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Ding X, Cui L, Li J, Cao J, Ding M, Wang H, Zhang F, Wang H. Assessing the diagnostic value of left ventricular synchrony indices derived from phase analysis by D-SPECT in identifying obstructive coronary artery disease. THE INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING 2024:10.1007/s10554-024-03182-z. [PMID: 38960945 DOI: 10.1007/s10554-024-03182-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 06/28/2024] [Indexed: 07/05/2024]
Abstract
This study aimed to assess the diagnostic efficacy of left ventricular synchrony (LVS) for detecting coronary artery disease (CAD). We explored whether the LVS index derived from phase analysis of D-SPECT provides superior diagnostic value compared to conventional perfusion analysis in identifying obstructive CAD. Patients with suspected or confirmed CAD underwent drug-stress/rest gated D-SPECT myocardial perfusion imaging (MPI) and coronary angiography (CAG). A 50% stenosis was set as the threshold for obstructive CAD. 110 participants were enrolled in this analysis. There were significant differences in phase standard deviation (PSD), phase histogram bandwidth (PHB) and entropy among the four groups. Patients without cardiac disease and those with mild-moderate stenosis exhibited no noticeable contraction asynchrony. However, LVS indices demonstrated a gradual increase with the progression of coronary stenosis when compared to NC (P < 0.001). Obstructive CAD was identified in 43 out of 110 participants (39%). Optimal cutoff values for diagnosing obstructive CAD during stress were determined as 7.6° for PSD, 24° for PHB, and 37% for entropy, respectively. Notably, PSD, PHB, and entropy indices exhibited higher sensitivity compared to MPI. The integration of the stress-induced LVS indices into routine MPI analysis resulted in a significantly greater area under the curve (AUC), leading to improved diagnostic performance and enhanced differential capacity. Stress-induced LVS indices increase with the severity of coronary artery stenosis by D-SPECT phase analysis. Further, the indices-derived phase analysis exhibits superior sensitivity and discriminatory ability compared to MPI in detecting obstructive CAD.
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Affiliation(s)
- Xinhua Ding
- Department of Nuclear Medicine, Gansu Province, Gansu Provincial Hospital, 204 Donggang West Road, Chengguan District, Lanzhou, 730000, China
| | - Lanlan Cui
- PET/CT Center, Gansu Provincial Hospital, Lanzhou, China
| | - Jianfeng Li
- Department of Cardiology, Gansu Province, Gansu Provincial Hospital, 204 Donggang West Road, Chengguan District, Lanzhou, 730000, China
| | - Jiancang Cao
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Mingjia Ding
- School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Haiyong Wang
- Department of Ultrasound, Gansu Provincial Hospital, Lanzhou, China
| | - Fu Zhang
- Department of Cardiology, Gansu Province, Gansu Provincial Hospital, 204 Donggang West Road, Chengguan District, Lanzhou, 730000, China.
| | - Haijun Wang
- Department of Nuclear Medicine, Gansu Province, Gansu Provincial Hospital, 204 Donggang West Road, Chengguan District, Lanzhou, 730000, China.
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Feher A, Pieszko K, Shanbhag A, Lemley M, Bednarski B, Miller RJH, Huang C, Miras L, Liu YH, Sinusas AJ, Slomka PJ, Miller EJ. CT attenuation correction improves quantitative risk prediction by cardiac SPECT in obese patients. Eur J Nucl Med Mol Imaging 2024; 51:695-706. [PMID: 37924340 DOI: 10.1007/s00259-023-06484-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 10/21/2023] [Indexed: 11/06/2023]
Abstract
PURPOSE This study aimed to compare the predictive value of CT attenuation-corrected stress total perfusion deficit (AC-sTPD) and non-corrected stress TPD (NC-sTPD) for major adverse cardiac events (MACE) in obese patients undergoing cadmium zinc telluride (CZT) SPECT myocardial perfusion imaging (MPI). METHODS The study included 4,585 patients who underwent CZT SPECT/CT MPI for clinical indications (chest pain: 56%, shortness of breath: 13%, other: 32%) at Yale New Haven Hospital (age: 64 ± 12 years, 45% female, body mass index [BMI]: 30.0 ± 6.3 kg/m2, prior coronary artery disease: 18%). The association between AC-sTPD or NC-sTPD and MACE defined as the composite end point of mortality, nonfatal myocardial infarction or late coronary revascularization (> 90 days after SPECT) was evaluated with survival analysis. RESULTS During a median follow-up of 25 months, 453 patients (10%) experienced MACE. In patients with BMI ≥ 35 kg/m2 (n = 931), those with AC-sTPD ≥ 3% had worse MACE-free survival than those with AC-sTPD < 3% (HR: 2.23, 95% CI: 1.40 - 3.55, p = 0.002) with no difference in MACE-free survival between patients with NC-sTPD ≥ 3% and NC-sTPD < 3% (HR:1.06, 95% CI:0.67 - 1.68, p = 0.78). AC-sTPD had higher AUC than NC-sTPD for the detection of 2-year MACE in patients with BMI ≥ 35 kg/m2 (0.631 versus 0.541, p = 0.01). In the overall cohort AC-sTPD had a higher ROC area under the curve (AUC, 0.641) than NC-sTPD (0.608; P = 0.01) for detection of 2-year MACE. In patients with BMI ≥ 35 kg/m2 AC sTPD provided significant incremental prognostic value beyond NC sTPD (net reclassification index: 0.14 [95% CI: 0.20 - 0.28]). CONCLUSIONS AC sTPD outperformed NC sTPD in predicting MACE in patients undergoing SPECT MPI with BMI ≥ 35 kg/m2. These findings highlight the superior prognostic value of AC-sTPD in this patient population and underscore the importance of CT attenuation correction.
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Affiliation(s)
- Attila Feher
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, Dana 3, P.O. Box 208017, New Haven, CT, 06520, USA.
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
| | - Konrad Pieszko
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Aakash Shanbhag
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Mark Lemley
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Bryan Bednarski
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Robert J H Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada
| | - Cathleen Huang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Leonidas Miras
- Division of Cardiology, Bridgeport Hospital, Yale University School of Medicine, Bridgeport, CT, USA
| | - Yi-Hwa Liu
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, Dana 3, P.O. Box 208017, New Haven, CT, 06520, USA
| | - Albert J Sinusas
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, Dana 3, P.O. Box 208017, New Haven, CT, 06520, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, Dana 3, P.O. Box 208017, New Haven, CT, 06520, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
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Feher A, Pieszko K, Shanbhag A, Lemley M, Miller RJ, Huang C, Miras L, Liu YH, Gerber J, Sinusas AJ, Miller EJ, Slomka PJ. Comparison of the prognostic value between quantification and visual estimation of coronary calcification from attenuation CT in patients undergoing SPECT myocardial perfusion imaging. Int J Cardiovasc Imaging 2024; 40:185-193. [PMID: 37845406 PMCID: PMC466934 DOI: 10.1007/s10554-023-02980-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 09/29/2023] [Indexed: 10/18/2023]
Abstract
We investigated the prognostic utility of visually estimated coronary artery calcification (VECAC) from low dose computed tomography attenuation correction (CTAC) scans obtained during SPECT/CT myocardial perfusion imaging (MPI), and assessed how it compares to coronary artery calcifications (CAC) quantified by calcium score on CTACs (QCAC). From the REFINE SPECT Registry 4,236 patients without prior coronary stenting with SPECT/CT performed at a single center were included (age: 64 ± 12 years, 47% female). VECAC in each coronary artery (left main, left anterior descending, circumflex, and right) were scored separately as 0 (absent), 1 (mild), 2 (moderate), or 3 (severe), yielding a possible score of 0-12 for each patient (overall VECAC grade zero:0, mild:1-2, moderate: 3-5, severe: >5). CAC scoring of CTACs was performed at the REFINE SPECT core lab with dedicated software. VECAC was correlated with categorized QCAC (zero: 0, mild: 1-99, moderate: 100-399, severe: ≥400). A high degree of correlation was observed between VECAC and QCAC, with 73% of VECACs in the same category as QCAC and 98% within one category. There was substantial agreement between VECAC and QCAC (weighted kappa: 0.78 with 95% confidence interval: 0.76-0.79, p < 0.001). During a median follow-up of 25 months, 372 patients (9%) experienced major adverse cardiovascular events (MACE). In survival analysis, both VECAC and QCAC were associated with MACE. The area under the receiver operating characteristic curve for 2-year-MACE was similar for VECAC when compared to QCAC (0.694 versus 0.691, p = 0.70). In conclusion, visual assessment of CAC on low-dose CTAC scans provides good estimation of QCAC in patients undergoing SPECT/CT MPI. Visually assessed CAC has similar prognostic value for MACE in comparison to QCAC.
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Affiliation(s)
- Attila Feher
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, Dana 3, P.O. Box 208017, New Haven, CT, 06520, USA.
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
| | - Konrad Pieszko
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Aakash Shanbhag
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Mark Lemley
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Robert Jh Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada
| | - Cathleen Huang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Leonidas Miras
- Division of Cardiology, Bridgeport Hospital, Yale University School of Medicine, Bridgeport, CT, USA
| | - Yi-Hwa Liu
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, Dana 3, P.O. Box 208017, New Haven, CT, 06520, USA
| | - Jamie Gerber
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, Dana 3, P.O. Box 208017, New Haven, CT, 06520, USA
| | - Albert J Sinusas
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, Dana 3, P.O. Box 208017, New Haven, CT, 06520, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, Dana 3, P.O. Box 208017, New Haven, CT, 06520, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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Feher A, Pieszko K, Miller R, Lemley M, Shanbhag A, Huang C, Miras L, Liu YH, Sinusas AJ, Miller EJ, Slomka PJ. Integration of coronary artery calcium scoring from CT attenuation scans by machine learning improves prediction of adverse cardiovascular events in patients undergoing SPECT/CT myocardial perfusion imaging. J Nucl Cardiol 2023; 30:590-603. [PMID: 36195826 DOI: 10.1007/s12350-022-03099-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 07/31/2022] [Indexed: 01/05/2023]
Abstract
BACKGROUND Machine learning (ML) has been previously applied for prognostication in patients undergoing SPECT myocardial perfusion imaging (MPI). We evaluated whether including attenuation CT coronary artery calcification (CAC) scoring improves ML prediction of major adverse cardiovascular events (MACE) in patients undergoing SPECT/CT MPI. METHODS From the REFINE SPECT Registry 4770 patients with SPECT/CT performed at a single center were included (age: 64 ± 12 years, 45% female). ML algorithm (XGBoost) inputs were clinical risk factors, stress variables, SPECT imaging parameters, and expert-observer CAC scoring using CT attenuation correction scans performed to obtain CT attenuation maps. The ML model was trained and validated using tenfold hold-out validation. Receiver Operator Characteristics (ROC) curves were analyzed for prediction of MACE. MACE-free survival was evaluated with standard survival analyses. RESULTS During a median follow-up of 24.1 months, 475 patients (10%) experienced MACE. Higher area under the ROC curve for MACE was observed with ML when CAC scoring was included (CAC-ML score, 0.77, 95% confidence interval [CI] 0.75-0.79) compared to ML without CAC (ML score, 0.75, 95% CI 0.73-0.77, P = .005) and when compared to CAC score alone (0.71, 95% CI 0.68-0.73, P < .001). Among clinical, imaging, and stress parameters, CAC score had highest variable importance for ML. On survival analysis patients with high CAC-ML score (> 0.091) had higher event rate when compared to patients with low CAC-ML score (hazard ratio 5.3, 95% CI 4.3-6.5, P < .001). CONCLUSION Integration of attenuation CT CAC scoring improves the predictive value of ML risk score for MACE prediction in patients undergoing SPECT MPI.
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Affiliation(s)
- Attila Feher
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, Dana 3, P.O. Box 208017, New Haven, CT, 06520, USA.
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
| | - Konrad Pieszko
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Robert Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada
| | - Mark Lemley
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Aakash Shanbhag
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Cathleen Huang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Leonidas Miras
- Division of Cardiology, Bridgeport Hospital, Yale University School of Medicine, Bridgeport, CT, USA
| | - Yi-Hwa Liu
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, Dana 3, P.O. Box 208017, New Haven, CT, 06520, USA
| | - Albert J Sinusas
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, Dana 3, P.O. Box 208017, New Haven, CT, 06520, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, Dana 3, P.O. Box 208017, New Haven, CT, 06520, USA
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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