Premium
Using physiologically based models for clinical translation: predictive modelling, data interpretation or something in‐between?
Author(s) -
Niederer Steven A.,
Smith Nic P.
Publication year - 2016
Publication title -
the journal of physiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.802
H-Index - 240
eISSN - 1469-7793
pISSN - 0022-3751
DOI - 10.1113/jp272003
Subject(s) - translation (biology) , interpretation (philosophy) , computer science , artificial intelligence , natural language processing , computational biology , neuroscience , psychology , chemistry , biology , programming language , biochemistry , messenger rna , gene
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.