The prognostic value of automated coronary calcium derived by a deep learning approach on non-ECG gated CT images from 82Rb-PET/CT myocardial perfusion imaging

被引：12

作者：

Dekker, Mirthe ^{[1
,2
]}

Waissi, Farahnaz ^{[1
,2
]}

Bank, Ingrid E. M. ^{[3
]}

Isgum, Ivana ^{[4
]}

Scholtens, Asbjorn M. ^{[5
]}

Velthuis, Birgitta K. ^{[6
]}

Pasterkamp, Gerard ^{[7
]}

de Winter, Robbert J. ^{[2
]}

Mosterd, Arend ^{[8
]}

Timmers, Leo ^{[3
,9
]}

de Kleijn, Dominique P., V ^{[1
,9
]}

机构：

[1] Univ Med Ctr Utrecht, Dept Vasc Surg, Utrecht, Netherlands

[2] Amsterdam Univ Med Ctr, Dept Cardiol, Amsterdam, Netherlands

[3] St Antonius Hosp Nieuwegein, Dept Cardiol, Nieuwegein, Netherlands

[4] Univ Med Ctr Utrecht, Image Sci Inst, Utrecht, Netherlands

[5] Meander Med Ctr, Dept Nucl Med, Hoogland, Netherlands

[6] Univ Med Ctr Utrecht, Dept Radiol, Utrecht, Netherlands

[7] Univ Med Ctr Utrecht, Dept Clin Chem & Haematol, Utrecht, Netherlands

[8] Meander Med Ctr Amersfoort, Dept Cardiol, Amersfoort, Netherlands

[9] Netherlands Heart Inst, Utrecht, Netherlands

来源：

INTERNATIONAL JOURNAL OF CARDIOLOGY | 2021年 / 329卷

关键词：

Coronary artery disease; Myocardial perfusion imaging; Coronary artery calcium; Deep learning; ARTERY CALCIUM; DISEASE; CALCIFICATION; SCORE; ATHEROSCLEROSIS; TOMOGRAPHY; VALIDATION; PREDICTION; ISCHEMIA; OUTCOMES;

D O I：

10.1016/j.ijcard.2020.12.079

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

Background: Assessment of both coronary artery calcium(CAC) scores and myocardial perfusion imaging(MPI) in patients suspected of coronary artery disease(CAD) provides incremental prognostic information. We used an automated method to determine CAC scores on low-dose attenuation correction CT(LDACT) images gathered during MPI in one single assessment. The prognostic value of this automated CAC score is unknown, we therefore investigated the association of this automated CAC scores and major adverse cardiovascular events(MACE) in a large chest-pain cohort. Method: We analyzed 747 symptomatic patients referred for (82)RubidiumPET/CT, without a history of coronary revascularization. Ischemia was defined as a summed difference score >= 2. We used a validated deep learning (DL) method to determine CAC scores. For survival analysis CAC scores were dichotomized as low(<400) and high(>= 400). MACE was defined as all cause death, late revascularization (>90 days after scanning) or nonfatal myocardial infarction. Cox proportional hazard analysis were performed to identify predictors of MACE. Results: During 4 years follow-up, 115 MACEs were observed. High CAC scores showed higher cumulative event rates, irrespective of ischemia (non ischemic: 25.8% vs 11.9% and ischemic: 57.6% vs 23.4%, P-values <0.001). Multivariable cox regression revealed both high CAC scores (HR 2.19 95%CI 1.43-3.35) and ischemia (HR 256 95%CI 1.71-3.35) as independent predictors of MACE. Addition of automated CAC scores showed a net reclassification improvement of 0.13(0.022-0.245). Conclusion: Automatically derived CAC scores determined during a single imaging session are independently associated with MACE. This validated DL method could improve risk stratification and subsequently lead to more personalized treatment in patients suspected of CAD. (C) 2021 The Authors. Published by Elsevier B.V.

引用

页码：9 / 15

页数：7