Prognostic Utility of Coronary Computed Tomography Angiography-derived Plaque Information on Long-term Outcome in Patients With and Without Diabetes Mellitus : Journal of Thoracic Imaging

Secondary Logo

Journal Logo

Advanced Search
Original Articles

Prognostic Utility of Coronary Computed Tomography Angiography-derived Plaque Information on Long-term Outcome in Patients With and Without Diabetes Mellitus

Tesche, Christian MD*,†,‡,§; Baquet, Moritz MD*; Bauer, Maximilian J. MD†,§; Straube, Florian MD†; Hartl, Stefan MD∥; Leonard, Tyler BSc§; Jochheim, David MD*; Fink, David Med†; Brandt, Verena MD§,¶; Baumann, Stefan MD§,#; Schoepf, U. Joseph MD§,**; Massberg, Steffen MD*; Hoffmann, Ellen MD†; Ebersberger, Ullrich MD*,†,§,††

Author Information

*Department of Cardiology, Munich University Clinic, Ludwig-Maximilians-University

†Department of Cardiology and Intensive Care Medicine, Heart Center Munich-Bogenhausen

††Kardiologie MVZ München-Nord, Munich

‡Department of Internal Medicine, Cardiology, St. Johannes-Hospital, Dortmund

∥Department of Cardiology, Pulmonology and Vascular Medicine, Faculty of Medicine, Heinrich-Heine-University, Düsseldorf

Department of Cardiology, Robert-Bosch-Krankenhaus, Stuttgart

#First Department of Medicine-Cardiology, University Medical Centre Mannheim, and DZHK (German Centre for Cardiovascular Research), Mannheim, Germany

§Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC

**Division of Cardiology, Medical University of South Carolina, Charleston, SC

Christian Tesche and Moritz Baquet contributed equally to this work.

U.J.S. received institutional research support and/or honoraria for speaking and consulting from Astellas, Bayer, Bracco, Elucid BioImaging, Guerbet, HeartFlow Inc., and Siemens Healthineers. C.T. has received speaker’s fees from Siemens Healthineers and Heartflow Inc. The remaining authors declare no conflicts of interest.

Correspondence to: Christian Tesche, MD, Department of Internal Medicine I, St. Johannes-Hospital Dortmund, Johannesstrasse 9-13, Dortmund 44137, Germany (e-mail: [email protected]).

Journal of Thoracic Imaging 38(3):p 179-185, May 2023. | DOI: 10.1097/RTI.0000000000000626
  • Free

Abstract

Coronary computed tomography angiography (cCTA) is a well-established modality for the assessment of coronary artery disease (CAD) and noninvasive plaque quantification.1–3 Recent studies have demonstrated the predictive value of coronary plaque measures (ie, extent, composition, location) for improved risk stratification.4–6

Diabetes mellitus (DM) is a well-known cardiovascular risk factor for CAD with increased rates in morbidity and mortality.7 Patients with diabetes present an overall higher coronary plaque burden and are at higher risk for adverse cardiovascular events when compared with nondiabetic patients.8 However, conventional risk scores recommended by societal guidelines are often challenging to apply effectively in patients with diabetes and do not necessarily meet the required prevention care of cardiovascular disease in this specific patient population.9 Recent investigations have demonstrated improved risk stratification in diabetic patients provided by cCTA-derived risk scores and plaque quantification.10–12 However, the impact of computed tomography (CT) scores, especially high-risk plaque features, on major adverse cardiovascular events (MACE) in patients with diabetes has yet to be investigated.

Thus, we sought to evaluate the long-term prognostic value of cCTA-derived coronary plaque information on MACE in patients with and without DM.

MATERIAL AND METHODS

Study Population

This retrospective single-center study was approved by the Institutional Review Board, and the need for written informed consent was waived due to the retrospective nature of this investigation. The study was performed in compliance with HIPAA. All consecutive patients with suspected or known CAD who underwent 64-slice cCTA as part of their clinical workup between April 2009 and October 2013 were included. Sixty-four consecutive patients with DM were matched with 297 patients without diabetes according to the following parameters: age, sex, cardiovascular risk factors, and statin and antithrombotic therapy. Portions of this patient population have been reported in a prior study.13 However, the prior study focused on outcome prediction in a general patient population using machine-learning principles, whereas the present study aims to investigate cCTA-derived plaque measures for risk prognostication in patients with and without diabetes.

MACE were recorded on follow-up. MACE were defined as cardiac death (fatal myocardial infarction [MI]), nonfatal MI (ST-segment elevation [STEMI] and non-ST-segment elevation MI [NSTEMI]), and unstable angina leading to coronary revascularization (percutaneous coronary intervention or coronary artery bypass grafting), with more than 6 weeks between cCTA and invasive coronary angiography with the revascularization procedure.14 The patients’ Framingham risk score was calculated to reflect the clinical risk for cardiovascular events.15 Diabetes was defined as fasting glucose ≥126 mg/dL and/or treatment with insulin or oral hypoglycemic medication.9 cCTA data with nondiagnostic image quality was excluded from further analysis. Likewise, patients were excluded if they underwent coronary revascularization within 6 weeks of the CT scan, or had a history of MI, percutaneous coronary intervention, or coronary artery bypass grafting. Demographic and clinical data were collected from medical records.

cCTA Acquisition

A 64-slice CT (Philips Brilliance 64, Philips Medical, Eindhoven, The Netherlands) was used for image acquisition. Patients initially underwent a non–contrast-enhanced calcium scoring scan (collimation, 32×1.2 mm; 120 kV tube voltage; tube current, 75 mA; 3 mm slice thickness with 1.5 mm increment). A retrospectively ECG-gated protocol in spiral technique was used for the subsequent contrast-enhanced cCTA with the following scan parameters: 120 kV, 600 mAs, temporal resolution of 165 ms, using a collimation of 64×0.6 mm. Contrast enhancement was achieved by injecting 50 to 80 mL contrast agent at 4 to 6 mL/s followed by a 30 mL saline bolus chaser. The attending radiologist determined the use of beta-blockers (heart rate >65 beats per minute) and nitroglycerin. Weighted filtered back projection image reconstruction was performed in the cardiac phase with the least motion: section thickness of 0.75 mm, reconstruction increment of 0.5 mm, using a smooth convolution kernel.

Analysis of cCTA Data, cCTA Scores, and Plaque Measures

cCTA datasets were analyzed on a commercially available post-processing software (Philips Medical). Two observers, who were blinded to the patients’ history, independently analyzed the lesion characteristics. All discordant cases were resolved by consensus. Transverse sections and automatically generated curved multiplanar reformations were used as the reference for diameter and area stenosis quantification. Average dimensions of nonaffected vessel segments immediately proximal and distal to the lesion of interest were measured at points free of atherosclerotic plaque. The CAD-RADS (CAD reporting and data system) was used to determine the degree of stenosis: (1) none (0%) or minimal (1% to 24%), (2) mild (25% to 49% stenosis), (3) moderate (50% to 69% stenosis), (4) severe (70% to 99% stenosis), and (5) total occlusion (100%). Obstructive CAD was defined as ≥50% luminal stenosis corresponding to a CAD-RADS classification of ≥3.16 CAD-RADS classification of 5 met the exclusion criteria. A coronary plaque was defined as a structure >1 mm2 located within or adjacent to the coronary artery lumen. Plaques with a CT attenuation of ≤30 HU were defined as low-attenuation plaques.17 On vessel cross sections, the presence of positive vessel remodeling was measured as the ratio of the vessel area of the lesion over the proximal luminal reference area. A remodeling index ≥1.1 was defined as positive vessel remodeling.17,18 The presence of a positive napkin-ring sign, described as a low attenuation plaque core circumscribed by an area of higher attenuation, was evaluated.19 Spotty calcifications were visually assessed as calcifications covering < 90 degrees of the vessel circumference while being >3 mm of length.20 Segment involvement score and segment stenosis score were calculated as previously reported.21 Briefly, the segment involvement score is a measure of overall coronary artery plaque distribution, whereas the segment stenosis score reflects the overall coronary artery plaque extent. Presence of low-attenuation plaque, napkin-ring sign, positive remodeling, and spotty calcifications deemed “high-risk” plaque features were evaluated.16,22

Statistical Analysis

MedCalc (MedCalc Software, version 15, Ostend, Belgium) and the python module scikit-survival23 were used for statistical analysis. Continuous variables are displayed as mean±SD or median with interquartile range when not normally distributed. Normal distribution was assessed using Kolmogorov-Smirnov test. Student t test and Mann-Whitney U test were used for parametric and nonparametric data, respectively. First, predictors of MACE were assessed for patients with and without diabetes using univariable and multivariable Cox proportional hazards analysis with backward elimination based on P-values as a selection criterion. The resulting hazard ratios (HR) and 95% confidence intervals (CIs) were reported. Second, the prognostic discriminatory capacity for predicting events in the overall dataset was evaluated by Cox proportional hazard analysis corrected for DM, and concordance (C)-indices were determined as proposed by Harrell et al.24 To avoid overfitting, a multivariable model was built utilizing recursive feature elimination with 5-fold cross-validation and C-indices as the performance criterion. Improvement in the prediction performance of MACE was calculated using continuous net reclassification improvement (NRI) according to Pencina et al.25 Event rates were estimated by Kaplan-Meier curves and compared by log-rank test in patients with and without diabetes according to segment stenosis score (≥10 vs. <10), presence of low-attenuation plaque (yes/no), and high-risk plaque features (≥2 vs. < 2). Statistical significance was assumed with a P-value ≤0.05.

RESULTS

Patient Characteristics

A total of 361 patients were included: 64 diabetic patients (63.3±10.1 y, 66% male) and 297 nondiabetics (61.6±10.4 y, 64% male). MACE occurred after a median follow-up of 5.4 years (interquartile range [IQR] 4.9 to 5.7 y) in 31 patients (8.6%), 21 with diabetes (67%) and 10 without diabetes (33%). Baseline characteristics were well balanced in both groups (Table 1). However, the Framingham risk score, which includes diabetes as a determining factor, was significantly higher in diabetic patients (13.2 [IQR 10.1 to 14.7] in comparison to nondiabetic patients (9.7 [IQR 7.8 to 13.0, P<0.001]. Diabetes was treated with oral antidiabetic medication in 47 patients (73%) and with insulin in 17 patients (27%).

TABLE 1 - Patient Demographics
Parameter All Patients (n=361) Patients With Diabetes (n=64) Patients Without Diabetes (n=297) P
Age (y) 61.9±10.3 63.3±10.1 61.6±10.4 0.28
Male sex, n (%) 233 (65) 42 (66) 191 (64) 0.84
Body-mass index (kg/m2) 28.3±5.4 28.7±6.0 28.2±5.3 0.80
Framingham risk score Median 10.2 (IQR 8.0, 13.2) Median 13.2 (IQR 10.1, 14.7) Median 9.7 (IQR 7.8, 13.0) <0.001
Cardiovascular risk factors
 Hypertension, n (%)* 205 (56) 41 (64) 164 (55) 0.17
 Dyslipidemia, n (%)† 161 (45) 26 (40) 135 (45) 0.48
 Tobacco abuse, n (%) 94 (26) 22 (34) 97 (32) 0.19
 CAD family history, n (%) 91 (25) 16 (25) 75 (25) 0.97
Medication at admission, n (%)
 Aspirin 126 (35) 24 (38) 102 (34) 0.29
 Statins 169 (47) 31 (48) 138 (46) 0.62
 Beta-blocker 86 (24) 18 (28) 68 (22) 0.58
 ACE inhibitor 76 (21) 15 (23) 61 (21) 0.46
 Diuretics 65 (18) 13 (20) 52 (18) 0.22
Clinical presentation at admission, n (%)
 No chest pain 130 (36) 33 (52) 97 (35) <0.01
 Noncardiac chest pain 58 (16) 8 (13) 50 (18)
 Typical chest pain 45 (12) 9 (14) 36 (13)
 Atypical chest pain 111 (31) 17 (278) 94 (34)
Total patient cohort (n=361).
Data presented as medians with 25th and 75th percentile, mean±SD or percentages in parentheses (%).
*Defined as blood pressure >140 mm Hg systolic, >90 mm Hg diastolic, or use of antihypertensive medication.
†Defined as a total cholesterol of >200 mg/dL or use of antilipidemic medication.
CAD indicates coronary artery disease.

Analysis of cCTA Scores and Plaque Measures in Patients With and Without Diabetes

cCTA analysis revealed obstructive CAD in 34 diabetic patients (53%) and 66 nondiabetic patients (22%, P<0.001). Of the 34 diabetics with obstructive CAD, 21 patients (62%) were categorized to CAD-RADS 3, and 13 patients (38%) to CAD-RADS 4. In the nondiabetic group, 38 patients (58%) had CAD-RADS category 3, and 28 patients (42%) were assigned to CAD-RADS 4. Median CT scores were significantly different between patients with and without diabetes (segment involvement score: 5 [IQR 2.0, 7.0] vs. 2.0 [IQR 0.0 to 4.0, P<0.001] and segment stenosis score: 7.5 [IQR 2.0, 13.0] vs. 3.0 [IQR 0.0 to 6.0, P<0.001]). Furthermore, prevalence of all plaque measures were significantly higher in the diabetes group when compared with nondiabetics (Table 2).

TABLE 2 - Quantitative Analysis of Coronary CT-derived Markers in Patients With Diabetes and Without Diabetes
Parameter All Patients (n=361) Patients With Diabetes (n=64) Patients Without Diabetes (n=297) P
Segment stenosis score Median 3 (IQR 1.0, 7.0) Median 7.5 (IQR 2.0, 13.0) Median 3.0 (IQR 0.0, 6.0) <0.001
Segment involvement score Median 3.0 (IQR 1.0, 5.0) Median 5.0 (IQR 2.0, 7.0) Median 2.0 (IQR 0.0, 4.0) <0.001
Agatston score Median 9.7 (IQR 0.0, 103.8) Median 64.1 (IQR 6.2, 482.7) Median 4.9 (IQR 0.0, 75.6) <0.001
Obstructive CAD 100 (28) 34 (53) 66 (22) <0.001
Low-attenuation plaque 74 (21) 22 (34) 52 (18) <0.001
Spotty calcification 76 (21) 23 (36) 53 (18) <0.001
Positive remodeling 94 (26) 20 (31) 74 (25) 0.022
Napkin-ring sign 67 (18) 19 (29) 48 (17) <0.001
≥2 high-risk features 92 (25) 30 (46) 62 (21) <0.001
1 vessel CAD 52 (14) 13 (20) 39 (13) 0.02
2 vessel CAD 32 (9) 12 (19) 20 (7)
3 vessel CAD 13 (4) 10 (15) 3 (1)
Data presented as medians with 25th and 75th percentile or numbers with percentages (%).
CAD indicates coronary artery disease.

Association of cCTA Scores and Plaque Measures With MACE

Patients with diabetes who suffered MACE yielded higher cCTA scores and prevalence of plaque measures (Table 3). Similar results were demonstrated for patients without diabetes. Results of the univariable and multivariable Cox regression analyses are displayed in Tables 4 and 5. In multivariable analysis, only segment stenosis score (HR: 1.20 [95% CI: 1.07-1.33], P<0.001) and low-attenuation plaque (HR: 3.47 [95% CI: 1.02-11.80], P=0.05) remained independent predictors for MACE in patients with diabetes. The following markers showed a predictive value of MACE in the nondiabetic group: segment stenosis score (HR: 1.92 [1.46-2.54], P<0.001), Agatston score (HR: 1.0009 [95% CI: 1.00006-1.0017, P=0.04], and low-attenuation plaque (HR: 4.15 [95% CI: 1.04-16.64], P=0.04).

TABLE 3 - Quantitative Analysis of Coronary CT-derived Markers in Patients With Diabetes and Without Diabetes According to MACE
Parameter All Patients (n=64) MACE (n=21) No MACE (n=43) P
Patients with diabetes
 Segment stenosis score Median 3 (IQR 0.0, 9.0) Median 12 (IQR 10.0, 16.3) Median 3 (IQR 1.0, 8.5) <0.001
 Segment involvement score Median 5.0 (IQR 0.0, 9.0) Median 7.0 (IQR 5.7, 8.3) Median 4.0 (IQR 1.0, 6.0) 0.001
 Agatston score Median 65.1 (IQR 6.2, 487.2) Median 482.7 (IQR 178.4, 1545.5) Median 4.9 (IQR 0.0, 75.6) 0.001
 Obstructive CAD 34 (53) 17 (81) 17 (40) 0.002
 Low-attenuation plaque 25 (39) 17 (81) 8 (19) <0.001
 Spotty calcification 27 (42) 14 13 (30) 0.007
 Positive remodeling 24 (38) 14 (67) 10 (23) 0.001
 Napkin-ring sign 21 (32) 14 (67) 7 (16) 0.001
 ≥2 high-risk features 30 (47) 19 (90) 11 (26) <0.001
All Patients (n=297) MACE (n=10) No MACE (n=287)
Patients without diabetes
 Segment stenosis score Median 5 (IQR 1.0, 7.0) Median 13.5 (IQR 12.0, 17.0) Median 2.0 (IQR 0.0, 5.0) <0.001
 Segment involvement score Median 2.0 (IQR 0.0, 4.0) Median 7.5 (IQR 6.0, 9.0) Median 2.0 (IQR 0.0, 4.0) <0.001
 Agatston score Median 4.9 (IQR 0.0, 75.7) Median 283.7 (IQR 67.5, 854.4) Median 4.2 (IQR 0.0, 64.6) 0.005
 Obstructive CAD 66 (22) 8 (80) 58 (20) <0.001
 Low-attenuation plaque 49 (16) 5 (50) 44 (15) 0.0041
 Spotty calcification 49 (16) 7 (70) 42 (14) <0.001
 Positive remodeling 70 (24) 5 (50) 65 (23) 0.046
 Napkin-ring sign 46 (15) 4 (40) 42 (15) 0.031
 ≥2 high-risk features 62 (21) 7 (70) 55 (19) 0.001
Data presented as medians with 25th and 75th percentile or numbers with percentages (%).
CPV indicates calcified plaque volume; NCPV, noncalcified plaque volume; SIS, segment involvement score; SSS, segment stenosis score; TPV, total plaque volume.

TABLE 4 - Univariate Cox Proportional Hazards Regression Analysis of Coronary CT-derived Markers in Patients With Diabetes and Without Diabetes for the Prediction of MACE
Parameter Hazard Ratio 95% CI of Hazard Ratio P
Patients with diabetes
 Segment stenosis score 1.26 1.15-1.39 <0.001
 Segment involvement score 1.44 1.21-1.72 <0.001
 Framingham risk score 1.09 0.98-1.21 0.11
 Agatston score 1.0003 1.00006-1.00058 0.01
 Obstructive CAD 4.83 1.62-14.39 0.005
 Low-attenuation plaque 10.01 3.35-29.96 <0.001
 Spotty calcification 3.42 1.38-8.50 0.01
 Positive remodeling 4.33 1.74-10.78 0.002
 Napkin-ring sign 5.55 2.23-13.85 <0.001
 ≥2 high-risk features 15.14 3.51-65.33 <0.001
Patients without diabetes
 Segment stenosis score 1.55 1.35-1.79 <0.001
 Segment involvement score 1.54 1.54-2.47 <0.001
 Framingham risk score 1.02 0.97-1.07 0.38
 Agatston score 0.998 0.994- 1.003 0.58
 Obstructive CAD 14.64 3.11-68.96 <0.001
 Low-attenuation plaque 5.24 1.52-18.10 0.01
 Spotty calcification 12.42 3.21-48.04 <0.001
 Positive remodeling 3.35 0.97-11.58 0.06
 Napkin-ring sign 3.72 1.05-13.17 0.04
 ≥2 high-risk features 9.24 2.39-35.72 0.001

TABLE 5 - Multivariable Cox Proportional Hazards Regression Analysis of Coronary CT-derived Markers in Patients With Diabetes and Without Diabetes for the Prediction of MACE
Parameter Hazard Ratio 95% CI of Hazard Ratio P
Patients with diabetes
 Segment stenosis score 1.20 1.07-1.33 <0.001
 Low-attenuation plaque 3.47 1.02-11.80 0.05
Patients without diabetes
 Segment stenosis score 1.92 1.46-2.54 <0.001
 Agatston score 1.0009 1.00006-1.0017 0.04
 Low-attenuation plaque 4.15 1.04-16.64 0.04

Receiver-operating characteristics analysis demonstrated that the multivariable model including DM, segment stenosis score, segment involvement score, low-attenuation plaque, and positive remodeling resulted in a significantly improved C-index of 0.96 (95% CI: 0.94-0.97) for MACE prediction, when compared with these parameters alone: low-attenuation plaque: C-index 0.82 (95% CI: 0.75-0.90, P<0.001), and ≥2 high-risk features: C-index 0.86 (95% CI: 0.80-0.93, P=0.003). Segment stenosis score yielded a C-index of 0.94 (95% CI: 0.92-0.96, P=0.049), comparable to that of the multivariable model (Fig. 1).

F1
FIGURE 1:
Diagnostic performance for the prediction of MACE. C-indices in the overall study population are shown with the receiver-operating characteristics curves for the multivariable model (0.96 [95% CI: 0.94-0.97]) in comparison to low-attenuation plaque (0.82 [95% CI: 0.75-0.90]), ≥2 high-risk plaque features (0.86 [95% CI: 0.80-0.93]), segment stenosis score (0.94 [95% CI: 0.92-0.96]), the Framingham risk score (0.80 [95% CI: 0.73-0.88]), and obstructive CAD (0.84 [95% CI: 0.78-0.91]) (each model corrected for diabetes mellitus).

To assess the ability of appropriate reclassification of patient risk for MACE, the NRI was calculated. The NRI of the multivariable model compared with segment stenosis score was 0.45 (95% CI: 0.08-0.81), 0.28 (95% CI: 0.004-0.51) when compared with low-attenuation plaque, and 0.29 (95% CI: 0.004-0.54) when compared with ≥2 high-risk features.

The Kaplan-Meier survival curves showed that patients with diabetes and addition of one of the following characteristics (≥2 high-risk plaque features, presence of low-attenuation plaque, or segment stenosis score >10) had substantially higher event rates than patients without these findings (P<0.001) (Fig. 2). A case example of coronary stenosis on cCTA with the corresponding high-risk plaque feature is shown in Figure 3.

F2
FIGURE 2:
Kaplan-Meier curves stratified by a combination of diabetes and segment stenosis score, presence of low-attenuation plaque, and ≥2 high-risk plaque features. Kaplan-Meier curves stratified by (A) a combination of diabetes and segment stenosis score and (B) a combination of diabetes and presence of low-attenuation plaque, and (C) a combination of diabetes and ≥2 high-risk plaque features. HRPF indicates high-risk plaque features; LAP, low-attenuation plaque; SSS, segment stenosis score.
F3
FIGURE 3:
Case example of coronary stenosis on cCTA with corresponding high-risk plaque features. A 72-year-old man with diabetes mellitus and chest pain who underwent cCTA for suspected CAD. cCTA shows mixed high-risk plaque of the medial LAD with a napkin-ring sign. Cross-sectional images of the lesion demonstrate an outer hyperdense fibrous rim and a hypodense lipid-rich necrotic core (<30 HU).

DISCUSSION

The present study assessed the long-term prognostic value of cCTA-derived plaque information on MACE in patients with and without DM. Our results demonstrate the predictive value of cCTA measures for MACE in patients with diabetes, with segment stenosis score (HR: 1.20, P<0.001) and low-attenuation plaque (HR: 3.47, P=0.05), showing predictive power beyond cCTA stenosis grading and the Framingham risk score. These markers portend improved risk stratification in both patients with and without diabetes. In addition, a multivariable model showed a significantly improved C-index of 0.96 (95% CI: 0.94-0.97) for MACE prediction, when compared with single measures alone (all P<0.05).

Several prior studies have evaluated the prognostic value of cCTA-derived plaque information using semiautomatic plaque quantifications and CT scores in patients with diabetes compared with patients without diabetes.8,10,11 A recent study by van Hoogen et al10 and Hadamitzky et al26 showed that plaque-related scores (ie, segment stenosis score/segment involvement score) were significantly different in patients with and without diabetes and demonstrated a predictive value. We also demonstrated a significant difference in CT scores between diabetic and nondiabetic patients (median segment stenosis score: 7.5 vs. 3.0, P<0.001, median segment involvement score: 5.0 vs. 2.0, P<0.001) with segment stenosis score serving as an independent predictor of MACE in both patients with diabetes (HR: 1.20, P<0.001) and without diabetes (HR: 1.92, P<0.001). A recent study by Deseive et al27 investigated the impact of different plaque features on adverse outcomes in diabetic patients. They demonstrated that the total plaque volume and noncalcified plaque volume were significantly different between both groups, with the total plaque volume providing independent predictive value for the identification of MACE in diabetic patients. However, they did not assess the impact of high-risk plaque features, which are established markers for MACE prediction. Our results are in line with the aforementioned results and go beyond their findings as we demonstrated that all high-risk features were significantly different in diabetic patients versus nondiabetic patients. Multivariable Cox regression analysis revealed that the presence of low-attenuation plaque demonstrated incremental predictive value in patients with diabetes (HR: 3.47, P=0.05) and without diabetes (HR: 4.15, P=0.04). We proved that the above-mentioned CT measures demonstrated superior discriminatory power in the prediction of MACE (95% CIs: 0.82-0.95) beyond the Framingham risk score (95% CI: 0.80, P<0.001). Whereas most recent studies focused on CT risk scores that reflect the cardiovascular disease burden (ie, Leiden cCTA risk score, CT Leaman score),10,28 we additionally investigated high-risk plaque features that have been demonstrated as independent predictors of adverse cardiovascular outcome and have yet to be investigated in patients with diabetes. We demonstrated that patients with diabetes exhibited higher overall plaque burden and higher prevalence of high-risk plaque features. The presence of plaque features was associated with significantly decreased event-free survival on Kaplan-Meier analysis (Fig. 2), which is in line with the findings by Blanke et al.28

A major drawback is the necessity for manually performed time-consuming plaque analysis that hampers its applicability in a real-world clinical setting. Although semiautomatic plaque software has been used in prior investigations,1,27,29 manual adjustment is still required. Technical advances such as machine-learning applications may reduce this limitation and allow for improved risk stratification in a timely and cost-effective manner.30,31

This study has several limitations that deserve mentioning. A relatively small number of patients with different types of diabetes and various medical therapies were included, which may incur selection bias. Furthermore, the number of MACE in nondiabetic patients is very small compared with diabetics, which may be explained by the retrospective study design resulting in a selection bias. Therefore, prospective studies on larger study cohorts are necessary to validate our findings. In addition, only a single CT scanner provided by one vendor was used for cCTA image acquisition. Our results of the multivariable analysis may be underpowered by the limited number of observations per variable included.32 Therefore, the data generated in this study should only be considered hypothesis generating. Patient follow-up was performed using electronic medical records of the hospitals, potentially resulting in missed events that may have occurred outside the hospital system.

In conclusion, this study demonstrates that the presence of diabetes is associated with a significantly higher extent of CAD and plaque features, which have independent predictive values for MACE. cCTA-derived plaque information portends improved risk stratification of patients with diabetes beyond the assessment of obstructive stenosis on cCTA alone.

REFERENCES

1. Tesche C, Plank F, De Cecco CN, et al. Prognostic implications of coronary CT angiography-derived quantitative markers for the prediction of major adverse cardiac events. J Cardiovasc Comput Tomogr. 2016;10:458–465.
2. Thomsen C, Abdulla J. Characteristics of high-risk coronary plaques identified by computed tomographic angiography and associated prognosis: a systematic review and meta-analysis. Eur Heart J Cardiovasc Imaging. 2016;17:120–129.
3. Brandt V, Emrich T, Schoepf UJ, et al. Ischemia and outcome prediction by cardiac CT based machine learning. Int J Cardiovasc Imaging. 2020;36:2429–2439.
4. Investigators S-H, Newby DE, Adamson PD, et al. Coronary CT angiography and 5-year risk of myocardial infarction. N Engl J Med. 2018;379:924–933.
5. Ferencik M, Mayrhofer T, Bittner DO, et al. Use of high-risk coronary atherosclerotic plaque detection for risk stratification of patients with stable chest pain: a secondary analysis of the PROMISE randomized clinical trial. JAMA Cardiol. 2018;3:144–152.
6. Lakshmanan S, Rezvanizadeh V, Budoff MJ. Comprehensive plaque assessment with serial coronary CT angiography: translation to bedside. Int J Cardiovasc Imaging. 2020;36:2335–2346.
7. Low Wang CC, Hess CN, Hiatt WR, et al. Clinical update: cardiovascular disease in diabetes mellitus: atherosclerotic cardiovascular disease and heart failure in type 2 diabetes mellitus—mechanisms, management, and clinical considerations. Circulation. 2016;133:2459–2502.
8. Nadjiri J, Hausleiter J, Jahnichen C, et al. Incremental prognostic value of quantitative plaque assessment in coronary CT angiography during 5 years of follow up. J Cardiovasc Comput Tomogr. 2016;10:97–104.
9. Authors/Task Force Members, Ryden L, Grant PJ, Anker SD, et al. ESC Committe for Practice Guidline, Document Reviewers. ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD: the Task Force on diabetes, pre-diabetes, and cardiovascular diseases of the European Society of Cardiology (ESC) and developed in collaboration with the European Association for the Study of Diabetes (EASD). Eur Heart J. 2013;34:3035–3087.
10. van den Hoogen IJ, van Rosendael AR, Lin FY, et al. Coronary atherosclerosis scoring with semiquantitative CCTA risk scores for prediction of major adverse cardiac events: Propensity score-based analysis of diabetic and non-diabetic patients. J Cardiovasc Comput Tomogr. 2020;14:251–257.
11. Deseive S, Straub R, Kupke M, et al. Automated quantification of coronary plaque volume from CT angiography improves CV risk prediction at long-term follow-up. JACC Cardiovasc Imaging. 2018;11(pt 1):280–282.
12. Maurovich-Horvat P, Hoffmann U, Vorpahl M, et al. The napkin-ring sign: CT signature of high-risk coronary plaques? JACC Cardiovasc Imaging. 2010;3:440–444.
13. Tesche C, Bauer MJ, Baquet M, et al. Improved long-term prognostic value of coronary CT angiography-derived plaque measures and clinical parameters on adverse cardiac outcome using machine learning. Eur Radiol. 2021;31:486–493.
14. Task Force on the Management of ST-segment Elevation Acute Myocardial Infraction of the European Society of Cardiology, Steg PG, James SK, Atar D, et al. ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation. Eur Heart J. 2012;33:2569–2619.
15. D’Agostino RB Sr, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117:743–753.
16. Cury RC, Abbara S, Achenbach S, et al. CAD-RADS(TM) coronary artery disease—reporting and data system. An expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Radiology (ACR) and the North American Society for Cardiovascular Imaging (NASCI). Endorsed by the American College of Cardiology. J Cardiovasc Comput Tomogr. 2016;10:269–281.
17. Motoyama S, Kondo T, Sarai M, et al. Multislice computed tomographic characteristics of coronary lesions in acute coronary syndromes. J Am Coll Cardiol. 2007;50:319–326.
18. Achenbach S, Ropers D, Hoffmann U, et al. Assessment of coronary remodeling in stenotic and nonstenotic coronary atherosclerotic lesions by multidetector spiral computed tomography. J Am Coll Cardiol. 2004;43:842–847.
19. Maurovich-Horvat P, Schlett CL, Alkadhi H, et al. The napkin-ring sign indicates advanced atherosclerotic lesions in coronary CT angiography. JACC Cardiovasc Imaging. 2012;5:1243–1252.
20. Park HB, Heo R, o Hartaigh B, et al. Atherosclerotic plaque characteristics by CT angiography identify coronary lesions that cause ischemia: a direct comparison to fractional flow reserve. JACC Cardiovasc Imaging. 2015;8:1–10.
21. Min JK, Feignoux J, Treutenaere J, et al. The prognostic value of multidetector coronary CT angiography for the prediction of major adverse cardiovascular events: a multicenter observational cohort study. Int J Cardiovasc Imaging. 2010;26:721–728.
22. Yamamoto H, Kihara Y, Kitagawa T, et al. Coronary plaque characteristics in computed tomography and 2-year outcomes: The PREDICT study. J Cardiovasc Comput Tomogr. 2018;12:436–443.
23. Poelsterl S. scikit-survival: a library for time-to-event analysis built on top of scikit-learn. J Mach Learn Res. 2020;21:1–6.
24. Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15:361–387.
25. Pencina MJ, D’Agostino RB Sr, D’Agostino RB Jr, et al. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27:157–172; discussion 207–112.
26. Hadamitzky M, Hein F, Meyer T, et al. Prognostic value of coronary computed tomographic angiography in diabetic patients without known coronary artery disease. Diabetes Care. 2010;33:1358–1363.
27. Deseive S, Straub R, Kupke M, et al. Impact of diabetes on coronary artery plaque volume by coronary CT angiography and subsequent adverse cardiac events. J Cardiovasc Comput Tomogr. 2019;13:31–37.
28. Blanke P, Naoum C, Ahmadi A, et al. Long-term prognostic utility of coronary CT angiography in stable patients with diabetes mellitus. JACC Cardiovasc Imaging. 2016;9:1280–1288.
29. Dey D, Schepis T, Marwan M, et al. Automated three-dimensional quantification of noncalcified coronary plaque from coronary CT angiography: comparison with intravascular US. Radiology. 2010;257:516–522.
30. Dey D, Slomka PJ, Leeson P, et al. Artificial intelligence in cardiovascular imaging: JACC State-of-the-Art Review. J Am Coll Cardiol. 2019;73:1317–1335.
31. Tesche C, Gray HN. Machine learning and deep neural networks applications in coronary flow assessment: the case of computed tomography fractional flow reserve. J Thorac Imaging. 2020;35(suppl 1):S66–S71.
32. Kunce JT, Cook DW, Miller DE. Random variables and correlational overkill. Educational and Psychological Measurement. 1975;35:529–534.
Keywords:

spiral computed tomography; coronary artery disease; outcome measures; diabetes mellitus

Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.