Diagnosis and Risk Prediction of Dilated Cardiomyopathy in the Era of Big Data and Genomics
Dilated cardiomyopathy (DCM) is a leading cause of heart failure and life-threatening ventricular arrhythmias (LTVA). Work-up and risk stratification of DCM is clinically challenging, as there is great heterogeneity in phenotype and genotype. Throughout the last decade, improved genetic testing of p...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/10/5/921 |
| _version_ | 1797395128973787136 |
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| author | Arjan Sammani Annette F. Baas Folkert W. Asselbergs Anneline S. J. M. te Riele |
| author_facet | Arjan Sammani Annette F. Baas Folkert W. Asselbergs Anneline S. J. M. te Riele |
| author_sort | Arjan Sammani |
| collection | DOAJ |
| description | Dilated cardiomyopathy (DCM) is a leading cause of heart failure and life-threatening ventricular arrhythmias (LTVA). Work-up and risk stratification of DCM is clinically challenging, as there is great heterogeneity in phenotype and genotype. Throughout the last decade, improved genetic testing of patients has identified genotype–phenotype associations and enhanced evaluation of at-risk relatives leading to better patient prognosis. The field is now ripe to explore opportunities to improve personalised risk assessments. Multivariable risk models presented as “risk calculators” can incorporate a multitude of clinical variables and predict outcome (such as heart failure hospitalisations or LTVA). In addition, genetic risk scores derived from genome/exome-wide association studies can estimate an individual’s lifetime genetic risk of developing DCM. The use of clinically granular investigations, such as late gadolinium enhancement on cardiac magnetic resonance imaging, is warranted in order to increase predictive performance. To this end, constructing big data infrastructures improves accessibility of data by using electronic health records, existing research databases, and disease registries. By applying methods such as machine and deep learning, we can model complex interactions, identify new phenotype clusters, and perform prognostic modelling. This review aims to provide an overview of the evolution of DCM definitions as well as its clinical work-up and considerations in the era of genomics. In addition, we present exciting examples in the field of big data infrastructures, personalised prognostic assessment, and artificial intelligence. |
| first_indexed | 2024-03-09T00:29:58Z |
| format | Article |
| id | doaj.art-e1515d4a6003472bb237572af0656425 |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-09T00:29:58Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-e1515d4a6003472bb237572af06564252023-12-11T18:39:07ZengMDPI AGJournal of Clinical Medicine2077-03832021-02-0110592110.3390/jcm10050921Diagnosis and Risk Prediction of Dilated Cardiomyopathy in the Era of Big Data and GenomicsArjan Sammani0Annette F. Baas1Folkert W. Asselbergs2Anneline S. J. M. te Riele3Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, 3582 CX Utrecht, The NetherlandsDepartment of Genetics, Division Laboratories, Pharmacy and Biomedical Genetics, University Medical Centre Utrecht, University of Utrecht, 3582 CX Utrecht, The NetherlandsDepartment of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, 3582 CX Utrecht, The NetherlandsDepartment of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, 3582 CX Utrecht, The NetherlandsDilated cardiomyopathy (DCM) is a leading cause of heart failure and life-threatening ventricular arrhythmias (LTVA). Work-up and risk stratification of DCM is clinically challenging, as there is great heterogeneity in phenotype and genotype. Throughout the last decade, improved genetic testing of patients has identified genotype–phenotype associations and enhanced evaluation of at-risk relatives leading to better patient prognosis. The field is now ripe to explore opportunities to improve personalised risk assessments. Multivariable risk models presented as “risk calculators” can incorporate a multitude of clinical variables and predict outcome (such as heart failure hospitalisations or LTVA). In addition, genetic risk scores derived from genome/exome-wide association studies can estimate an individual’s lifetime genetic risk of developing DCM. The use of clinically granular investigations, such as late gadolinium enhancement on cardiac magnetic resonance imaging, is warranted in order to increase predictive performance. To this end, constructing big data infrastructures improves accessibility of data by using electronic health records, existing research databases, and disease registries. By applying methods such as machine and deep learning, we can model complex interactions, identify new phenotype clusters, and perform prognostic modelling. This review aims to provide an overview of the evolution of DCM definitions as well as its clinical work-up and considerations in the era of genomics. In addition, we present exciting examples in the field of big data infrastructures, personalised prognostic assessment, and artificial intelligence.https://www.mdpi.com/2077-0383/10/5/921dilated cardiomyopathydiagnosisprognosisbig dataartificial intelligencedeep learning |
| spellingShingle | Arjan Sammani Annette F. Baas Folkert W. Asselbergs Anneline S. J. M. te Riele Diagnosis and Risk Prediction of Dilated Cardiomyopathy in the Era of Big Data and Genomics Journal of Clinical Medicine dilated cardiomyopathy diagnosis prognosis big data artificial intelligence deep learning |
| title | Diagnosis and Risk Prediction of Dilated Cardiomyopathy in the Era of Big Data and Genomics |
| title_full | Diagnosis and Risk Prediction of Dilated Cardiomyopathy in the Era of Big Data and Genomics |
| title_fullStr | Diagnosis and Risk Prediction of Dilated Cardiomyopathy in the Era of Big Data and Genomics |
| title_full_unstemmed | Diagnosis and Risk Prediction of Dilated Cardiomyopathy in the Era of Big Data and Genomics |
| title_short | Diagnosis and Risk Prediction of Dilated Cardiomyopathy in the Era of Big Data and Genomics |
| title_sort | diagnosis and risk prediction of dilated cardiomyopathy in the era of big data and genomics |
| topic | dilated cardiomyopathy diagnosis prognosis big data artificial intelligence deep learning |
| url | https://www.mdpi.com/2077-0383/10/5/921 |
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