRATION Registration: http://www.umin.ac.jp/; Unique identifier: UMIN000024006.
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Affiliation(s)
| | | | - Masaharu Akao
- Department of Cardiology, National Hospital Organization Kyoto Medical Center, Kyoto, Japan
| | | | - Takanori Ikeda
- Department of Cardiovascular Medicine, Toho University Faculty of Medicine, Tokyo, Japan
| | - Ken Okumura
- Division of Cardiology, Saiseikai Kumamoto Hospital Cardiovascular Center, Kumamoto, Japan
| | | | - Wataru Shimizu
- Department of Cardiovascular Medicine, Graduate School of Medicine, Nippon Medical School, Tokyo, Japan
| | - Hiroyuki Tsutsui
- Department of Cardiovascular Medicine, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kazunori Toyoda
- Department of Cerebrovascular Medicine, National Cerebral and Cardiovascular Center, Osaka, Japan
| | | | - Masahiro Yasaka
- Department of Cerebrovascular Medicine and Neurology, Cerebrovascular Center, National Hospital Organization Kyushu Medical Center, Fukuoka, Japan
| | - Takenori Yamaguchi
- Department of Cerebrovascular Medicine, National Cerebral and Cardiovascular Center, Osaka, Japan
| | - Satoshi Teramukai
- Department of Biostatistics, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Tetsuya Kimura
- Primary Medical Science Department, Daiichi Sankyo, Tokyo, Japan
| | | | - Atsushi Takita
- Data Intelligence Department, Daiichi Sankyo Co., Ltd., Tokyo, Japan
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23
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Zeitler EP, Li Y, Silverstein AP, Russo AM, Poole JE, Daniels MR, Al‐Khalidi HR, Lee KL, Bahnson T, Anstrom KJ, Packer DL, Mark DB. Effects of Ablation Versus Drug Therapy on Quality of Life by Sex in Atrial Fibrillation: Results From the CABANA Trial. J Am Heart Assoc 2023; 12:e027871. [PMID: 36688367 PMCID: PMC9973617 DOI: 10.1161/jaha.122.027871] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 12/13/2022] [Indexed: 01/24/2023]
Abstract
Background Women with atrial fibrillation (AF) demonstrate more AF-related symptoms and worse quality of life (QOL). Whether increased use of ablation in women reduces sex-related QOL differences is unknown. Sex-related outcomes for ablation versus drug therapy was a prespecified analysis in the CABANA (Catheter Ablation Versus Antiarrhythmic Drug Therapy for Atrial Fibrillation) trial. Methods and Results Symptoms were assessed periodically over 60 months with the Mayo AF-Specific Symptom Inventory (MAFSI) frequency score, and QOL was assessed with the Atrial Fibrillation Effect on Quality of Life (AFEQT) summary and component scores. Women had lower baseline QOL scores than men (mean AFEQT scores 55.9 and 65.6, respectively). Ablation patients improved more than drug therapy patients with similar treatment effect by sex: AFEQT 12-month mean adjusted treatment difference in women 6.1 points (95% CI, 3.5-8.6) and men 4.9 points (95% CI, 3.0-6.9). Participants with baseline AFEQT summary scores <70 had greater QOL improvement, with a mean treatment difference at 12 months of 7.6 points for women (95% CI, 4.3-10.9) and 6.4 points for men (95% CI, 3.3-9.4). The mean adjusted difference in MAFSI frequency score between women randomized to ablation versus drug therapy at 12 months was -2.5 (95% CI, -3.4 to -1.6); for men, the difference was -1.3 (95% CI, -2.0 to -0.6). Conclusions Compared with drug therapy for AF, ablation resulted in more QOL improvement in both sexes, primarily driven by improvements in those with lower baseline QOL. Ablation did not eliminate the AF-related QOL gap between women and men. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT00911508.
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Affiliation(s)
| | - Yanhong Li
- Duke Clinical Research Institute, Duke UniversityDurhamNCUSA
| | | | - Andrea M. Russo
- Division of Cardiovascular DiseaseCooper Medical School of Rowan UniversityCamdenNJUSA
| | | | | | - Hussein R. Al‐Khalidi
- Duke Clinical Research Institute, Duke UniversityDurhamNCUSA
- Department of Biostatistics and BioinformaticsDuke UniversityDurhamNCUSA
| | - Kerry L. Lee
- Duke Clinical Research Institute, Duke UniversityDurhamNCUSA
- Department of Biostatistics and BioinformaticsDuke UniversityDurhamNCUSA
| | - Tristram D. Bahnson
- Duke Clinical Research Institute, Duke UniversityDurhamNCUSA
- Division of Cardiology, Department of MedicineDuke University Medical CenterDurhamNCUSA
| | | | | | - Daniel B. Mark
- Duke Clinical Research Institute, Duke UniversityDurhamNCUSA
- Division of Cardiology, Department of MedicineDuke University Medical CenterDurhamNCUSA
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24
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Pujadas ER, Raisi-Estabragh Z, Szabo L, Morcillo CI, Campello VM, Martin-Isla C, Vago H, Merkely B, Harvey NC, Petersen SE, Lekadir K. Atrial fibrillation prediction by combining ECG markers and CMR radiomics. Sci Rep 2022; 12:18876. [PMID: 36344532 PMCID: PMC9640662 DOI: 10.1038/s41598-022-21663-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 09/29/2022] [Indexed: 11/09/2022] Open
Abstract
Atrial fibrillation (AF) is the most common cardiac arrhythmia. It is associated with a higher risk of important adverse health outcomes such as stroke and death. AF is linked to distinct electro-anatomic alterations. The main tool for AF diagnosis is the Electrocardiogram (ECG). However, an ECG recorded at a single time point may not detect individuals with paroxysmal AF. In this study, we developed machine learning models for discrimination of prevalent AF using a combination of image-derived radiomics phenotypes and ECG features. Thus, we characterize the phenotypes of prevalent AF in terms of ECG and imaging alterations. Moreover, we explore sex-differential remodelling by building sex-specific models. Our integrative model including radiomics and ECG together resulted in a better performance than ECG alone, particularly in women. ECG had a lower performance in women than men (AUC: 0.77 vs 0.88, p < 0.05) but adding radiomics features, the accuracy of the model was able to improve significantly. The sensitivity also increased considerably in women by adding the radiomics (0.68 vs 0.79, p < 0.05) having a higher detection of AF events. Our findings provide novel insights into AF-related electro-anatomic remodelling and its variations by sex. The integrative radiomics-ECG model also presents a potential novel approach for earlier detection of AF.
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Affiliation(s)
- Esmeralda Ruiz Pujadas
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain.
| | - Zahra Raisi-Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK
| | - Liliana Szabo
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK
| | - Cristian Izquierdo Morcillo
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Víctor M Campello
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Carlos Martin-Isla
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Hajnalka Vago
- Semmelweis University Heart and Vascular Center, Budapest, Hungary
| | - Bela Merkely
- Semmelweis University Heart and Vascular Center, Budapest, Hungary
| | - Nicholas C Harvey
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK
- NIHR Southampton Biomedical Research Centre, University of Southampton, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK
- Health Data Research UK, London, UK
- Alan Turing Institute, London, UK
| | - Karim Lekadir
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
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25
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Aebersold H, Serra-Burriel M, Foster-Wittassek F, Moschovitis G, Aeschbacher S, Auricchio A, Beer JH, Blozik E, Bonati LH, Conen D, Felder S, Huber CA, Kuehne M, Mueller A, Oberle J, Paladini RE, Reichlin T, Rodondi N, Springer A, Stauber A, Sticherling C, Szucs TD, Osswald S, Schwenkglenks M. Patient clusters and cost trajectories in the Swiss Atrial Fibrillation cohort. Heart 2022; 109:763-770. [PMID: 36332981 DOI: 10.1136/heartjnl-2022-321520] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 10/07/2022] [Indexed: 11/06/2022] Open
Abstract
ObjectiveEvidence on long-term costs of atrial fibrillation (AF) and associated factors is scarce. As part of the Swiss-AF prospective cohort study, we aimed to characterise AF costs and their development over time, and to assess specific patient clusters and their cost trajectories.MethodsSwiss-AF enrolled 2415 patients with variable duration of AF between 2014 and 2017. Patient clusters were identified using hierarchical cluster analysis of baseline characteristics. Ongoing yearly follow-ups include health insurance clinical and claims data. An algorithm was developed to adjudicate costs to AF and related complications.ResultsA subpopulation of 1024 Swiss-AF patients with available claims data was followed up for a median (IQR) of 3.24 (1.09) years. Average yearly AF-adjudicated costs amounted to SFr5679 (€5163), remaining stable across the observation period. AF-adjudicated costs consisted mainly of inpatient and outpatient AF treatment costs (SFr4078; €3707), followed by costs of bleeding (SFr696; €633) and heart failure (SFr494; €449). Hierarchical analysis identified three patient clusters: cardiovascular (CV; N=253 with claims), isolated-symptomatic (IS; N=586) and severely morbid without cardiovascular disease (SM; N=185). The CV cluster and SM cluster depicted similarly high costs across all cost outcomes; IS patients accrued the lowest costs.ConclusionOur results highlight three well-defined patient clusters with specific costs that could be used for stratification in both clinical and economic studies. Patient characteristics associated with adjudicated costs as well as cost trajectories may enable an early understanding of the magnitude of upcoming AF-related healthcare costs.
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Affiliation(s)
- Helena Aebersold
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Miquel Serra-Burriel
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | | | - Giorgio Moschovitis
- Division of Cardiology, Ente Ospedaliero Cantonale (EOC), Opsedale Regionale di Lugano, Lugano, Switzerland
| | - Stefanie Aeschbacher
- Cardiology Division, Department of Medicine, University Hospital Basel, Basel, Switzerland
- Cardiovascular Research Institute Basel, University Hospital Basel, Basel, Switzerland
| | - Angelo Auricchio
- Department of Cardiology, Instituto Cardiocentro Ticino, Ente Ospedaliero Cantonale (EOC), Lugano, Switzerland
| | - Jürg Hans Beer
- Department of Medicine, Cantonal Hospital of Baden, Baden, Switzerland
- Center for Molecular Cardiology, University of Zurich, Zurich, Switzerland
| | - Eva Blozik
- Institute of Primary Care, University of Zurich, Zurich, Switzerland
| | - Leo H Bonati
- Research Department, Reha Rheinfelden, Rheinfelden, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - David Conen
- Population Health Research Institute, McMaster University, Hamilton, Ontario, Canada
| | - Stefan Felder
- Faculty of Business and Economics, University of Basel, Basel, Switzerland
| | - Carola A Huber
- Department of Health Sciences, Helsana Group, Zurich, Switzerland
| | - Michael Kuehne
- Cardiology Division, Department of Medicine, University Hospital Basel, Basel, Switzerland
- Cardiovascular Research Institute Basel, University Hospital Basel, Basel, Switzerland
| | - Andreas Mueller
- Department of Cardiology, Triemli Hospital Zurich, Zurich, Switzerland
| | - Jolanda Oberle
- Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
- Department of General Internal Medicine, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Rebecca E Paladini
- Cardiology Division, Department of Medicine, University Hospital Basel, Basel, Switzerland
- Cardiovascular Research Institute Basel, University Hospital Basel, Basel, Switzerland
| | - Tobias Reichlin
- Department of Cardiology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Nicolas Rodondi
- Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
- Department of General Internal Medicine, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Anne Springer
- Cardiology Division, Department of Medicine, University Hospital Basel, Basel, Switzerland
- Cardiovascular Research Institute Basel, University Hospital Basel, Basel, Switzerland
| | - Annina Stauber
- Department of Cardiology, Triemli Hospital Zurich, Zurich, Switzerland
| | - Christian Sticherling
- Cardiology Division, Department of Medicine, University Hospital Basel, Basel, Switzerland
- Cardiovascular Research Institute Basel, University Hospital Basel, Basel, Switzerland
| | - Thomas D Szucs
- Institute of Pharmaceutical Medicine (ECPM), University of Basel, Basel, Switzerland
| | - Stefan Osswald
- Cardiology Division, Department of Medicine, University Hospital Basel, Basel, Switzerland
- Cardiovascular Research Institute Basel, University Hospital Basel, Basel, Switzerland
| | - Matthias Schwenkglenks
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
- Institute of Pharmaceutical Medicine (ECPM), University of Basel, Basel, Switzerland
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Qin X, Zhang Y, Zheng Q. Metabolic Inflexibility as a Pathogenic Basis for Atrial Fibrillation. Int J Mol Sci 2022; 23:ijms23158291. [PMID: 35955426 PMCID: PMC9368187 DOI: 10.3390/ijms23158291] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 07/22/2022] [Accepted: 07/25/2022] [Indexed: 12/10/2022] Open
Abstract
Atrial fibrillation (AF), the most common sustained arrhythmia, is closely intertwined with metabolic abnormalities. Recently, a metabolic paradox in AF pathogenesis has been suggested: under different forms of pathogenesis, the metabolic balance shifts either towards (e.g., obesity and diabetes) or away from (e.g., aging, heart failure, and hypertension) fatty acid oxidation, yet they all increase the risk of AF. This has raised the urgent need for a general consensus regarding the metabolic changes that predispose patients to AF. “Metabolic flexibility” aptly describes switches between substrates (fatty acids, glucose, amino acids, and ketones) in response to various energy stresses depending on availability and requirements. AF, characterized by irregular high-frequency excitation and the contraction of the atria, is an energy challenge and triggers a metabolic switch from preferential fatty acid utilization to glucose metabolism to increase the efficiency of ATP produced in relation to oxygen consumed. Therefore, the heart needs metabolic flexibility. In this review, we will briefly discuss (1) the current understanding of cardiac metabolic flexibility with an emphasis on the specificity of atrial metabolic characteristics; (2) metabolic heterogeneity among AF pathogenesis and metabolic inflexibility as a common pathological basis for AF; and (3) the substrate-metabolism mechanism underlying metabolic inflexibility in AF pathogenesis.
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Affiliation(s)
- Xinghua Qin
- Xi’an Key Laboratory of Special Medicine and Health Engineering, School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, China;
| | - Yudi Zhang
- Department of Cardiology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, China;
| | - Qiangsun Zheng
- Department of Cardiology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, China;
- Correspondence: or
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27
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Geurts S, Lu Z, Kavousi M. Perspectives on Sex- and Gender-Specific Prediction of New-Onset Atrial Fibrillation by Leveraging Big Data. Front Cardiovasc Med 2022; 9:886469. [PMID: 35898269 PMCID: PMC9309362 DOI: 10.3389/fcvm.2022.886469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 06/17/2022] [Indexed: 02/01/2023] Open
Abstract
Atrial fibrillation (AF), the most common sustained cardiac arrhythmia, has a large impact on quality of life and is associated with increased risk of hospitalization, morbidity, and mortality. Over the past two decades advances regarding the clinical epidemiology and management of AF have been established. Moreover, sex differences in the prevalence, incidence, prediction, pathophysiology, and prognosis of AF have been identified. Nevertheless, AF remains to be a complex and heterogeneous disorder and a comprehensive sex- and gender-specific approach to predict new-onset AF is lacking. The exponential growth in various sources of big data such as electrocardiograms, electronic health records, and wearable devices, carries the potential to improve AF risk prediction. Leveraging these big data sources by artificial intelligence (AI)-enabled approaches, in particular in a sex- and gender-specific manner, could lead to substantial advancements in AF prediction and ultimately prevention. We highlight the current status, premise, and potential of big data to improve sex- and gender-specific prediction of new-onset AF.
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28
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Should Atrial Fibrillation Be Included in Preoperative Risk Assessment for Noncardiac Surgery? J Am Coll Cardiol 2022; 79:2486-2488. [PMID: 35738708 DOI: 10.1016/j.jacc.2022.04.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 04/20/2022] [Indexed: 11/24/2022]
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29
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Clinical Phenotypes of Atrial Fibrillation and Mortality Risk-A Cluster Analysis from the Nationwide Italian START Registry. J Pers Med 2022; 12:jpm12050785. [PMID: 35629207 PMCID: PMC9143727 DOI: 10.3390/jpm12050785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 05/06/2022] [Accepted: 05/10/2022] [Indexed: 01/27/2023] Open
Abstract
Patients with atrial fibrillation (AF) still experience a high mortality rate despite optimal antithrombotic treatment. We aimed to identify clinical phenotypes of patients to stratify mortality risk in AF. Cluster analysis was performed on 5171 AF patients from the nationwide START registry. The risk of all-cause mortality in each cluster was analyzed. We identified four clusters. Cluster 1 was composed of the youngest patients, with low comorbidities; Cluster 2 of patients with low cardiovascular risk factors and high prevalence of cancer; Cluster 3 of men with diabetes and coronary disease and peripheral artery disease; Cluster 4 included the oldest patients, mainly women, with previous cerebrovascular events. During 9857 person-years of observation, 386 deaths (3.92%/year) occurred. Mortality rates increased across clusters: 0.42%/year (cluster 1, reference group), 2.12%/year (cluster 2, adjusted hazard ratio (aHR) 3.306, 95% confidence interval (CI) 1.204−9.077, p = 0.020), 4.41%/year (cluster 3, aHR 6.702, 95%CI 2.433−18.461, p < 0.001), and 8.71%/year (cluster 4, aHR 8.927, 95%CI 3.238−24.605, p < 0.001). We identified four clusters of AF patients with progressive mortality risk. The use of clinical phenotypes may help identify patients at a higher risk of mortality.
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30
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Nishi H, Oishi N, Ogawa H, Natsue K, Doi K, Kawakami O, Aoki T, Fukuda S, Akao M, Tsukahara T. Predicting cerebral infarction in patients with atrial fibrillation using machine learning: The Fushimi AF registry. J Cereb Blood Flow Metab 2022; 42:746-756. [PMID: 34851764 PMCID: PMC9254038 DOI: 10.1177/0271678x211063802] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The CHADS2 and CHA2DS2-VASc scores are widely used to assess ischemic risk in the patients with atrial fibrillation (AF). However, the discrimination performance of these scores is limited. Using the data from a community-based prospective cohort study, we sought to construct a machine learning-based prediction model for cerebral infarction in patients with AF, and to compare its performance with the existing scores. All consecutive patients with AF treated at 81 study institutions from March 2011 to May 2017 were enrolled (n = 4396). The whole dataset was divided into a derivation cohort (n = 1005) and validation cohort (n = 752) after excluding the patients with valvular AF and anticoagulation therapy. Using the derivation cohort dataset, a machine learning model based on gradient boosting tree algorithm (ML) was built to predict cerebral infarction. In the validation cohort, the receiver operating characteristic area under the curve of the ML model was higher than those of the existing models according to the Hanley and McNeil method: ML, 0.72 (95%CI, 0.66-0.79); CHADS2, 0.61 (95%CI, 0.53-0.69); CHA2DS2-VASc, 0.62 (95%CI, 0.54-0.70). As a conclusion, machine learning algorithm have the potential to perform better than the CHADS2 and CHA2DS2-VASc scores for predicting cerebral infarction in patients with non-valvular AF.
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Affiliation(s)
- Hidehisa Nishi
- Department of Neurosurgery, National Hospital Organization Kyoto Medical Center, Kyoto, Japan.,Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Naoya Oishi
- Medical Innovation Center, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Hisashi Ogawa
- Department of Cardiology, National Hospital Organization Kyoto Medical Center, Kyoto, Japan
| | - Kishida Natsue
- Department of Neurosurgery, National Hospital Organization Kyoto Medical Center, Kyoto, Japan
| | - Kento Doi
- Department of Neurosurgery, National Hospital Organization Kyoto Medical Center, Kyoto, Japan
| | - Osamu Kawakami
- Department of Neurosurgery, National Hospital Organization Kyoto Medical Center, Kyoto, Japan
| | - Tomokazu Aoki
- Department of Neurosurgery, National Hospital Organization Kyoto Medical Center, Kyoto, Japan
| | - Shunichi Fukuda
- Department of Neurosurgery, National Hospital Organization Kyoto Medical Center, Kyoto, Japan
| | - Masaharu Akao
- Department of Cardiology, National Hospital Organization Kyoto Medical Center, Kyoto, Japan
| | - Tetsuya Tsukahara
- Department of Neurosurgery, National Hospital Organization Kyoto Medical Center, Kyoto, Japan
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31
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Pensa AV, Baman JR, Puckelwartz MJ, Wilcox JE. Genetically Based Atrial Fibrillation: Current Considerations for Diagnosis and Management. J Cardiovasc Electrophysiol 2022; 33:1944-1953. [PMID: 35262243 DOI: 10.1111/jce.15446] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 03/01/2022] [Accepted: 03/04/2022] [Indexed: 11/30/2022]
Abstract
Atrial fibrillation (AF) is the most common atrial arrhythmia and is subcategorized into numerous clinical phenotypes. Given its heterogeneity, investigations into the genetic mechanisms underlying AF have been pursued in recent decades, with predominant analyses focusing on early onset or lone AF. Linkage analyses, genome wide association studies (GWAS), and single gene analyses have led to the identification of rare and common genetic variants associated with AF risk. Significant overlap with genetic variants implicated in dilated cardiomyopathy syndromes, including truncating variants of the sarcomere protein titin, have been identified through these analyses, in addition to other genes associated with cardiac structure and function. Despite this, widespread utilization of genetic testing in AF remains hindered by the unclear impact of genetic risk identification on clinical outcomes and the high prevalence of variants of unknown significance (VUS). However, genetic testing is a reasonable option for patients with early onset AF and in those with significant family history of arrhythmia. While many knowledge gaps remain, emerging data support genotyping to inform selection of AF therapeutics. In this review we highlight the current understanding of the complex genetic basis of AF and explore the overlap of AF with inherited cardiomyopathy syndromes. We propose a set of criteria for clinical genetic testing in AF patients and outline future steps for the integration of genetics into AF care. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Anthony V Pensa
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Jayson R Baman
- Department of Medicine, Division of Cardiology, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Megan J Puckelwartz
- Department of Pharmacology, Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Jane E Wilcox
- Department of Medicine, Division of Cardiology, Northwestern University Feinberg School of Medicine, Chicago, IL
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32
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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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33
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Steinberg BA, Li Z, Shrader P, Chew DS, Bunch TJ, Mark DB, Nabutovsky Y, Shah RU, Greiner MA, Piccini JP. Bimodal distribution of atrial fibrillation burden in 3 distinct cohorts: What is 'paroxysmal' atrial fibrillation? Am Heart J 2022; 244:149-156. [PMID: 34838507 PMCID: PMC8727503 DOI: 10.1016/j.ahj.2021.11.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 11/19/2021] [Accepted: 11/22/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND Burden of atrial fibrillation (AF), as a continuous measure, is an emerging alternative classification often assumed to increase linearly with progression of disease. Yet there are no descriptions of AF burden distributions across populations. METHODS We examined patterns of AF burden (% time in AF) across 3 different cohorts: outpatients with AF undergoing Holter monitoring in a national registry (ORBIT-AF II), routine outpatients undergoing Holter monitoring in a tertiary healthcare system (UHealth), and patients >= 65 years with cardiac implantable electronic devices (Merlin.netTM linked to Medicare). RESULTS We included 2,058 ORBIT-AF II patients, 4,537 UHealth patients, and 39,710 from Merlin.net. Mean age ranged from 56 to 77 years, sex ranged from 40% to 61% male, and mean CHA2DS2-VASc scores ranged from 2.2 to 4.9. Across all cohorts, AF burden demonstrated skewed frequency towards the extremes, with the vast majority of patients having either very low or very high AF burden. This bimodal distribution was consistent across cohorts, across clinically-documented AF types (paroxysmal v persistent), patients with or without a known AF diagnosis, and among patients with different types of cardiac implantable electronic devices. CONCLUSIONS Across 3 broad, diverse cohorts with continuous monitoring, distribution of AF burden was consistently skewed towards the extremes without an even, linear distribution or progression. As AF burden is increasingly recognized as a descriptor and potential risk-stratifier, these findings have important implications for future research and patient care.
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Affiliation(s)
- Benjamin A Steinberg
- Division of Cardiovascular Medicine, University of Utah Health Sciences Center, Salt Lake City, UT.
| | - Zhen Li
- Department of Population Health, Duke University, Durham, NC
| | - Peter Shrader
- Duke Clinical Research Institute, Duke University, Durham, NC
| | - Derek S Chew
- Department of Population Health, Duke University, Durham, NC; Duke Clinical Research Institute, Duke University, Durham, NC
| | - T Jared Bunch
- Division of Cardiovascular Medicine, University of Utah Health Sciences Center, Salt Lake City, UT
| | - Daniel B Mark
- Duke Clinical Research Institute, Duke University, Durham, NC; Division of Cardiology, Duke University Medical Center, Durham, NC
| | | | - Rashmee U Shah
- Division of Cardiovascular Medicine, University of Utah Health Sciences Center, Salt Lake City, UT
| | | | - Jonathan P Piccini
- Department of Population Health, Duke University, Durham, NC; Duke Clinical Research Institute, Duke University, Durham, NC; Division of Cardiology, Duke University Medical Center, Durham, NC
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34
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Chen S, Mei Q, Guo L, Yang X, Luo W, Qu X, Li X, Zhou B, Chen K, Zeng C. Association between triglyceride-glucose index and atrial fibrillation: A retrospective observational study. Front Endocrinol (Lausanne) 2022; 13:1047927. [PMID: 36568072 PMCID: PMC9773201 DOI: 10.3389/fendo.2022.1047927] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 11/11/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Insulin resistance is associated with atrial remodeling as well as atrial fibrillation (AF). However, there was limited evidence on the relationship of triglyceride-glucose index (TyG) index, a simple, valuable marker of insulin resistance, with AF. Thus, we aimed to investigate the association between TyG index and AF among hospitalized patients. METHODS A retrospective observational study was conducted in Daping Hospital, which included 356 hospitalized patients from the Department of Cardiology. Clinical and biochemical parameters were collected from electronic medical records and AF was diagnosed from electrocardiogram (ECG) findings. RESULTS We found that the TyG index was significantly higher in the AF group than in the group without AF. Multivariate logistic regression revealed that hypertension (OR = 1.756, 95%CI 1.135-2.717, P = 0.011) and TyG index (OR = 2.092, 95%CI 1.412-3.100, P<0.001) were positively associated with AF. The analysis of the area under the ROC curve was performed and revealed that area under curve (AUC) of TyG index was 0.600 (95%CI, 0.542-0.659, P = 0.001), the optimal critical value was 8.35, the sensitivity was 65.4%, and the specificity was 52.0%. Additional subgroup analyses of diabetic and non-diabetic subjects were also performed and found the TyG index was increased in non-diabetic subjects with AF. Furthermore, a logistic regression analysis showed TyG index was associated with AF (OR = 3.065, 95% CI, 1.819-5.166, P<0.001) in non-diabetic subjects. However, TyG index was not associated with AF in diabetic subjects. CONCLUSION Elevated TyG index is an independent risk factor for AF among non-diabetic hospitalized patients.
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Affiliation(s)
- Shengnan Chen
- ChongQing Medical University, Chongqing, China
- Department of Cardiology, Daping Hospital, The Third Military Medical University (Army Medical University), Chongqing, China
- Cardiovascular Research Center of Chongqing College, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Chongqing, China
| | - Qiao Mei
- ChongQing Medical University, Chongqing, China
- Department of Cardiology, Daping Hospital, The Third Military Medical University (Army Medical University), Chongqing, China
- Cardiovascular Research Center of Chongqing College, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Chongqing, China
| | - Li Guo
- Department of Endocrinology, Southwest Hospital, The Third Military Medical University (Army Medical University), Chongqing, China
| | - Xiaoli Yang
- Department of Cardiology, Daping Hospital, The Third Military Medical University (Army Medical University), Chongqing, China
- Chongqing Key Laboratory for Hypertension Research, Chongqing Cardiovascular Clinical Research Center Chongqing Institute of Cardiology, Chongqing, China
| | - Wenbin Luo
- Department of Cardiology, Daping Hospital, The Third Military Medical University (Army Medical University), Chongqing, China
- Chongqing Key Laboratory for Hypertension Research, Chongqing Cardiovascular Clinical Research Center Chongqing Institute of Cardiology, Chongqing, China
| | - Xuemei Qu
- Department of Cardiology, The Fifth People’s Hospital of Chongqing, Chongqing, China
| | - Xiaoping Li
- Department of Cardiology, Daping Hospital, The Third Military Medical University (Army Medical University), Chongqing, China
- Chongqing Key Laboratory for Hypertension Research, Chongqing Cardiovascular Clinical Research Center Chongqing Institute of Cardiology, Chongqing, China
| | - Bingqing Zhou
- Department of Cardiology, Daping Hospital, The Third Military Medical University (Army Medical University), Chongqing, China
- Chongqing Key Laboratory for Hypertension Research, Chongqing Cardiovascular Clinical Research Center Chongqing Institute of Cardiology, Chongqing, China
| | - Ken Chen
- Cardiovascular Research Center of Chongqing College, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Chongqing, China
- Department of Cardiology, The Fifth People’s Hospital of Chongqing, Chongqing, China
- *Correspondence: Chunyu Zeng, ; Ken Chen,
| | - Chunyu Zeng
- ChongQing Medical University, Chongqing, China
- Department of Cardiology, Daping Hospital, The Third Military Medical University (Army Medical University), Chongqing, China
- Cardiovascular Research Center of Chongqing College, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Chongqing, China
- Chongqing Key Laboratory for Hypertension Research, Chongqing Cardiovascular Clinical Research Center Chongqing Institute of Cardiology, Chongqing, China
- *Correspondence: Chunyu Zeng, ; Ken Chen,
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Flores AM, Schuler A, Eberhard AV, Olin JW, Cooke JP, Leeper NJ, Shah NH, Ross EG. Unsupervised Learning for Automated Detection of Coronary Artery Disease Subgroups. J Am Heart Assoc 2021; 10:e021976. [PMID: 34845917 PMCID: PMC9075403 DOI: 10.1161/jaha.121.021976] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 10/14/2021] [Indexed: 12/24/2022]
Abstract
Background The promise of precision population health includes the ability to use robust patient data to tailor prevention and care to specific groups. Advanced analytics may allow for automated detection of clinically informative subgroups that account for clinical, genetic, and environmental variability. This study sought to evaluate whether unsupervised machine learning approaches could interpret heterogeneous and missing clinical data to discover clinically important coronary artery disease subgroups. Methods and Results The Genetic Determinants of Peripheral Arterial Disease study is a prospective cohort that includes individuals with newly diagnosed and/or symptomatic coronary artery disease. We applied generalized low rank modeling and K-means cluster analysis using 155 phenotypic and genetic variables from 1329 participants. Cox proportional hazard models were used to examine associations between clusters and major adverse cardiovascular and cerebrovascular events and all-cause mortality. We then compared performance of risk stratification based on clusters and the American College of Cardiology/American Heart Association pooled cohort equations. Unsupervised analysis identified 4 phenotypically and prognostically distinct clusters. All-cause mortality was highest in cluster 1 (oldest/most comorbid; 26%), whereas major adverse cardiovascular and cerebrovascular event rates were highest in cluster 2 (youngest/multiethnic; 41%). Cluster 4 (middle-aged/healthiest behaviors) experienced more incident major adverse cardiovascular and cerebrovascular events (30%) than cluster 3 (middle-aged/lowest medication adherence; 23%), despite apparently similar risk factor and lifestyle profiles. In comparison with the pooled cohort equations, cluster membership was more informative for risk assessment of myocardial infarction, stroke, and mortality. Conclusions Unsupervised clustering identified 4 unique coronary artery disease subgroups with distinct clinical trajectories. Flexible unsupervised machine learning algorithms offer the ability to meaningfully process heterogeneous patient data and provide sharper insights into disease characterization and risk assessment. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT00380185.
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Affiliation(s)
- Alyssa M. Flores
- Division of Vascular SurgeryDepartment of SurgeryStanford University School of MedicineStanfordCA
| | - Alejandro Schuler
- Center for Biomedical Informatics ResearchStanford UniversityStanfordCA
| | - Anne Verena Eberhard
- Division of Vascular SurgeryDepartment of SurgeryStanford University School of MedicineStanfordCA
| | - Jeffrey W. Olin
- Zena and Michael A. Wiener Cardiovascular InstituteMarie‐Josée and Henry R. Kravis Center for Cardiovascular HealthIcahn School of Medicine at Mount SinaiNew YorkNY
| | - John P. Cooke
- Department of Cardiovascular SciencesHouston Methodist Research InstituteHoustonTX
| | - Nicholas J. Leeper
- Division of Vascular SurgeryDepartment of SurgeryStanford University School of MedicineStanfordCA
- Division of Cardiovascular MedicineDepartment of MedicineStanford University School of MedicineStanfordCA
- Stanford Cardiovascular InstituteStanfordCA
| | - Nigam H. Shah
- Center for Biomedical Informatics ResearchStanford UniversityStanfordCA
| | - Elsie G. Ross
- Division of Vascular SurgeryDepartment of SurgeryStanford University School of MedicineStanfordCA
- Center for Biomedical Informatics ResearchStanford UniversityStanfordCA
- Stanford Cardiovascular InstituteStanfordCA
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Clinical phenotypes of patients with non-valvular atrial fibrillation as defined by a cluster analysis: A report from the J-RHYTHM registry. IJC HEART & VASCULATURE 2021; 37:100885. [PMID: 34692988 PMCID: PMC8515385 DOI: 10.1016/j.ijcha.2021.100885] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 09/24/2021] [Accepted: 09/25/2021] [Indexed: 01/21/2023]
Abstract
Background Atrial fibrillation (AF) is a heterogeneous condition caused by various underlying disorders and comorbidities. A cluster analysis is a statistical technique that attempts to group populations by shared traits. Applied to AF, it could be useful in classifying the variables and complex presentations of AF into phenotypes of coherent, more tractable subpopulations. Objectives This study aimed to characterize the clinical phenotypes of AF using a national AF patient registry using a cluster analysis. Methods We used data of an observational cohort that included 7406 patients with non-valvular AF enrolled from 158 sites participating in a nationwide AF registry (J-RHYTHM). The endpoints analyzed were all-cause mortality, thromboembolisms, and major bleeding. Results The optimal number of clusters was found to be 4 based on 40 characteristics. They were those with (1) a younger age and low rate of comorbidities (n = 1876), (2) a high rate of hypertension (n = 4579), (3) high bleeding risk (n = 302), and (4) prior coronary artery disease and other atherosclerotic comorbidities (n = 649). The patients in the younger/low comorbidity cluster demonstrated the lowest risk for all 3 endpoints. The atherosclerotic comorbidity cluster had significantly higher adjusted risks of total mortality (odds ratio [OR], 3.70; 95% confidence interval [CI], 2.37–5.80) and major bleeding (OR, 5.19; 95% CI, 2.58–10.9) than the younger/low comorbidity cluster. Conclusions A cluster analysis identified 4 distinct groups of non-valvular AF patients with different clinical characteristics and outcomes. Awareness of these groupings may lead to a differentiated patient management for AF.
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Proietti M, Vitolo M, Harrison SL, Lane DA, Fauchier L, Marin F, Nabauer M, Potpara TS, Dan GA, Boriani G, Lip GYH. Impact of clinical phenotypes on management and outcomes in European atrial fibrillation patients: a report from the ESC-EHRA EURObservational Research Programme in AF (EORP-AF) General Long-Term Registry. BMC Med 2021; 19:256. [PMID: 34666757 PMCID: PMC8527730 DOI: 10.1186/s12916-021-02120-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 09/08/2021] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Epidemiological studies in atrial fibrillation (AF) illustrate that clinical complexity increase the risk of major adverse outcomes. We aimed to describe European AF patients' clinical phenotypes and analyse the differential clinical course. METHODS We performed a hierarchical cluster analysis based on Ward's Method and Squared Euclidean Distance using 22 clinical binary variables, identifying the optimal number of clusters. We investigated differences in clinical management, use of healthcare resources and outcomes in a cohort of European AF patients from a Europe-wide observational registry. RESULTS A total of 9363 were available for this analysis. We identified three clusters: Cluster 1 (n = 3634; 38.8%) characterized by older patients and prevalent non-cardiac comorbidities; Cluster 2 (n = 2774; 29.6%) characterized by younger patients with low prevalence of comorbidities; Cluster 3 (n = 2955;31.6%) characterized by patients' prevalent cardiovascular risk factors/comorbidities. Over a mean follow-up of 22.5 months, Cluster 3 had the highest rate of cardiovascular events, all-cause death, and the composite outcome (combining the previous two) compared to Cluster 1 and Cluster 2 (all P < .001). An adjusted Cox regression showed that compared to Cluster 2, Cluster 3 (hazard ratio (HR) 2.87, 95% confidence interval (CI) 2.27-3.62; HR 3.42, 95%CI 2.72-4.31; HR 2.79, 95%CI 2.32-3.35), and Cluster 1 (HR 1.88, 95%CI 1.48-2.38; HR 2.50, 95%CI 1.98-3.15; HR 2.09, 95%CI 1.74-2.51) reported a higher risk for the three outcomes respectively. CONCLUSIONS In European AF patients, three main clusters were identified, differentiated by differential presence of comorbidities. Both non-cardiac and cardiac comorbidities clusters were found to be associated with an increased risk of major adverse outcomes.
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Affiliation(s)
- Marco Proietti
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, UK. .,Geriatric Unit, IRCCS Istituti Clinici Scientifici Maugeri, Milan, Italy. .,Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy.
| | - Marco Vitolo
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, UK.,Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, Modena, Italy.,Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, Modena, Italy
| | - Stephanie L Harrison
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, UK
| | - Deirdre A Lane
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, UK.,Aalborg Thrombosis Research Unit, Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Laurent Fauchier
- Service de Cardiologie, Centre Hospitalier Universitaire Trousseau, Tours, France
| | - Francisco Marin
- Department of Cardiology, Hospital Universitario Virgen de la Arrixaca, IMIB-Arrixaca, University of Murcia, CIBER-CV, Murcia, Spain
| | - Michael Nabauer
- Department of Cardiology, Ludwig-Maximilians-University, Munich, Germany
| | - Tatjana S Potpara
- School of Medicine, University of Belgrade, Belgrade, Serbia.,Intensive Arrhythmia Care, Cardiology Clinic, Clinical Center of Serbia, Belgrade, Serbia
| | - Gheorghe-Andrei Dan
- University of Medicine, 'Carol Davila', Colentina University Hospital, Bucharest, Romania
| | - Giuseppe Boriani
- Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, Modena, Italy
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, UK. .,Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, Modena, Italy.
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Suzuki S, Yamashita T, Otsuka T, Arita T, Yagi N, Kishi M, Semba H, Kano H, Matsuno S, Kato Y, Uejima T, Oikawa Y, Matsuhama M, Iida M, Inoue T, Yajima J. Identifying risk patterns in older adults with atrial fibrillation by hierarchical cluster analysis: A retrospective approach based on the risk probability for clinical events. IJC HEART & VASCULATURE 2021; 37:100883. [PMID: 34632044 PMCID: PMC8487977 DOI: 10.1016/j.ijcha.2021.100883] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 09/05/2021] [Accepted: 09/21/2021] [Indexed: 12/25/2022]
Abstract
In older AF patients, representative AF-related outcomes compete, causing difficulty in decision making. We proposed cluster analysis using risk probability for four AF-related outcomes. Older adults with AF were classified into 3 clusters. The clusters could possibly identify older adults with AF with good/poor responses to AF-related treatment.
Background Older adults with atrial fibrillation (AF) have highly diverse risk levels for mortality, heart failure (HF), thromboembolism (TE), and major bleeding (MB), thus an integrated risk-pattern algorithm is warranted. Methods We analyzed 573 AF patients aged ≥ 75 years from our single-center cohort (Shinken Database 2010–2018). The 3-year risk scores (risk probability) for mortality (M-score), HF (HF-score), TE (TE-score), and MB (MB-score) were estimated for each patient by logistic regression analysis. Using the four risk scores, cluster analysis was performed with Ward’s linkage hierarchical algorithm. Results Three clusters were identified: Clusters 1 (n = 429, 74%), 2 (n = 24, 5%), and 3 (n = 120, 21%). The clusters were characterized as standard risk (Cluster 1), high TE- and MB-risk (Cluster 2), and high M- and HF-risk (Cluster 3). Oral anticoagulants were prescribed for over 80% of the patients in each cluster. Catheter ablation for AF was performed only in Cluster 1 (8.9%). Compared with Cluster 1, Cluster 2 was more closely associated with males, asymptomatic AF, history of cerebral infarction or transient ischemic attack, history of intracranial hemorrhage, high HAS-BLED score (≥3), and low body mass index (<18.0 kg/m2). Cluster 3 was more closely associated with old age, heart failure, and low estimated creatinine clearance (<30 mL/min). Conclusion The cluster analysis identified those at a high risk for all-cause death and HF or a high risk for TE and MB and could support decision making in older adults with AF.
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Affiliation(s)
- Shinya Suzuki
- Department of Cardiovascular Medicine, The Cardiovascular Institute, Tokyo, Japan
| | - Takeshi Yamashita
- Department of Cardiovascular Medicine, The Cardiovascular Institute, Tokyo, Japan
| | - Takayuki Otsuka
- Department of Cardiovascular Medicine, The Cardiovascular Institute, Tokyo, Japan
| | - Takuto Arita
- Department of Cardiovascular Medicine, The Cardiovascular Institute, Tokyo, Japan
| | - Naoharu Yagi
- Department of Cardiovascular Medicine, The Cardiovascular Institute, Tokyo, Japan
| | - Mikio Kishi
- Department of Cardiovascular Medicine, The Cardiovascular Institute, Tokyo, Japan
| | - Hiroaki Semba
- Department of Cardiovascular Medicine, The Cardiovascular Institute, Tokyo, Japan
| | - Hiroto Kano
- Department of Cardiovascular Medicine, The Cardiovascular Institute, Tokyo, Japan
| | - Shunsuke Matsuno
- Department of Cardiovascular Medicine, The Cardiovascular Institute, Tokyo, Japan
| | - Yuko Kato
- Department of Cardiovascular Medicine, The Cardiovascular Institute, Tokyo, Japan
| | - Tokuhisa Uejima
- Department of Cardiovascular Medicine, The Cardiovascular Institute, Tokyo, Japan
| | - Yuji Oikawa
- Department of Cardiovascular Medicine, The Cardiovascular Institute, Tokyo, Japan
| | - Minoru Matsuhama
- Department of Cardiovascular Surgery, The Cardiovascular Institute, Tokyo, Japan
| | - Mitsuru Iida
- Department of Cardiovascular Surgery, The Cardiovascular Institute, Tokyo, Japan
| | - Tatsuya Inoue
- Department of Cardiovascular Surgery, The Cardiovascular Institute, Tokyo, Japan
| | - Junji Yajima
- Department of Cardiovascular Medicine, The Cardiovascular Institute, Tokyo, Japan
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Lip GYH, Tran G, Genaidy A, Marroquin P, Estes C. Revisiting the dynamic risk profile of cardiovascular/non-cardiovascular multimorbidity in incident atrial fibrillation patients and five cardiovascular/non-cardiovascular outcomes: A machine-learning approach. J Arrhythm 2021; 37:931-941. [PMID: 34386119 PMCID: PMC8339094 DOI: 10.1002/joa3.12555] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Accepted: 05/01/2021] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Patients with atrial fibrillation (AF) usually have a heterogeneous co-morbid history, with dynamic changes in risk factors impacting on multiple adverse outcomes. We investigated a large prospective cohort of patients with multimorbidity, using a machine-learning approach, accounting for the dynamic nature of comorbidity risks and incident AF. METHODS Using machine-learning, we studied a prospective US cohort using medical/pharmacy databases of 1 091 911 patients, with an incident AF cohort of 14 078 and non-AF cohort of 1 077 833 enrolled in the 4-year study. Five incident clinical outcomes (heart failure, stroke, myocardial infarction, major bleeding, and cognitive impairment) were examined in relationship to AF status (AF vs non-AF), diverse multi-morbid (conditions and medications) history, and demographic parameters (age and gender), with supervised machine-learning techniques. RESULTS Complex inter-relationships of various comorbidities were uncovered for AF cases, leading to 6-fold higher risk of heart failure relative to the non-AF cohort (OR 6.02, 95% CI 5.72-6.33), followed by myocardial infarction (OR=2.68), stroke (OR=2.19), and major bleeding (OR=1.36). Supervised machine learning algorithms on the original populations yielded comparable results for both neural network and logistic regression algorithms in terms of discriminant validity, with c-indexes for incident adverse outcomes: heart failure (0.924, 95%CI 0.923-0.925), stroke (0.871, 95%CI 0.869-0.873), myocardial infarction (0.901, 95% CI 0.899-0.903), major bleeding (0.700, 95%CI 0.697-0.703), and cognitive impairment (0.919, 95% CI 0.9170.921). External calibration of all models demonstrated a good fit between the predicted probabilities and observed events. Decision curve analysis demonstrated that the obtained models were much more clinically useful than the "treat all" strategy. CONCLUSIONS Complex multimorbidity relationships uncovered using a machine learning approach for incident AF cases have major consequences for integrated care management, with implications for risk stratification and adverse clinical outcomes. This approach may facilitate automated approaches in the presence of multimorbidity, potentially helping decision making.
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Affiliation(s)
- Gregory Y. H. Lip
- Liverpool Centre for Cardiovascular ScienceUniversity of Liverpool and Liverpool Heart & Chest HospitalLiverpoolUK
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Proietti M, Esteve-Pastor MA, Rivera-Caravaca JM, Roldán V, Roldán Rabadán I, Muñiz J, Cequier Á, Bertomeu-Martínez V, Badimón L, Anguita M, Lip GYH, Marín F. Relationship between multimorbidity and outcomes in atrial fibrillation. Exp Gerontol 2021; 153:111482. [PMID: 34303775 DOI: 10.1016/j.exger.2021.111482] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 07/09/2021] [Accepted: 07/12/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND Multimorbidity is common in atrial fibrillation (AF) patients. Charlson comorbidity index (CCI) is used to evaluate multimorbidity in the general population. Limited long-term data are available on the relationship between CCI and AF. We examined the association between CCI, anticoagulation control and outcomes in AF patients. METHODS We studied 1956 from the FANTASIIA registry, an observational Spanish nationwide study on anticoagulated AF patients. Time in therapeutic range (TTR) was used to evaluate anticoagulation control. Stroke/TIA, major bleeding, cardiovascular (CV) death and all-cause death were study outcomes. RESULTS Mean ± SD CCI was 1.1 ± 1.2. Based on CCI quartiles, patients were categorised in four groups: 676 (34.6%) in Q1 (CCI 0); 683 (34.9%) in Q2 (CCI 1); 345 (17.6%) in Q3 (CCI 2); and 252 (12.9%) in Q4 (CCI ≥3). In vitamin K antagonist treated patients, the highest CCI quartile was inversely associated with TTR >70% (odds ratio:0.67, 95% confidence interval (CI):0.45-0.99). During observation, a progressively higher rate of major bleeding, CV death and all-cause death was found across the quartiles (all p < 0.001). The final Cox multivariable regression analysis showed an association with increasing risk for major bleeding occurrence in Q3 and Q4 (hazard ratio (HR):1.69, 95%CI:1.00-2.87 and HR:1.92, 95%CI:1.08-3.41). An increasing risk for all-cause death and CV death was found across CCI quartiles. CONCLUSIONS In a nationwide contemporary cohort of AF anticoagulated patients, multimorbidity was inversely associated with good anticoagulation control. A progressively higher risk for major bleeding, CV death and all-cause death was found across CCI quartiles.
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Affiliation(s)
- Marco Proietti
- Geriatric Unit, IRCCS Istituti Clinici Scientifici Maugeri, Milan, Italy; Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy; Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom.
| | - María Asunción Esteve-Pastor
- Department of Cardiology, Hospital Clínico Universitario Virgen de la Arrixaca, University of Murcia, Instituto Murciano de Investigación Biosanitaria (IMIB-Arrixaca), CIBERCV, Murcia, Spain
| | - José Miguel Rivera-Caravaca
- Department of Cardiology, Hospital Clínico Universitario Virgen de la Arrixaca, University of Murcia, Instituto Murciano de Investigación Biosanitaria (IMIB-Arrixaca), CIBERCV, Murcia, Spain
| | - Vanessa Roldán
- Department of Hematology and Clinical Oncology, Hospital Universitario Morales Meseguer, University of Murcia, Instituto Murciano de Investigación Biosanitaria (IMIB-Arrixaca), Murcia, Spain
| | | | - Javier Muñiz
- Universidade da Coruña, Grupo de Investigación Cardiovascular, Departamento de Ciencias de la Salud e Instituto de Investigación Biomédica de A Coruña (INIBIC), CIBERCV, Spain
| | - Ángel Cequier
- University Hospital of Bellvitge, Department of Cardiology, Barcelona, CIBERCV, Spain
| | | | - Lina Badimón
- Cardiovascular Research Center (CSIC-ICCC), Barcelona, Spain
| | - Manuel Anguita
- University Hospital Reina Sofia, Department of Cardiology, Cordoba, Spain
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom; Department of Clinical Medicine, Faculty of Health, Aalborg University, Aalborg, Denmark
| | - Francisco Marín
- Department of Cardiology, Hospital Clínico Universitario Virgen de la Arrixaca, University of Murcia, Instituto Murciano de Investigación Biosanitaria (IMIB-Arrixaca), CIBERCV, Murcia, Spain
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Vitolo M, Proietti M, Shantsila A, Boriani G, Lip GYH. Clinical Phenotype Classification of Atrial Fibrillation Patients Using Cluster Analysis and Associations with Trial-Adjudicated Outcomes. Biomedicines 2021; 9:biomedicines9070843. [PMID: 34356907 PMCID: PMC8301818 DOI: 10.3390/biomedicines9070843] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 07/10/2021] [Accepted: 07/15/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND AND PURPOSE Given the great clinical heterogeneity of atrial fibrillation (AF) patients, conventional classification only based on disease subtype or arrhythmia patterns may not adequately characterize this population. We aimed to identify different groups of AF patients who shared common clinical phenotypes using cluster analysis and evaluate the association between identified clusters and clinical outcomes. METHODS We performed a hierarchical cluster analysis in AF patients from AMADEUS and BOREALIS trials. The primary outcome was a composite of stroke/thromboembolism (TE), cardiovascular (CV) death, myocardial infarction, and/or all-cause death. Individual components of the primary outcome and major bleeding were also assessed. RESULTS We included 3980 AF patients treated with the Vitamin-K Antagonist from the AMADEUS and BOREALIS studies. The analysis identified four clusters in which patients varied significantly among clinical characteristics. Cluster 1 was characterized by patients with low rates of CV risk factors and comorbidities; Cluster 2 was characterized by patients with a high burden of CV risk factors; Cluster 3 consisted of patients with a high burden of CV comorbidities; Cluster 4 was characterized by the highest rates of non-CV comorbidities. After a mean follow-up of 365 (standard deviation 187) days, Cluster 4 had the highest cumulative risk of outcomes. Compared with Cluster 1, Cluster 4 was independently associated with an increased risk for the composite outcome (hazard ratio (HR) 2.43, 95% confidence interval (CI) 1.70-3.46), all-cause death (HR 2.35, 95% CI 1.58-3.49) and major bleeding (HR 2.18, 95% CI 1.19-3.96). CONCLUSIONS Cluster analysis identified four different clinically relevant phenotypes of AF patients that had unique clinical characteristics and different outcomes. Cluster analysis highlights the high degree of heterogeneity in patients with AF, suggesting the need for a phenotype-driven approach to comorbidities, which could provide a more holistic approach to management aimed to improve patients' outcomes.
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Affiliation(s)
- Marco Vitolo
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool L7 8TX, UK; (M.V.); (M.P.); (A.S.)
- Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, 41125 Modena, Italy;
- Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, 41125 Modena, Italy
| | - Marco Proietti
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool L7 8TX, UK; (M.V.); (M.P.); (A.S.)
- Department of Clinical Sciences and Community Health, University of Milan, 20138 Milan, Italy
- Geriatric Unit, IRCCS Istituti Clinici Scientifici Maugeri, 20138 Milan, Italy
| | - Alena Shantsila
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool L7 8TX, UK; (M.V.); (M.P.); (A.S.)
| | - Giuseppe Boriani
- Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, 41125 Modena, Italy;
| | - Gregory Y. H. Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool L7 8TX, UK; (M.V.); (M.P.); (A.S.)
- Aalborg Thrombosis Research Unit, Department of Clinical Medicine, Aalborg University, 9000 Aalborg, Denmark
- Correspondence: ; Tel.: +44-0151-794-9020
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Proietti M, Cesari M. Describing the relationship between atrial fibrillation and frailty: Clinical implications and open research questions. Exp Gerontol 2021; 152:111455. [PMID: 34153440 DOI: 10.1016/j.exger.2021.111455] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 06/13/2021] [Indexed: 11/26/2022]
Abstract
In the recent years a lot of attention has been gathered by the issue of frailty outside the boundaries of the geriatric medicine, for example in the field of cardiovascular medicine. Atrial fibrillation (AF) is known as a very common cardiological condition, often burdened by high level of clinical complexity. Aim of this narrative review is to examine the most relevant evidence about the relationship between frailty and AF, focusing also on its impact on clinical management and natural history of patients with this condition. Data reported underline how a relevant relationship exists between these two conditions, even though the burden of frailty among AF cohorts is still unclear. Frailty seems to affect the clinical management, even though no definitive data are yet available. Lastly, frailty significantly increases the risk of all-cause mortality but it's still unclear the impact on thromboembolic and bleeding events. Despite several data are already available, more research is still needed to fully elucidate the relationship between these two clinical entities.
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Affiliation(s)
- Marco Proietti
- Geriatric Unit, IRCCS Istituti Clinici Scientifici Maugeri, Milan, Italy; Department of Clinical Sciences and Community Health, University of Milan, Italy; Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom.
| | - Matteo Cesari
- Geriatric Unit, IRCCS Istituti Clinici Scientifici Maugeri, Milan, Italy; Department of Clinical Sciences and Community Health, University of Milan, Italy
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Heijman J, Sutanto H, Crijns HJGM, Nattel S, Trayanova NA. Computational models of atrial fibrillation: achievements, challenges, and perspectives for improving clinical care. Cardiovasc Res 2021; 117:1682-1699. [PMID: 33890620 PMCID: PMC8208751 DOI: 10.1093/cvr/cvab138] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Indexed: 12/11/2022] Open
Abstract
Despite significant advances in its detection, understanding and management, atrial fibrillation (AF) remains a highly prevalent cardiac arrhythmia with a major impact on morbidity and mortality of millions of patients. AF results from complex, dynamic interactions between risk factors and comorbidities that induce diverse atrial remodelling processes. Atrial remodelling increases AF vulnerability and persistence, while promoting disease progression. The variability in presentation and wide range of mechanisms involved in initiation, maintenance and progression of AF, as well as its associated adverse outcomes, make the early identification of causal factors modifiable with therapeutic interventions challenging, likely contributing to suboptimal efficacy of current AF management. Computational modelling facilitates the multilevel integration of multiple datasets and offers new opportunities for mechanistic understanding, risk prediction and personalized therapy. Mathematical simulations of cardiac electrophysiology have been around for 60 years and are being increasingly used to improve our understanding of AF mechanisms and guide AF therapy. This narrative review focuses on the emerging and future applications of computational modelling in AF management. We summarize clinical challenges that may benefit from computational modelling, provide an overview of the different in silico approaches that are available together with their notable achievements, and discuss the major limitations that hinder the routine clinical application of these approaches. Finally, future perspectives are addressed. With the rapid progress in electronic technologies including computing, clinical applications of computational modelling are advancing rapidly. We expect that their application will progressively increase in prominence, especially if their added value can be demonstrated in clinical trials.
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Affiliation(s)
- Jordi Heijman
- Department of Cardiology, Cardiovascular Research Institute Maastricht, Faculty of Health, Medicine, and Life Sciences, Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands
| | - Henry Sutanto
- Department of Cardiology, Cardiovascular Research Institute Maastricht, Faculty of Health, Medicine, and Life Sciences, Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands
| | - Harry J G M Crijns
- Department of Cardiology, Cardiovascular Research Institute Maastricht, Faculty of Health, Medicine, and Life Sciences, Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands
| | - Stanley Nattel
- Department of Medicine, Montreal Heart Institute and Université de Montréal, Montreal, Canada
- Department of Pharmacology and Therapeutics, McGill University, Montreal, Canada
- Institute of Pharmacology, West German Heart and Vascular Center, Faculty of Medicine, University Duisburg-Essen, Duisburg, Germany
- IHU Liryc and Fondation Bordeaux Université, Bordeaux, France
| | - Natalia A Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Loring Z, Piccini JP. The reward of understanding risk in atrial fibrillation. Eur J Prev Cardiol 2021; 28:622-623. [PMID: 33611467 PMCID: PMC11115192 DOI: 10.1177/2047487320925215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Zak Loring
- Duke Clinical Research Institute, Durham, USA
- Duke University Medical Center, Durham, USA
| | - Jonathan P Piccini
- Duke Clinical Research Institute, Durham, USA
- Duke University Medical Center, Durham, USA
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Ogawa H, An Y, Nishi H, Fukuda S, Ishigami K, Ikeda S, Doi K, Ide Y, Hamatani Y, Fujino A, Ishii M, Iguchi M, Masunaga N, Esato M, Tsuji H, Wada H, Hasegawa K, Abe M, Tsukahara T, Lip GYH, Akao M. Characteristics and clinical outcomes in atrial fibrillation patients classified using cluster analysis: the Fushimi AF Registry. Europace 2021; 23:1369-1379. [PMID: 33930126 DOI: 10.1093/europace/euab079] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 03/18/2021] [Indexed: 11/13/2022] Open
Abstract
AIMS The risk of adverse events in atrial fibrillation (AF) patients was commonly stratified by risk factors or clinical risk scores. Risk factors often do not occur in isolation and are often found in multimorbidity 'clusters' which may have prognostic implications. We aimed to perform cluster analysis in a cohort of AF patients and to assess the outcomes and prognostic implications of the identified comorbidity cluster phenotypes. METHODS AND RESULTS The Fushimi AF Registry is a community-based prospective survey of the AF patients in Fushimi-ku, Kyoto, Japan. Hierarchical cluster analysis was performed on 4304 patients (mean age: 73.6 years, female; 40.3%, mean CHA2DS2-VASc score 3.37 ± 1.69), using 42 baseline clinical characteristics. On hierarchical cluster analysis, AF patients could be categorized into six statistically driven comorbidity clusters: (i) younger ages (mean age: 48.3 years) with low prevalence of risk factors and comorbidities (n = 209); (ii) elderly (mean age: 74.0 years) with low prevalence of risk factors and comorbidities (n = 1301); (iii) those with high prevalence of atherosclerotic risk factors, but without atherosclerotic disease (n = 1411); (iv) those with atherosclerotic comorbidities (n = 440); (v) those with history of any-cause stroke (n = 681); and (vi) the very elderly (mean age: 83.4 years) (n = 262). Rates of all-cause mortality and major adverse cardiovascular or neurological events can be stratified by these six identified clusters (log-rank test; P < 0.001 and P < 0.001, respectively). CONCLUSIONS We identified six clinically relevant phenotypes of AF patients on cluster analysis. These phenotypes can be associated with various types of comorbidities and associated with the incidence of clinical outcomes. CLINICAL TRIAL REGISTRATION INFORMATION https://www.umin.ac.jp/ctr/index.htm. Unique identifier: UMIN000005834.
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Affiliation(s)
- Hisashi Ogawa
- Department of Cardiology, National Hospital Organization Kyoto Medical Center, 1-1, Mukaihata-cho, Fukakusa, Fushimi-ku, Kyoto 612-8555, Japan
| | - Yoshimori An
- Department of Cardiology, National Hospital Organization Kyoto Medical Center, 1-1, Mukaihata-cho, Fukakusa, Fushimi-ku, Kyoto 612-8555, Japan
| | - Hidehisa Nishi
- Department of Neurosurgery, National Hospital Organization Kyoto Medical Center, Kyoto, Japan
| | - Shunichi Fukuda
- Department of Neurosurgery, National Hospital Organization Kyoto Medical Center, Kyoto, Japan
| | - Kenjiro Ishigami
- Department of Cardiology, National Hospital Organization Kyoto Medical Center, 1-1, Mukaihata-cho, Fukakusa, Fushimi-ku, Kyoto 612-8555, Japan
| | - Syuhei Ikeda
- Department of Cardiology, National Hospital Organization Kyoto Medical Center, 1-1, Mukaihata-cho, Fukakusa, Fushimi-ku, Kyoto 612-8555, Japan
| | - Kosuke Doi
- Department of Cardiology, National Hospital Organization Kyoto Medical Center, 1-1, Mukaihata-cho, Fukakusa, Fushimi-ku, Kyoto 612-8555, Japan
| | - Yuya Ide
- Department of Cardiology, National Hospital Organization Kyoto Medical Center, 1-1, Mukaihata-cho, Fukakusa, Fushimi-ku, Kyoto 612-8555, Japan
| | - Yasuhiro Hamatani
- Department of Cardiology, National Hospital Organization Kyoto Medical Center, 1-1, Mukaihata-cho, Fukakusa, Fushimi-ku, Kyoto 612-8555, Japan
| | - Akiko Fujino
- Department of Cardiology, National Hospital Organization Kyoto Medical Center, 1-1, Mukaihata-cho, Fukakusa, Fushimi-ku, Kyoto 612-8555, Japan
| | - Mitsuru Ishii
- Department of Cardiology, National Hospital Organization Kyoto Medical Center, 1-1, Mukaihata-cho, Fukakusa, Fushimi-ku, Kyoto 612-8555, Japan
| | - Moritake Iguchi
- Department of Cardiology, National Hospital Organization Kyoto Medical Center, 1-1, Mukaihata-cho, Fukakusa, Fushimi-ku, Kyoto 612-8555, Japan
| | - Nobutoyo Masunaga
- Department of Cardiology, National Hospital Organization Kyoto Medical Center, 1-1, Mukaihata-cho, Fukakusa, Fushimi-ku, Kyoto 612-8555, Japan
| | - Masahiro Esato
- Department of Cardiology, Heart Rhythm Section, Ogaki Tokushukai Hospital, Gifu, Japan
| | | | - Hiromichi Wada
- Division of Translational Research, National Hospital Organization Kyoto Medical Center, Kyoto, Japan
| | - Koji Hasegawa
- Division of Translational Research, National Hospital Organization Kyoto Medical Center, Kyoto, Japan
| | - Mitsuru Abe
- Department of Cardiology, National Hospital Organization Kyoto Medical Center, 1-1, Mukaihata-cho, Fukakusa, Fushimi-ku, Kyoto 612-8555, Japan
| | - Tetsuya Tsukahara
- Department of Neurosurgery, National Hospital Organization Kyoto Medical Center, Kyoto, Japan
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, UK.,Aalborg Thrombosis Research Unit, Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Masaharu Akao
- Department of Cardiology, National Hospital Organization Kyoto Medical Center, 1-1, Mukaihata-cho, Fukakusa, Fushimi-ku, Kyoto 612-8555, Japan
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Boriani G, Vitolo M, Diemberger I, Proietti M, Valenti AC, Malavasi VL, Lip GYH. Optimizing indices of AF susceptibility and burden to evaluate AF severity, risk and outcomes. Cardiovasc Res 2021; 117:1-21. [PMID: 33913486 PMCID: PMC8707734 DOI: 10.1093/cvr/cvab147] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 03/15/2021] [Accepted: 04/29/2021] [Indexed: 02/06/2023] Open
Abstract
Atrial fibrillation (AF) has heterogeneous patterns of presentation concerning symptoms,
duration of episodes, AF burden, and the tendency to progress towards the terminal step of
permanent AF. AF is associated with a risk of stroke/thromboembolism traditionally
considered dependent on patient-level risk factors rather than AF type, AF burden, or
other characterizations. However, the time spent in AF appears related to an incremental
risk of stroke, as suggested by the higher risk of stroke in patients with clinical AF vs.
subclinical episodes and in patients with non-paroxysmal AF vs. paroxysmal AF. In patients
with device-detected atrial tachyarrhythmias, AF burden is a dynamic process with
potential transitions from a lower to a higher maximum daily arrhythmia burden, thus
justifying monitoring its temporal evolution. In clinical terms, the appearance of the
first episode of AF, the characterization of the arrhythmia in a specific AF type, the
progression of AF, and the response to rhythm control therapies, as well as the clinical
outcomes, are all conditioned by underlying heart disease, risk factors, and
comorbidities. Improved understanding is needed on how to monitor and modulate the effect
of factors that condition AF susceptibility and modulate AF-associated outcomes. The
increasing use of wearables and apps in practice and clinical research may be useful to
predict and quantify AF burden and assess AF susceptibility at the individual patient
level. This may help us reveal why AF stops and starts again, or why AF episodes, or
burden, cluster. Additionally, whether the distribution of burden is associated with
variations in the propensity to thrombosis or other clinical adverse events. Combining the
improved methods for data analysis, clinical and translational science could be the basis
for the early identification of the subset of patients at risk of progressing to a longer
duration/higher burden of AF and the associated adverse outcomes.
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Affiliation(s)
- Giuseppe Boriani
- Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, Modena, Italy
| | - Marco Vitolo
- Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, Modena, Italy.,Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom.,Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, Modena, Italy
| | - Igor Diemberger
- Department of Experimental, Diagnostic and Specialty Medicine, Institute of Cardiology, University of Bologna, Policlinico S. Orsola-Malpighi, Bologna, Italy
| | - Marco Proietti
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom.,Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy.,Geriatric Unit, IRCCS Istituti Clinico Scientifici Maugeri, Milan, Italy
| | - Anna Chiara Valenti
- Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, Modena, Italy
| | - Vincenzo Livio Malavasi
- Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, Modena, Italy
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom.,Aalborg Thrombosis Research Unit, Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
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Banerjee A, Chen S, Fatemifar G, Zeina M, Lumbers RT, Mielke J, Gill S, Kotecha D, Freitag DF, Denaxas S, Hemingway H. Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility. BMC Med 2021; 19:85. [PMID: 33820530 PMCID: PMC8022365 DOI: 10.1186/s12916-021-01940-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 02/12/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Machine learning (ML) is increasingly used in research for subtype definition and risk prediction, particularly in cardiovascular diseases. No existing ML models are routinely used for cardiovascular disease management, and their phase of clinical utility is unknown, partly due to a lack of clear criteria. We evaluated ML for subtype definition and risk prediction in heart failure (HF), acute coronary syndromes (ACS) and atrial fibrillation (AF). METHODS For ML studies of subtype definition and risk prediction, we conducted a systematic review in HF, ACS and AF, using PubMed, MEDLINE and Web of Science from January 2000 until December 2019. By adapting published criteria for diagnostic and prognostic studies, we developed a seven-domain, ML-specific checklist. RESULTS Of 5918 studies identified, 97 were included. Across studies for subtype definition (n = 40) and risk prediction (n = 57), there was variation in data source, population size (median 606 and median 6769), clinical setting (outpatient, inpatient, different departments), number of covariates (median 19 and median 48) and ML methods. All studies were single disease, most were North American (n = 61/97) and only 14 studies combined definition and risk prediction. Subtype definition and risk prediction studies respectively had limitations in development (e.g. 15.0% and 78.9% of studies related to patient benefit; 15.0% and 15.8% had low patient selection bias), validation (12.5% and 5.3% externally validated) and impact (32.5% and 91.2% improved outcome prediction; no effectiveness or cost-effectiveness evaluations). CONCLUSIONS Studies of ML in HF, ACS and AF are limited by number and type of included covariates, ML methods, population size, country, clinical setting and focus on single diseases, not overlap or multimorbidity. Clinical utility and implementation rely on improvements in development, validation and impact, facilitated by simple checklists. We provide clear steps prior to safe implementation of machine learning in clinical practice for cardiovascular diseases and other disease areas.
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Affiliation(s)
- Amitava Banerjee
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK.
- Health Data Research UK, University College London, London, UK.
- University College London Hospitals NHS Trust, 235 Euston Road, London, UK.
- Barts Health NHS Trust, The Royal London Hospital, Whitechapel Rd, London, UK.
| | - Suliang Chen
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK
- Health Data Research UK, University College London, London, UK
| | - Ghazaleh Fatemifar
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK
- Health Data Research UK, University College London, London, UK
| | | | - R Thomas Lumbers
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK
- Health Data Research UK, University College London, London, UK
- University College London Hospitals NHS Trust, 235 Euston Road, London, UK
| | - Johanna Mielke
- Bayer AG, Division Pharmaceuticals, Open Innovation & Digital Technologies, Wuppertal, Germany
| | - Simrat Gill
- University of Birmingham Institute of Cardiovascular Sciences and University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Dipak Kotecha
- University of Birmingham Institute of Cardiovascular Sciences and University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Department of Cardiology, University Medical Centre Utrecht, Utrecht, the Netherlands
| | - Daniel F Freitag
- Bayer AG, Division Pharmaceuticals, Open Innovation & Digital Technologies, Wuppertal, Germany
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK
- Health Data Research UK, University College London, London, UK
- The Alan Turing Institute, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK
- Health Data Research UK, University College London, London, UK
- University College London Hospitals Biomedical Research Centre (UCLH BRC), London, UK
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Coombes CE, Abrams ZB, Nakayiza S, Brock G, Coombes KR. Umpire 2.0: Simulating realistic, mixed-type, clinical data for machine learning. F1000Res 2021. [DOI: 10.12688/f1000research.25877.2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
The Umpire 2.0 R-package offers a streamlined, user-friendly workflow to simulate complex, heterogeneous, mixed-type data with known subgroup identities, dichotomous outcomes, and time-to-event data, while providing ample opportunities for fine-tuning and flexibility. Here, we describe how we have expanded the core Umpire 1.0 R-package, developed to simulate gene expression data, to generate clinically realistic, mixed-type data for use in evaluating unsupervised and supervised machine learning (ML) methods. As the availability of large-scale clinical data for ML has increased, clinical data has posed unique challenges, including widely variable size, individual biological heterogeneity, data collection and measurement noise, and mixed data types. Developing and validating ML methods for clinical data requires data sets with known ground truth, generated from simulation. Umpire 2.0 addresses challenges to simulating realistic clinical data by providing the user a series of modules to generate survival parameters and subgroups, apply meaningful additive noise, and discretize to single or mixed data types. Umpire 2.0 provides broad functionality across sample sizes, feature spaces, and data types, allowing the user to simulate correlated, heterogeneous, binary, continuous, categorical, or mixed type data from the scale of a small clinical trial to data on thousands of patients drawn from electronic health records. The user may generate elaborate simulations by varying parameters in order to compare algorithms or interrogate operating characteristics of an algorithm in both supervised and unsupervised ML.
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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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50
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Ribeiro JM, Astudillo P, de Backer O, Budde R, Nuis RJ, Goudzwaard J, Van Mieghem NM, Lumens J, Mortier P, Mattace-Raso F, Boersma E, Cummins P, Bruining N, de Jaegere PP. Artificial Intelligence and Transcatheter Interventions for Structural Heart Disease: A glance at the (near) future. Trends Cardiovasc Med 2021; 32:153-159. [PMID: 33581255 DOI: 10.1016/j.tcm.2021.02.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 02/03/2021] [Accepted: 02/04/2021] [Indexed: 01/16/2023]
Abstract
With innovations in therapeutic technologies and changes in population demographics, transcatheter interventions for structural heart disease have become the preferred treatment and will keep growing. Yet, a thorough clinical selection and efficient pathway from diagnosis to treatment and follow-up are mandatory. In this review we reflect on how artificial intelligence may help to improve patient selection, pre-procedural planning, procedure execution and follow-up so to establish efficient and high quality health care in an increasing number of patients.
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Affiliation(s)
- Joana Maria Ribeiro
- Department of Cardiology, Thoraxcenter, Erasmus Medical Center, Rotterdam, the Netherlands; Department of Cardiology, Centro Hospitalar de Entre o Douro e Vouga, Santa Maria da Feira, Portugal; Department of Cardiology, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
| | | | - Ole de Backer
- Department of Cardiology, Rigshospitalet University Hospital, Copenhagen, Denmark
| | - Ricardo Budde
- Department of Radiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Rutger Jan Nuis
- Department of Cardiology, Thoraxcenter, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Jeanette Goudzwaard
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Nicolas M Van Mieghem
- Department of Cardiology, Thoraxcenter, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Joost Lumens
- CARIM School for Cardiovascular Diseases, Maastricht University Medical Center, Maastricht, the Netherlands
| | | | | | - Eric Boersma
- Department of Cardiology, Thoraxcenter, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Paul Cummins
- Department of Cardiology, Thoraxcenter, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Nico Bruining
- Department of Cardiology, Thoraxcenter, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Peter Pt de Jaegere
- Department of Cardiology, Thoraxcenter, Erasmus Medical Center, Rotterdam, the Netherlands.
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