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type=\u0022text\/css\u0022 rel=\u0022stylesheet\u0022 href=\u0022\/\/d282kpwvnogo5m.cloudfront.net\/sites\/default\/files\/advagg_css\/css__ce2QY63WIanKyr8eSq7eavr1XQRRmFD6ZSmwpyJi8lM__zXwFqpqmxrZOXXcd_TpBQpjuELbmIP9wBR5UuTDWAO4__YJWWMMdfCJuAFm5cUEp88OsodhO3ZA-2lzRfoBsSlk4.css\u0022 media=\u0022all\u0022 \/\u003E\n\u003Clink rel=\u0027stylesheet\u0027 type=\u0027text\/css\u0027 href=\u0027\/sites\/all\/modules\/contrib\/panels\/plugins\/layouts\/onecol\/onecol.css\u0027 \/\u003E\u003C\/head\u003E\u003Cbody\u003E\u003Cdiv class=\u0022panels-ajax-tab-panel panels-ajax-tab-panel-sageoa-tab-art\u0022\u003E\u003Cdiv class=\u0022panel-display panel-1col clearfix\u0022 \u003E\n  \u003Cdiv class=\u0022panel-panel panel-col\u0022\u003E\n    \u003Cdiv\u003E\u003Cdiv class=\u0022panel-pane pane-highwire-markup\u0022 \u003E\n  \n      \n  \n  \u003Cdiv class=\u0022pane-content\u0022\u003E\n    \u003Cdiv class=\u0022highwire-markup\u0022\u003E\u003Cdiv xmlns=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022 id=\u0022content-block-markup\u0022 xmlns:xhtml=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022\u003E\u003Cdiv class=\u0022article fulltext-view \u0022\u003E\u003Cspan class=\u0022highwire-journal-article-marker-start\u0022\u003E\u003C\/span\u003E\u003Cdiv class=\u0022section abstract\u0022 id=\u0022abstract-1\u0022\u003E\u003Ch2\u003ESummary\u003C\/h2\u003E\n            \u003Cp id=\u0022p-1\u0022\u003EChallenges in the identification of individuals at high risk for sudden cardiac death (SCD) persist. Nearly 1,000 deaths occur every day in the United States due to SCD. Data-based alternatives for SCD risk stratification dates back to the mid-1990s with the Cardiac Arrythmia Patient Outcomes Research Team (PORT) and several studies published in the \u003Cem\u003EAmerican Heart Journal\u003C\/em\u003E (September 2002, Volume 144). Although these were landmark studies, Mario Talajic, MD, Chief of Medicine, Montreal Heart Institute, Montreal, Canada, says \u201cwe still have limited clinical applications for what we\u0027ve learned.\u201d\u003C\/p\u003E\n         \u003C\/div\u003E\u003Cul class=\u0022kwd-group\u0022\u003E\u003Cli class=\u0022kwd\u0022\u003Ecoronary artery disease\u003C\/li\u003E\u003C\/ul\u003E\u003Cdiv class=\u0022section\u0022 id=\u0022sec-1\u0022\u003E\n         \n         \u003Cp id=\u0022p-2\u0022\u003EChallenges in the identification of individuals at high risk for sudden cardiac death (SCD) persist. Nearly 1,000 deaths occur every day in the United States due to SCD. Data-based alternatives for SCD risk stratification dates back to the mid-1990s with the Cardiac Arrythmia Patient Outcomes Research Team (PORT) and several studies published in the \u003Cem\u003EAmerican Heart Journal\u003C\/em\u003E (September 2002, Volume 144). Although these were landmark studies, Mario Talajic, MD, Chief of Medicine, Montreal Heart Institute, Montreal, Canada, says \u201cwe still have limited clinical applications for what we\u0027ve learned.\u201d\u003C\/p\u003E\n         \u003Cp id=\u0022p-3\u0022\u003EOtto Costantini, MD, Assistant Professor of Medicine, Case Western Reserve University, looked at the role of T-wave alternans (TWA) as a risk stratification tool. TWAs noted on ECG can predict the risk of life threatening arrythmia. \u201cBut they can also be hard to see, or are obscured by electrical interference,\u201d according to Dr. Constantini.\u003C\/p\u003E\n         \u003Cp id=\u0022p-4\u0022\u003EDr. Constantini suggested that TWA has promise, but \u201cmany risk stratification techniques that were promising later hit the junkpile. At the moment, we can\u0027t beat a lowered ejection fraction (EF) as a reliable risk predictor. We look forward to data on combining EF with TWA to create a risk score.\u201d (TWA may soon be mandated by Centers for Medicare and Medicaid Services for patients being evaluated for ICD for primary prevention of SCD.)\u003C\/p\u003E\n         \u003Cp id=\u0022p-5\u0022\u003EMaria Teresa La Rovere, MD, of the Department of Cardiology, Scientific Institute of Montescano, Italy, discussed autonomic markers of SCD. Many studies confirm the role of the autonomic nervous system (ANS) as a SCD risk factor, particularly when vagal and sympathetic output collide to increase the risk of ventricular dysrythmia.\u003C\/p\u003E\n         \u003Cp id=\u0022p-6\u0022\u003EThe key ANS markers are heart rate variability (HRV) and baroreflex sensitivity. Depressed HRV along with reduced (\u0026lt;40%) EF and reduced baro-sensitivity (\u0026lt;3 ms\/mm Hg) are strongly associated with increased SCD risk.\u003C\/p\u003E\n         \u003Cp id=\u0022p-7\u0022\u003EWhile multiple studies have demonstrated that autonomic markers can be used as risk stratification tools for SCD, some observers note a lack of consensus regarding autonomic markers while suggesting that the arm of mathematics known as nonlinear dynamics may offer more powerful risk prediction.\u003C\/p\u003E\n         \u003Cdiv id=\u0022F1\u0022 class=\u0022fig pos-float  odd\u0022\u003E\u003Cdiv class=\u0022highwire-figure\u0022\u003E\u003Cdiv class=\u0022fig-inline-img-wrapper\u0022\u003E\u003Cdiv class=\u0022fig-inline-img\u0022\u003E\u003Ca href=\u0022http:\/\/d282kpwvnogo5m.cloudfront.net\/content\/spmdc\/6\/1\/22\/F1.large.jpg?width=800\u0026amp;height=600\u0026amp;carousel=1\u0022 title=\u0022\u0022 class=\u0022fragment-images colorbox-load\u0022 rel=\u0022gallery-fragment-images-1880904076\u0022 data-figure-caption=\u0022\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003E\u003Cimg class=\u0022fragment-image\u0022 alt=\u0022Figure1\u0022 src=\u0022http:\/\/d282kpwvnogo5m.cloudfront.net\/content\/spmdc\/6\/1\/22\/F1.medium.gif\u0022\/\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cul class=\u0022highwire-figure-links inline\u0022\u003E\u003Cli class=\u00220 first\u0022\u003E\u003Ca href=\u0022http:\/\/d282kpwvnogo5m.cloudfront.net\/content\/spmdc\/6\/1\/22\/F1.large.jpg?download=true\u0022 class=\u0022highwire-figure-link highwire-figure-link-download\u0022 title=\u0022Download Figure1\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003EDownload figure\u003C\/a\u003E\u003C\/li\u003E\u003Cli class=\u00221\u0022\u003E\u003Ca href=\u0022http:\/\/d282kpwvnogo5m.cloudfront.net\/content\/spmdc\/6\/1\/22\/F1.large.jpg\u0022 class=\u0022highwire-figure-link highwire-figure-link-newtab\u0022 target=\u0022_blank\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003EOpen in new tab\u003C\/a\u003E\u003C\/li\u003E\u003Cli class=\u00222 last\u0022\u003E\u003Ca href=\u0022\/highwire\/powerpoint\/16092\u0022 class=\u0022highwire-figure-link highwire-figure-link-ppt\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003EDownload powerpoint\u003C\/a\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003C\/div\u003E\u003C\/div\u003E\n         \u003Cp id=\u0022p-8\u0022\u003EBut does nonlinear dynamics have any role in forecasting arrythmias? \u201cNot yet,\u201d according to Leon Glass, PhD, Department of Physiology, McGill University, Montreal, Canada.\u003C\/p\u003E\n         \u003Cp id=\u0022p-9\u0022\u003ENonlinear dynamics developed out of chaos theory, and applies equations and computer projections to model ebb, flow, and aberrations in complex rhythmic environments\u2014like the human heart. Largely the province of mathematicians and physicists, few physician researchers have yet delved deeply into nonlinear dynamics.\u003C\/p\u003E\n         \u003Cp id=\u0022p-10\u0022\u003E\u201cTo the casual observer the pacing dynamics of the heart can look like wild disorder,\u201d Dr. Glass said. \u201cWe chaos researchers search for inherent order in apparent disorder. Cardiac arrythmias are a perfect example.\u201d While the goal is to develop new tools with clinical applicability, Dr. Glass noted that mathematical studies have not yet yielded useful tools. \u201cWe\u0027re just beginning to lay the groundwork,\u201d he said.\u003C\/p\u003E\n         \u003Cp id=\u0022p-11\u0022\u003E\u201cA summation of all the talks today\u2014mine included\u2014is that we don\u0027t yet have consistently reliable methods to stratify people at risk for SCD. A big part of this challenge is that people at SCD risk are very hetereogeneous.\u201d\u003C\/p\u003E\n         \u003Cp id=\u0022p-12\u0022\u003EWith such a various patient population, Dr. Glass noted that \u201cpulling all our findings to date into a coherent system is still beyond us. But we know that clinical applications often follow basic research by many years. I think we\u0027re looking at great opportunities in improving risk stratification for SCD, not just in nonlinear dynamics but with all the techniques discussed here today.\u201d\u003C\/p\u003E\n      \u003C\/div\u003E\u003Cul class=\u0022copyright-statement\u0022\u003E\u003Cli class=\u0022fn\u0022 id=\u0022copyright-statement-1\u0022\u003E\u00a9 2006 MD Conference Express\u003C\/li\u003E\u003C\/ul\u003E\u003Cspan class=\u0022highwire-journal-article-marker-end\u0022\u003E\u003C\/span\u003E\u003C\/div\u003E\u003Cspan id=\u0022related-urls\u0022\u003E\u003C\/span\u003E\u003C\/div\u003E\u003Ca href=\u0022http:\/\/mdc.sagepub.com\/content\/6\/1\/22.abstract\u0022 class=\u0022hw-link hw-link-article-abstract\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003EView Summary\u003C\/a\u003E\u003C\/div\u003E  \u003C\/div\u003E\n\n  \n  \u003C\/div\u003E\n\u003C\/div\u003E\n  \u003C\/div\u003E\n\u003C\/div\u003E\n\u003C\/div\u003E\u003Cscript type=\u0022text\/javascript\u0022 src=\u0022http:\/\/mdc.sagepub.com\/sites\/all\/modules\/highwire\/highwire\/plugins\/highwire_markup_process\/js\/highwire_figures.js?nzlxyp\u0022\u003E\u003C\/script\u003E\n\u003Cscript type=\u0022text\/javascript\u0022 src=\u0022http:\/\/mdc.sagepub.com\/sites\/all\/modules\/highwire\/highwire\/plugins\/highwire_markup_process\/js\/highwire_openurl.js?nzlxyp\u0022\u003E\u003C\/script\u003E\n\u003C\/body\u003E\u003C\/html\u003E"}