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Schmitz B, Wirtz S, Sestayo-Fernández M, Schäfer H, Douma ER, Alonso Vazquez M, González-Salvado V, Habibovic M, Gatsios D, Kop WJ, Peña-Gil C, Mooren F. Living Lab Data of Patient Needs and Expectations for eHealth-Based Cardiac Rehabilitation in Germany and Spain From the TIMELY Study: Cross-Sectional Analysis. J Med Internet Res 2024; 26:e53991. [PMID: 38386376 PMCID: PMC10921324 DOI: 10.2196/53991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 12/28/2023] [Accepted: 01/30/2024] [Indexed: 02/23/2024] Open
Abstract
BACKGROUND The use of eHealth technology in cardiac rehabilitation (CR) is a promising approach to enhance patient outcomes since adherence to healthy lifestyles and risk factor management during phase III CR maintenance is often poorly supported. However, patients' needs and expectations have not been extensively analyzed to inform the design of such eHealth solutions. OBJECTIVE The goal of this study was to provide a detailed patient perspective on the most important functionalities to include in an eHealth solution to assist them in phase III CR maintenance. METHODS A guided survey as part of a Living Lab approach was conducted in Germany (n=49) and Spain (n=30) involving women (16/79, 20%) and men (63/79, 80%) with coronary artery disease (mean age 57 years, SD 9 years) participating in a structured center-based CR program. The survey covered patients' perceived importance of different CR components in general, current usage of technology/technical devices, and helpfulness of the potential features of eHealth in CR. Questionnaires were used to identify personality traits (psychological flexibility, optimism/pessimism, positive/negative affect), potentially predisposing patients to acceptance of an app/monitoring devices. RESULTS All the patients in this study owned a smartphone, while 30%-40% used smartwatches and fitness trackers. Patients expressed the need for an eHealth platform that is user-friendly, personalized, and easily accessible, and 71% (56/79) of the patients believed that technology could help them to maintain health goals after CR. Among the offered components, support for regular physical exercise, including updated schedules and progress documentation, was rated the highest. In addition, patients rated the availability of information on diagnosis, current medication, test results, and risk scores as (very) useful. Of note, for each item, except smoking cessation, 35%-50% of the patients indicated a high need for support to achieve their long-term health goals, suggesting the need for individualized care. No major differences were detected between Spanish and German patients (all P>.05) and only younger age (P=.03) but not sex, education level, or personality traits (all P>.05) were associated with the acceptance of eHealth components. CONCLUSIONS The patient perspectives collected in this study indicate high acceptance of personalized user-friendly eHealth platforms with remote monitoring to improve adherence to healthy lifestyles among patients with coronary artery disease during phase III CR maintenance. The identified patient needs comprise support in physical exercise, including regular updates on personalized training recommendations. Availability of diagnoses, laboratory results, and medications, as part of a mobile electronic health record were also rated as very useful. TRIAL REGISTRATION ClinicalTrials.gov NCT05461729; https://clinicaltrials.gov/study/NCT05461729.
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Affiliation(s)
- Boris Schmitz
- Department of Rehabilitation Sciences, Faculty of Health, University of Witten/Herdecke, Witten, Germany
- Center for Medical Rehabilitation, DRV Clinic Königsfeld, Ennepetal, Germany
| | - Svenja Wirtz
- Department of Rehabilitation Sciences, Faculty of Health, University of Witten/Herdecke, Witten, Germany
- Center for Medical Rehabilitation, DRV Clinic Königsfeld, Ennepetal, Germany
| | | | - Hendrik Schäfer
- Department of Rehabilitation Sciences, Faculty of Health, University of Witten/Herdecke, Witten, Germany
- Center for Medical Rehabilitation, DRV Clinic Königsfeld, Ennepetal, Germany
| | - Emma R Douma
- Center of Research on Psychological Disorders and Somatic Diseases, Tilburg University, Tilburg, Netherlands
| | - Marta Alonso Vazquez
- Health Research Institute of Santiago de Compostela, Santiago de Compostela, Spain
| | - Violeta González-Salvado
- Cardiology and Coronary Care Department, IDIS, CIBER CV, University Hospital of Santiago de Compostela, Santiago de Compostela, Spain
| | - Mirela Habibovic
- Center of Research on Psychological Disorders and Somatic Diseases, Tilburg University, Tilburg, Netherlands
| | | | - Willem Johan Kop
- Center of Research on Psychological Disorders and Somatic Diseases, Tilburg University, Tilburg, Netherlands
| | - Carlos Peña-Gil
- Cardiology and Coronary Care Department, IDIS, CIBER CV, University Hospital of Santiago de Compostela, Santiago de Compostela, Spain
| | - Frank Mooren
- Department of Rehabilitation Sciences, Faculty of Health, University of Witten/Herdecke, Witten, Germany
- Center for Medical Rehabilitation, DRV Clinic Königsfeld, Ennepetal, Germany
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Nazar W, Szymanowicz S, Nazar K, Kaufmann D, Wabich E, Braun-Dullaeus R, Daniłowicz-Szymanowicz L. Artificial intelligence models in prediction of response to cardiac resynchronization therapy: a systematic review. Heart Fail Rev 2024; 29:133-150. [PMID: 37861853 PMCID: PMC10904439 DOI: 10.1007/s10741-023-10357-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/28/2023] [Indexed: 10/21/2023]
Abstract
The aim of the presented review is to summarize the literature data on the accuracy and clinical applicability of artificial intelligence (AI) models as a valuable alternative to the current guidelines in predicting cardiac resynchronization therapy (CRT) response and phenotyping of patients eligible for CRT implantation. This systematic review was performed according to the PRISMA guidelines. After a search of Scopus, PubMed, Cochrane Library, and Embase databases, 675 records were identified. Twenty supervised (prediction of CRT response) and 9 unsupervised (clustering and phenotyping) AI models were analyzed qualitatively (22 studies, 14,258 patients). Fifty-five percent of AI models were based on retrospective studies. Unsupervised AI models were able to identify clusters of patients with significantly different rates of primary outcome events (death, heart failure event). In comparison to the guideline-based CRT response prediction accuracy of 70%, supervised AI models trained on cohorts with > 100 patients achieved up to 85% accuracy and an AUC of 0.86 in their prediction of response to CRT for echocardiographic and clinical outcomes, respectively. AI models seem to be an accurate and clinically applicable tool in phenotyping of patients eligible for CRT implantation and predicting potential responders. In the future, AI may help to increase CRT response rates to over 80% and improve clinical decision-making and prognosis of the patients, including reduction of mortality rates. However, these findings must be validated in randomized controlled trials.
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Affiliation(s)
- Wojciech Nazar
- Faculty of Medicine, Medical University of Gdańsk, Marii Skłodowskiej-Curie 3a, 80-210, Gdańsk, Poland
| | | | - Krzysztof Nazar
- Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Gabriela Narutowicza 11/12, 80-233, Gdańsk, Poland
| | - Damian Kaufmann
- Department of Cardiology and Electrotherapy, Faculty of Medicine, Medical University of Gdańsk, Smoluchowskiego 17, 80-213, Gdańsk, Poland
| | - Elżbieta Wabich
- Department of Cardiology and Electrotherapy, Faculty of Medicine, Medical University of Gdańsk, Smoluchowskiego 17, 80-213, Gdańsk, Poland
| | - Rüdiger Braun-Dullaeus
- Department of Cardiology and Angiology, Otto von Guericke University Magdeburg, Leipziger Street 44, 39120, Magdeburg, Germany
| | - Ludmiła Daniłowicz-Szymanowicz
- Department of Cardiology and Electrotherapy, Faculty of Medicine, Medical University of Gdańsk, Smoluchowskiego 17, 80-213, Gdańsk, Poland.
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Gou Q, Zhao Q, Dong M, Liang L, You H. Diagnostic potential of energy metabolism-related genes in heart failure with preserved ejection fraction. Front Endocrinol (Lausanne) 2023; 14:1296547. [PMID: 38089628 PMCID: PMC10711684 DOI: 10.3389/fendo.2023.1296547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 11/07/2023] [Indexed: 12/18/2023] Open
Abstract
Background Heart failure with preserved ejection fraction (HFpEF) is associated with changes in cardiac metabolism that affect energy supply in the heart. However, there is limited research on energy metabolism-related genes (EMRGs) in HFpEF. Methods The HFpEF mouse dataset (GSE180065, containing heart tissues from 10 HFpEF and five control samples) was sourced from the Gene Expression Omnibus database. Gene expression profiles in HFpEF and control groups were compared to identify differentially expressed EMRGs (DE-EMRGs), and the diagnostic biomarkers with diagnostic value were screened using machine learning algorithms. Meanwhile, we constructed a biomarker-based nomogram model for its predictive power, and functionality of diagnostic biomarkers were conducted using single-gene gene set enrichment analysis, drug prediction, and regulatory network analysis. Additionally, consensus clustering analysis based on the expression of diagnostic biomarkers was utilized to identify differential HFpEF-related genes (HFpEF-RGs). Immune microenvironment analysis in HFpEF and subtypes were performed for analyzing correlations between immune cells and diagnostic biomarkers as well as HFpEF-RGs. Finally, qRT-PCR analysis on the HFpEF mouse model was used to validate the expression levels of diagnostic biomarkers. Results We selected 5 biomarkers (Chrna2, Gnb3, Gng7, Ddit4l, and Prss55) that showed excellent diagnostic performance. The nomogram model we constructed demonstrated high predictive power. Single-gene gene set enrichment analysis revealed enrichment in aerobic respiration and energy derivation. Further, various miRNAs and TFs were predicted by Gng7, such as Gng7-mmu-miR-6921-5p, ETS1-Gng7. A lot of potential therapeutic targets were predicted as well. Consensus clustering identified two distinct subtypes of HFpEF. Functional enrichment analysis highlighted the involvement of DEGs-cluster in protein amino acid modification and so on. Additionally, we identified five HFpEF-RGs (Kcnt1, Acot1, Kcnc4, Scn3a, and Gpam). Immune analysis revealed correlations between Macrophage M2, T cell CD4+ Th1 and diagnostic biomarkers, as well as an association between Macrophage and HFpEF-RGs. We further validated the expression trends of the selected biomarkers through experimental validation. Conclusion Our study identified 5 diagnostic biomarkers and provided insights into the prediction and treatment of HFpEF through drug predictions and network analysis. These findings contribute to a better understanding of HFpEF and may guide future research and therapy development.
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Affiliation(s)
- Qiling Gou
- Department of Cardiovascular Medicine, Shaanxi Provincial People’s Hospital, Xi’an, Shaanxi, China
| | - Qianqian Zhao
- Department of Cardiopulmonary Rehabilitation, Xi’an International Medical Center Hospital-Rehabilitation Hospital, Xi’an, Shaanxi, China
| | - Mengya Dong
- Department of Cardiovascular Medicine, Shaanxi Provincial People’s Hospital, Xi’an, Shaanxi, China
| | - Lei Liang
- Department of Cardiovascular Medicine, Shaanxi Provincial People’s Hospital, Xi’an, Shaanxi, China
| | - Hongjun You
- Department of Cardiovascular Medicine, Shaanxi Provincial People’s Hospital, Xi’an, Shaanxi, China
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Kabra R, Israni S, Vijay B, Baru C, Mendu R, Fellman M, Sridhar A, Mason P, Cheung JW, DiBiase L, Mahapatra S, Kalifa J, Lubitz SA, Noseworthy PA, Navara R, McManus DD, Cohen M, Chung MK, Trayanova N, Gopinathannair R, Lakkireddy D. Emerging role of artificial intelligence in cardiac electrophysiology. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2022; 3:263-275. [PMID: 36589314 PMCID: PMC9795267 DOI: 10.1016/j.cvdhj.2022.09.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Artificial intelligence (AI) and machine learning (ML) have significantly impacted the field of cardiovascular medicine, especially cardiac electrophysiology (EP), on multiple fronts. The goal of this review is to familiarize readers with the field of AI and ML and their emerging role in EP. The current review is divided into 3 sections. In the first section, we discuss the definitions and basics of AI, ML, and big data. In the second section, we discuss their application to EP in the context of detection, prediction, and management of arrhythmias. Finally, we discuss the regulatory issues, challenges, and future directions of AI in EP.
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Affiliation(s)
- Rajesh Kabra
- Kansas City Heart Rhythm Institute, Kansas City, Kansas
| | - Sharat Israni
- Bakar Computational Health Sciences Institute, University of California, San Francisco, California
| | | | - Chaitanya Baru
- San Diego Supercomputer Center, University of California, San Diego, San Diego, California
| | | | | | | | - Pamela Mason
- Department of Medicine, University of Virginia, Charlottesville, Virginia
| | - Jim W. Cheung
- Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Luigi DiBiase
- Albert Einstein College of Medicine at Montefiore Hospital, New York, New York
| | - Srijoy Mahapatra
- Department of Medicine, University of Minnesota, Minneapolis, Minnesota
| | - Jerome Kalifa
- Department of Cardiology, Brown University, Providence, Rhode Island
| | - Steven A. Lubitz
- Cardiac Arrhythmia Service, Massachusetts General Hospital, Boston, Massachusetts
| | | | - Rachita Navara
- Division of Cardiac Electrophysiology, University of California, San Francisco, San Francisco, California
| | - David D. McManus
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts
| | - Mitchell Cohen
- Division of Pediatric Cardiology, INOVA Children’s Hospital, Fairfax, Virginia
| | - Mina K. Chung
- Division of Cardiovascular Medicine, Cleveland Clinic, Cleveland, Ohio
| | - Natalia Trayanova
- Department of Biomedical Engineering and Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland
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Błaziak M, Urban S, Wietrzyk W, Jura M, Iwanek G, Stańczykiewicz B, Kuliczkowski W, Zymliński R, Pondel M, Berka P, Danel D, Biegus J, Siennicka A. An Artificial Intelligence Approach to Guiding the Management of Heart Failure Patients Using Predictive Models: A Systematic Review. Biomedicines 2022; 10:biomedicines10092188. [PMID: 36140289 PMCID: PMC9496386 DOI: 10.3390/biomedicines10092188] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 08/19/2022] [Accepted: 08/27/2022] [Indexed: 11/23/2022] Open
Abstract
Heart failure (HF) is one of the leading causes of mortality and hospitalization worldwide. The accurate prediction of mortality and readmission risk provides crucial information for guiding decision making. Unfortunately, traditional predictive models reached modest accuracy in HF populations. We therefore aimed to present predictive models based on machine learning (ML) techniques in HF patients that were externally validated. We searched four databases and the reference lists of the included papers to identify studies in which HF patient data were used to create a predictive model. Literature screening was conducted in Academic Search Ultimate, ERIC, Health Source Nursing/Academic Edition and MEDLINE. The protocol of the current systematic review was registered in the PROSPERO database with the registration number CRD42022344855. We considered all types of outcomes: mortality, rehospitalization, response to treatment and medication adherence. The area under the receiver operating characteristic curve (AUC) was used as the comparator parameter. The literature search yielded 1649 studies, of which 9 were included in the final analysis. The AUCs for the machine learning models ranged from 0.6494 to 0.913 in independent datasets, whereas the AUCs for statistical predictive scores ranged from 0.622 to 0.806. Our study showed an increasing number of ML predictive models concerning HF populations, although external validation remains infrequent. However, our findings revealed that ML approaches can outperform conventional risk scores and may play important role in HF management.
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Affiliation(s)
- Mikołaj Błaziak
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
- Correspondence: (M.B.); (W.K.); Tel.: +48-71-733-11-12 (M.B.)
| | - Szymon Urban
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
| | - Weronika Wietrzyk
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
| | - Maksym Jura
- Department of Physiology and Pathophysiology, Wroclaw Medical University, 50-368 Wroclaw, Poland
| | - Gracjan Iwanek
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
| | - Bartłomiej Stańczykiewicz
- Department of Psychiatry, Division of Consultation Psychiatry and Neuroscience, Wroclaw Medical University, 50-367 Wroclaw, Poland
| | - Wiktor Kuliczkowski
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
- Correspondence: (M.B.); (W.K.); Tel.: +48-71-733-11-12 (M.B.)
| | - Robert Zymliński
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
| | - Maciej Pondel
- Institute of Information Systems in Economics, Wroclaw University of Economics and Business, 53-345 Wroclaw, Poland
| | - Petr Berka
- Department of Information and Knowledge Engineering, Prague University of Economics and Business, W. Churchill Sq. 1938/4, 130 67 Prague, Czech Republic
| | - Dariusz Danel
- Department of Anthropology, Ludwik Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, 53-114 Wroclaw, Poland
| | - Jan Biegus
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
| | - Agnieszka Siennicka
- Department of Physiology and Pathophysiology, Wroclaw Medical University, 50-368 Wroclaw, Poland
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Asher C, Puyol-Antón E, Rizvi M, Ruijsink B, Chiribiri A, Razavi R, Carr-White G. The Role of AI in Characterizing the DCM Phenotype. Front Cardiovasc Med 2021; 8:787614. [PMID: 34993240 PMCID: PMC8724536 DOI: 10.3389/fcvm.2021.787614] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 12/02/2021] [Indexed: 12/13/2022] Open
Abstract
Dilated Cardiomyopathy is conventionally defined by left ventricular dilatation and dysfunction in the absence of coronary disease. Emerging evidence suggests many patients remain vulnerable to major adverse outcomes despite clear therapeutic success of modern evidence-based heart failure therapy. In this era of personalized medical care, the conventional assessment of left ventricular ejection fraction falls short in fully predicting evolution and risk of outcomes in this heterogenous group of heart muscle disease, as such, a more refined means of phenotyping this disease appears essential. Cardiac MRI (CMR) is well-placed in this respect, not only for its diagnostic utility, but the wealth of information captured in global and regional function assessment with the addition of unique tissue characterization across different disease states and patient cohorts. Advanced tools are needed to leverage these sensitive metrics and integrate with clinical, genetic and biochemical information for personalized, and more clinically useful characterization of the dilated cardiomyopathy phenotype. Recent advances in artificial intelligence offers the unique opportunity to impact clinical decision making through enhanced precision image-analysis tasks, multi-source extraction of relevant features and seamless integration to enhance understanding, improve diagnosis, and subsequently clinical outcomes. Focusing particularly on deep learning, a subfield of artificial intelligence, that has garnered significant interest in the imaging community, this paper reviews the main developments that could offer more robust disease characterization and risk stratification in the Dilated Cardiomyopathy phenotype. Given its promising utility in the non-invasive assessment of cardiac diseases, we firstly highlight the key applications in CMR, set to enable comprehensive quantitative measures of function beyond the standard of care assessment. Concurrently, we revisit the added value of tissue characterization techniques for risk stratification, showcasing the deep learning platforms that overcome limitations in current clinical workflows and discuss how they could be utilized to better differentiate at-risk subgroups of this phenotype. The final section of this paper is dedicated to the allied clinical applications to imaging, that incorporate artificial intelligence and have harnessed the comprehensive abundance of data from genetics and relevant clinical variables to facilitate better classification and enable enhanced risk prediction for relevant outcomes.
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Affiliation(s)
- Clint Asher
- Department of Cardiovascular Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Department of Cardiology, Guys and St Thomas' NHS Trust, London, United Kingdom
| | - Esther Puyol-Antón
- Department of Cardiovascular Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Maleeha Rizvi
- Department of Cardiovascular Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Department of Cardiology, Guys and St Thomas' NHS Trust, London, United Kingdom
| | - Bram Ruijsink
- Department of Cardiovascular Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Department of Cardiology, Guys and St Thomas' NHS Trust, London, United Kingdom
- Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Amedeo Chiribiri
- Department of Cardiovascular Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Department of Cardiology, Guys and St Thomas' NHS Trust, London, United Kingdom
| | - Reza Razavi
- Department of Cardiovascular Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Department of Cardiology, Guys and St Thomas' NHS Trust, London, United Kingdom
| | - Gerry Carr-White
- Department of Cardiovascular Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Department of Cardiology, Guys and St Thomas' NHS Trust, London, United Kingdom
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7
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Cai C, Tafti AP, Ngufor C, Zhang P, Xiao P, Dai M, Liu H, Noseworthy P, Chen M, Friedman PA, Cha YM. Using ensemble of ensemble machine learning methods to predict outcomes of cardiac resynchronization. J Cardiovasc Electrophysiol 2021; 32:2504-2514. [PMID: 34260141 DOI: 10.1111/jce.15171] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 05/08/2021] [Accepted: 06/14/2021] [Indexed: 11/29/2022]
Abstract
INTRODUCTION The efficacy of cardiac resynchronization therapy (CRT) has been widely studied in the medical literature; however, about 30% of candidates fail to respond to this treatment strategy. Smart computational approaches based on clinical data can help expose hidden patterns useful for identifying CRT responders. METHODS We retrospectively analyzed the electronic health records of 1664 patients who underwent CRT procedures from January 1, 2002 to December 31, 2017. An ensemble of ensemble (EoE) machine learning (ML) system composed of a supervised and an unsupervised ML layers was developed to generate a prediction model for CRT response. RESULTS We compared the performance of EoE against traditional ML methods and the state-of-the-art convolutional neural network (CNN) model trained on raw electrocardiographic (ECG) waveforms. We observed that the models exhibited improvement in performance as more features were incrementally used for training. Using the most comprehensive set of predictors, the performance of the EoE model in terms of the area under the receiver operating characteristic curve and F1-score were 0.76 and 0.73, respectively. Direct application of the CNN model on the raw ECG waveforms did not generate promising results. CONCLUSION The proposed CRT risk calculator effectively discriminates which heart failure (HF) patient is likely to respond to CRT significantly better than using clinical guidelines and traditional ML methods, thus suggesting that the tool can enhanced care management of HF patients by helping to identify high-risk patients.
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Affiliation(s)
- Cheng Cai
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.,Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Ahmad P Tafti
- College of Science, Technology, and Health, University of Southern Maine, Portland, Maine, USA
| | - Che Ngufor
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Pei Zhang
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.,Department of Cardiology, Sir Run Run Shaw Hospital, School of Medicine Zhejiang University, Hangzhou, China
| | - Peilin Xiao
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.,Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Mingyan Dai
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.,Department of Cardiology, Renmin Hospital of Wuhan University; Cardiovascular Research Institute, Wuhan University, Hubei Key Laboratory of Cardiology, Wuhan, China
| | - Hongfang Liu
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Peter Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Minglong Chen
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Yong-Mei Cha
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
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Łacina P, Butrym A, Humiński M, Dratwa M, Frontkiewicz D, Mazur G, Bogunia-Kubik K. Association of RANK and RANKL gene polymorphism with survival and calcium levels in multiple myeloma. Mol Carcinog 2020; 60:106-112. [PMID: 33283899 DOI: 10.1002/mc.23272] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 11/15/2020] [Accepted: 11/24/2020] [Indexed: 12/30/2022]
Abstract
Multiple myeloma (MM) is a heterogeneous bone marrow cancer characterized by proliferation of malignant plasma cells in the bone marrow. One of its major symptoms are hypercalcaemia and bone lesions, which may result in pathologic bone fractures. Receptor activator for nuclear factor κB (RANK) and its ligand, RANKL, are part of an activation pathway for osteoclasts and are thus responsible for bone resorption. Furthermore, RANKL expression is increased in multiple myeloma. In the present study, we investigated the role of single nucleotide polymorphisms (SNPs) in the genes coding for RANK (rs1805034, rs8086340), RANKL (rs7325635, rs7988338), and TACI (rs34562254), a receptor for osteoclast-derived pro-survival factors. The study involved 222 patients and 222 healthy individuals, and the analysis included disease susceptibility, survival, bone lesions, calcium levels, and vascular endothelial growth factor levels. Patients with allele RANK rs1805034 C had higher survival (p = .003). This relationship was especially evident in women (p = .006). Furthermore, allele rs1805034 C was associated with slightly lower median age at diagnosis (64.0 vs. 65.5, p = .008). Allele RANKL rs7325635 A correlated with lower progression-free survival (p = .027), and with lack of early progression (p = .023). Additionally, women with allele rs7325635 G were found to have higher calcium blood concentration (p = .040). Allele TACI rs34562254 A was more common in MM patients in more advanced stages (II and III stage International Staging System) at diagnosis (p = .017), and the SNP showed a slight trend towards association in a multivariate analysis (p = .084). Taken together, our results suggest that RANK rs1805034 and RANKL rs7325635 may have a role in MM development and progression.
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Affiliation(s)
- Piotr Łacina
- Department of Clinical Immunology, Laboratory of Clinical Immunogenetics and Pharmacogenetics, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wrocław, Poland
| | - Aleksandra Butrym
- Department of Cancer Prevention and Therapy, Wroclaw Medical University, Wrocław, Poland
| | - Michał Humiński
- Department of Clinical Immunology, Laboratory of Clinical Immunogenetics and Pharmacogenetics, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wrocław, Poland
| | - Marta Dratwa
- Department of Clinical Immunology, Laboratory of Clinical Immunogenetics and Pharmacogenetics, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wrocław, Poland
| | - Diana Frontkiewicz
- Department of Internal Occupational Diseases, Hypertension, and Clinical Oncology, Wroclaw Medical University, Wrocław, Poland
| | - Grzegorz Mazur
- Department of Internal Occupational Diseases, Hypertension, and Clinical Oncology, Wroclaw Medical University, Wrocław, Poland
| | - Katarzyna Bogunia-Kubik
- Department of Clinical Immunology, Laboratory of Clinical Immunogenetics and Pharmacogenetics, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wrocław, Poland
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9
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Heggermont W, Auricchio A, Vanderheyden M. Biomarkers to predict the response to cardiac resynchronization therapy. Europace 2020; 21:1609-1620. [PMID: 31681965 DOI: 10.1093/europace/euz168] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Accepted: 05/23/2019] [Indexed: 12/17/2022] Open
Abstract
Cardiac resynchronization therapy (CRT) is an established non-pharmacological treatment for selected heart failure patients with wide QRS duration. However, there is a persistent number of non-responders throughout. The prediction of the CRT response is paramount to adequately select the correct patients for CRT. One of the expanding fields of research is the development of biomarkers that predict the response to CRT. A review of the available literature on biomarkers in CRT patients has been performed to formulate a critical appraisal of the available data. The main conclusion of our review is that biomarker research in this patient population is very fragmented and broad. This results in the use of non-uniform endpoints to define the CRT response, which precludes an in-depth comparison of the available data. To improve research development in this field, a uniform definition of the CRT response and relevant endpoints is necessary to better predict the CRT response.
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Affiliation(s)
- Ward Heggermont
- Cardiovascular Research Centre, OLV Hospital Aalst, Moorselbaan 164, B, Aalst, Belgium.,Cardiovascular Research Institute Maastricht, Maastricht University, Universiteitssingel 50, Maastricht, The Netherlands
| | - Angelo Auricchio
- Cardiocentro Ticino, Department of Electrophysiology, Via Tesserete 48, CH, Lugano, Switzerland.,Centre for Computational Medicine in Cardiology, Via Buffi 13, CH-6900, Lugano, Switzerland
| | - Marc Vanderheyden
- Cardiovascular Research Centre, OLV Hospital Aalst, Moorselbaan 164, B, Aalst, Belgium
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10
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Wielińska J, Kolossa K, Świerkot J, Dratwa M, Iwaszko M, Bugaj B, Wysoczańska B, Chaszczewska-Markowska M, Jeka S, Bogunia-Kubik K. Polymorphisms within the RANK and RANKL Encoding Genes in Patients with Rheumatoid Arthritis: Association with Disease Progression and Effectiveness of the Biological Treatment. Arch Immunol Ther Exp (Warsz) 2020; 68:24. [PMID: 32815001 PMCID: PMC7438366 DOI: 10.1007/s00005-020-00590-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 06/17/2020] [Indexed: 12/11/2022]
Abstract
Inconsistency of the results regarding the genetic variability within genes coding for receptor activator of nuclear factor κB (RANK) and its ligand (RANKL) in rheumatoid arthritis (RA) prompted us to study the RANK and RANKL polymorphisms as potential biomarkers associated with disease predisposition and response to anti-TNF treatment in a group of Polish patients with RA. This study enrolled 318 RA patients and 163 controls. RANK (rs8086340, C > G; rs1805034, C > T) and RANKL (rs7325635, G > A; rs7988338 G > A) alleles were determined by real-time PCR with melting curve analysis and related with clinical parameters. In addition, RANKL serum levels were measured by ELISA. The RANK rs8086340-G allele was overrepresented among patients as compared to controls (OD = 1.777, p = 0.038). C-reactive protein (CRP) levels were significantly (p < 0.05) associated with RANK rs8086340 polymorphism and were higher in the CC-homozygotes at the baseline while lower in the GG-carriers at the 12th week of the treatment. At the latter time point RANKL rs7325635-GG-positive patients also showed significantly lower CRP concentrations. Higher alkaline phosphatase levels before induction of anti-TNF therapy were observed in RANK rs8086340 and RANK rs1805034 CC homozygotes (p = 0.057 and p = 0.035, respectively). The GG homozygosity of both RANKL single nucleotide polymorphisms was significantly associated with the number of swollen joints (rs7988338 and rs7325635, before and at the 12th week of therapy, respectively, p < 0.05 in both cases). These results imply that polymorphisms within the RANK and RANKL genes affect RA susceptibility and anti-TNF treatment outcome.
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Affiliation(s)
- Joanna Wielińska
- Laboratory of Clinical Immunogenetics and Pharmacogenetics, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wroclaw, Poland
| | - Katarzyna Kolossa
- Department of Rheumatology and Connective Tissue Diseases, Jan Biziel University Hospital No. 2, Bydgoszcz, Poland
| | - Jerzy Świerkot
- Department of Rheumatology and Internal Medicine, Wroclaw Medical University, Wroclaw, Poland
| | - Marta Dratwa
- Laboratory of Clinical Immunogenetics and Pharmacogenetics, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wroclaw, Poland
| | - Milena Iwaszko
- Laboratory of Clinical Immunogenetics and Pharmacogenetics, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wroclaw, Poland
| | - Bartosz Bugaj
- Department of Rheumatology and Internal Medicine, Wroclaw Medical University, Wroclaw, Poland
| | - Barbara Wysoczańska
- Laboratory of Clinical Immunogenetics and Pharmacogenetics, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wroclaw, Poland
| | - Monika Chaszczewska-Markowska
- Laboratory of Clinical Immunogenetics and Pharmacogenetics, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wroclaw, Poland
| | - Sławomir Jeka
- Department of Rheumatology and Connective Tissue Diseases, Jan Biziel University Hospital No. 2, Bydgoszcz, Poland
- Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University, Torun, Poland
| | - Katarzyna Bogunia-Kubik
- Laboratory of Clinical Immunogenetics and Pharmacogenetics, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wroclaw, Poland.
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11
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Sessa M, Khan AR, Liang D, Andersen M, Kulahci M. Artificial Intelligence in Pharmacoepidemiology: A Systematic Review. Part 1-Overview of Knowledge Discovery Techniques in Artificial Intelligence. Front Pharmacol 2020; 11:1028. [PMID: 32765261 PMCID: PMC7378532 DOI: 10.3389/fphar.2020.01028] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 06/24/2020] [Indexed: 12/14/2022] Open
Abstract
Aim To perform a systematic review on the application of artificial intelligence (AI) based knowledge discovery techniques in pharmacoepidemiology. Study Eligibility Criteria Clinical trials, meta-analyses, narrative/systematic review, and observational studies using (or mentioning articles using) artificial intelligence techniques were eligible. Articles without a full text available in the English language were excluded. Data Sources Articles recorded from 1950/01/01 to 2019/05/06 in Ovid MEDLINE were screened. Participants Studies including humans (real or simulated) exposed to a drug. Results In total, 72 original articles and 5 reviews were identified via Ovid MEDLINE. Twenty different knowledge discovery methods were identified, mainly from the area of machine learning (66/72; 91.7%). Classification/regression (44/72; 61.1%), classification/regression + model optimization (13/72; 18.0%), and classification/regression + features selection (12/72; 16.7%) were the three most frequent tasks in reviewed literature that machine learning methods has been applied to solve. The top three used techniques were artificial neural networks, random forest, and support vector machines models. Conclusions The use of knowledge discovery techniques of artificial intelligence techniques has increased exponentially over the years covering numerous sub-topics of pharmacoepidemiology. Systematic Review Registration Systematic review registration number in PROSPERO: CRD42019136552.
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Affiliation(s)
- Maurizio Sessa
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Abdul Rauf Khan
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark.,Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - David Liang
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Morten Andersen
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Murat Kulahci
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark.,Department of Business Administration, Technology and Social Sciences, Luleå University of Technology, Luleå, Sweden
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12
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de Marvao A, Dawes TJW, O'Regan DP. Artificial Intelligence for Cardiac Imaging-Genetics Research. Front Cardiovasc Med 2020; 6:195. [PMID: 32039240 PMCID: PMC6985036 DOI: 10.3389/fcvm.2019.00195] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 12/27/2019] [Indexed: 12/18/2022] Open
Abstract
Cardiovascular conditions remain the leading cause of mortality and morbidity worldwide, with genotype being a significant influence on disease risk. Cardiac imaging-genetics aims to identify and characterize the genetic variants that influence functional, physiological, and anatomical phenotypes derived from cardiovascular imaging. High-throughput DNA sequencing and genotyping have greatly accelerated genetic discovery, making variant interpretation one of the key challenges in contemporary clinical genetics. Heterogeneous, low-fidelity phenotyping and difficulties integrating and then analyzing large-scale genetic, imaging and clinical datasets using traditional statistical approaches have impeded process. Artificial intelligence (AI) methods, such as deep learning, are particularly suited to tackle the challenges of scalability and high dimensionality of data and show promise in the field of cardiac imaging-genetics. Here we review the current state of AI as applied to imaging-genetics research and discuss outstanding methodological challenges, as the field moves from pilot studies to mainstream applications, from one dimensional global descriptors to high-resolution models of whole-organ shape and function, from univariate to multivariate analysis and from candidate gene to genome-wide approaches. Finally, we consider the future directions and prospects of AI imaging-genetics for ultimately helping understand the genetic and environmental underpinnings of cardiovascular health and disease.
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Affiliation(s)
| | | | - Declan P. O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
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13
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POLYMORPHISM OF C825T (RS5443) G-PROTEIN 3-SUBUNIT GENE AND THE LONG-TERM PROGNOSIS FOR PATIENTS WITH HEART FAILURE. WORLD OF MEDICINE AND BIOLOGY 2019. [DOI: 10.26724/2079-8334-2019-1-67-88] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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14
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Kouvas N, Kontogiannis C, Georgiopoulos G, Spartalis M, Tsilimigras DI, Spartalis E, Kapelouzou A, Kosmopoulos M, Chatzidou S. The complex crosstalk between inflammatory cytokines and ventricular arrhythmias. Cytokine 2018; 111:171-177. [PMID: 30172113 DOI: 10.1016/j.cyto.2018.08.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Revised: 08/07/2018] [Accepted: 08/08/2018] [Indexed: 12/23/2022]
Affiliation(s)
- N Kouvas
- Department of Clinical Therapeutics, "Alexandra" Hospital, University of Athens, Athens, Greece
| | - C Kontogiannis
- Department of Clinical Therapeutics, "Alexandra" Hospital, University of Athens, Athens, Greece
| | - G Georgiopoulos
- Department of Clinical Therapeutics, "Alexandra" Hospital, University of Athens, Athens, Greece
| | - M Spartalis
- Department of Electrophysiology and Pacing, Onassis Cardiac Surgery Center, Greece
| | - D I Tsilimigras
- Department of Clinical Therapeutics, "Alexandra" Hospital, University of Athens, Athens, Greece
| | - E Spartalis
- Laboratory of Experimental Surgery and Surgical Research, National and Kapodistrian University of Athens, Medical School, Greece
| | - A Kapelouzou
- Center for Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, Athens, Greece
| | - M Kosmopoulos
- Department of Clinical Therapeutics, "Alexandra" Hospital, University of Athens, Athens, Greece.
| | - S Chatzidou
- Department of Clinical Therapeutics, "Alexandra" Hospital, University of Athens, Athens, Greece
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15
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Nguyên UC, Verzaal NJ, van Nieuwenhoven FA, Vernooy K, Prinzen FW. Pathobiology of cardiac dyssynchrony and resynchronization therapy. Europace 2018; 20:1898-1909. [DOI: 10.1093/europace/euy035] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Accepted: 02/16/2018] [Indexed: 02/04/2023] Open
Affiliation(s)
- Uyên Châu Nguyên
- Department of Physiology, Cardiovascular Research Institute Maastricht, Universiteitssingel 50, ER Maastricht, The Netherlands
- Department of Cardiology, Cardiovascular Research Institute Maastricht, Universiteitssingel 50, ER Maastricht, The Netherlands
| | - Nienke J Verzaal
- Department of Physiology, Cardiovascular Research Institute Maastricht, Universiteitssingel 50, ER Maastricht, The Netherlands
| | - Frans A van Nieuwenhoven
- Department of Physiology, Cardiovascular Research Institute Maastricht, Universiteitssingel 50, ER Maastricht, The Netherlands
| | - Kevin Vernooy
- Department of Cardiology, Cardiovascular Research Institute Maastricht, Universiteitssingel 50, ER Maastricht, The Netherlands
| | - Frits W Prinzen
- Department of Physiology, Cardiovascular Research Institute Maastricht, Universiteitssingel 50, ER Maastricht, The Netherlands
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16
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Kalscheur MM, Kipp RT, Tattersall MC, Mei C, Buhr KA, DeMets DL, Field ME, Eckhardt LL, Page CD. Machine Learning Algorithm Predicts Cardiac Resynchronization Therapy Outcomes: Lessons From the COMPANION Trial. Circ Arrhythm Electrophysiol 2018; 11:e005499. [PMID: 29326129 PMCID: PMC5769699 DOI: 10.1161/circep.117.005499] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Accepted: 11/27/2017] [Indexed: 01/27/2023]
Abstract
BACKGROUND Cardiac resynchronization therapy (CRT) reduces morbidity and mortality in heart failure patients with reduced left ventricular function and intraventricular conduction delay. However, individual outcomes vary significantly. This study sought to use a machine learning algorithm to develop a model to predict outcomes after CRT. METHODS AND RESULTS Models were developed with machine learning algorithms to predict all-cause mortality or heart failure hospitalization at 12 months post-CRT in the COMPANION trial (Comparison of Medical Therapy, Pacing, and Defibrillation in Heart Failure). The best performing model was developed with the random forest algorithm. The ability of this model to predict all-cause mortality or heart failure hospitalization and all-cause mortality alone was compared with discrimination obtained using a combination of bundle branch block morphology and QRS duration. In the 595 patients with CRT-defibrillator in the COMPANION trial, 105 deaths occurred (median follow-up, 15.7 months). The survival difference across subgroups differentiated by bundle branch block morphology and QRS duration did not reach significance (P=0.08). The random forest model produced quartiles of patients with an 8-fold difference in survival between those with the highest and lowest predicted probability for events (hazard ratio, 7.96; P<0.0001). The model also discriminated the risk of the composite end point of all-cause mortality or heart failure hospitalization better than subgroups based on bundle branch block morphology and QRS duration. CONCLUSIONS In the COMPANION trial, a machine learning algorithm produced a model that predicted clinical outcomes after CRT. Applied before device implant, this model may better differentiate outcomes over current clinical discriminators and improve shared decision-making with patients.
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Affiliation(s)
- Matthew M Kalscheur
- From the Division of Cardiovascular Medicine, Department of Medicine, School of Medicine and Public Health (M.M.K., R.T.K., M.C.T., M.E.F., L.L.E.), Department of Biostatistics and Medical Informatics (C.M., K.A.B., D.L.D., C.D.P.), University of Wisconsin Institute for Clinical and Translational Research (C.M.), and Department of Computer Sciences (C.D.P.), University of Wisconsin-Madison.
| | - Ryan T Kipp
- From the Division of Cardiovascular Medicine, Department of Medicine, School of Medicine and Public Health (M.M.K., R.T.K., M.C.T., M.E.F., L.L.E.), Department of Biostatistics and Medical Informatics (C.M., K.A.B., D.L.D., C.D.P.), University of Wisconsin Institute for Clinical and Translational Research (C.M.), and Department of Computer Sciences (C.D.P.), University of Wisconsin-Madison
| | - Matthew C Tattersall
- From the Division of Cardiovascular Medicine, Department of Medicine, School of Medicine and Public Health (M.M.K., R.T.K., M.C.T., M.E.F., L.L.E.), Department of Biostatistics and Medical Informatics (C.M., K.A.B., D.L.D., C.D.P.), University of Wisconsin Institute for Clinical and Translational Research (C.M.), and Department of Computer Sciences (C.D.P.), University of Wisconsin-Madison
| | - Chaoqun Mei
- From the Division of Cardiovascular Medicine, Department of Medicine, School of Medicine and Public Health (M.M.K., R.T.K., M.C.T., M.E.F., L.L.E.), Department of Biostatistics and Medical Informatics (C.M., K.A.B., D.L.D., C.D.P.), University of Wisconsin Institute for Clinical and Translational Research (C.M.), and Department of Computer Sciences (C.D.P.), University of Wisconsin-Madison
| | - Kevin A Buhr
- From the Division of Cardiovascular Medicine, Department of Medicine, School of Medicine and Public Health (M.M.K., R.T.K., M.C.T., M.E.F., L.L.E.), Department of Biostatistics and Medical Informatics (C.M., K.A.B., D.L.D., C.D.P.), University of Wisconsin Institute for Clinical and Translational Research (C.M.), and Department of Computer Sciences (C.D.P.), University of Wisconsin-Madison
| | - David L DeMets
- From the Division of Cardiovascular Medicine, Department of Medicine, School of Medicine and Public Health (M.M.K., R.T.K., M.C.T., M.E.F., L.L.E.), Department of Biostatistics and Medical Informatics (C.M., K.A.B., D.L.D., C.D.P.), University of Wisconsin Institute for Clinical and Translational Research (C.M.), and Department of Computer Sciences (C.D.P.), University of Wisconsin-Madison
| | - Michael E Field
- From the Division of Cardiovascular Medicine, Department of Medicine, School of Medicine and Public Health (M.M.K., R.T.K., M.C.T., M.E.F., L.L.E.), Department of Biostatistics and Medical Informatics (C.M., K.A.B., D.L.D., C.D.P.), University of Wisconsin Institute for Clinical and Translational Research (C.M.), and Department of Computer Sciences (C.D.P.), University of Wisconsin-Madison
| | - Lee L Eckhardt
- From the Division of Cardiovascular Medicine, Department of Medicine, School of Medicine and Public Health (M.M.K., R.T.K., M.C.T., M.E.F., L.L.E.), Department of Biostatistics and Medical Informatics (C.M., K.A.B., D.L.D., C.D.P.), University of Wisconsin Institute for Clinical and Translational Research (C.M.), and Department of Computer Sciences (C.D.P.), University of Wisconsin-Madison
| | - C David Page
- From the Division of Cardiovascular Medicine, Department of Medicine, School of Medicine and Public Health (M.M.K., R.T.K., M.C.T., M.E.F., L.L.E.), Department of Biostatistics and Medical Informatics (C.M., K.A.B., D.L.D., C.D.P.), University of Wisconsin Institute for Clinical and Translational Research (C.M.), and Department of Computer Sciences (C.D.P.), University of Wisconsin-Madison
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17
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Sheppard R, Hsich E, Damp J, Elkayam U, Kealey A, Ramani G, Zucker M, Alexis JD, Horne BD, Hanley-Yanez K, Pisarcik J, Halder I, Fett JD, McNamara DM. GNB3 C825T Polymorphism and Myocardial Recovery in Peripartum Cardiomyopathy: Results of the Multicenter Investigations of Pregnancy-Associated Cardiomyopathy Study. Circ Heart Fail 2016; 9:e002683. [PMID: 26915373 DOI: 10.1161/circheartfailure.115.002683] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Black women are at greater risk for peripartum cardiomyopathy (PPCM). The guanine nucleotide-binding proteins β-3 subunit (GNB3) has a polymorphism C825T. The GNB3 TT genotype more prevalent in blacks is associated with poorer outcomes. We evaluated GNB3 genotype and myocardial recovery in PPCM. METHODS AND RESULTS A total of 97 women with PPCM were enrolled and genotyped for the GNB3 T/C polymorphism. Left ventricular ejection fraction (LVEF) was assessed by echocardiography at entry, 6 and 12 months postpartum. LVEF over time in subjects with the GNB3 TT genotype was compared with those with the C allele overall and in black and white subsets. The cohort was 30% black, age 30+6, LVEF 0.34+0.10 at entry 31+25 days postpartum. The % GNB3 genotype for TT/CT/CC=23/41/36 and differed markedly by race (blacks=52/38/10 versus whites=10/44/46, P<0.001). In subjects with the TT genotype, LVEF at entry was lower (TT=0.31+0.09; CT+CC=0.35+0.09, P=0.054) and this difference increased at 6 (TT=0.45+0.15; CT+CC=0.53+0.08, P=0.002) and 12 months (TT=0.45+0.15; CT+CC=0.56+0.07, P<0.001.). The difference in LVEF at 12 months by genotype was most pronounced in blacks (12 months LVEF for GNB3 TT=0.39+0.16; versus CT+CC=0.53+0.09, P=0.02) but evident in whites (TT=0.50++0.11; CT+CC=0.56+0.06, P=0.04). CONCLUSIONS The GNB3 TT genotype was associated with lower LVEF at 6 and 12 months in women with PPCM, and this was particularly evident in blacks. Racial differences in the prevalence and impact of GNB3 TT may contribute to poorer outcomes in black women with PPCM.
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Affiliation(s)
- Richard Sheppard
- From the Division of Cardiology, Jewish General Hospital, McGill University, Montreal, QC, Canada (R.S.); Department of Cardiovascular Medicine, Cleveland Clinic Foundation, OH (E.H.); Department of Cardiology, Vanderbilt University, Nashville, TN (J.D.); Division of Cardiovascular Medicine, University of Southern California, Los Angeles (U.E.); Department of Medicine and Cardiovascular Sciences, University of Calgary, Calgary, AB, Canada (A.K.); Department of Cardiology, University of Maryland, Baltimore (G.R.); Cardiac Transplant Center, Beth Israel Newark Medical Center, NJ (M.Z.); Department of Cardiology, University of Rochester, NY (J.D.A.); Division of Cardiology, Intermountain Medical Center, Salt Lake City, Utah (B.D.H.); and Division of Cardiology, Heart, Lung, Blood, and Vascular Medicine Institute, University of Pittsburgh, PA (K.H.-Y., J.P., I.H., J.D.F., D.M.M.N.).
| | - Eileen Hsich
- From the Division of Cardiology, Jewish General Hospital, McGill University, Montreal, QC, Canada (R.S.); Department of Cardiovascular Medicine, Cleveland Clinic Foundation, OH (E.H.); Department of Cardiology, Vanderbilt University, Nashville, TN (J.D.); Division of Cardiovascular Medicine, University of Southern California, Los Angeles (U.E.); Department of Medicine and Cardiovascular Sciences, University of Calgary, Calgary, AB, Canada (A.K.); Department of Cardiology, University of Maryland, Baltimore (G.R.); Cardiac Transplant Center, Beth Israel Newark Medical Center, NJ (M.Z.); Department of Cardiology, University of Rochester, NY (J.D.A.); Division of Cardiology, Intermountain Medical Center, Salt Lake City, Utah (B.D.H.); and Division of Cardiology, Heart, Lung, Blood, and Vascular Medicine Institute, University of Pittsburgh, PA (K.H.-Y., J.P., I.H., J.D.F., D.M.M.N.)
| | - Julie Damp
- From the Division of Cardiology, Jewish General Hospital, McGill University, Montreal, QC, Canada (R.S.); Department of Cardiovascular Medicine, Cleveland Clinic Foundation, OH (E.H.); Department of Cardiology, Vanderbilt University, Nashville, TN (J.D.); Division of Cardiovascular Medicine, University of Southern California, Los Angeles (U.E.); Department of Medicine and Cardiovascular Sciences, University of Calgary, Calgary, AB, Canada (A.K.); Department of Cardiology, University of Maryland, Baltimore (G.R.); Cardiac Transplant Center, Beth Israel Newark Medical Center, NJ (M.Z.); Department of Cardiology, University of Rochester, NY (J.D.A.); Division of Cardiology, Intermountain Medical Center, Salt Lake City, Utah (B.D.H.); and Division of Cardiology, Heart, Lung, Blood, and Vascular Medicine Institute, University of Pittsburgh, PA (K.H.-Y., J.P., I.H., J.D.F., D.M.M.N.)
| | - Uri Elkayam
- From the Division of Cardiology, Jewish General Hospital, McGill University, Montreal, QC, Canada (R.S.); Department of Cardiovascular Medicine, Cleveland Clinic Foundation, OH (E.H.); Department of Cardiology, Vanderbilt University, Nashville, TN (J.D.); Division of Cardiovascular Medicine, University of Southern California, Los Angeles (U.E.); Department of Medicine and Cardiovascular Sciences, University of Calgary, Calgary, AB, Canada (A.K.); Department of Cardiology, University of Maryland, Baltimore (G.R.); Cardiac Transplant Center, Beth Israel Newark Medical Center, NJ (M.Z.); Department of Cardiology, University of Rochester, NY (J.D.A.); Division of Cardiology, Intermountain Medical Center, Salt Lake City, Utah (B.D.H.); and Division of Cardiology, Heart, Lung, Blood, and Vascular Medicine Institute, University of Pittsburgh, PA (K.H.-Y., J.P., I.H., J.D.F., D.M.M.N.)
| | - Angela Kealey
- From the Division of Cardiology, Jewish General Hospital, McGill University, Montreal, QC, Canada (R.S.); Department of Cardiovascular Medicine, Cleveland Clinic Foundation, OH (E.H.); Department of Cardiology, Vanderbilt University, Nashville, TN (J.D.); Division of Cardiovascular Medicine, University of Southern California, Los Angeles (U.E.); Department of Medicine and Cardiovascular Sciences, University of Calgary, Calgary, AB, Canada (A.K.); Department of Cardiology, University of Maryland, Baltimore (G.R.); Cardiac Transplant Center, Beth Israel Newark Medical Center, NJ (M.Z.); Department of Cardiology, University of Rochester, NY (J.D.A.); Division of Cardiology, Intermountain Medical Center, Salt Lake City, Utah (B.D.H.); and Division of Cardiology, Heart, Lung, Blood, and Vascular Medicine Institute, University of Pittsburgh, PA (K.H.-Y., J.P., I.H., J.D.F., D.M.M.N.)
| | - Gautam Ramani
- From the Division of Cardiology, Jewish General Hospital, McGill University, Montreal, QC, Canada (R.S.); Department of Cardiovascular Medicine, Cleveland Clinic Foundation, OH (E.H.); Department of Cardiology, Vanderbilt University, Nashville, TN (J.D.); Division of Cardiovascular Medicine, University of Southern California, Los Angeles (U.E.); Department of Medicine and Cardiovascular Sciences, University of Calgary, Calgary, AB, Canada (A.K.); Department of Cardiology, University of Maryland, Baltimore (G.R.); Cardiac Transplant Center, Beth Israel Newark Medical Center, NJ (M.Z.); Department of Cardiology, University of Rochester, NY (J.D.A.); Division of Cardiology, Intermountain Medical Center, Salt Lake City, Utah (B.D.H.); and Division of Cardiology, Heart, Lung, Blood, and Vascular Medicine Institute, University of Pittsburgh, PA (K.H.-Y., J.P., I.H., J.D.F., D.M.M.N.)
| | - Mark Zucker
- From the Division of Cardiology, Jewish General Hospital, McGill University, Montreal, QC, Canada (R.S.); Department of Cardiovascular Medicine, Cleveland Clinic Foundation, OH (E.H.); Department of Cardiology, Vanderbilt University, Nashville, TN (J.D.); Division of Cardiovascular Medicine, University of Southern California, Los Angeles (U.E.); Department of Medicine and Cardiovascular Sciences, University of Calgary, Calgary, AB, Canada (A.K.); Department of Cardiology, University of Maryland, Baltimore (G.R.); Cardiac Transplant Center, Beth Israel Newark Medical Center, NJ (M.Z.); Department of Cardiology, University of Rochester, NY (J.D.A.); Division of Cardiology, Intermountain Medical Center, Salt Lake City, Utah (B.D.H.); and Division of Cardiology, Heart, Lung, Blood, and Vascular Medicine Institute, University of Pittsburgh, PA (K.H.-Y., J.P., I.H., J.D.F., D.M.M.N.)
| | - Jeffrey D Alexis
- From the Division of Cardiology, Jewish General Hospital, McGill University, Montreal, QC, Canada (R.S.); Department of Cardiovascular Medicine, Cleveland Clinic Foundation, OH (E.H.); Department of Cardiology, Vanderbilt University, Nashville, TN (J.D.); Division of Cardiovascular Medicine, University of Southern California, Los Angeles (U.E.); Department of Medicine and Cardiovascular Sciences, University of Calgary, Calgary, AB, Canada (A.K.); Department of Cardiology, University of Maryland, Baltimore (G.R.); Cardiac Transplant Center, Beth Israel Newark Medical Center, NJ (M.Z.); Department of Cardiology, University of Rochester, NY (J.D.A.); Division of Cardiology, Intermountain Medical Center, Salt Lake City, Utah (B.D.H.); and Division of Cardiology, Heart, Lung, Blood, and Vascular Medicine Institute, University of Pittsburgh, PA (K.H.-Y., J.P., I.H., J.D.F., D.M.M.N.)
| | - Benjamin D Horne
- From the Division of Cardiology, Jewish General Hospital, McGill University, Montreal, QC, Canada (R.S.); Department of Cardiovascular Medicine, Cleveland Clinic Foundation, OH (E.H.); Department of Cardiology, Vanderbilt University, Nashville, TN (J.D.); Division of Cardiovascular Medicine, University of Southern California, Los Angeles (U.E.); Department of Medicine and Cardiovascular Sciences, University of Calgary, Calgary, AB, Canada (A.K.); Department of Cardiology, University of Maryland, Baltimore (G.R.); Cardiac Transplant Center, Beth Israel Newark Medical Center, NJ (M.Z.); Department of Cardiology, University of Rochester, NY (J.D.A.); Division of Cardiology, Intermountain Medical Center, Salt Lake City, Utah (B.D.H.); and Division of Cardiology, Heart, Lung, Blood, and Vascular Medicine Institute, University of Pittsburgh, PA (K.H.-Y., J.P., I.H., J.D.F., D.M.M.N.)
| | - Karen Hanley-Yanez
- From the Division of Cardiology, Jewish General Hospital, McGill University, Montreal, QC, Canada (R.S.); Department of Cardiovascular Medicine, Cleveland Clinic Foundation, OH (E.H.); Department of Cardiology, Vanderbilt University, Nashville, TN (J.D.); Division of Cardiovascular Medicine, University of Southern California, Los Angeles (U.E.); Department of Medicine and Cardiovascular Sciences, University of Calgary, Calgary, AB, Canada (A.K.); Department of Cardiology, University of Maryland, Baltimore (G.R.); Cardiac Transplant Center, Beth Israel Newark Medical Center, NJ (M.Z.); Department of Cardiology, University of Rochester, NY (J.D.A.); Division of Cardiology, Intermountain Medical Center, Salt Lake City, Utah (B.D.H.); and Division of Cardiology, Heart, Lung, Blood, and Vascular Medicine Institute, University of Pittsburgh, PA (K.H.-Y., J.P., I.H., J.D.F., D.M.M.N.)
| | - Jessica Pisarcik
- From the Division of Cardiology, Jewish General Hospital, McGill University, Montreal, QC, Canada (R.S.); Department of Cardiovascular Medicine, Cleveland Clinic Foundation, OH (E.H.); Department of Cardiology, Vanderbilt University, Nashville, TN (J.D.); Division of Cardiovascular Medicine, University of Southern California, Los Angeles (U.E.); Department of Medicine and Cardiovascular Sciences, University of Calgary, Calgary, AB, Canada (A.K.); Department of Cardiology, University of Maryland, Baltimore (G.R.); Cardiac Transplant Center, Beth Israel Newark Medical Center, NJ (M.Z.); Department of Cardiology, University of Rochester, NY (J.D.A.); Division of Cardiology, Intermountain Medical Center, Salt Lake City, Utah (B.D.H.); and Division of Cardiology, Heart, Lung, Blood, and Vascular Medicine Institute, University of Pittsburgh, PA (K.H.-Y., J.P., I.H., J.D.F., D.M.M.N.)
| | - Indrani Halder
- From the Division of Cardiology, Jewish General Hospital, McGill University, Montreal, QC, Canada (R.S.); Department of Cardiovascular Medicine, Cleveland Clinic Foundation, OH (E.H.); Department of Cardiology, Vanderbilt University, Nashville, TN (J.D.); Division of Cardiovascular Medicine, University of Southern California, Los Angeles (U.E.); Department of Medicine and Cardiovascular Sciences, University of Calgary, Calgary, AB, Canada (A.K.); Department of Cardiology, University of Maryland, Baltimore (G.R.); Cardiac Transplant Center, Beth Israel Newark Medical Center, NJ (M.Z.); Department of Cardiology, University of Rochester, NY (J.D.A.); Division of Cardiology, Intermountain Medical Center, Salt Lake City, Utah (B.D.H.); and Division of Cardiology, Heart, Lung, Blood, and Vascular Medicine Institute, University of Pittsburgh, PA (K.H.-Y., J.P., I.H., J.D.F., D.M.M.N.)
| | - James D Fett
- From the Division of Cardiology, Jewish General Hospital, McGill University, Montreal, QC, Canada (R.S.); Department of Cardiovascular Medicine, Cleveland Clinic Foundation, OH (E.H.); Department of Cardiology, Vanderbilt University, Nashville, TN (J.D.); Division of Cardiovascular Medicine, University of Southern California, Los Angeles (U.E.); Department of Medicine and Cardiovascular Sciences, University of Calgary, Calgary, AB, Canada (A.K.); Department of Cardiology, University of Maryland, Baltimore (G.R.); Cardiac Transplant Center, Beth Israel Newark Medical Center, NJ (M.Z.); Department of Cardiology, University of Rochester, NY (J.D.A.); Division of Cardiology, Intermountain Medical Center, Salt Lake City, Utah (B.D.H.); and Division of Cardiology, Heart, Lung, Blood, and Vascular Medicine Institute, University of Pittsburgh, PA (K.H.-Y., J.P., I.H., J.D.F., D.M.M.N.)
| | - Dennis M McNamara
- From the Division of Cardiology, Jewish General Hospital, McGill University, Montreal, QC, Canada (R.S.); Department of Cardiovascular Medicine, Cleveland Clinic Foundation, OH (E.H.); Department of Cardiology, Vanderbilt University, Nashville, TN (J.D.); Division of Cardiovascular Medicine, University of Southern California, Los Angeles (U.E.); Department of Medicine and Cardiovascular Sciences, University of Calgary, Calgary, AB, Canada (A.K.); Department of Cardiology, University of Maryland, Baltimore (G.R.); Cardiac Transplant Center, Beth Israel Newark Medical Center, NJ (M.Z.); Department of Cardiology, University of Rochester, NY (J.D.A.); Division of Cardiology, Intermountain Medical Center, Salt Lake City, Utah (B.D.H.); and Division of Cardiology, Heart, Lung, Blood, and Vascular Medicine Institute, University of Pittsburgh, PA (K.H.-Y., J.P., I.H., J.D.F., D.M.M.N.)
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Cardiac dyssynchrony and response to cardiac resynchronisation therapy in heart failure: can genetic predisposition play a role? Neth Heart J 2015; 24:11-5. [PMID: 26645708 PMCID: PMC4692826 DOI: 10.1007/s12471-015-0766-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Cardiac resynchronisation therapy (CRT) is an accepted treatment for heart failure patients with depressed left ventricular (LV) function and dyssynchrony. However, despite better clinical outcome and improved cardiac function after CRT in the majority of eligible heart failure patients, a large proportion of implanted patients do not seem to benefit clinically from this therapy. In this review we consider whether genetic factors may play a role in modulating response to CRT and summarise the few genetic studies that have investigated the role of genetic variation in candidate genes.
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