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CC BY-NC-ND 4.0 · Indian J Radiol Imaging
DOI: 10.1055/s-0045-1810066
Original Article

Association of Pericardial Fat with Severity of Coronary Artery Disease

Sunil Kumar K. S.
1   Department of Radiodiagnosis, All India Institute of Medical Sciences, Rishikesh, Uttarakhand, India
,
Pankaj Sharma 
1   Department of Radiodiagnosis, All India Institute of Medical Sciences, Rishikesh, Uttarakhand, India
,
Barun Kumar
2   Department of Cardiology, All India Institute of Medical Sciences, Rishikesh, Uttarakhand, India
,
Udit Chauhan
1   Department of Radiodiagnosis, All India Institute of Medical Sciences, Rishikesh, Uttarakhand, India
› Author Affiliations

Funding None.
› Further Information
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Abstract

Background

Obesity is a well-known risk factor for cardiovascular disease. Measurement of pericardial fat (PF) by multidetector computed tomography (MDCT) might have potential for early diagnosis and assessment of risk of coronary artery disease (CAD). Our study is the first study from India, wherein we have tried to find the association of PF with the severity of CAD.

Aim and Objective

To evaluate the association of PF quantified by MDCT with the severity of CAD.

Materials and Methods

This cross-sectional study was performed at a single-tertiary-care center over a period of 18 months, and included patients who referred for coronary computed tomography: (1) suspected case of CAD, (2) symptomatic patient with intermediate pretest probability of CAD, or uninterpretable electrocardiogram, or unable to exercise on stress test. Periprostatic fat volume (PFV) was quantified using semiautomated technique, for measuring the amount of fat during end-systolic phase. Patients who had evidence of CAD were considered “CAD positive” in this study.

Observation and Results

A total of 87 patients were enrolled in this study. However, 7 patients were excluded due to motion artifacts and 80 patients (females, 27.5%, and males, 72.5%; mean age, 49.30 ± 12.27 years) were eligible for this study. PFV ranged from 28 to 547 mL, and the median value of PFV was around 100 mL.

Conclusion

Our study demonstrated a significant association between PF and age/body mass index/risk factors such as diabetes or family history. Higher PF was strongly associated with calcium score and severity of stenosis on computed tomography coronary angiography. Thus, PF is a risk predictor in subclinical CAD patients.


 

Keywords

pericardial fat volume - epicardial fat volume - coronary artery disease - multidetector computer tomography - coronary angiography - coronary artery calcium score

Introduction

Obesity is a growing health concern in India with an associated risk for coronary artery disease (CAD).[1] Accumulation of fat is associated with dyslipidemia, type 2 diabetes, hypertension, and insulin resistance.[2] Visceral adipose tissue secretes a variety of proatherogenic and proinflammatory adipokines, and these act at both the local and systemic levels.[3] [4] The adipokines released from pericardial fat (PF) may act locally on adjacent coronary vasculature and aggravate vessel wall inflammation.[5] [6] Regional thoracic fat depots such as PF have recently emerged as a strong predictor for CAD.

Fat deposited around the heart can be classified into different compartments:

  1. Epicardial fat (EF): adipose tissue located between the myocardium and the visceral pericardium.

  2. Paracardial fat: adipose tissue deposited outside the parietal pericardium ([Fig. 1]).

  3. PF: sum of EF and paracardial fat.

Zoom
Fig. 1 (A) Paracardial fat (green outlined). (B) Epicardial fat (purple outlined).

Multidetector computed tomography (MDCT) is a noninvasive, cardiac imaging technique that allows detection of subclinical CAD at an early stage.[7] Earlier published studies have established that PF is related to metabolic risk factors, which increase the chance of CAD.[8] [9] Our study is the first study from India, wherein we have tried to find the association of PF with the severity of CAD.


 

Aim and Objective

This article aimed to evaluate the association of PF quantified by MDCT with the severity of CAD.


 

Materials and Methods

This cross-sectional study was performed at a single-tertiary-care center over a period of 18 months.

Inclusion Criteria

Patients were referred for computed tomography coronary angiography (CTCA) for the following indications:

  • Suspected case of CAD.

  • Symptomatic patient with intermediate pretest probability of CAD, or uninterpretable electrocardiogram, or unable to exercise on stress test.


 

Exclusion Criteria

  • Patients who did not provide informed consent.

  • Renal dysfunction: estimated glomerular filtration rate <30 mL/min/1.73 m2.

  • History of allergy to iodinated contrast media.

  • Patients with a history of open-heart surgery/history of coronary artery stenting/post–valve replacement/pericardial effusion/those with pacing leads.

  • Seven patients were excluded due to motion artifacts.

Enrolled patients gave written informed consent and were asked to fast for 4 hours before CTCA. Clinical signs and symptoms, cardiovascular risk factors, and patient demographic data were evaluated at the time of CTCA.

The following cutoffs were taken as normal:

  • Serum low-density lipoprotein: less than 100 mg/dL.

  • Serum high-density lipoprotein: more than 40 mg/dL.

  • Serum triglyceride (STG): less than 150 mg/dL.[10]

For patient with a heart rate (HR) > 60 beats/min, a steady HR of ∼60 beats/min was achieved using β-blocker (metoprolol), and/or an anxiolytic (alprazolam), both in standard dose. Calcium channel blocker (oral/intravenous: diltiazem/verapamil) was used if there was a contraindication to the use of β-blockers (specially in patients with asthma).

Patients were positioned feet first in the gantry. Initial scout image was obtained. Prospectively gated, electrocardiogram (ECG)-triggered, noncontrast scan was acquired for coronary calcium scoring (CCS). Patients with CCS more than 400 were excluded from the study.

Retrospective gated, ECG-triggered, CTCA was done, after intravenous administration of 80 to 100 mL of nonionic, iodinated, water soluble contrast (iohexol 350 mgI/mL), injected using dual head Medrad Pressure Injector with a flow rate of 5 mL/s, through an 18G intravenous cannula, followed by saline flush of 20 mL. Acquisition was done on low-dose, 128-slice MDCT scanner (SOMATOM Definition Flash CT, Siemens Healthineers India). Acquisition started at the level of the tracheal bifurcation (above the origin of coronary arteries) and ended at the level of the dome of the diaphragm. Initiation of scan was done using Bolus triggering technique (trigger point at threshold of 140 Hounsfield units [HU], placed on descending thoracic aorta). After manual adjustment of field of view, data were reconstructed at various phases of the cardiac cycle, keeping slice thickness: 0.8 mm, and reconstruction increment: 0.4 mm, in dedicated soft-tissue kernel setting. Curved multiplanar reformation, maximum intensity projection, volume rendering technique (VRT), two-dimensional, and globe images were reconstructed, and all scans were analyzed on Syngo Acquisition Workstation (Siemens Healthineers India).

Correlation was done with conventional angiography (CA) findings, wherever possible. PF was quantified using a semiautomated technique (we adjusted region of interest if there was any discrepancy), for measuring the amount of fat, during end-systolic phase. Batch film was reconstructed keeping slice thickness: 3 mm and reconstruction increment: 1.5 mm. The chest area where PF was visualized was delimited by upper slice limit at origin of left main coronary artery and lower slice limit at cardiac apex (just below posterior descending artery). Boundaries were marked by manual tracing. In the end, software recognized the delimited area (content with attenuation value between −30 and −250 HU, characteristic of fatty tissue).

Patients were divided into two groups:

  1. High-PF group: patients with PF >100 mL and

  2. Low-PF group: patients with PF <100 mL ([Fig. 2]).[11]

Zoom
Fig. 2 Method of pericardial fat volume quantification using semiautomated technique on Syngo acquisition workplace. Yellow colored area represents pericardial fat.

Based on the degree of coronary artery stenosis (CAS), patients were divided into three groups:

  1. No stenosis: CAD absent.

  2. Nonobstructive stenosis: <50% stenosis.

  3. Obstructive stenosis (significant): >50% stenosis ([Fig. 3]).[12] [13]

Zoom
Fig. 3 Based on degree of stenosis: classified as “normal/no stenosis/CAD-RADS 0 (CAD absent),” “nonobstructive/CAD-RADS 1 and 2 (<50% stenosis),” and “obstructive/CAD-RADS 3-5 (>50% stenosis).”

If the patient had a different degree of stenosis in different coronary artery segments, then the plaque with the highest degree of stenosis was used for classification of the degree of CAS.

Patients who had evidence of CAD were referred to as “CAD present” in this study.

We followed CAS Coronary Artery Disease—Reporting and Data System (CAD-RADS) classification as proposed by Society for Cardiovascular Computed Tomography, the American College of Radiology, and the North American Society for Cardiovascular Imaging.

  • CAD-RADS 0: degree of coronary stenosis was 0%.

  • CAD-RADS 1: degree of coronary stenosis was 1 to 24% (minimal).

  • CAD-RADS 2: degree of coronary stenosis was 24 to 49% (mild).

  • CAD-RADS 3: degree of coronary stenosis was 50 to 69% (moderate).

  • CAD-RADS 4: degree of coronary stenosis was >70 to 99% (severe).

  • CAD-RADS 5: degree of coronary stenosis was 100% (total coronary artery occlusion).

All CT scans were interpreted by two radiologists, one who quantified PF and the other who evaluated CTCA. A degree of association was seen between PF and severity of CAD. Association was also seen between PF and other risk factors of CAD such as age/sex/body mass index (BMI)/family history of CAD (grandfather/grandmother/father/mother/brother/sister)/hypertension/diabetes/CCS. Clinical information of the patient was not shared with radiologists, who were interpreting scans. In case of any discrepancy in interpretation, the final result was reached by consensus.


 

Statistical Analysis

All calculations were done using IBM Statistical Package for Social Sciences 26.0 (SPSS) software. The level of significance was set at below 5%. A p-value was calculated using chi-square test and Fisher's exact test, then compared with the level of significance to reach an inference.


 

 

Observation and Results

A total of 80 patients in age group of 20 to 80 years were included in this study. Out of 80 patients, 37 (46.25%) patients had high PF, and 43 (53.75%) patients had low PF ([Table 1]). Twenty-two (27.50%) patients were female, and 58 (72.50%) patients were male. In the high-PF group, 29 (78.37%) patients were male, and 8 (21.62%) patients were female. In the low-PF group, 29 (67.44%) patients were male, and 14 (32.55%) patients were female.

Table 1

Association between PF and parameters

Parameters

PF

p-Value

≤ 100 mL (n = 43)

> 100 mL (n = 37)

Age (y)a

45.30 ± 11.90

53.95 ± 11.14

0.001

Age (y)

 21–30

6 (85.7%)

1 (14.3%)

0.075

 31–40

11 (78.6%)

3 (21.4%)

 41–50

11 (52.4%)

10 (47.6%)

 51–60

10 (40.0%)

15 (60.0%)

 61–70

4 (44.4%)

5 (55.6%)

 71–80

1 (25.0%)

3 (75.0%)

Gender

 Male

29 (50.0%)

29 (50.0%)

0.275

 Female

14 (63.6%)

8 (36.4%)

Female age group (y)

 ≤ 45

4 (80.0%)

1 (20.0%)

0.613

 > 45

10 (58.8%)

7 (41.2%)

BMI (kg/m2)a

23.87 ± 3.79

25.72 ± 3.40

0.024

BMI (kg/m2)

 < 18.5

4 (100.0%)

0 (0.0%)

0.284

 18.5–22.9

12 (63.2%)

7 (36.8%)

 23.0–24.9

8 (50.0%)

8 (50.0%)

 25.0–29.9

15 (48.4%)

16 (51.6%)

 30.0–34.9

4 (40.0%)

6 (60.0%)

Risk factor: smoking (yes)

14 (53.8%)

12 (46.2%)

0.990

Risk factor: hypertension (yes)

9 (50.0%)

9 (50.0%)

0.717

Risk factor: diabetes (yes)a

7 (30.4%)

16 (69.6%)

0.008

Risk factor: family history (yes)a

2 (18.2%)

9 (81.8%)

0.011

Calcium scorea

17.40 ± 72.93

128.63 ± 209.34

< 0.001

Calcium score categorya

 0

37 (69.8%)

16 (30.2%)

< 0.001

 1–9

2 (66.7%)

1 (33.3%)

 10–99

2 (20.0%)

8 (80.0%)

 100–400

1 (10.0%)

9 (90.0%)

 > 400

1 (25.0%)

3 (75.0%)

Stenosis on CTCA (present)a

13 (33.3%)

26 (66.7%)

< 0.001

Extent of stenosis on CTCA

 < 50% stenosis

7 (53.8%)

6 (46.2%)

0.077

 > 50% stenosis

6 (23.1%)

20 (76.9%)

CTCA impressiona

 Normal

30 (73.2%)

11 (26.8%)

< 0.001

 < 50% stenosis

7 (53.8%)

6 (46.2%)

 > 50% stenosis

6 (23.1%)

20 (76.9%)

Number of vessels involved

 SVD

7 (16.27%)

6 (16.21%)

0.059

 DVD

5 (11.62%)

8 (21.62%)

 TVD

1 (2.32%)

12 (32.43%)

LADa

 Involved

9 (20.93%)

19 (67.9%)

0.004

 Not involved

34 (86.04%)

18 (34.6%)

LCXa

 Involved

6 (13.95%)

19 (76.0%)

< 0.001

 Not involved

37 (67.3%)

18 (32.7%)

RCAa

 Involved

5 (11.62%)

20 (80.0%)

< 0.001

 Not involved

38 (88.37%)

17 (30.9%)

CAD (present)a

13 (30.23%)

26 (70.27%)

< 0.001

Abbreviations: BMI, body mass index; CAD, coronary artery disease; CTCA, computed tomography coronary angiography; DVD, double vessel disease; LAD, left anterior descending; LCX, left circumflex artery; PF, pericardial fat; RCA, right coronary artery; SVD, single vessel disease; TVD, triple vessel disease.


Note: Mean age (years)+/- Standard Deviation.


In the high-PF group, 15 (40.54%) patients were in age group of 51 to 60 years, 10 (27.02%) patients were in age group of 41 to 50 years, 5 (13.51%) patients were in age group of 61 to 70 years, 3 (8.10%) patients were in age group of 31 to 40 years, 3 (8.10%) patients were in age group of 71 to 80 years, and 1 (2.70%) patient were in age group of 21 to 30 years. In the low-PF group, 11 (25.58%) patients were in age group of 31 to 40 years, 11 (25.58%) patients were in age group of 41 to 50 years, 10 (23.25%) patients were in age group of 51 to 60 years, 6 (13.95%) patients were in age group of 21 to 30 years, 4 (9.30%) patients were in age group of 61 to 70 years, and 1 (2.32%) patient were in age group of 71 to 80 years.

Mean age group (±standard deviation [SD]) was 53.95 ± 11.14 in the high-PF group and 45.30 ± 11.90 in the low-PF group. BMI (± SD) was 25.72 ± 3.40 in the high-PF group and 23.87 ± 3.79 in the low-PF group. Calcium score (± SD) was 128.63 ± 209.34 in the high-PF group and 17.40 ± 72.93 in the low-PF group. Twenty-six out of 37 (70.27%) patients had stenosis on CTCA in the high-PF group, and 13 out of 43 (30.23%) patients had stenosis on CTCA in the low-PF group.

In the high-PF group, 20 (54.05%) patients had >50% stenosis, 11 (29.72%) patients were normal, and 6 (16.21%) patients had <50% stenosis. In the low-PF group, 30 (69.76%) patients were normal, 7 (16.27%) patients had <50% stenosis, and 6 (13.95%) patients had >50% stenosis.

In the high-PF group, the left anterior descending (LAD) artery was involved in 19 (51.35%) patients, and LAD was not involved in 18 (48.64%) patients. In the high-PF group, the left circumflex artery (LCX) was involved in 19 (51.35%) patients, and LCX was not involved in 18 (48.64%) patients. In the high-PF group, the right coronary artery (RCA) was involved in 20 (54.05%) patients, and RCA was not involved in 17 (45.94%) patients.

In the low-PF group, LAD was involved in 9 (20.93%) patients, and LAD was not involved in 34 (79.06%) patients. In the low-PF group, LCX was involved in 6 (13.95%) patients, and LCX was not involved in 37 (86.04%) patients. In the low-PF group, RCA was involved in 5 (11.62%) patients, and RCA was not involved in 38 (88.37%) patients.

In the high-PF group, 12 (32.43%) patients had triple vessel disease, 8 (21.62%) patients had double vessel disease (DVD), and 6 (16.21%) patients had single vessel disease. In the low-PF group, one (2.32%) patient had triple vessel disease, five (11.62%) patients had DVD, and seven (16.27%) patients had single vessel disease. CAD was present in 26 out of 37 (70.27%) high-PF group patients and in 13 out of 43 (30.23%) low-PF group patients.

Our results revealed a significant association (p < 0.05) between the variable “PF” with age (years), BMI (kg/m2), risk factors such as diabetes, and family history. Higher PF was also strongly associated with calcium score and severity of stenosis on CTCA. We found a significant difference between two population groups in terms of distribution of PF (p < 0.001) and extent of stenosis on CTCA ([Table 1]), and this correlation was statistically significant (p < 0.001). However, no statistically significant correlation was observed between PF with gender, smoking, and hypertension.

Following variables were significantly associated (p < 0.05) with PF: age (years), BMI (kg/m2), risk factors such as diabetes and family history, calcium score, calcium score category, stenosis on CTCA, CTCA impression, LAD, LCX, RCA, and CAD.

Chi-square test was used to explore the association between “PF” and “stenosis on CTCA” ([Table 2]). We found a significant difference between various groups in terms of distribution of PF (χ2 = 12.760, p < 0.001).

Table 2

Association between PF and stenosis severity on CTCA (n = 80)

Stenosis on CTCA

PF

Chi-square test

≤ 100 mL

> 100 mL

Total

χ 2

p-Value

Present

13 (33.3%)

26 (66.7%)

39 (100.0%)

12.760

< 0.001

Absent

30 (73.2%)

11 (26.8%)

41 (100.0%)

Total

43 (53.8%)

37 (46.2%)

80 (100.0%)

Abbreviations: CTCA, computed tomography coronary angiography; PF, pericardial fat.


Fisher's exact test was used to explore the association between “PF” and “extent of stenosis on CTCA” as more than 20% of the total number of cells had an expected count of less than 5 ([Table 3]). We found a significant difference between various groups in terms of the distribution of PF (χ2 = 3.692, p = 0.077).

Table 3

Association between PF and extent of stenosis on CTCA (n = 39)

Extent of stenosis on CTCA

PF

Fisher's exact test

≤ 100 mL

 > 100 mL

Total

χ 2

p-Value

< 50% stenosis

7 (53.8%)

6 (46.2%)

13 (100.0%)

3.692

0.077

> 50% stenosis

6 (23.1%)

20 (76.9%)

26 (100.0%)

Total

13 (33.3%)

26 (66.7%)

39 (100.0%)

Abbreviations: CTCA, computed tomography coronary angiography; PF, pericardial fat.


As can be seen from [Tables 1] to [3], PF cutoff of 100 mL had significant discriminatory power on CTCA, to pick up CAS. However, periprostatic fat volume cutoff of 100 mL did not had significant discriminatory power on CTCA, to pick up >50% CAS.


 

Discussion

Metabolically active PF is responsible for the development of plaque in adjacent coronary vessel and has now established itself as a new risk factor for CAD.[14] [15] [16] With its high spatial and temporal resolution, MDCT allows accurate quantification of PF. In our study, PF ranged from 28.00 to 547.00 mL, with a median value around 100 mL; which was used as a cutoff value to categorize patients into two groups (high PF: >100 mL group and low PF: <100 mL group). Our study showed a significant association (p < 0.05) of PF with age (years), BMI (kg/m2), risk factors such as diabetes and family history. Higher PF was strongly associated with calcium score and severity of stenosis on CTCA. Similar results were shown by Rosito et al and Nafakhi et al. However, there was no significant difference between the two groups in terms of PF and gender/smoking/hypertension.

Kim et al[17] conducted a study on participants from the cohort of Korean Atherosclerosis Study 2 group (n = 402, mean age of 54 years, 57.0% men). 64-slice MDCT was used to assess CCS, plaque characteristics, severity of CAS, and PF. Patients with atherosclerotic lesions had significantly larger volume of PF than patients without atherosclerosis (p < 0.01). This study showed strong agreement with our study. However, we had enrolled only 80 patients, as compared with 402 patients enrolled in study done by Kim et al. Our study has clinical relevance as this is first study from India, in which we have tried to evaluate correlation of PF more than 100 mL, with severity of CAS.

Nafakhi et al conducted study on a total of 115 consecutive Iraqi patients using 64-slice MDCT angiography examination. Only 74 patients (females: 38% and males: 68%) with mean age of 54 years were found to be eligible for statistical analysis. Median value of PF in their study was 100 mL (range: 17–319 mL). They found a significant association between high PF and significant CAS (p = 0.005), between high PF and presence of coronary plaque (p = 0.005), and this is in agreement with our study. In their study, there was no significant correlation between high PF and CCS (p = 0.188), between high PF and number of diseased coronary vessels (p = 0.3), and between high PF and body weight/body mass index, and these results are in conflict with our present study.

Rodriguez-Granillo et al included 75 patients in their study, with CA as gold standard.[18] Like our study, Rodriguez-Granillo et al concluded that PF did not differed between patients with (>50% stenosis) or without (<50% stenosis) obstructive CAD. However, in our study, CA was not done in all patients, and assessment was done by CTCA.

Khurana et al[19] did an ambivalent (prospective cum retrospective) cross-sectional observational study on 950 Indian subjects (suspected cases of CAD), who were referred for CTCA. They correlated EF with the severity of CAD, and concluded that higher quantity of EF was found in patients with a greater degree of CAS. EF correlated with age, weight, and BMI, and EF was found to be an independent risk factor for CAD. Study done by Khurana et al is in agreement with our present study, with the difference being that we have used PF (sum of EF and paracardial fat) for cardiac risk assessment, whereas Khurana et al have used EF for cardiac risk assessment.

Recent study by Ma et al[20] concluded that epicardial adipose tissue density showed a significant association with pericoronary adipose tissue mean attenuation in CAD and non-CAD patients. Our study differs from the study done by Ma et al as we did not measure epicardial adipose tissue density in our study.


 

What Adds to Literature

  • This study demonstrated a significant association (p < 0.05) between variable PF with severity of CAD/diabetes/family history of CAD and showed no significant correlation with gender/female age group/smoking/hypertension.

  • Our study also found discriminatory power of PF > 100 mL for assessing the severity of CAS in the Indian population.

  • However, we still do not know which is a better predictor for assessing the severity of CAS: EF or PF?

  • Many studies have shown inconclusive results in diabetics. However, our study showed a significant correlation between PF and diabetes in patients with CAD. The high prevalence of the diabetic population in India better explains the correlation of PF with CAD in the diabetic population.


 

Limitations

  • Single-center-based study was conducted in the Indian subcontinent population only. Different ethnic groups have variable amount of PF, and this may significantly affect cardiac risk assessment.

  • The present study had a small sample size.

  • Gender variability: Only 22 females out of 80 people were included in this study.

  • No interrater agreement was established in the study.

  • No gold standard comparison of CTCA result with invasive CA is a limitation of this study.

  • Confounding bias can be there when there are multiple risk factors. This was not analyzed with subgroup analysis.

  • Till date, we do not have the value of PF in the normal population.


 

Conclusion

Our study demonstrated a significant association between PF and age/BMI/diabetes/family history. Higher PF was strongly associated with CCS and the severity of CAS on CTCA. Thus, we conclude that PF is significantly associated with the severity of CAD. Therefore, PF can act as a risk predictor in subclinical CAD patients.

PF > 10 mL was found to have significant discriminatory power for assessing the severity of CAS in the Indian population. Assessment of PF on CTCA can be used for early risk stratification and management of patients who are at high risk for CAD, by initiation of preventive lifestyle modifications, thereby reducing morbidity and mortality.


 

 

Conflict of Interest

None declared.

Acknowledgment

The authors would like to express their deepest gratitude to Dr. Rishi Gupta for his time and effort in providing the statistical data for the study.

Ethical Approval

Ethical approval was obtained from the Institutional Ethics Committee. We obtained informed consent from every patient. The study conforms to the Ethical Guidelines of the Declaration of Helsinki.


Patients' Consent

Written informed consent to publish the personal and clinical details of the participant was obtained from patients.



Address for correspondence

Pankaj Sharma, DNB, FRCR
Department of Diagnostic and Interventional Radiology, All India Institute of Medical Sciences
Rishikesh, Uttarakhand 249203
India   

Publication History

Article published online:
18 July 2025

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  Permissions and Reprints
Zoom
Fig. 1 (A) Paracardial fat (green outlined). (B) Epicardial fat (purple outlined).
Zoom
Fig. 2 Method of pericardial fat volume quantification using semiautomated technique on Syngo acquisition workplace. Yellow colored area represents pericardial fat.
Zoom
Fig. 3 Based on degree of stenosis: classified as “normal/no stenosis/CAD-RADS 0 (CAD absent),” “nonobstructive/CAD-RADS 1 and 2 (<50% stenosis),” and “obstructive/CAD-RADS 3-5 (>50% stenosis).”