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2. Sex differences in coronary atherosclerotic plaque activity using 18F-sodium fluoride positron emission tomography

Author

Kwiecinski, Jacek, Wang, Kang-Ling, Tzolos, Evangelos, Moss, Alastair, Daghem, Marwa, Adamson, Philip D., Dey, Damini, Molek-Dziadosz, Patrycja, Dawson, Dana, Arumugam, Parthiban, Sabharwal, Nikant, Greenwood, John P., Townend, John N., Calvert, Patrick A., Rudd, James HF., Berman, Daniel, Verjans, Johan W., Williams, Michelle C., Slomka, Piotr, Dweck, Marc R., and Newby, David E.

Published
2024
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3. Patient-Specific Myocardial Infarction Risk Thresholds From AI-Enabled Coronary Plaque Analysis

Author

Miller, Robert J.H., Manral, Nipun, Lin, Andrew, Shanbhag, Aakash, Park, Caroline, Kwiecinski, Jacek, Killekar, Aditya, McElhinney, Priscilla, Matsumoto, Hidenari, Razipour, Aryabod, Grodecki, Kajetan, Kwan, Alan C., Han, Donghee, Kuronuma, Keiichiro, Flores Tomasino, Guadalupe, Geers, Jolien, Goeller, Markus, Marwan, Mohamed, Gransar, Heidi, Tamarappoo, Balaji K., Cadet, Sebastien, Cheng, Victor Y., Achenbach, Stephan, Nicholls, Stephen J., Wong, Dennis T., Chen, Lu, Cao, J. Jane, Berman, Daniel S., Dweck, Marc R., Newby, David E., Williams, Michelle C., Slomka, Piotr J., and Dey, Damini

Published
2024
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4. Coronary Atherosclerotic Plaque Activity and Risk of Myocardial Infarction

Author

Wang, Kang-Ling, Balmforth, Craig, Meah, Mohammed N., Daghem, Marwa, Moss, Alastair J., Tzolos, Evangelos, Kwiecinski, Jacek, Molek-Dziadosz, Patrycja, Craig, Neil, Bularga, Anda, Adamson, Philip D., Dawson, Dana K., Arumugam, Parthiban, Sabharwal, Nikant K., Greenwood, John P., Townend, Jonathan N., Calvert, Patrick A., Rudd, James H.F., Verjans, Johan W., Berman, Daniel S., Slomka, Piotr J., Dey, Damini, Mills, Nicholas L., van Beek, Edwin J.R., Williams, Michelle C., Dweck, Marc R., and Newby, David E.

Published
2024
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5. Relationship between impaired myocardial blood flow by positron emission tomography and low-attenuation plaque burden and pericoronary adipose tissue attenuation from coronary computed tomography: From the prospective PACIFIC trial

Author

Kuronuma, Keiichiro, van Diemen, Pepijn A., Han, Donghee, Lin, Andrew, Grodecki, Kajetan, Kwiecinski, Jacek, Motwani, Manish, McElhinney, Priscilla, Tomasino, Guadalupe Flores, Park, Caroline, Kwan, Alan, Tzolos, Evangelos, Klein, Eyal, Shou, Benjamin, Tamarappoo, Balaji, Cadet, Sebastien, Danad, Ibrahim, Driessen, Roel S., Berman, Daniel S., Slomka, Piotr J., Dey, Damini, and Knaapen, Paul

Published
2023
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6. Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging

Author

Williams, Michelle C., Bednarski, Bryan P., Pieszko, Konrad, Miller, Robert J. H., Kwiecinski, Jacek, Shanbhag, Aakash, Liang, Joanna X., Huang, Cathleen, Sharir, Tali, Dorbala, Sharmila, Di Carli, Marcelo F., Einstein, Andrew J., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Fish, Mathews B., Ruddy, Terrence D., Acampa, Wanda, Hauser, M. Timothy, Kaufmann, Philipp A., Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2023
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9. Clinical phenotypes among patients with normal cardiac perfusion using unsupervised learning: a retrospective observational study

Author

Miller, Robert J.H., Bednarski, Bryan P., Pieszko, Konrad, Kwiecinski, Jacek, Williams, Michelle C., Shanbhag, Aakash, Liang, Joanna X., Huang, Cathleen, Sharir, Tali, Hauser, M. Timothy, Dorbala, Sharmila, Di Carli, Marcelo F., Fish, Mathews B., Ruddy, Terrence D., Bateman, Timothy M., Einstein, Andrew J., Kaufmann, Philipp A., Miller, Edward J., Sinusas, Albert J., Acampa, Wanda, Han, Donghee, Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2024
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16. Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning

Author

Singh, Ananya, Miller, Robert J.H., Otaki, Yuka, Kavanagh, Paul, Hauser, Michael T., Tzolos, Evangelos, Kwiecinski, Jacek, Van Kriekinge, Serge, Wei, Chih-Chun, Sharir, Tali, Einstein, Andrew J., Fish, Mathews B., Ruddy, Terrence D., Kaufmann, Philipp A., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Dorbala, Sharmila, Di Carli, Marcelo, Liang, Joanna X., Huang, Cathleen, Han, Donghee, Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2023
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19. Cardiovascular magnetic resonance and positron emission tomography in the assessment of aortic stenosis

Author

Kwiecinski, Jacek Kajetan, Jansen, Maurits, and Dweck, Marc

Subjects
616.1, aortic stenosis, cardiovascular magnetic resonance imaging, positron emission tomography, asymmetric wall thickening, blood pressure measurements, gender differences
Abstract

Background: Aortic stenosis is not only characterized by progressive valve narrowing but also by the hypertrophic response of the left ventricle that ensues. In this most common valvular condition novel imaging approaches (cardiovascular magnetic resonance [CMR] and positron emission tomography [PET]) have shown promise in the assessment of disease progression and risk stratification. The central aim of this thesis was to investigate the potential of CMR imaging to refine risk prediction and to improve the imaging protocol of 18F-sodium fluoride PET for aortic stenosis. Methods and Results: Asymmetric wall thickening in aortic stenosis In a prospective observational cohort study, 166 patients with aortic stenosis (age 69, 69% males, mean aortic valve area 1.0±0.4cm2) and 37 age and sex-matched healthy volunteers underwent phenotypic characterisation with comprehensive clinical, imaging and biomarker evaluation. Asymmetric wall thickening on both echocardiography and cardiovascular magnetic resonance was defined as regional wall thickening ≥13 mm and >1.5-fold the thickness of the opposing myocardial segment. Asymmetric wall thickening was observed in 26% (n=43) of patients with aortic stenosis using magnetic resonance and 17% (n=29) using echocardiography. Despite similar demographics, co-morbidities, valve narrowing, myocardial hypertrophy and fibrosis, patients with asymmetric wall thickening had increased cardiac troponin I and brain natriuretic peptide concentrations (both p < 0.001). Over 28 [22, 33] months of follow-up, asymmetric wall thickening was an independent predictor of aortic valve replacement or death whether detected by magnetic resonance (HR=2.15; 95 CI 1.29 to 3.59; p=0.003) or echocardiography (HR=1.79; 95 CI 1.08 to 3.69; p=0.021). Animal model of pressure overload We performed serial Cardiac Magnetic Resonance (CMR) imaging every 2-week in 31 mice subjected to pressure overload (continuous angiotensin II infusion) for 6 weeks and investigated reverse remodelling by repeating CMR 1 month following normalization of afterload (n=9). Cine CMR was used to measure left ventricular volumes, mass, and systolic function whilst myocardial fibrosis was assessed using indexed ECV (iECV) calculated from T1-relaxation times acquired with a small animal modified look-locker inversion recovery sequence. During the initial phase of increased pressure afterload indices of left ventricular hypertrophy (0.091 [0.083, 0.105] vs 0.123 [0.111, 0.138] g) and myocardial fibrosis (iECV: 0.022 [0.019, 0.024] vs 0.022 [0.019, 0.024] mL) increased in line with blood pressure measurements (65.1±12.0 vs 84.7±9.2 mmHg) whilst left ventricular ejection fraction (LVEF, 59.3 [57.6, 59.9] vs 46.9 [38.5, 49.6] %) deteriorated significantly (all p≤0.01 compared to baseline). During the reverse remodelling phase blood pressure normalized (68.8±5.4 vs 65.1±12.0 mmHg, p=0.42 compared to baseline). Whilst LV mass (0.108 [0.098, 0.116] vs 0.091 [0.083, 0.105] g) and iECV (0.034 [0.032, 0.036] vs 0.022 [0.019, 0.024] mL) improved both remained elevated compared to baseline (p < 0.05). Similarly, the LVEF remained impaired 51.1 [42.9, 52.8] vs 59.3 [57.6, 59.9] %, p=0.03. There was a strong association between LVEF and iECV values during pressure overload (r=-0.88, p < 0.001). Gender differences in aortic stenosis Two hundred forty-nine patients (66±13 years, 30% women) with at least mild AS were recruited from two prospective observational cohort studies and underwent comprehensive Doppler echocardiography and CMR exams. On CMR, T1 mapping was used to quantify extracellular volume (ECV) fraction as a marker of diffuse fibrosis, and late gadolinium enhancement (LGE) was used to assess focal fibrosis. There was no difference in age between women and men (66±15 vs 66±12 years, p=0.78). However, women presented a better cardiovascular risk profile than men with less hypertension, dyslipidemia, diabetes, and coronary artery disease (all p≤0.10). As expected, LV mass index measured by CMR was smaller in women than in men (p < 0.0001). Despite fewer comorbidities, women presented larger ECV fraction [29.0 (27.4-30.6) vs. 26.8 (25.1-28.7) %, p < 0.0001] and similar LGE [4.5 (2.3- 7.0) vs. 2.8 (0.6-6.8) %, p=0.20] than men. In multivariable analysis, female sex remained an independent determinant of higher ECV fraction and LGE (both p≤0.05). Prior CT angiography for PET Forty-five patients (age 67.1±6.9 years, 76% males) underwent CTA (CTA1) and combined 18F-NaF PET/CTA (CTA2) imaging within 14 [10,21] days. We fused CTA1 from visit one with 18F-NaF PET from the second visit (PET) and compared visual pattern of activity, maximal standard uptake values (SUVmax) and target to background (TBR) measurements on (PET/CTA1) fused versus hybrid (PET/CTA2) data. On PET/CTA2, 226 coronary plaques were identified. Fifty-eight coronary segments from 28 (62%) patients had high 18F-NaF uptake (TBR>1.25), whilst 168 segments had lesions with 18F-NaF TBR ≤1.25. Uptake in all lesions was categorized identically on co-registered PET/CTA1. There was no significant difference in 18F-NaF uptake values between PET/CTA1 and PET/CTA2 (SUVmax: 1.16±0.40 vs. 1.15±0.39, p=0.53; TBR:1.10±0.45 vs. 1.09±0.46, p=0.55). The intraclass correlation coefficient for SUVmax and TBR was 0.987 (95%CI 0.983 to 0.991) and 0.986 (95%CI 0.981 to 0.992). There was no fixed or proportional bias between PET/CTA1 and PET/CTA2 for SUVmax and TBR. Cardiac motion correction of PET scans improved reproducibility with tighter 95% limits of agreement (±0.14 for SUVmax and ±0.15 for TBR vs. ±0.20 and ±0.20 on diastolic imaging; p < 0.001). Delayed PET imaging Twenty patients (67±7years old, 55% male) with stable coronary artery disease underwent coronary CT angiography and PET/CT both 1 h and 3 h after the injection of 266.2±13.3 MBq of 18F-NaF. We compared the visual pattern of coronary uptake, maximal background (blood pool) activity, noise, standard uptake values (SUVmax), corrected SUV (cSUVmax) and target to background (TBR) measurements in lesions defined by CTA on 1h vs 3h post injection 18F-NaF PET. On 1h PET 26 CTA lesions with 18F-NaF PET uptake were identified in 12 (60%) patients. On 3h PET we detected 18F-NaF PET uptake in 7 lesions which were not identified on the 1h PET. The median cSUVmax and TBR values of these lesions were 0.48 [interquartile range (IQR) 0.44-0.51] and 1.45 [IQR, 1.39-1.52] compared to -0.01 [IQR, -0.03-0.001] and 0.95 [IQR, 0.90-0.98] on 1h PET, both p < 0.001. Across the entire cohort 3h PET SUVmax values were similar to 1h PET measurements 1.63 [IQR, 1.37-1.98] vs. 1.55 [IQR, 1.43-1.89], p=0.30 and the background activity was lower 0.71 [IQR, 0.65-0.81] vs. 1.24 [IQR, 1.05-1.31], p < 0.001. On 3h PET, the TBR values, cSUVmax and the noise were significantly higher (2.30 [IQR, 1.70-2.68] vs 1.28 [IQR, 0.98-1.56], p < 0.001; 0.38 [IQR, 0.27-0.70] vs 0.90 [IQR, 0.64-1.17], p < 0.001 and 0.10 [IQR, 0.09-0.12] vs. 0.07 [IQR, 0.06-0.09], p=0.02). The median cSUVmax and TBR values increased by 92% (range: 33-225%) and 80% (range: 20-177%). Conclusions: In aortic stenosis, asymmetric wall thickening is associated with adverse prognosis, in this condition there are significant differences in the fibrosis burden between male and female patients and the adverse remodeling of the ventricle can be reproduced in a simple animal model of pressure overload. For 18F-NaF PET utilizing a CT angiography acquired before the PET acquisition enables adequate uptake quantification and delayed emission scanning facilitates image analysis.

Published
2019

20. Quantifying microcalcification activity in the thoracic aorta

Author

Fletcher, Alexander J., Lembo, Maria, Kwiecinski, Jacek, Syed, Maaz B. J., Nash, Jennifer, Tzolos, Evangelos, Bing, Rong, Cadet, Sebastien, MacNaught, Gillian, van Beek, Edwin J. R., Moss, Alistair J., Doris, Mhairi K., Walker, Niki L., Dey, Damini, Adamson, Philip D., Newby, David E., Slomka, Piotr J., and Dweck, Marc R.

Published
2022
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23. Thoracic Aortic 18F-Sodium Fluoride Activity and Ischemic Stroke in Patients With Established Cardiovascular Disease

Author

Fletcher, Alexander J., Tew, Yong Y., Tzolos, Evangelos, Joshi, Shruti S., Kaczynski, Jakub, Nash, Jennifer, Debono, Samuel, Lembo, Maria, Kwiecinski, Jacek, Bing, Rong, Syed, Maaz B.J., Doris, Mhairi K., van Beek, Edwin J.R., Moss, Alistair J., Jenkins, William S., Walker, Niki L., Joshi, Nikhil V., Pawade, Tania A., Adamson, Philip D., Whiteley, William N., Wardlaw, Joanna M., Slomka, Piotr J., Williams, Michelle C., Newby, David E., and Dweck, Marc R.

Published
2022
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24. Pericoronary Adipose Tissue Attenuation, Low-Attenuation Plaque Burden, and 5-Year Risk of Myocardial Infarction

Author

Tzolos, Evangelos, Williams, Michelle C., McElhinney, Priscilla, Lin, Andrew, Grodecki, Kajetan, Flores Tomasino, Guadalupe, Cadet, Sebastien, Kwiecinski, Jacek, Doris, Mhairi, Adamson, Philip D., Moss, Alastair J., Alam, Shirjel, Hunter, Amanda, Shah, Anoop S.V., Mills, Nicholas L., Pawade, Tania, Wang, Chengjia, Weir-McCall, Jonathan R., Roditi, Giles, van Beek, Edwin J.R., Shaw, Leslee J., Nicol, Edward D., Berman, Daniel S., Slomka, Piotr J., Dweck, Marc R., Newby, David E., and Dey, Damini

Published
2022
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27. Bypass Grafting and Native Coronary Artery Disease Activity

Author

Kwiecinski, Jacek, Tzolos, Evangelos, Fletcher, Alexander J., Nash, Jennifer, Meah, Mohammed N., Cadet, Sebastien, Adamson, Philip D., Grodecki, Kajetan, Joshi, Nikhil, Williams, Michelle C., van Beek, Edwin J.R., Lai, Chi, Tavares, Adriana A.S., MacAskill, Mark G., Dey, Damini, Baker, Andrew H., Leipsic, Jonathon, Berman, Daniel S., Sellers, Stephanie L., Newby, David E., Dweck, Marc R., and Slomka, Piotr J.

Published
2022
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28. Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study

Author

Lin, Andrew, Manral, Nipun, McElhinney, Priscilla, Killekar, Aditya, Matsumoto, Hidenari, Kwiecinski, Jacek, Pieszko, Konrad, Razipour, Aryabod, Grodecki, Kajetan, Park, Caroline, Otaki, Yuka, Doris, Mhairi, Kwan, Alan C, Han, Donghee, Kuronuma, Keiichiro, Flores Tomasino, Guadalupe, Tzolos, Evangelos, Shanbhag, Aakash, Goeller, Markus, Marwan, Mohamed, Gransar, Heidi, Tamarappoo, Balaji K, Cadet, Sebastien, Achenbach, Stephan, Nicholls, Stephen J, Wong, Dennis T, Berman, Daniel S, Dweck, Marc, Newby, David E, Williams, Michelle C, Slomka, Piotr J, and Dey, Damini

Published
2022
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31. Association of Lipoprotein(a) With Atherosclerotic Plaque Progression

Author

Kaiser, Yannick, Daghem, Marwa, Tzolos, Evangelos, Meah, Mohammed N., Doris, Mhairi K., Moss, Alistair J., Kwiecinski, Jacek, Kroon, Jeffrey, Nurmohamed, Nick S., van der Harst, Pim, Adamson, Philip D., Williams, Michelle C., Dey, Damini, Newby, David E., Stroes, Erik S.G., Zheng, Kang H., and Dweck, Marc R.

Published
2022
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40. Detection and Prediction of Bioprosthetic Aortic Valve Degeneration

Author

Cartlidge, Timothy R.G., Doris, Mhairi K., Sellers, Stephanie L., Pawade, Tania A., White, Audrey C., Pessotto, Renzo, Kwiecinski, Jacek, Fletcher, Alison, Alcaide, Carlos, Lucatelli, Christophe, Densem, Cameron, Rudd, James H.F., van Beek, Edwin J.R., Tavares, Adriana, Virmani, Renu, Berman, Daniel, Leipsic, Jonathon A., Newby, David E., and Dweck, Marc R.

Published
2019
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44. Low-Attenuation Noncalcified Plaque on Coronary Computed Tomography Angiography Predicts Myocardial Infarction: Results From the Multicenter SCOT-HEART Trial (Scottish Computed Tomography of the HEART)

Author

Williams, Michelle C., Kwiecinski, Jacek, Doris, Mhairi, McElhinney, Priscilla, D’Souza, Michelle S., Cadet, Sebastien, Adamson, Philip D., Moss, Alastair J., Alam, Shirjel, Hunter, Amanda, Shah, Anoop S.V., Mills, Nicholas L., Pawade, Tania, Wang, Chengjia, Weir McCall, Jonathan, Bonnici-Mallia, Michael, Murrills, Christopher, Roditi, Giles, van Beek, Edwin J.R., Shaw, Leslee J., Nicol, Edward D., Berman, Daniel S., Slomka, Piotr J., Newby, David E., Dweck, Marc R., and Dey, Damini

Published
2020
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45. Coronary Atherosclerotic Plaque Activity and Future Coronary Events

Author

Moss, Alastair, Daghem, Marwa, Tzolos, Evangelos, MEAH, MOHAMMED N, Wang, Kang-Ling, Bularga, Anda, Adamson, Philip D, Kwiecinski, Jacek, Fletcher, Alison, Dawson, Dana, Arumugam, Parthiban, Sabharwal, Nikant, Greenwood, John P, Townend, Jon N, Calvert, Patrick A., Rudd, James H F, Berman, Dan, Verjans , Johan, Slomka, Piotr, Dey, Damini, Forsyth, Laura, Murdoch, Lauren, Lee, Robert J, Lewis, Steff, Mills, Nicholas L, van Beek, Edwin J R, Williams, Michelle C, Dweck, Marc R, and Newby, David E

Abstract

Importance: Recurrent coronary events in patients with recent myocardial infarction remain a major clinical problem. Non-invasive measures of coronary atherosclerotic disease activity have the potential to identify those at greatest risk.Objective: To determine whether non-invasive assessment of coronary atherosclerotic plaque activity could be associated with recurrent coronary events. Design: Prospective observational longitudinal cohort study recruiting participants between September 2015 and February 2020 with a minimum 2 years follow up.Setting: International multicenter study.Participants: Patients with multivessel coronary artery disease and recent myocardial infarction were eligible for inclusion. From 2,684 patients screened, 995 were eligible, 712 attended for imaging, and 704 had completed an interpretable scan and comprised the study population.Intervention: Coronary 18F-sodium fluoride positron emission tomography and coronary computed tomography angiography. Main Outcomes and Measures: Total coronary atherosclerotic plaque activity was assessed by 18F-sodium fluoride uptake. The primary endpoint was cardiac death or non-fatal myocardial infarction but was expanded during study conduct to include unscheduled coronary revascularization due to lower than anticipated primary event rates.Results: Participants were middle-aged (63.8±8.2 years) and predominantly male (85%). Total coronary atherosclerotic plaque activity was identified in 421 (60%) participants. After a median of 4 years follow-up, 141 participants experienced the primary endpoint: 9 had cardiac death, 49 non-fatal myocardial infarction and 83 unscheduled coronary revascularizations. Increased coronary plaque activity had no demonstrable association with the primary endpoint (hazard ratio (HR) 1.25 [95% confidence interval (CI) 0.89 to 1.76], P=0.20) or unscheduled revascularization (HR 0.98 [95% CI 0.64 to 1.49], P=0.91) but was associated with the secondary endpoints of cardiac death or non-fatal myocardial infarction (47 versus 19; HR 1.82 [95% CI 1.07 to 3.10], P=0.03) and all-cause mortality (30 versus 9; HR 2.43 [95% CI 1.15 to 5.12], P=0.02). These associations were similar after adjustment for differences in baseline clinical, coronary angiographic, and GRACE score characteristics (HR 1.76 [95% CI 1.00 to 3.10] (P=0.05) and HR 2.01 [95% CI 0.90 to 4.49], (P=0.09) respectively).Conclusions and Relevance: In patients with recent myocardial infarction, coronary atherosclerotic plaque activity was not associated with the primary composite endpoint. The findings suggesting risk of cardiovascular death or myocardial infarction in patients with elevated plaque activity warrants further research to explore its incremental prognostic implications.

Published
2023

46. Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging

Author

Williams, Michelle Claire, Bednarski, Bryan P, Pieszko, Konrad, Miller, Robert J H, Kwiecinski, Jacek, Shanbhag, Aakash, Liang, Joanna X, Huang, Cathleen, Sharir, Tali, Dorbala, Sharmila, Di Carli, Marcelo F, Einstein, Andrew J, Sinusas, Albert J, Miller, Edward J, Bateman, Timothy M, Fish, Mathews B, Ruddy, Terrence D, Acampa, Wanda, Hauser, M Timothy, Kaufmann, Philipp A, Dey, Damini, Berman, Daniel S, Slomka, Piotr J, Williams, Mc, Bednarski, Bp, Pieszko, K, Miller, Rjh, Kwiecinski, J, Shanbhag, A, Liang, Jx, Huang, C, Sharir, T, Dorbala, S, Di Carli, Mf, Einstein, Aj, Sinusas, Aj, Miller, Ej, Bateman, Tm, Fish, Mb, Ruddy, Td, Acampa, W, Hauser, Mt, Kaufmann, Pa, Dey, D, Berman, D, Slomka, Pj., University of Zurich, and Slomka, Piotr J

Subjects
SPECT myocardial perfusion, Cluster analysis, CARDIOVASCULAR RISK, Machine learning, 2741 Radiology, Nuclear Medicine and Imaging, Radiology, Nuclear Medicine and imaging, 610 Medicine & health, General Medicine, 10181 Clinic for Nuclear Medicine, Coronary artery disease
Abstract

Purpose Patients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion. We use cluster analysis to risk-stratify patients with known CAD undergoing single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). Methods From 37,298 patients in the REFINE SPECT registry, we identified 9221 patients with known coronary artery disease. Unsupervised machine learning was performed using clinical (23), acquisition (17), and image analysis (24) parameters from 4774 patients (internal cohort) and validated with 4447 patients (external cohort). Risk stratification for all-cause mortality was compared to stress total perfusion deficit ( Results Three clusters were identified, with patients in Cluster 3 having a higher body mass index, more diabetes mellitus and hypertension, and less likely to be male, have dyslipidemia, or undergo exercise stress imaging (p p p p Conclusions Our unsupervised cluster analysis in patients with known CAD undergoing SPECT MPI identified three distinct phenotypic clusters and predicted all-cause mortality better than ischemia alone.

Published
2023

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