Study population
In this study, 173 hospitalized patients with D2M and suspected CAD were consecutively recruited in the First Affiliated Hospital of Xi’an Jiaotong University between 2012 and 2014. The inclusion criteria included the following: (1) CCTA performed within 1 month before or after hospitalization; (2) patients under the very high risk and high risk categories according to the 2019 ESC guidelines[4]. Very high risk was defined as a history of established CVD, other target-organ damage (i.e., proteinuria, renal impairment [eGFR >30 mL/min/1.73 m2], left ventricular hypertrophy, or retinopathy), or three or more major risk factors [age, hypertension, dyslipidemia, smoking, obesity). High risk was defined as patients with D2M duration ≥10 years without target-organ damage plus any other additional risk factors; and (3) medical records with adequate baseline clinical status. The exclusion criteria were (1) active cancer or blood disease, immune disease, thyroid dysfunction; (2) previous valvular heart disease, congenital heart disease, myocardial infarction, cerebral infarction, heart failure, coronary stent, or bypass therapy; and (3) subjects with poor CCTA image quality, with insufficient clinical data, or failing to complete follow-up (Fig. 1). This retrospective study was approved by the local institutional review board.
Data collection
We obtained data on demographic characteristics, traditional CVD risk factors, diabetes characteristics, laboratory tests, and medical treatment on admission from electronic medical records.
Outcome data
CVEVs including non-fatal myocardial infarction (MI), non-fatal stroke, cardiac deaths, hospitalization for unstable angina, or hospitalization for congestive heart failure were defined as endpoints. Participants were followed up by telephone interviews and hospital records. In patients who experienced two CVEVs, the first event was chosen. When two CVEVs occurred simultaneously, the worse event was chosen (i.e., death over MI, MI over revascularization, and revascularization over hospital readmission). All those CVEVs and outcomes were performed by individuals blinded to the patients’ CT data.
CT data acquisition
Cardiac CT was performed using a 128-section multidetector CT (Brilliance iCT; Philips, Medical Systems, Best, The Netherlands). First, the patients underwent non-enhanced prospective electrocardiography (ECG)-gated sequential scan to measure the coronary artery calcium score. Thereafter, CCTA was performed using retrospective ECG-gated tube current modulation. A weight-dependent bolus of 70–90 ml iodine contrast agent (iohexol [350 mg iodine/ml]; GE Healthcare, Shanghai, China) was administered at a speed of 4 to 5.5 ml/s, which was followed by a 30-ml saline flush. Reconstructed images were at 75% and 45% of the RR interval.
CT Image interpretation
All the scans were retrospectively analyzed on an offline workstation (EBW 4.4, Philips Medical Systems, Best, The Netherlands). Total calcium burden in the coronary arteries was quantified using the scoring algorithm proposed by Agatston et al.[8], and predefined calcium score categories (0, 1–100, 101–400, and >400) were employed[9].
The coronary artery tree was divided into 16 segments according to the Society of Cardiovascular Computed Tomography guidelines[10]. The degree of stenosis was classified as significant if the patient had >50% diameter stenosis on the longitudinal images. We evaluated the plaque extent and stenosis rate by summing the number of epicardial vessels with significant stenosis (i.e., no plaque, no obstruction, 1-vessel disease, 2-vessel disease, 3-vessel disease). Atheroma burden obstructive score (ABOS), segment involvement score (SIS), and segment stenosis score (SSS) were measured. ABOS was defined as the number of plaques with >50% stenosis in the entire coronary artery tree. SIS was calculated as the total number of coronary artery segments that exhibited plaque, irrespective of the degree of luminal stenosis within each segment (minimum = 0; maximum = 16)[11]. SSS was used as a measure of the overall extent of the coronary atherosclerosis. To determine the SSS, each coronary segment was graded based on Coronary Artery Disease—Reporting and Data System (scores ranged from 0 to 5)[12]. The extent scores of all 16 segments were then summed to yield a total score ranging from 0 to 80.
Moreover, coronary plaques were classified as calcified (composed exclusively of a high-density material >130 HU), non-calcified (composed exclusively of a material with a density ≤130 HU), and mixed (with components of both calcified and non-calcified plaques)[13]. Vulnerable plaques were confirmed by the following characteristics: positive remodeling, low-attenuation plaque, spotty calcification, and the napkin-ring sign[14].
EAT depot was defined as the fat tissue between the outer wall of the myocardium and the visceral layer of the pericardium[15]. Epicardial fat volume was assessed using a dedicated workstation (Advantage Workstation 4.6; GE Healthcare). The pericardium was manually traced from the right pulmonary artery to the diaphragm to determine a region of interest. Within the region of interest, fat was defined as pixels within a window of –190 to –30 HU. Overall, only pixels with Hounsfield units equivalent to fat within the pericardial sac were counted as EAT (Fig. 2). Reproducibility was excellent (for interobserver variability, intraclass correlation coefficient=0.889, p<0.05; for intraobserver variability, intraclass correlation coefficient=0.814, p<0.05).
Two experienced computed tomography readers who were blinded to the clinical characteristics and procedural outcomes and to each other’s assessment measured the characteristics of CTA and EAT volume separately. In cases of discrepancy, consensus was reached by discussion.
Statistical analysis
Patient characteristics were expressed as mean±standard deviation for continuous data and counts and proportions for categorical data. Kolmogorov-Smirnov test was used to test the normal distribution of the continuous variables. Chi-square test, Student’s t-test, and non-parametric equivalent tests were employed when appropriate.A comparative analysis of diabetes with and without CVEVs was performed to evaluate potential predictors. The independent predictors of CVEVs were identified by multivariate regression analysis.
The following prediction models were created through multivariate analysis (binary logistic regression with the method Enter) using event predictors: clinical model, which includes diabetes duration, mean creatinine level, and hypertension); CT model, which includes ABOS, SSS, and EAT volume; and a combined model, which is composed of parameters included in both the clinical and CT models. The regression coefficients obtained were used to calculate predicted risks according to prediction models.
SPSS Statistics for Windows v18.0 (SPSS Inc., Chicago, USA) and MedCalc Statistical Software version 13.0 (MedCalc Software bvba, Ostend, Belgium; http://www.medcalc.org; 2014) were used for data analysis. A two-tailed p value <0.05 indicated statistical significance.