Predicting drug-mediated pro-arrhythmic effects using pre-drug electrocardiograms

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dc.contributor.author Peng, Tommy
dc.contributor.author Malik, Avinash
dc.contributor.author Trew, Mark L
dc.date.accessioned 2021-08-06T02:56:54Z
dc.date.available 2021-08-06T02:56:54Z
dc.date.issued 2021-7-1
dc.identifier.citation Biomedical Signal Processing and Control 68:102712 01 Jul 2021
dc.identifier.issn 1746-8094
dc.identifier.uri https://hdl.handle.net/2292/55881
dc.description.abstract Altered electrocardiogram (ECG) morphology is important for assessing cardiac pro-arrhythmic risk of drugs. We propose a basis function method to predict morphological and QT, JT, Tpeak—Tend timing interval changes in ECGs due to drug effects. The method systematically decomposes ECGs for a study population recorded at varying pharmacokinetic states into Gaussian Mesa Functions (GMFs). The GMF parameters are then fit to linear mixed effects drug sensitivity models. For a new subject, post-drug GMF parameter values and ECG morphology at varying pharmacokinetic states are predicted from the pre-drug GMF parameter values using the drug sensitivity models. The proposed methodology is validated with clinical ECGs of human subjects administered Dofetilide, Quinidine, Ranolazine, and Verapamil. The datasets are obtained from the ECGRVDQ database. The proposed method predicts post-drug timing intervals not significantly different to expert annotated intervals (pair-wise t-test p>0.05) for 153 out of 180 scenarios (drug type, hours post-dose, and ECG timing interval combinations). A comparative method based on expert annotations predicts 105 out of 180 scenarios. Importantly, realistic predictions of post-dose ECG morphology are reconstructed from predicted GMF parameters. Our study suggests that the GMF parameter space can provide important new biomarkers for assessing and visualizing drug-induced changes in ECGs.
dc.language en
dc.publisher Elsevier BV
dc.relation.ispartofseries Biomedical Signal Processing and Control
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher.
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm
dc.subject Science & Technology
dc.subject Technology
dc.subject Engineering, Biomedical
dc.subject Engineering
dc.subject Electrocardiogram decomposition
dc.subject Pro-arrhythmic risk
dc.subject Gaussian Mesa Functions
dc.subject Electrocardiogram prediction
dc.subject INDUCED QT PROLONGATION
dc.subject T-WAVE MORPHOLOGY
dc.subject DYNAMICAL MODEL
dc.subject ARRHYTHMIAS
dc.subject RANOLAZINE
dc.subject QUINIDINE
dc.subject SIGNALS
dc.subject 0903 Biomedical Engineering
dc.subject 0906 Electrical and Electronic Engineering
dc.subject 1004 Medical Biotechnology
dc.title Predicting drug-mediated pro-arrhythmic effects using pre-drug electrocardiograms
dc.type Journal Article
dc.identifier.doi 10.1016/j.bspc.2021.102712
pubs.begin-page 102712
pubs.volume 68
dc.date.updated 2021-07-27T02:56:20Z
dc.rights.holder Copyright: Elsevier Ltd. All rights reserved. en
pubs.author-url http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000670368300007&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=6e41486220adb198d0efde5a3b153e7d
pubs.publication-status Published
dc.rights.accessrights http://purl.org/eprint/accessRights/RetrictedAccess en
pubs.subtype Article
pubs.subtype Journal
pubs.elements-id 853163
dc.identifier.eissn 1746-8108
pubs.number 102712


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