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<PubmedArticle><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">25862307</PMID><DateCompleted><Year>2016</Year><Month>08</Month><Day>04</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1532-2092</ISSN><JournalIssue CitedMedium="Internet"><Volume>17</Volume><Issue>10</Issue><PubDate><Year>2015</Year><Month>Oct</Month></PubDate></JournalIssue><Title>Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology</Title><ISOAbbreviation>Europace</ISOAbbreviation></Journal><ArticleTitle>Automated detection of effective left-ventricular pacing: going beyond percentage pacing counters.</ArticleTitle><Pagination><StartPage>1555</StartPage><EndPage>1562</EndPage><MedlinePgn>1555-62</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/europace/euv062</ELocationID><Abstract><AbstractText Label="AIMS" NlmCategory="OBJECTIVE">Cardiac resynchronization therapy (CRT) devices report percentage pacing as a diagnostic but cannot determine the effectiveness of each paced beat in capturing left-ventricular (LV) myocardium. Reasons for ineffective LV pacing include improper timing (i.e. pseudofusion) or inadequate pacing output. Device-based determination of effective LV pacing may facilitate optimization of CRT response.</AbstractText><AbstractText Label="METHODS AND RESULTS" NlmCategory="RESULTS">Effective capture at the LV cathode results in a negative deflection (QS or QS-r morphology) on a unipolar electrogram (EGM). Morphological features of LV cathode-RV coil EGMs were analysed to develop a device-based automatic algorithm, which classified each paced beat as effective or ineffective LV pacing. The algorithm was validated using acute data from 28 CRT-defibrillator patients. Effective LV pacing and pseudofusion was simulated by pacing at various AV delays. Loss of LV capture was simulated by RV-only pacing. The algorithm always classified LV or biventricular (BV) pacing with AV delays &#x2264;60% of patient's intrinsic AV delay as effective pacing. As AV delays increased, the percentage of beats classified as effective LV pacing decreased. Algorithm results were compared against a classification truth based on correlation coefficients between paced QRS complexes and intrinsic rhythm QRS templates from three surface ECG leads. An average correlation &gt;0.9 defined a classification truth of ineffective pacing. Compared against the classification truth, the algorithm correctly classified 98.2% (3240/3300) effective LV pacing beats, 75.8% (561/740) of pseudofusion beats, and 100% (540/540) of beats with loss of LV capture.</AbstractText><AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">A device-based algorithm for beat-by-beat monitoring of effective LV pacing is feasible.</AbstractText><CopyrightInformation>&#xa9; The Author 2015. Published by Oxford University Press on behalf of the European Society of Cardiology.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Ghosh</LastName><ForeName>Subham</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Medtronic Inc., 8200 Coral Sea St, Mounds View, MN 55112, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Stadler</LastName><ForeName>Robert W</ForeName><Initials>RW</Initials><AffiliationInfo><Affiliation>Medtronic Inc., 8200 Coral Sea St, Mounds View, MN 55112, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Mittal</LastName><ForeName>Suneet</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Arrhythmia Institute at Valley Hospital, 223 N. Van Dien Avenue, Ridgewood, NJ 07450, USA mittsu@valleyhealth.com.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2015</Year><Month>04</Month><Day>09</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Europace</MedlineTA><NlmUniqueID>100883649</NlmUniqueID><ISSNLinking>1099-5129</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="Y">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058406" MajorTopicYN="Y">Cardiac Resynchronization Therapy</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058409" MajorTopicYN="Y">Cardiac Resynchronization Therapy Devices</DescriptorName><QualifierName UI="Q000009" MajorTopicYN="N">adverse effects</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006333" MajorTopicYN="N">Heart Failure</DescriptorName><QualifierName UI="Q000628" MajorTopicYN="Y">therapy</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006352" MajorTopicYN="N">Heart Ventricles</DescriptorName><QualifierName UI="Q000503" MajorTopicYN="Y">physiopathology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Cardiac resynchronization therapy</Keyword><Keyword MajorTopicYN="N">Effective LV pacing</Keyword><Keyword MajorTopicYN="N">Electrograms</Keyword><Keyword MajorTopicYN="N">Percent pacing</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2015</Year><Month>1</Month><Day>2</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2015</Year><Month>2</Month><Day>23</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2015</Year><Month>4</Month><Day>12</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2015</Year><Month>4</Month><Day>12</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2016</Year><Month>8</Month><Day>5</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pmc-release"><Year>2015</Year><Month>4</Month><Day>10</Day></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">25862307</ArticleId><ArticleId IdType="pmc">PMC4617370</ArticleId><ArticleId IdType="doi">10.1093/europace/euv062</ArticleId><ArticleId IdType="pii">euv062</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>Brignole M, Aurrichio A, Baron-Esquivias G, Bordachar P, Boriani G, Breithardt OA, et al. 2013 ESC Guidelines on cardiac pacing and cardiac resynchronization therapy: the task force on cardiac pacing and resynchronization therapy of the European Society of Cardiology (ESC). 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               <h2><a href="https://www.litmetric.com/detail/25862307/Automated-detection-of-effective-left-ventricular-pacing-going-beyond-percentage-pacing-counters" >Automated detection of effective left-ventricular pacing: going beyond percentage pacing counters.</a></h2>
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                <div class="publication-auther"><a href="https://www.litmetric.com/author/Subham+Ghosh" class="tag" style="margin-bottom: 5px;">Subham Ghosh</a> <a href="https://www.litmetric.com/author/Robert+W+Stadler" class="tag" style="margin-bottom: 5px;">Robert W Stadler</a> <a href="https://www.litmetric.com/author/Suneet+Mittal" class="tag" style="margin-bottom: 5px;">Suneet Mittal</a></div>                   
                    <p class="mb-0 dark-color fz15 fw500 list-inline-item ml15 mb5-sm ml0-xs"><i class="flaticon-contract vam fz20 me-2"></i> Europace</p>
              
                   <p class="mb-0 dark-color fz15 fw500 list-inline-item mr15  mb5-sm ml0-xs"><i class="flaticon-place text-thm2 vam fz20 me-2"></i>  <a href='https://www.litmetric.com/search/Arrhythmia+Institute+at+Valley+Hospital%5BAffiliation%5D' style='text-decoration: underline'>Arrhythmia Institute at Valley Hospital</a>, 223 N. Van Dien Avenue, Ridgewood, NJ 07450, USA </p>
                    
                     
                   <p class="mb-0 dark-color fz15 fw500 list-inline-item ml15 mb5-sm ml0-xs"><i class="flaticon-calendar vam fz20 me-2"></i> Published: October 2015</p>
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                   <p class="text mb30"><p class="text mt10"><em>Aims</em>: Cardiac resynchronization therapy (CRT) devices report percentage pacing as a diagnostic but cannot determine the effectiveness of each paced beat in capturing left-ventricular (LV) myocardium. Reasons for ineffective LV pacing include improper timing (i.e. pseudofusion) or inadequate pacing output. Device-based determination of effective LV pacing may facilitate optimization of CRT response.<BR><BR><em>Methods And Results</em>: Effective capture at the LV cathode results in a negative deflection (QS or QS-r morphology) on a unipolar electrogram (EGM). Morphological features of LV cathode-RV coil EGMs were analysed to develop a device-based automatic algorithm, which classified each paced beat as effective or ineffective LV pacing. The algorithm was validated using acute data from 28 CRT-defibrillator patients. Effective LV pacing and pseudofusion was simulated by pacing at various AV delays. Loss of LV capture was simulated by RV-only pacing. The algorithm always classified LV or biventricular (BV) pacing with AV delays ≤60% of patient's intrinsic AV delay as effective pacing. As AV delays increased, the percentage of beats classified as effective LV pacing decreased. Algorithm results were compared against a classification truth based on correlation coefficients between paced QRS complexes and intrinsic rhythm QRS templates from three surface ECG leads. An average correlation >0.9 defined a classification truth of ineffective pacing. Compared against the classification truth, the algorithm correctly classified 98.2% (3240/3300) effective LV pacing beats, 75.8% (561/740) of pseudofusion beats, and 100% (540/540) of beats with loss of LV capture.<BR><BR><em>Conclusion</em>: A device-based algorithm for beat-by-beat monitoring of effective LV pacing is feasible.</p><table class="table table-bordered table-striped downloadTable" >
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