Prediction of Cardiac Resynchronization Therapy Response Using a Lead Placement Score Derived From 4-Dimensional Computed Tomography
Cardiac resynchronization therapy (CRT) is an effective treatment for patients with heart failure; however, 30% of patients do not respond to the treatment. We sought to derive patient-specific left ventricle maps of lead placement scores (LPS) that highlight target pacing lead sites for achieving a...
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Published in | Circulation. Cardiovascular imaging Vol. 15; no. 8; p. e014165 |
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Main Authors | , , , , , , , , , |
Format | Journal Article |
Language | English |
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United States
01.08.2022
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Abstract | Cardiac resynchronization therapy (CRT) is an effective treatment for patients with heart failure; however, 30% of patients do not respond to the treatment. We sought to derive patient-specific left ventricle maps of lead placement scores (LPS) that highlight target pacing lead sites for achieving a higher probability of CRT response.
Eighty-two subjects recruited for the ImagingCRT trial (Empiric Versus Imaging Guided Left Ventricular Lead Placement in Cardiac Resynchronization Therapy) were retrospectively analyzed. All 82 subjects had 2 contrast-enhanced full cardiac cycle 4-dimensional computed tomography scans: a baseline and a 6-month follow-up scan. CRT response was defined as a reduction in computed tomography-derived end-systolic volume ≥15%. Eight left ventricle features derived from the baseline scans were used to train a support vector machine via a bagging approach. An LPS map over the left ventricle was created for each subject as a linear combination of the support vector machine feature weights and the subject's own feature vector. Performance for distinguishing responders was performed on the original 82 subjects.
Fifty-two (63%) subjects were responders. Subjects with an LPS≤Q
(lower-quartile) had a posttest probability of responding of 14% (3/21), while subjects with an LPS≥ Q
(upper-quartile) had a posttest probability of responding of 90% (19/21). Subjects with Q
<LPS<Q
had a posttest probability of responding that was essentially unchanged from the pretest probability (75% versus 63%,
=0.2). An LPS threshold that maximized the geometric mean of true-negative and true-positive rates identified 26/30 of the nonresponders. The area under the curve of the receiver operating characteristic curve for identifying responders with an LPS threshold was 87%.
An LPS map was defined using 4-dimensional computed tomography-derived features of left ventricular mechanics. The LPS correlated with CRT response, reclassifying 25% of the subjects into low probability of response, 25% into high probability of response, and 50% unchanged. These encouraging results highlight the potential utility of 4-dimensional computed tomography in guiding patient selection for CRT. The present findings need verification in larger independent data sets and prospective trials. |
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AbstractList | Background:
Cardiac resynchronization therapy (CRT) is an effective treatment for patients with heart failure; however, 30% of patients do not respond to the treatment. We sought to derive patient-specific left ventricle maps of lead placement scores (LPS) that highlight target pacing lead sites for achieving a higher probability of CRT response.
Methods:
Eighty-two subjects recruited for the ImagingCRT trial (Empiric Versus Imaging Guided Left Ventricular Lead Placement in Cardiac Resynchronization Therapy) were retrospectively analyzed. All 82 subjects had 2 contrast-enhanced full cardiac cycle 4-dimensional computed tomography scans: a baseline and a 6-month follow-up scan. CRT response was defined as a reduction in computed tomography–derived end-systolic volume ≥15%. Eight left ventricle features derived from the baseline scans were used to train a support vector machine via a bagging approach. An LPS map over the left ventricle was created for each subject as a linear combination of the support vector machine feature weights and the subject’s own feature vector. Performance for distinguishing responders was performed on the original 82 subjects.
Results:
Fifty-two (63%) subjects were responders. Subjects with an LPS≤Q
1
(lower-quartile) had a posttest probability of responding of 14% (3/21), while subjects with an LPS≥ Q
3
(upper-quartile) had a posttest probability of responding of 90% (19/21). Subjects with Q
1
<LPS<Q
3
had a posttest probability of responding that was essentially unchanged from the pretest probability (75% versus 63%,
P
=0.2). An LPS threshold that maximized the geometric mean of true-negative and true-positive rates identified 26/30 of the nonresponders. The area under the curve of the receiver operating characteristic curve for identifying responders with an LPS threshold was 87%.
Conclusions:
An LPS map was defined using 4-dimensional computed tomography–derived features of left ventricular mechanics. The LPS correlated with CRT response, reclassifying 25% of the subjects into low probability of response, 25% into high probability of response, and 50% unchanged. These encouraging results highlight the potential utility of 4-dimensional computed tomography in guiding patient selection for CRT. The present findings need verification in larger independent data sets and prospective trials. Cardiac resynchronization therapy (CRT) is an effective treatment for patients with heart failure; however, 30% of patients do not respond to the treatment. We sought to derive patient-specific left ventricle maps of lead placement scores (LPS) that highlight target pacing lead sites for achieving a higher probability of CRT response. Eighty-two subjects recruited for the ImagingCRT trial (Empiric Versus Imaging Guided Left Ventricular Lead Placement in Cardiac Resynchronization Therapy) were retrospectively analyzed. All 82 subjects had 2 contrast-enhanced full cardiac cycle 4-dimensional computed tomography scans: a baseline and a 6-month follow-up scan. CRT response was defined as a reduction in computed tomography-derived end-systolic volume ≥15%. Eight left ventricle features derived from the baseline scans were used to train a support vector machine via a bagging approach. An LPS map over the left ventricle was created for each subject as a linear combination of the support vector machine feature weights and the subject's own feature vector. Performance for distinguishing responders was performed on the original 82 subjects. Fifty-two (63%) subjects were responders. Subjects with an LPS≤Q (lower-quartile) had a posttest probability of responding of 14% (3/21), while subjects with an LPS≥ Q (upper-quartile) had a posttest probability of responding of 90% (19/21). Subjects with Q <LPS<Q had a posttest probability of responding that was essentially unchanged from the pretest probability (75% versus 63%, =0.2). An LPS threshold that maximized the geometric mean of true-negative and true-positive rates identified 26/30 of the nonresponders. The area under the curve of the receiver operating characteristic curve for identifying responders with an LPS threshold was 87%. An LPS map was defined using 4-dimensional computed tomography-derived features of left ventricular mechanics. The LPS correlated with CRT response, reclassifying 25% of the subjects into low probability of response, 25% into high probability of response, and 50% unchanged. These encouraging results highlight the potential utility of 4-dimensional computed tomography in guiding patient selection for CRT. The present findings need verification in larger independent data sets and prospective trials. Cardiac resynchronization therapy (CRT) is an effective treatment for patients with heart failure; however, 30% of patients do not respond to the treatment. We sought to derive patient-specific left ventricle maps of lead placement scores (LPS) that highlight target pacing lead sites for achieving a higher probability of CRT response.BACKGROUNDCardiac resynchronization therapy (CRT) is an effective treatment for patients with heart failure; however, 30% of patients do not respond to the treatment. We sought to derive patient-specific left ventricle maps of lead placement scores (LPS) that highlight target pacing lead sites for achieving a higher probability of CRT response.Eighty-two subjects recruited for the ImagingCRT trial (Empiric Versus Imaging Guided Left Ventricular Lead Placement in Cardiac Resynchronization Therapy) were retrospectively analyzed. All 82 subjects had 2 contrast-enhanced full cardiac cycle 4-dimensional computed tomography scans: a baseline and a 6-month follow-up scan. CRT response was defined as a reduction in computed tomography-derived end-systolic volume ≥15%. Eight left ventricle features derived from the baseline scans were used to train a support vector machine via a bagging approach. An LPS map over the left ventricle was created for each subject as a linear combination of the support vector machine feature weights and the subject's own feature vector. Performance for distinguishing responders was performed on the original 82 subjects.METHODSEighty-two subjects recruited for the ImagingCRT trial (Empiric Versus Imaging Guided Left Ventricular Lead Placement in Cardiac Resynchronization Therapy) were retrospectively analyzed. All 82 subjects had 2 contrast-enhanced full cardiac cycle 4-dimensional computed tomography scans: a baseline and a 6-month follow-up scan. CRT response was defined as a reduction in computed tomography-derived end-systolic volume ≥15%. Eight left ventricle features derived from the baseline scans were used to train a support vector machine via a bagging approach. An LPS map over the left ventricle was created for each subject as a linear combination of the support vector machine feature weights and the subject's own feature vector. Performance for distinguishing responders was performed on the original 82 subjects.Fifty-two (63%) subjects were responders. Subjects with an LPS≤Q1 (lower-quartile) had a posttest probability of responding of 14% (3/21), while subjects with an LPS≥ Q3 (upper-quartile) had a posttest probability of responding of 90% (19/21). Subjects with Q1<LPS<Q3 had a posttest probability of responding that was essentially unchanged from the pretest probability (75% versus 63%, P=0.2). An LPS threshold that maximized the geometric mean of true-negative and true-positive rates identified 26/30 of the nonresponders. The area under the curve of the receiver operating characteristic curve for identifying responders with an LPS threshold was 87%.RESULTSFifty-two (63%) subjects were responders. Subjects with an LPS≤Q1 (lower-quartile) had a posttest probability of responding of 14% (3/21), while subjects with an LPS≥ Q3 (upper-quartile) had a posttest probability of responding of 90% (19/21). Subjects with Q1<LPS<Q3 had a posttest probability of responding that was essentially unchanged from the pretest probability (75% versus 63%, P=0.2). An LPS threshold that maximized the geometric mean of true-negative and true-positive rates identified 26/30 of the nonresponders. The area under the curve of the receiver operating characteristic curve for identifying responders with an LPS threshold was 87%.An LPS map was defined using 4-dimensional computed tomography-derived features of left ventricular mechanics. The LPS correlated with CRT response, reclassifying 25% of the subjects into low probability of response, 25% into high probability of response, and 50% unchanged. These encouraging results highlight the potential utility of 4-dimensional computed tomography in guiding patient selection for CRT. The present findings need verification in larger independent data sets and prospective trials.CONCLUSIONSAn LPS map was defined using 4-dimensional computed tomography-derived features of left ventricular mechanics. The LPS correlated with CRT response, reclassifying 25% of the subjects into low probability of response, 25% into high probability of response, and 50% unchanged. These encouraging results highlight the potential utility of 4-dimensional computed tomography in guiding patient selection for CRT. The present findings need verification in larger independent data sets and prospective trials. |
Author | Ledesma-Carbayo, Maria J Colvert, Gabrielle M Kronborg, Mads Brix Chen, Zhennong Nielsen, Jens Cosedis McVeigh, Elliot R Yang, James Manohar, Ashish Sommer, Anders Nørgaard, Bjarne L |
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References | e_1_3_3_50_2 e_1_3_3_16_2 e_1_3_3_18_2 e_1_3_3_39_2 e_1_3_3_12_2 e_1_3_3_37_2 e_1_3_3_14_2 e_1_3_3_35_2 e_1_3_3_33_2 e_1_3_3_10_2 e_1_3_3_31_2 e_1_3_3_40_2 e_1_3_3_5_2 e_1_3_3_7_2 e_1_3_3_9_2 e_1_3_3_27_2 e_1_3_3_29_2 e_1_3_3_23_2 e_1_3_3_48_2 e_1_3_3_25_2 e_1_3_3_46_2 e_1_3_3_44_2 e_1_3_3_3_2 e_1_3_3_21_2 e_1_3_3_42_2 e_1_3_3_51_2 e_1_3_3_17_2 e_1_3_3_19_2 e_1_3_3_38_2 e_1_3_3_13_2 e_1_3_3_36_2 e_1_3_3_15_2 e_1_3_3_34_2 e_1_3_3_32_2 e_1_3_3_11_2 e_1_3_3_30_2 e_1_3_3_6_2 e_1_3_3_8_2 e_1_3_3_28_2 e_1_3_3_49_2 e_1_3_3_24_2 e_1_3_3_47_2 e_1_3_3_26_2 e_1_3_3_45_2 e_1_3_3_2_2 e_1_3_3_20_2 e_1_3_3_43_2 e_1_3_3_4_2 e_1_3_3_22_2 e_1_3_3_41_2 |
References_xml | – ident: e_1_3_3_22_2 doi: 10.1161/CIRCEP.119.007316 – ident: e_1_3_3_20_2 doi: 10.1080/24748706.2021.1934617 – ident: e_1_3_3_15_2 doi: 10.1016/j.hrthm.2017.04.041 – ident: e_1_3_3_24_2 doi: 10.1016/j.hrthm.2018.04.012 – ident: e_1_3_3_48_2 doi: 10.1152/ajpheart.2002.282.1.H372 – ident: e_1_3_3_11_2 doi: 10.1002/jmri.25613 – ident: e_1_3_3_26_2 doi: 10.1007/BF00994018 – ident: e_1_3_3_44_2 doi: 10.1161/01.cir.0000035037.11968.5c – ident: e_1_3_3_7_2 doi: 10.1161/CIRCULATIONAHA.107.743120 – ident: e_1_3_3_8_2 doi: 10.1186/1532-429X-12-64 – ident: e_1_3_3_35_2 doi: 10.1161/JAHA.118.010972 – ident: e_1_3_3_27_2 doi: 10.1023/A:1007515423169 – ident: e_1_3_3_28_2 doi: 10.1109/EUSIPCO.2015.7362366 – ident: e_1_3_3_47_2 doi: 10.1152/ajpheart.1999.276.3.H881 – ident: e_1_3_3_16_2 doi: 10.1111/jce.14896 – ident: e_1_3_3_34_2 doi: 10.1093/europace/euy292 – ident: e_1_3_3_49_2 doi: 10.1002/9781118646106 – ident: e_1_3_3_3_2 doi: 10.1038/nrcardio.2014.67 – ident: e_1_3_3_10_2 doi: 10.1002/mrm.1910390402 – ident: e_1_3_3_46_2 doi: 10.1136/heartjnl-2016-310052 – ident: e_1_3_3_4_2 doi: 10.1016/j.crvasa.2015.08.001 – ident: e_1_3_3_17_2 doi: 10.1097/RCT.0000000000000824 – ident: e_1_3_3_30_2 doi: 10.1161/CIRCULATIONAHA.110.945345 – ident: e_1_3_3_45_2 doi: 10.1148/radiol.14141578 – ident: e_1_3_3_29_2 doi: 10.1016/j.jcmg.2011.11.006 – ident: e_1_3_3_37_2 doi: 10.1016/j.hrthm.2019.03.011 – ident: e_1_3_3_25_2 doi: 10.1161/CIRCULATIONAHA.106.626143 – ident: e_1_3_3_19_2 doi: 10.1002/ejhf.530 – ident: e_1_3_3_50_2 doi: 10.1016/j.jclinepi.2006.01.014 – ident: e_1_3_3_51_2 doi: 10.1093/bib/bbs006 – ident: e_1_3_3_33_2 doi: 10.1007/s00259-010-1621-z – ident: e_1_3_3_38_2 doi: 10.1093/europace/euu056 – ident: e_1_3_3_42_2 doi: 10.1002/mp.15709 – ident: e_1_3_3_36_2 doi: 10.1016/j.hrthm.2019.06.016 – ident: e_1_3_3_9_2 doi: 10.1016/j.hrthm.2015.10.024 – ident: e_1_3_3_41_2 doi: 10.1097/RLI.0000000000000601 – ident: e_1_3_3_31_2 doi: 10.2967/jnumed.111.095448 – ident: e_1_3_3_12_2 doi: 10.1093/ehjci/jes270 – ident: e_1_3_3_18_2 doi: 10.1186/1745-6215-14-113 – ident: e_1_3_3_40_2 doi: 10.1093/eurheartj/ehy546 – ident: e_1_3_3_23_2 doi: 10.1093/eurheartj/ehw270 – ident: e_1_3_3_2_2 doi: 10.1161/CIRCULATIONAHA.112.000112 – ident: e_1_3_3_14_2 doi: 10.1016/j.hrthm.2018.03.020 – ident: e_1_3_3_5_2 doi: 10.1016/j.jacc.2011.12.030 – ident: e_1_3_3_32_2 doi: 10.1093/europace/eum133 – ident: e_1_3_3_6_2 doi: 10.1016/j.euje.2006.12.005 – ident: e_1_3_3_13_2 doi: 10.1093/europace/eus222 – ident: e_1_3_3_39_2 doi: 10.1016/j.rcl.2008.10.006 – ident: e_1_3_3_21_2 doi: 10.1117/1.JMI.6.4.045001 – ident: e_1_3_3_43_2 doi: 10.1002/mp.15550 |
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Snippet | Cardiac resynchronization therapy (CRT) is an effective treatment for patients with heart failure; however, 30% of patients do not respond to the treatment. We... Background: Cardiac resynchronization therapy (CRT) is an effective treatment for patients with heart failure; however, 30% of patients do not respond to the... |
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SubjectTerms | Cardiac Resynchronization Therapy Clinical Trials as Topic Heart Failure - diagnostic imaging Heart Failure - therapy Humans Lipopolysaccharides Prospective Studies Retrospective Studies Tomography Treatment Outcome Ventricular Function, Left |
Title | Prediction of Cardiac Resynchronization Therapy Response Using a Lead Placement Score Derived From 4-Dimensional Computed Tomography |
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