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Using physiologically based models for clinical translation: predictive modelling data interpretation or something in‐between?

机译:使用基于生理学的模型进行临床翻译:预测模型数据解释或两者之间的某些东西?

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摘要

Heart disease continues to be a significant clinical problem in Western society. Predictive models and simulations that integrate physiological understanding with patient information derived from clinical data have huge potential to contribute to improving our understanding of both the progression and treatment of heart disease. In particular they provide the potential to improve patient selection and optimisation of cardiovascular interventions across a range of pathologies. Currently a significant proportion of this potential is still to be realised. In this paper we discuss the opportunities and challenges associated with this realisation. Reviewing the successful elements of model translation for biophysically based models and the emerging supporting technologies, we propose three distinct modes of clinical translation. Finally we outline the challenges ahead that will be fundamental to overcome if the ultimate goal of fully personalised clinical cardiac care is to be achieved.
机译:在西方社会,心脏病仍然是重要的临床问题。将生理学理解与从临床数据中获得的患者信息相结合的预测模型和模拟具有巨大的潜力,可有助于增进我们对心脏病发展和治疗的理解。特别是,它们提供了改善多种病理情况下患者选择和优化心血管干预措施的潜力。目前,这种潜力的很大一部分仍有待实现。在本文中,我们讨论了与此实现相关的机遇和挑战。回顾基于物理模型的模型翻译的成功要素和新兴的支持技术,我们提出了三种不同的临床翻译模式。最后,我们概述了要实现完全个性化的临床心脏护理的最终目标所要克服的根本挑战。

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