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Role of myocardial perfusion study in differentiating ischemic versus nonischemic cardiomyopathy using quantitative parameters
Purpose: Ischemic cardiomyopathy (ICM) and non-ICM (NICM) causes of dilated cardiomyopathy with similar clinical presentation have different management and prognosis. This study employed myocardial perfusion imaging (MPI) to differentiate between the two using quantitative parameters in Indian popul...
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Published in: | Indian journal of nuclear medicine 2018-01, Vol.33 (1), p.32-38 |
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description | Purpose: Ischemic cardiomyopathy (ICM) and non-ICM (NICM) causes of dilated cardiomyopathy with similar clinical presentation have different management and prognosis. This study employed myocardial perfusion imaging (MPI) to differentiate between the two using quantitative parameters in Indian population. Methods and Materials: Fifty patients prospectively underwent MPI and 18F-fluorodeoxyglucose metabolism studies. P values (0.05 as significant) were calculated for the left ventricular ejection fraction (EF), end diastolic volume (EDV) at rest and stress, end systolic volume (ESV) at rest and stress, summed rest score (SRS), summed difference score (SDS), and eccentricity. On 6-month follow-up, rate of hospital admission, change in management and death was correlated for ICM and NICM. Coronary angiography (CAG) being gold standard, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and level of agreement were calculated for MPI. Results: MPI and CAG had a moderate level of agreement (κ = 0.463) for differentiating ICM and NICM. The sensitivity, specificity, PPV, NPV, and diagnostic accuracy were 79.31%, 66.67%, 76.67%, 70.0%, and 74% for ICM and 66.67%, 79.31%, 70%, 76.67%, and 74% for NICM, respectively. Significant differences were seen in EDV stress (P = 0.045), EDV rest (P = 0.031), ESV rest (P = 0.034), SRS (P = 0.004), Left ventricular EF rest (P = 0.049) and SDS in ICM and NICM, respectively. Conclusion: EDV at rest and stress, ESV at rest, SRS, SDS, and EF at rest obtained using MPI provides precise quantitative information to differentiate ICM and NICM. It is wide and easy availability, noninvasiveness, objectivity, and near absence of complications favors it as a preferable diagnostic tool with its given sensitivity, specificity, and accuracy for the purpose. |
doi_str_mv | 10.4103/ijnm.IJNM_118_17 |
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This study employed myocardial perfusion imaging (MPI) to differentiate between the two using quantitative parameters in Indian population. Methods and Materials: Fifty patients prospectively underwent MPI and 18F-fluorodeoxyglucose metabolism studies. P values (0.05 as significant) were calculated for the left ventricular ejection fraction (EF), end diastolic volume (EDV) at rest and stress, end systolic volume (ESV) at rest and stress, summed rest score (SRS), summed difference score (SDS), and eccentricity. On 6-month follow-up, rate of hospital admission, change in management and death was correlated for ICM and NICM. Coronary angiography (CAG) being gold standard, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and level of agreement were calculated for MPI. Results: MPI and CAG had a moderate level of agreement (κ = 0.463) for differentiating ICM and NICM. The sensitivity, specificity, PPV, NPV, and diagnostic accuracy were 79.31%, 66.67%, 76.67%, 70.0%, and 74% for ICM and 66.67%, 79.31%, 70%, 76.67%, and 74% for NICM, respectively. Significant differences were seen in EDV stress (P = 0.045), EDV rest (P = 0.031), ESV rest (P = 0.034), SRS (P = 0.004), Left ventricular EF rest (P = 0.049) and SDS in ICM and NICM, respectively. Conclusion: EDV at rest and stress, ESV at rest, SRS, SDS, and EF at rest obtained using MPI provides precise quantitative information to differentiate ICM and NICM. It is wide and easy availability, noninvasiveness, objectivity, and near absence of complications favors it as a preferable diagnostic tool with its given sensitivity, specificity, and accuracy for the purpose.</description><identifier>ISSN: 0972-3919</identifier><identifier>EISSN: 0974-0244</identifier><identifier>DOI: 10.4103/ijnm.IJNM_118_17</identifier><identifier>PMID: 29430112</identifier><language>eng</language><publisher>India: Wolters Kluwer India Pvt. Ltd</publisher><subject>Adenosine ; Angiography ; Cameras ; Cardiology ; Cardiomyopathy ; Cardiovascular disease ; Coronary vessels ; Diagnostic software ; Diagnostic systems ; Ejection fraction ; Fasting ; Glucose ; Heart failure ; Hospitals ; Ischemia ; Mathematical analysis ; Medical imaging ; Medical prognosis ; Metabolism ; Myocardial diseases ; NMR ; Nuclear magnetic resonance ; Nuclear medicine ; Original ; Patients ; Rest ; Sensitivity ; Stresses ; Studies ; Tomography</subject><ispartof>Indian journal of nuclear medicine, 2018-01, Vol.33 (1), p.32-38</ispartof><rights>COPYRIGHT 2018 Medknow Publications and Media Pvt. Ltd.</rights><rights>Copyright Medknow Publications & Media Pvt. Ltd. Jan/Mar 2018</rights><rights>Copyright: © 2018 Indian Journal of Nuclear Medicine 2018</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5798095/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1989484741?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,4024,25753,27458,27923,27924,27925,37012,37013,44590,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29430112$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Singh, Preeti</creatorcontrib><creatorcontrib>Bhatt, Bhairavi</creatorcontrib><creatorcontrib>Pawar, Shwetal</creatorcontrib><creatorcontrib>Kamra, Ashish</creatorcontrib><creatorcontrib>Shetye, Suruchi</creatorcontrib><creatorcontrib>Ghorpade, Mangala</creatorcontrib><title>Role of myocardial perfusion study in differentiating ischemic versus nonischemic cardiomyopathy using quantitative parameters</title><title>Indian journal of nuclear medicine</title><addtitle>Indian J Nucl Med</addtitle><description>Purpose: Ischemic cardiomyopathy (ICM) and non-ICM (NICM) causes of dilated cardiomyopathy with similar clinical presentation have different management and prognosis. This study employed myocardial perfusion imaging (MPI) to differentiate between the two using quantitative parameters in Indian population. Methods and Materials: Fifty patients prospectively underwent MPI and 18F-fluorodeoxyglucose metabolism studies. P values (0.05 as significant) were calculated for the left ventricular ejection fraction (EF), end diastolic volume (EDV) at rest and stress, end systolic volume (ESV) at rest and stress, summed rest score (SRS), summed difference score (SDS), and eccentricity. On 6-month follow-up, rate of hospital admission, change in management and death was correlated for ICM and NICM. Coronary angiography (CAG) being gold standard, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and level of agreement were calculated for MPI. Results: MPI and CAG had a moderate level of agreement (κ = 0.463) for differentiating ICM and NICM. The sensitivity, specificity, PPV, NPV, and diagnostic accuracy were 79.31%, 66.67%, 76.67%, 70.0%, and 74% for ICM and 66.67%, 79.31%, 70%, 76.67%, and 74% for NICM, respectively. Significant differences were seen in EDV stress (P = 0.045), EDV rest (P = 0.031), ESV rest (P = 0.034), SRS (P = 0.004), Left ventricular EF rest (P = 0.049) and SDS in ICM and NICM, respectively. Conclusion: EDV at rest and stress, ESV at rest, SRS, SDS, and EF at rest obtained using MPI provides precise quantitative information to differentiate ICM and NICM. It is wide and easy availability, noninvasiveness, objectivity, and near absence of complications favors it as a preferable diagnostic tool with its given sensitivity, specificity, and accuracy for the purpose.</description><subject>Adenosine</subject><subject>Angiography</subject><subject>Cameras</subject><subject>Cardiology</subject><subject>Cardiomyopathy</subject><subject>Cardiovascular disease</subject><subject>Coronary vessels</subject><subject>Diagnostic software</subject><subject>Diagnostic systems</subject><subject>Ejection fraction</subject><subject>Fasting</subject><subject>Glucose</subject><subject>Heart failure</subject><subject>Hospitals</subject><subject>Ischemia</subject><subject>Mathematical analysis</subject><subject>Medical imaging</subject><subject>Medical prognosis</subject><subject>Metabolism</subject><subject>Myocardial diseases</subject><subject>NMR</subject><subject>Nuclear magnetic resonance</subject><subject>Nuclear medicine</subject><subject>Original</subject><subject>Patients</subject><subject>Rest</subject><subject>Sensitivity</subject><subject>Stresses</subject><subject>Studies</subject><subject>Tomography</subject><issn>0972-3919</issn><issn>0974-0244</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNp1kt2LEzEUxQdR3HX13ScJCIsvU5NJ5iMvwrL4UVkVRJ9Dmrlp080k3WSmpS_-7Wba3dqKkoeE5HfO5d6cLHtJ8IQRTN-apesm089fvwhCGkHqR9k55jXLccHY4925yCkn_Cx7FuMS44rVVfU0Oys4o5iQ4jz79d1bQF6jbuuVDK2RFq0g6CEa71Dsh3aLjEOt0RoCuN7I3rg5MlEtoDMKrSHEISLn3eFqZ-OT30r2iy1KTklwN8gk7pN6DWglg-ygT9Ln2RMtbYQX9_tF9vPD-x_Xn_Kbbx-n11c3uWKcm1yXnLW8ropWlbRkbFbyilBKy1rVTeoVZrhQja50y4GAlhzamSINUViSilJNL7J3e9_VMOugVamTIK1YBdPJsBVeGnH64sxCzP1alDVvMC-TwZt7g-DvBoi96FLDYK104IcoCowJw2VFWEJf_4Uu_RBcak8Q3nDWsJqRP9RcWhDGaZ_qqtFUXJUFI4yXu7KTf1BpteOovQNt0v2J4PJIsABp-0X0dujTd8ZTEO9BFXyMAfRhGASLMVxiDJc4CleSvDoe4kHwkKYETPfAxtvxd2_tsIEgEnvr_Oa_xoIWYoyh8Fo8xJD-BpF-6Ik</recordid><startdate>20180101</startdate><enddate>20180101</enddate><creator>Singh, Preeti</creator><creator>Bhatt, Bhairavi</creator><creator>Pawar, Shwetal</creator><creator>Kamra, Ashish</creator><creator>Shetye, Suruchi</creator><creator>Ghorpade, Mangala</creator><general>Wolters Kluwer India Pvt. 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Bhatt, Bhairavi ; Pawar, Shwetal ; Kamra, Ashish ; Shetye, Suruchi ; Ghorpade, Mangala</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c499i-f594d9762dc53544b596133357c78244eb02c8f6fd9e1efa9edbc181c0a1633f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Adenosine</topic><topic>Angiography</topic><topic>Cameras</topic><topic>Cardiology</topic><topic>Cardiomyopathy</topic><topic>Cardiovascular disease</topic><topic>Coronary vessels</topic><topic>Diagnostic software</topic><topic>Diagnostic systems</topic><topic>Ejection fraction</topic><topic>Fasting</topic><topic>Glucose</topic><topic>Heart failure</topic><topic>Hospitals</topic><topic>Ischemia</topic><topic>Mathematical analysis</topic><topic>Medical imaging</topic><topic>Medical prognosis</topic><topic>Metabolism</topic><topic>Myocardial diseases</topic><topic>NMR</topic><topic>Nuclear magnetic resonance</topic><topic>Nuclear medicine</topic><topic>Original</topic><topic>Patients</topic><topic>Rest</topic><topic>Sensitivity</topic><topic>Stresses</topic><topic>Studies</topic><topic>Tomography</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Singh, Preeti</creatorcontrib><creatorcontrib>Bhatt, Bhairavi</creatorcontrib><creatorcontrib>Pawar, Shwetal</creatorcontrib><creatorcontrib>Kamra, Ashish</creatorcontrib><creatorcontrib>Shetye, Suruchi</creatorcontrib><creatorcontrib>Ghorpade, Mangala</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection (Proquest)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>ProQuest Research Library</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Research Library China</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Indian journal of nuclear medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Singh, Preeti</au><au>Bhatt, Bhairavi</au><au>Pawar, Shwetal</au><au>Kamra, Ashish</au><au>Shetye, Suruchi</au><au>Ghorpade, Mangala</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Role of myocardial perfusion study in differentiating ischemic versus nonischemic cardiomyopathy using quantitative parameters</atitle><jtitle>Indian journal of nuclear medicine</jtitle><addtitle>Indian J Nucl Med</addtitle><date>2018-01-01</date><risdate>2018</risdate><volume>33</volume><issue>1</issue><spage>32</spage><epage>38</epage><pages>32-38</pages><issn>0972-3919</issn><eissn>0974-0244</eissn><abstract>Purpose: Ischemic cardiomyopathy (ICM) and non-ICM (NICM) causes of dilated cardiomyopathy with similar clinical presentation have different management and prognosis. This study employed myocardial perfusion imaging (MPI) to differentiate between the two using quantitative parameters in Indian population. Methods and Materials: Fifty patients prospectively underwent MPI and 18F-fluorodeoxyglucose metabolism studies. P values (0.05 as significant) were calculated for the left ventricular ejection fraction (EF), end diastolic volume (EDV) at rest and stress, end systolic volume (ESV) at rest and stress, summed rest score (SRS), summed difference score (SDS), and eccentricity. On 6-month follow-up, rate of hospital admission, change in management and death was correlated for ICM and NICM. Coronary angiography (CAG) being gold standard, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and level of agreement were calculated for MPI. Results: MPI and CAG had a moderate level of agreement (κ = 0.463) for differentiating ICM and NICM. The sensitivity, specificity, PPV, NPV, and diagnostic accuracy were 79.31%, 66.67%, 76.67%, 70.0%, and 74% for ICM and 66.67%, 79.31%, 70%, 76.67%, and 74% for NICM, respectively. Significant differences were seen in EDV stress (P = 0.045), EDV rest (P = 0.031), ESV rest (P = 0.034), SRS (P = 0.004), Left ventricular EF rest (P = 0.049) and SDS in ICM and NICM, respectively. Conclusion: EDV at rest and stress, ESV at rest, SRS, SDS, and EF at rest obtained using MPI provides precise quantitative information to differentiate ICM and NICM. It is wide and easy availability, noninvasiveness, objectivity, and near absence of complications favors it as a preferable diagnostic tool with its given sensitivity, specificity, and accuracy for the purpose.</abstract><cop>India</cop><pub>Wolters Kluwer India Pvt. Ltd</pub><pmid>29430112</pmid><doi>10.4103/ijnm.IJNM_118_17</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Adenosine Angiography Cameras Cardiology Cardiomyopathy Cardiovascular disease Coronary vessels Diagnostic software Diagnostic systems Ejection fraction Fasting Glucose Heart failure Hospitals Ischemia Mathematical analysis Medical imaging Medical prognosis Metabolism Myocardial diseases NMR Nuclear magnetic resonance Nuclear medicine Original Patients Rest Sensitivity Stresses Studies Tomography |
title | Role of myocardial perfusion study in differentiating ischemic versus nonischemic cardiomyopathy using quantitative parameters |
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