Sammani Arjan, Baas Annette F, Asselbergs Folkert W, Te Riele Anneline S J M
Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, 3582 CX Utrecht, The Netherlands.
Department of Genetics, Division Laboratories, Pharmacy and Biomedical Genetics, University Medical Centre Utrecht, University of Utrecht, 3582 CX Utrecht, The Netherlands.
J Clin Med. 2021 Feb 26;10(5):921. doi: 10.3390/jcm10050921.
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.
扩张型心肌病（DCM）是心力衰竭和危及生命的室性心律失常（LTVA）的主要原因。DCM的检查和风险分层在临床上具有挑战性，因为其表型和基因型存在很大的异质性。在过去十年中，对患者进行的基因检测有所改进，确定了基因型与表型的关联，并加强了对高危亲属的评估，从而改善了患者的预后。现在该领域已经成熟，可以探索改善个性化风险评估的机会。以“风险计算器”形式呈现的多变量风险模型可以纳入多种临床变量并预测结果（如心力衰竭住院或LTVA）。此外，从全基因组/外显子组关联研究得出的遗传风险评分可以估计个体患DCM的终生遗传风险。为了提高预测性能，有必要使用临床精细检查，如心脏磁共振成像上的延迟钆增强。为此，通过使用电子健康记录、现有研究数据库和疾病登记处来构建大数据基础设施，可以提高数据的可及性。通过应用机器学习和深度学习等方法，我们可以对复杂的相互作用进行建模，识别新的表型集群，并进行预后建模。本综述旨在概述DCM定义的演变及其在基因组学时代的临床检查和注意事项。此外，我们还展示了大数据基础设施、个性化预后评估和人工智能领域的一些令人兴奋的例子。
