Abstract

Aims

Peri-procedural myocardial infarction (PMI) after percutaneous coronary intervention (PCI) has been shown to be associated with worse clinical outcomes. We aimed to investigate the value of coronary plaque characteristics and physiologic disease patterns (focal vs. diffuse) assessed by coronary computed tomography angiography (CTA) in predicting PMI and adverse events.

Methods and results

Three hundred fifty-nine patients with normal pre-PCI high-sensitivity cardiac troponin T (hs-cTnT) underwent CTA before PCI were analysed. The high-risk plaque characteristics (HRPC) were assessed on CTA. The physiologic disease pattern was characterized using CTA fractional flow reserve-derived pullback pressure gradients (FFRCT PPG). PMI was defined as an increase in hs-cTnT to >5 times the upper limit of normal after PCI. The major adverse cardiovascular events (MACE) were a composite of cardiac death, spontaneous myocardial infarction, and target vessel revascularization. The presence of ≥3 HRPC in the target lesions [odds ratio (OR) 2.21, 95% confidence interval (CI) 1.29–3.80, P = 0.004] and low FFRCT PPG (OR 1.23, 95% CI 1.02–1.52, P = 0.028) were independent predictors of PMI. In a four-group classification according to HRPC and FFRCT PPG, patients with ≥3 HRPC and low FFRCT PPG had the highest risk of MACE (19.3%; overall P = 0.001). Moreover, the presence of ≥3 HRPC and low FFRCT PPG was an independent predictor of MACE and showed incremental prognostic value compared with a model with clinical risk factors alone [C index = 0.78 vs. 0.60, P = 0.005, net reclassification index = 0.21 (95% CI: 0.04–0.48), P = 0.020].

Conclusions

Coronary CTA can evaluate plaque characteristics and physiologic disease patterns simultaneously, which plays an important role for risk stratification before PCI.

With coronary CTA imaging, high-risk plaque characteristics (including minimum lumen area <4 mm2, plaque burden ≥ 70%, low-attenuation plaque, positive remodelling, napkin-ring sign, or spotty calcification) and physiologic disease patterns represented by FFRCT PPG can be assessed and used for risk stratification for post-PCI myocardial infarction and major adverse cardiovascular events. (i) With HRPC and FFRCT PPG adding into a model with clinical factors alone, the discrimination ability for predicting PMI improved; (ii) when patients were classified into four groups according to HRPC and FFRCT PPG, those with ≥3 HRPC and low FFRCT PPG show worst clinical outcomes. Abbreviations: CAD: coronary artery disease; PCI: percutaneous coronary intervention; other abbreviations as in Figures 1 and 3.
Graphical Abstract

With coronary CTA imaging, high-risk plaque characteristics (including minimum lumen area <4 mm2, plaque burden ≥ 70%, low-attenuation plaque, positive remodelling, napkin-ring sign, or spotty calcification) and physiologic disease patterns represented by FFRCT PPG can be assessed and used for risk stratification for post-PCI myocardial infarction and major adverse cardiovascular events. (i) With HRPC and FFRCT PPG adding into a model with clinical factors alone, the discrimination ability for predicting PMI improved; (ii) when patients were classified into four groups according to HRPC and FFRCT PPG, those with ≥3 HRPC and low FFRCT PPG show worst clinical outcomes. Abbreviations: CAD: coronary artery disease; PCI: percutaneous coronary intervention; other abbreviations as in Figures 1 and 3.

Introduction

Peri-procedural myocardial infarction (PMI) following percutaneous coronary intervention (PCI) was associated with worse clinical outcomes.1 The pathophysiology might be occlusion of coronary microvasculature by distal embolization attributable to mechanical plaque disruption or diffused disease associated with longer stents used.2 However, PMI may uncover the risk too late in the treatment pathway. It is important to identify patients most likely to develop PMI prior to PCI, thus allowing the preventive strategy.

Coronary computed tomography angiography (CTA) is a non-invasive test enabling atherosclerotic quantification and characterization with high diagnostic performance.3 Moreover, advances in computational fluid dynamics (CFD) enable haemodynamic assessments including fractional flow reserve by CTA (FFRCT).4 A fractional flow reserve pullback–based quantitative metric called pullback pressure gradient (PPG) has been proved to be able to characterize the spatial distribution of epicardial resistances and discriminate between focal and diffused disease.5 Similarly, FFRCT-derived PPG could also be measured to define the severity and the functional patterns of CAD.6

In this regard, we sought (i) to explore the relationship of plaque features and functional disease patterns assessed by CTA with PMI in patients undergoing elective PCI and (ii) to assess the additive and independent risk stratification value of CTA-derived plaque features and physiologic disease patterns for PMI and subsequent events.

Methods

Study design and population

Consecutive patients with normal baseline (pre-procedural) high-sensitivity cardiac troponin T (hs-cTnT) levels who were referred for elective PCI and underwent coronary CTA within 90 days before the procedure at Zhongshan Hospital, Fudan University, between 2016 and 2019 (see Supplementary material online, Figure S1)7 were retrospectively included. This study was approved by the Ethical Committee of Zhongshan Hospital, Fudan University (Approval NO.: B2016-018, Date: 29 February 2016), and all patients gave their written, informed consent. The study was conducted in accordance with the tenets of the Declaration of Helsinki.

High-sensitivity troponin T measurements

Blood samples were collected at admission and 12–18 h after PCI. Troponin T was measured by a high-sensitivity assay using electrochemiluminescence technology (Roche Diagnostics, Risch-Rotkreuz, Switzerland). The limit of blank for this assay (the concentration below which analyte-free samples are found with 95% probability) is ≤0.003 ng/mL. The 99th percentile URL was 0.014 ng/mL. PMI was adjudicated by an independent central events committee using the most recent Fourth Universal Definition of MI (UDMI) criterion (post PCI hs-cTnT >5×URL with additional supporting evidence of new myocardial infarction).7,8

Procedures

Angiography and PCI were performed according to standard practices. The choice of drug-eluting stent, technique, and use of adjunctive devices, physiologic or intravascular imaging guidance, and drugs was left to the operator’s discretion. Coronary angiograms were independently assessed at the angiographic core laboratory.

Coronary CTA and plaque characteristics analysis

CTA was performed in accordance with the Society of Cardiovascular Computed Tomography Guidelines, 9 with 128-channel dual-source scanner platforms (Siemens Medical System, Forchheim, Germany) with electrocardiographic gating. All CTA images were analysed in a blinded fashion using a semi-automated plaque analysis software (Syngo Via Frontier, version 4.2.0, Siemens Healthcare) at a core laboratory (Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China).10

The lesions that received PCI were deemed as target lesions, and the following plaque parameters were assessed: the site of maximal stenosis was chosen to calculate %DS [diameter stenosis, calculated as 100%× (reference diameter − minimal lumen diameter)/reference diameter]. Plaque volumes (in mm3) were measured for the following plaque subtypes: total plaque, calcified plaque, and non-calcified plaque. Total plaque volume (calcified plus non-calcified plaque volume) was calculated by subtracting the lumen volume from the outer wall volume. Plaque burden (PB) for each of these plaque subtypes was defined as 100% × (plaque volume/vessel volume) of the region assessed. Specifically, the lipid, fibrous, and calcified plaque composition was represented by voxels in the ranges between −100 and 29 HU, 30 and 189 HU, and 350 and 1000 HU, respectively. Voxels in the range between 190 and 349 HU corresponded to lumen density.11 To define high-risk plaque characteristics (HRPC), the following parameters were considered: low-attenuation plaque, positive remodelling, napkin-ring sign, spotty calcification (representative images as seen in Supplementary material online, Figure S2), minimum lumen area <4 mm2, or PB ≥70%, which was pre-specified based on previous studies.12–15 The number of HRPC was calculated on a per-lesion basis. For patients with multi-vessel disease/multiple lesions, the lesions with most high-risk characteristics were included for analysis.

Analysis of haemodynamic plaque characteristics in coronary CTA

Haemodynamic parameters derived from coronary CTA were analysed at a core laboratory (Core CHART, Chinese Non-invasive Imaging and Physiology Study Group, Shanghai, China), which was informed of the target vessels and segments that received stent implantation for analysis; the results of PMI and cardiovascular events were blinded to the core lab. FFRCT calculations were performed using dedicated software (RuiXin-FFR, Raysight Medical, Shenzhen, China).16 Briefly, coronary models were constructed using segmentation algorithms that extract the luminal surface of the epicardial coronary arteries and branches. Coronary flow and pressure were computed by solving the Navier–Stokes equations. Then, physiologic distribution of coronary atherosclerosis was assessed using FFRCT-derived PPG index (FFRCT PPG) (detailed method in Appendix).5,17–19 The lower tertile value of FFRCT PPG index (0.61) in the present population was used to define predominant focal (>0.61) vs. diffuse (≤0.61) disease. This cut-off value was same as that derived from ROC analysis to predict PMI (see Supplementary material online, Figure S3).

Data collection and clinical outcomes

Demographic data and cardiovascular risk factors were recorded at the time of the index procedure. Follow-up data were collected through telephone interviews or clinic visits. All clinical events were adjudicated by an independent clinical events committee in a blinded fashion. The primary endpoint was major adverse cardiovascular events (MACE), which were defined as the composite of cardiovascular death, spontaneous myocardial infarction, and target vessel revascularization (TVR). All included patients completed a 24-month follow-up.

Statistical analysis

All discrete or categorical variables are presented as numbers and relative frequencies (percentages) and continuous variables as mean ± SD or median with interquartile range normally distributed data. Comparisons of continuous variables were analysed by unpaired t-test or Mann–Whitney U test according to the data distribution. Comparisons of categorical variables between groups were performed by χ2 test or Fisher’s exact test, as appropriate.

Multivariable logistic regression analysis was used to calculate odds ratios (ORs) with 95% confidence intervals (CIs) to find independent predictors for PMI. Adjusted covariates included age, sex, hypertension, diabetes mellitus, eGFR, prior PCI, left ventricular ejection fraction (LVEF), and peri-procedural glycoprotein IIb/IIIa inhibitor application. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV), and diagnostic accuracy of the CTA plaque characteristics and functional disease patterns in combination with clinical factors were calculated for prediction of PMI.

In comparisons of clinical outcomes amongst groups classified by HRPC and FFRCT PPG, event rates were calculated on the basis of Kaplan–Meier censoring estimates and presented with the cumulative incidence, and the log-rank test or the Breslow test was used to compare survival curves between groups. Multivariable Cox proportional hazard regression was used to calculate adjusted hazard ratio (HRadj) and 95% confidence interval (CI) to compare between-group differences for MACE. Adjusted covariables were for age, sex, hypertension, diabetes, smoking, prior PCI, and LVEF. Multivariable logistic regression analysis was used to calculate hazard ratios (HRs) with 95% CI to find independent predictors for MACE, and the discriminant function of the predictive model was evaluated using Harrell’s C-statistics with 95% CIs. The additive prognostic implications of CTA evaluated plaque characteristics, and physiologic disease patterns over clinical (age, sex, hypertension, diabetes mellitus, eGFR, and LVEF) or angiographic factors were evaluated by assessing improvement in discriminant and reclassification ability using the category-free net reclassification index (NRI) and integrated discrimination improvement (IDI) (see supplemental statistical methods in Supplementary material online, Appendix). All probability values were two-sided, and P values <0.05 were considered to indicate statistical significance.

Results

Characteristics of patients and vessels

Overall, the study included 359 patients with a mean age of 63.6 ± 9.3 years; 69.9% were male (Table 1). Aspirin plus a P2Y12 inhibitor was prescribed in all patients according to the current guideline. Proportions of patients on ticagrelor and aspirin and those prescribed between 6 and 12 months were similar between subgroups with and without PMI. Table 2 shows the medications, target lesion locations, and procedural characteristics; no significant differences were found amongst subgroups according to HRPC and FFRCT PPG. There were 154 (42.9%) patients with PB ≥70% and 275 (76.6%) patients with MLA < 4mm2, with a total plaque volume of 307.5 (180.1–495.8) mm3. Adverse plaques were present in 145 (40.3%) patients.

Table 1

Baseline patient characteristics

DemographicsTotal (N = 359)With PMI (N = 86)Without PMI (N = 273)P value
 Age, years63.6 ± 9.366.8 ± 8.962.6 ± 9.20.966
 Male251 (69.9%)62 (70.5%)189 (69.7%)0.899
 Body mass index, kg/m224.4 ± 2.324.0 ± 2.824.7 ± 3.30.376
 Hypertension238 (66.3%)62 (70.5%)176 (64.9%)0.342
 Diabetes mellitus97 (27.0%)22 (25.0%)75 (27.7%)0.623
 Hypercholesterolaemia98 (27.3%)22 (25.0%)76 (28.0%)0.578
 Current smoker121 (33.7%)28 (31.8%)93 (34.3%)0.667
 Prior PCI26 (7.2%)9 (10.2%)17 (6.3%)0.214
 Prior myocardial infarction7 (1.9%)2 (2.3%)5 (1.8%)0.801
 Left ventricular ejection fraction, %64.4 ± 5.864.2 ± 6.564.5 ± 5.60.054
Laboratory data
 Total cholesterol, mmol/L3.79 ± 1.063.78 ± 0.943.79 ± 1.090.169
 Low-density lipoprotein, mmol/L1.90 ± 0.921.95 ± 0.831.89 ± 0.950.451
 High-density lipoprotein, mmol/L1.08 ± 0.301.14 ± 0.331.07 ± 0.290.078
 Triglycerides, mmol/L2.00 ± 1.381.72 ± 1.192.09 ± 1.430.107
 HbA1c (%)6.2 ± 1.26.2 ± 1.16.2 ± 1.20.908
 Pre-PCI hs-cTnT, ng/mL0.010 ± 0.0050.011 ± 0.0060.009 ± 0.0050.005
 Post-PCI hs-cTnT, ng/mL0.097 ± 0.3580.350 ± 0.6810.027 ± 0.016<0.001
 eGFR, mL/min/1.73m284.0 ± 14.182.2 ± 14.384.5 ± 14.10.780
Medications
 Ticagrelor and aspirin as DAPT35 (9.7%)8 (9.1%)27 (10.0%)0.811
 DAPT for 6–12 months22 (6.1%)6 (6.8%)16 (5.9%)0.756
DemographicsTotal (N = 359)With PMI (N = 86)Without PMI (N = 273)P value
 Age, years63.6 ± 9.366.8 ± 8.962.6 ± 9.20.966
 Male251 (69.9%)62 (70.5%)189 (69.7%)0.899
 Body mass index, kg/m224.4 ± 2.324.0 ± 2.824.7 ± 3.30.376
 Hypertension238 (66.3%)62 (70.5%)176 (64.9%)0.342
 Diabetes mellitus97 (27.0%)22 (25.0%)75 (27.7%)0.623
 Hypercholesterolaemia98 (27.3%)22 (25.0%)76 (28.0%)0.578
 Current smoker121 (33.7%)28 (31.8%)93 (34.3%)0.667
 Prior PCI26 (7.2%)9 (10.2%)17 (6.3%)0.214
 Prior myocardial infarction7 (1.9%)2 (2.3%)5 (1.8%)0.801
 Left ventricular ejection fraction, %64.4 ± 5.864.2 ± 6.564.5 ± 5.60.054
Laboratory data
 Total cholesterol, mmol/L3.79 ± 1.063.78 ± 0.943.79 ± 1.090.169
 Low-density lipoprotein, mmol/L1.90 ± 0.921.95 ± 0.831.89 ± 0.950.451
 High-density lipoprotein, mmol/L1.08 ± 0.301.14 ± 0.331.07 ± 0.290.078
 Triglycerides, mmol/L2.00 ± 1.381.72 ± 1.192.09 ± 1.430.107
 HbA1c (%)6.2 ± 1.26.2 ± 1.16.2 ± 1.20.908
 Pre-PCI hs-cTnT, ng/mL0.010 ± 0.0050.011 ± 0.0060.009 ± 0.0050.005
 Post-PCI hs-cTnT, ng/mL0.097 ± 0.3580.350 ± 0.6810.027 ± 0.016<0.001
 eGFR, mL/min/1.73m284.0 ± 14.182.2 ± 14.384.5 ± 14.10.780
Medications
 Ticagrelor and aspirin as DAPT35 (9.7%)8 (9.1%)27 (10.0%)0.811
 DAPT for 6–12 months22 (6.1%)6 (6.8%)16 (5.9%)0.756

Data are shown as median (interquartile range) or n (%).

Abbreviations: DAPT: dual antiplatelet therapy; eGFR: estimated glomerular filtration rate; HbA1c: glycosylated haemoglobin, type A1c; hs-cTnT: high-sensitivity cardiac troponin T; PCI: percutaneous coronary intervention.

Table 1

Baseline patient characteristics

DemographicsTotal (N = 359)With PMI (N = 86)Without PMI (N = 273)P value
 Age, years63.6 ± 9.366.8 ± 8.962.6 ± 9.20.966
 Male251 (69.9%)62 (70.5%)189 (69.7%)0.899
 Body mass index, kg/m224.4 ± 2.324.0 ± 2.824.7 ± 3.30.376
 Hypertension238 (66.3%)62 (70.5%)176 (64.9%)0.342
 Diabetes mellitus97 (27.0%)22 (25.0%)75 (27.7%)0.623
 Hypercholesterolaemia98 (27.3%)22 (25.0%)76 (28.0%)0.578
 Current smoker121 (33.7%)28 (31.8%)93 (34.3%)0.667
 Prior PCI26 (7.2%)9 (10.2%)17 (6.3%)0.214
 Prior myocardial infarction7 (1.9%)2 (2.3%)5 (1.8%)0.801
 Left ventricular ejection fraction, %64.4 ± 5.864.2 ± 6.564.5 ± 5.60.054
Laboratory data
 Total cholesterol, mmol/L3.79 ± 1.063.78 ± 0.943.79 ± 1.090.169
 Low-density lipoprotein, mmol/L1.90 ± 0.921.95 ± 0.831.89 ± 0.950.451
 High-density lipoprotein, mmol/L1.08 ± 0.301.14 ± 0.331.07 ± 0.290.078
 Triglycerides, mmol/L2.00 ± 1.381.72 ± 1.192.09 ± 1.430.107
 HbA1c (%)6.2 ± 1.26.2 ± 1.16.2 ± 1.20.908
 Pre-PCI hs-cTnT, ng/mL0.010 ± 0.0050.011 ± 0.0060.009 ± 0.0050.005
 Post-PCI hs-cTnT, ng/mL0.097 ± 0.3580.350 ± 0.6810.027 ± 0.016<0.001
 eGFR, mL/min/1.73m284.0 ± 14.182.2 ± 14.384.5 ± 14.10.780
Medications
 Ticagrelor and aspirin as DAPT35 (9.7%)8 (9.1%)27 (10.0%)0.811
 DAPT for 6–12 months22 (6.1%)6 (6.8%)16 (5.9%)0.756
DemographicsTotal (N = 359)With PMI (N = 86)Without PMI (N = 273)P value
 Age, years63.6 ± 9.366.8 ± 8.962.6 ± 9.20.966
 Male251 (69.9%)62 (70.5%)189 (69.7%)0.899
 Body mass index, kg/m224.4 ± 2.324.0 ± 2.824.7 ± 3.30.376
 Hypertension238 (66.3%)62 (70.5%)176 (64.9%)0.342
 Diabetes mellitus97 (27.0%)22 (25.0%)75 (27.7%)0.623
 Hypercholesterolaemia98 (27.3%)22 (25.0%)76 (28.0%)0.578
 Current smoker121 (33.7%)28 (31.8%)93 (34.3%)0.667
 Prior PCI26 (7.2%)9 (10.2%)17 (6.3%)0.214
 Prior myocardial infarction7 (1.9%)2 (2.3%)5 (1.8%)0.801
 Left ventricular ejection fraction, %64.4 ± 5.864.2 ± 6.564.5 ± 5.60.054
Laboratory data
 Total cholesterol, mmol/L3.79 ± 1.063.78 ± 0.943.79 ± 1.090.169
 Low-density lipoprotein, mmol/L1.90 ± 0.921.95 ± 0.831.89 ± 0.950.451
 High-density lipoprotein, mmol/L1.08 ± 0.301.14 ± 0.331.07 ± 0.290.078
 Triglycerides, mmol/L2.00 ± 1.381.72 ± 1.192.09 ± 1.430.107
 HbA1c (%)6.2 ± 1.26.2 ± 1.16.2 ± 1.20.908
 Pre-PCI hs-cTnT, ng/mL0.010 ± 0.0050.011 ± 0.0060.009 ± 0.0050.005
 Post-PCI hs-cTnT, ng/mL0.097 ± 0.3580.350 ± 0.6810.027 ± 0.016<0.001
 eGFR, mL/min/1.73m284.0 ± 14.182.2 ± 14.384.5 ± 14.10.780
Medications
 Ticagrelor and aspirin as DAPT35 (9.7%)8 (9.1%)27 (10.0%)0.811
 DAPT for 6–12 months22 (6.1%)6 (6.8%)16 (5.9%)0.756

Data are shown as median (interquartile range) or n (%).

Abbreviations: DAPT: dual antiplatelet therapy; eGFR: estimated glomerular filtration rate; HbA1c: glycosylated haemoglobin, type A1c; hs-cTnT: high-sensitivity cardiac troponin T; PCI: percutaneous coronary intervention.

Table 2

Baseline characteristics according to HRPC numbers and FFRCT PPG

Total N = 359<3 HRPC≥3 HRPC
Group 1, N = 152 Focal diseaseGroup 2, N = 62 Diffused diseaseP valueaGroup 3, N = 88 Focal diseaseGroup 4, N = 57 Diffused diseaseP valuea
Medications
 Aspirin359 (100.0)152 (100.0)62 (100.0)NA88 (100.0)57 (100.0)NA
 P2Y12 Inhibitors359 (100.0)152 (100.0)62 (100.0)NA88 (100.0)57 (100.0)NA
 Statin354 (98.6)149 (98.0)61 (98.4)0.86088 (100.0)56 (98.2)0.826
 Peri-procedural GP IIb/IIIa inhibitors64 (17.8)23 (15.1)14 (22.6)0.26817 (19.3)10 (17.5)0.960
Target vessel location
 LAD194 (54.0)98 (64.5)28 (45.2)0.00748 (54.5)20 (35.1)0.017
 LCX81 (22.6)23 (15.1)18 (29.0)0.01822 (25.0)18 (31.6)0.249
 RCA84 (23.4)31 (20.4)16 (25.8)0.24418 (20.5)19 (33.3)0.062
Target lesion location
 Proximal215 (59.9)94 (61.8)36 (58.1)0.60849 (55.7)36 (63.2)0.371
 Middle108 (30.1)43 (28.3)20 (32.3)0.56329 (33.0)16 (28.1)0.535
 Distal36 (10.0)15 (9.9)6 (8.7)0.96610 (11.4)5 (8.8)0.617
 Pre-PCI hs-cTnT, ng/mL0.010 (0.007–0.010)0.010 (0.006–0.010)0.010 (0.009–0.010)0.3480.010 (0.007–0.010)0.010 (0.007–0.011)0.973
 Post-PCI hs-cTnT, ng/mL0.030 (0.018–0.070)0.026 (0.013–0.050)0.028 (0.018–0.069)0.8620.038 (0.020–0.072)0.058 (0.020–0.160)0.049
 Proportion of post-PCI hs-cTnT >5 UNL86 (24.0)22 (14.5)16 (25.8)0.04123 (26.1)27 (47.4)0.007
Procedural characteristics
 Number of stents2 (1–2)1 (1–2)2 (1–3)0.0042 (1–3)2 (1–3)0.478
 Total stent length, mm43 (27–70)36 (24–54)56 (34–75)0.00150 (28–77)56 (33–96)0.051
 Intravascular physiologic guidance29 (8.1)12 (7.9)5 (8.1)0.9677 (8.0)5 (8.8)0.861
 Intravascular imaging guidance103 (28.7)42 (27.6)22 (35.5)0.38229 (33.0)16 (28.1)0.535
 Pre-dilation79 (22.0)28 (18.4)12 (19.4)0.87425 (28.4)14 (24.6)0.750
 Post-dilation253 (70.5)102 (67.1)45 (72.6)0.53567 (76.1)39 (68.4)0.406
 Rotablator7 (1.9)3 (2.0%)1 (1.6%)0.8602 (2.3%)1 (1.8%)0.831
 Bifurcation stenting63 (17.5%)22 (14.5%)8 (12.9%)0.76418 (20.5%)15 (26.3%)0.411
 Small vessel disease4 (1.1%)2 (1.3%)0 (0%)0.3642 (2.3%)0 (0%)0.252
 Side branch loss1 (0.3%)0 (0%)1 (1.6%)0.1170 (0%)0 (0%)NA
 Slow flow3 (0.8%)0 (0%)1 (1.6%)0.1171 (1.1%)1 (1.8%)0.755
 Non-flow-limiting dissention3 (0.8%)1 (0.7%)1 (1.6%)0.5101 (1.1%)0 (0%)0.419
Computed tomography parametersb
Quantitative parameters
 Minimum lumen area, mm22.5 (1.4–3.9)2.7 (1.7–3.6)2.5 (1.6–3.8)0.9262.6 (1.3–4.3)2.1 (1.0–3.9)0.480
 MLA <4 mm2275 (76.6)11 9 (78.2)49 (79.0)0.53163 (71.6)44 (77.2)0.291
 Plaque burden, %68.0 (58.2–75.7)63.5 (54.3–69.8)62.8 (53.9–69.1)0.98974.3 (68.2–78.7)74.9 (70.1–79.6)0.772
 Plaque burden ≥70%154 (42.9)35 (23.0)12 (19.4)0.34764 (72.7)43 (75.4)0.435
 Diameter stenosis, %62.1 (41.0–77.1)63.8 (46.2–76.1)57.1 (32.4–69.9)0.08861.3 (38.4–81.8)67.2 (34.9–79.4)0.680
 Lesion length, mm30.5 (22.0–42.3)27.5 (18.7–37.9)31.1 (21.9–41.0)0.12531.9 (24.6–46.1)36.2 (25.0–46.5)0.398
 Remodelling index0.86 (0.58–1.22)0.76 (0.49–0.95)0.71 (0.54–1.07)0.4411.18 (0.74–1.32)1.16 (0.76–1.54)0.250
 TPV, mm3307.5 (180.1–495.8)242.5 (146.9–393.5)231.4 (151.0–336.9)0.598448.3 (255.7–721.2)438.6 (289.3–638.1)0.650
 Percent TPV, %68.0 (58.1–75.7)63.5 (54.3–69.8)62.8 (53.9–69.1)0.98974.3 (68.2–78.7)74.9 (70.1–79.6)0.772
 Non-calcified volume, mm3222.9 (135.8–356.0)192.1 (110.5–302.6)169.9 (98.5–247.1)0.374310.0 (185.2–454.9)299.4 (186.4–480.7)0.922
 Non-calcified volume, %79.0 (63.7–89.5)81.5 (71.0–90.4)80.7 (64.5–88.1)0.69772.3 (56.8–84.6)79.9 (61.0–89.4)0.071
 Fibrous volume, mm3207.0 (128.2–339.0)184.7 (108.4–265.0)154.3 (98.0–237.1)0.143285.5 (168.6–430.9)288.2 (167.7–443.4)0.761
 Fibrous volume, %74.4 (58.9–83.5)76.8 (65.0–84.5)76.7 (64.1–84.7)0.57967.9 (54.6–78.4)73.1 (53.9–83.4)0.577
 Lipid volume, mm313.3 (2.3–25.3)9.7 (2.1–23.7)5.3 (0.6–20.2)0.05419.3 (7.4–31.6)20.3 (5.5–34.0)0.991
 Lipid volume, %3.9 (1.1–6.7)3.9 (1.0–7.8)3.2 (0.4–6.5)0.1563.9 (1.8–6.3)4.1 (1.3–7.3)0.221
 Calcified plaque volume, mm354.6 (22.4–142.7)37.4 (20.2–82.8)35.0 (17.2–110.9)0.306127.5 (45.3–234.5)80.5 (41.2–169.2)0.044
 Calcified plaque volume, %20.7 (10.5–36.3)18.5 (9.6–29.3)18.9 (10.8–30.4)0.83027.7 (13.9–43.2)20.1 (10.6–39.0)0.235
Qualitative parameters
 Low-attenuation plaque129 (35.9)32 (21.1)22 (35.5)0.02340 (45.5)35 (61.4)0.044
 Positive remodelling119 (33.1)25 (16.4)14 (22.6)0.19449 (55.7)31 (54.4)0.507
 Spotty calcification58 (16.2)7 (4.6)3 (4.8)0.59330 (34.1)18 (31.6)0.449
 Napkin-ring sign34 (9.5)5 (3.3)0 (0)0.17716 (18.2)13 (22.8)0.318
Physiologic index
 FFRCT0.69 (0.55–0.79)0.72 (0.59–0.84)0.66 (0.63–0.72)0.0290.69 (0.52–0.84)0.64 (0.50–0.70)0.017
 FFRCT PPG0.69 (0.58–0.80)0.78 (0.69–0.85)0.55 (0.51–0.58)<0.0010.73 (0.68–0.80)0.51 (0.47–0.58)<0.001
Total N = 359<3 HRPC≥3 HRPC
Group 1, N = 152 Focal diseaseGroup 2, N = 62 Diffused diseaseP valueaGroup 3, N = 88 Focal diseaseGroup 4, N = 57 Diffused diseaseP valuea
Medications
 Aspirin359 (100.0)152 (100.0)62 (100.0)NA88 (100.0)57 (100.0)NA
 P2Y12 Inhibitors359 (100.0)152 (100.0)62 (100.0)NA88 (100.0)57 (100.0)NA
 Statin354 (98.6)149 (98.0)61 (98.4)0.86088 (100.0)56 (98.2)0.826
 Peri-procedural GP IIb/IIIa inhibitors64 (17.8)23 (15.1)14 (22.6)0.26817 (19.3)10 (17.5)0.960
Target vessel location
 LAD194 (54.0)98 (64.5)28 (45.2)0.00748 (54.5)20 (35.1)0.017
 LCX81 (22.6)23 (15.1)18 (29.0)0.01822 (25.0)18 (31.6)0.249
 RCA84 (23.4)31 (20.4)16 (25.8)0.24418 (20.5)19 (33.3)0.062
Target lesion location
 Proximal215 (59.9)94 (61.8)36 (58.1)0.60849 (55.7)36 (63.2)0.371
 Middle108 (30.1)43 (28.3)20 (32.3)0.56329 (33.0)16 (28.1)0.535
 Distal36 (10.0)15 (9.9)6 (8.7)0.96610 (11.4)5 (8.8)0.617
 Pre-PCI hs-cTnT, ng/mL0.010 (0.007–0.010)0.010 (0.006–0.010)0.010 (0.009–0.010)0.3480.010 (0.007–0.010)0.010 (0.007–0.011)0.973
 Post-PCI hs-cTnT, ng/mL0.030 (0.018–0.070)0.026 (0.013–0.050)0.028 (0.018–0.069)0.8620.038 (0.020–0.072)0.058 (0.020–0.160)0.049
 Proportion of post-PCI hs-cTnT >5 UNL86 (24.0)22 (14.5)16 (25.8)0.04123 (26.1)27 (47.4)0.007
Procedural characteristics
 Number of stents2 (1–2)1 (1–2)2 (1–3)0.0042 (1–3)2 (1–3)0.478
 Total stent length, mm43 (27–70)36 (24–54)56 (34–75)0.00150 (28–77)56 (33–96)0.051
 Intravascular physiologic guidance29 (8.1)12 (7.9)5 (8.1)0.9677 (8.0)5 (8.8)0.861
 Intravascular imaging guidance103 (28.7)42 (27.6)22 (35.5)0.38229 (33.0)16 (28.1)0.535
 Pre-dilation79 (22.0)28 (18.4)12 (19.4)0.87425 (28.4)14 (24.6)0.750
 Post-dilation253 (70.5)102 (67.1)45 (72.6)0.53567 (76.1)39 (68.4)0.406
 Rotablator7 (1.9)3 (2.0%)1 (1.6%)0.8602 (2.3%)1 (1.8%)0.831
 Bifurcation stenting63 (17.5%)22 (14.5%)8 (12.9%)0.76418 (20.5%)15 (26.3%)0.411
 Small vessel disease4 (1.1%)2 (1.3%)0 (0%)0.3642 (2.3%)0 (0%)0.252
 Side branch loss1 (0.3%)0 (0%)1 (1.6%)0.1170 (0%)0 (0%)NA
 Slow flow3 (0.8%)0 (0%)1 (1.6%)0.1171 (1.1%)1 (1.8%)0.755
 Non-flow-limiting dissention3 (0.8%)1 (0.7%)1 (1.6%)0.5101 (1.1%)0 (0%)0.419
Computed tomography parametersb
Quantitative parameters
 Minimum lumen area, mm22.5 (1.4–3.9)2.7 (1.7–3.6)2.5 (1.6–3.8)0.9262.6 (1.3–4.3)2.1 (1.0–3.9)0.480
 MLA <4 mm2275 (76.6)11 9 (78.2)49 (79.0)0.53163 (71.6)44 (77.2)0.291
 Plaque burden, %68.0 (58.2–75.7)63.5 (54.3–69.8)62.8 (53.9–69.1)0.98974.3 (68.2–78.7)74.9 (70.1–79.6)0.772
 Plaque burden ≥70%154 (42.9)35 (23.0)12 (19.4)0.34764 (72.7)43 (75.4)0.435
 Diameter stenosis, %62.1 (41.0–77.1)63.8 (46.2–76.1)57.1 (32.4–69.9)0.08861.3 (38.4–81.8)67.2 (34.9–79.4)0.680
 Lesion length, mm30.5 (22.0–42.3)27.5 (18.7–37.9)31.1 (21.9–41.0)0.12531.9 (24.6–46.1)36.2 (25.0–46.5)0.398
 Remodelling index0.86 (0.58–1.22)0.76 (0.49–0.95)0.71 (0.54–1.07)0.4411.18 (0.74–1.32)1.16 (0.76–1.54)0.250
 TPV, mm3307.5 (180.1–495.8)242.5 (146.9–393.5)231.4 (151.0–336.9)0.598448.3 (255.7–721.2)438.6 (289.3–638.1)0.650
 Percent TPV, %68.0 (58.1–75.7)63.5 (54.3–69.8)62.8 (53.9–69.1)0.98974.3 (68.2–78.7)74.9 (70.1–79.6)0.772
 Non-calcified volume, mm3222.9 (135.8–356.0)192.1 (110.5–302.6)169.9 (98.5–247.1)0.374310.0 (185.2–454.9)299.4 (186.4–480.7)0.922
 Non-calcified volume, %79.0 (63.7–89.5)81.5 (71.0–90.4)80.7 (64.5–88.1)0.69772.3 (56.8–84.6)79.9 (61.0–89.4)0.071
 Fibrous volume, mm3207.0 (128.2–339.0)184.7 (108.4–265.0)154.3 (98.0–237.1)0.143285.5 (168.6–430.9)288.2 (167.7–443.4)0.761
 Fibrous volume, %74.4 (58.9–83.5)76.8 (65.0–84.5)76.7 (64.1–84.7)0.57967.9 (54.6–78.4)73.1 (53.9–83.4)0.577
 Lipid volume, mm313.3 (2.3–25.3)9.7 (2.1–23.7)5.3 (0.6–20.2)0.05419.3 (7.4–31.6)20.3 (5.5–34.0)0.991
 Lipid volume, %3.9 (1.1–6.7)3.9 (1.0–7.8)3.2 (0.4–6.5)0.1563.9 (1.8–6.3)4.1 (1.3–7.3)0.221
 Calcified plaque volume, mm354.6 (22.4–142.7)37.4 (20.2–82.8)35.0 (17.2–110.9)0.306127.5 (45.3–234.5)80.5 (41.2–169.2)0.044
 Calcified plaque volume, %20.7 (10.5–36.3)18.5 (9.6–29.3)18.9 (10.8–30.4)0.83027.7 (13.9–43.2)20.1 (10.6–39.0)0.235
Qualitative parameters
 Low-attenuation plaque129 (35.9)32 (21.1)22 (35.5)0.02340 (45.5)35 (61.4)0.044
 Positive remodelling119 (33.1)25 (16.4)14 (22.6)0.19449 (55.7)31 (54.4)0.507
 Spotty calcification58 (16.2)7 (4.6)3 (4.8)0.59330 (34.1)18 (31.6)0.449
 Napkin-ring sign34 (9.5)5 (3.3)0 (0)0.17716 (18.2)13 (22.8)0.318
Physiologic index
 FFRCT0.69 (0.55–0.79)0.72 (0.59–0.84)0.66 (0.63–0.72)0.0290.69 (0.52–0.84)0.64 (0.50–0.70)0.017
 FFRCT PPG0.69 (0.58–0.80)0.78 (0.69–0.85)0.55 (0.51–0.58)<0.0010.73 (0.68–0.80)0.51 (0.47–0.58)<0.001

Values are presented as n (%) or median (IQR).

Generalized estimating equation model or maximum likelihood χ2 tests were used for overall and between-group comparison in per-patient analysis.

FFRCT: fractional flow reserve by computed tomography angiography; GP IIb/IIIa: glycoprotein IIb/IIIa; HRPC: high-risk plaque characteristics; LAD: left anterior descending coronary artery; LCX: left circumflex branch coronary artery; MLA: minimal lumen area; N: number of patients; PPG: pullback pressure gradient; RCA: right coronary artery; TPV: total plaque volume; UNL: upper reference limit; other abbreviations as in Table 1.

P values for the comparison of variables between focal and diffused disease groups.

Quantitative or qualitative plaque analysis was performed for target lesions that received PCI.

Table 2

Baseline characteristics according to HRPC numbers and FFRCT PPG

Total N = 359<3 HRPC≥3 HRPC
Group 1, N = 152 Focal diseaseGroup 2, N = 62 Diffused diseaseP valueaGroup 3, N = 88 Focal diseaseGroup 4, N = 57 Diffused diseaseP valuea
Medications
 Aspirin359 (100.0)152 (100.0)62 (100.0)NA88 (100.0)57 (100.0)NA
 P2Y12 Inhibitors359 (100.0)152 (100.0)62 (100.0)NA88 (100.0)57 (100.0)NA
 Statin354 (98.6)149 (98.0)61 (98.4)0.86088 (100.0)56 (98.2)0.826
 Peri-procedural GP IIb/IIIa inhibitors64 (17.8)23 (15.1)14 (22.6)0.26817 (19.3)10 (17.5)0.960
Target vessel location
 LAD194 (54.0)98 (64.5)28 (45.2)0.00748 (54.5)20 (35.1)0.017
 LCX81 (22.6)23 (15.1)18 (29.0)0.01822 (25.0)18 (31.6)0.249
 RCA84 (23.4)31 (20.4)16 (25.8)0.24418 (20.5)19 (33.3)0.062
Target lesion location
 Proximal215 (59.9)94 (61.8)36 (58.1)0.60849 (55.7)36 (63.2)0.371
 Middle108 (30.1)43 (28.3)20 (32.3)0.56329 (33.0)16 (28.1)0.535
 Distal36 (10.0)15 (9.9)6 (8.7)0.96610 (11.4)5 (8.8)0.617
 Pre-PCI hs-cTnT, ng/mL0.010 (0.007–0.010)0.010 (0.006–0.010)0.010 (0.009–0.010)0.3480.010 (0.007–0.010)0.010 (0.007–0.011)0.973
 Post-PCI hs-cTnT, ng/mL0.030 (0.018–0.070)0.026 (0.013–0.050)0.028 (0.018–0.069)0.8620.038 (0.020–0.072)0.058 (0.020–0.160)0.049
 Proportion of post-PCI hs-cTnT >5 UNL86 (24.0)22 (14.5)16 (25.8)0.04123 (26.1)27 (47.4)0.007
Procedural characteristics
 Number of stents2 (1–2)1 (1–2)2 (1–3)0.0042 (1–3)2 (1–3)0.478
 Total stent length, mm43 (27–70)36 (24–54)56 (34–75)0.00150 (28–77)56 (33–96)0.051
 Intravascular physiologic guidance29 (8.1)12 (7.9)5 (8.1)0.9677 (8.0)5 (8.8)0.861
 Intravascular imaging guidance103 (28.7)42 (27.6)22 (35.5)0.38229 (33.0)16 (28.1)0.535
 Pre-dilation79 (22.0)28 (18.4)12 (19.4)0.87425 (28.4)14 (24.6)0.750
 Post-dilation253 (70.5)102 (67.1)45 (72.6)0.53567 (76.1)39 (68.4)0.406
 Rotablator7 (1.9)3 (2.0%)1 (1.6%)0.8602 (2.3%)1 (1.8%)0.831
 Bifurcation stenting63 (17.5%)22 (14.5%)8 (12.9%)0.76418 (20.5%)15 (26.3%)0.411
 Small vessel disease4 (1.1%)2 (1.3%)0 (0%)0.3642 (2.3%)0 (0%)0.252
 Side branch loss1 (0.3%)0 (0%)1 (1.6%)0.1170 (0%)0 (0%)NA
 Slow flow3 (0.8%)0 (0%)1 (1.6%)0.1171 (1.1%)1 (1.8%)0.755
 Non-flow-limiting dissention3 (0.8%)1 (0.7%)1 (1.6%)0.5101 (1.1%)0 (0%)0.419
Computed tomography parametersb
Quantitative parameters
 Minimum lumen area, mm22.5 (1.4–3.9)2.7 (1.7–3.6)2.5 (1.6–3.8)0.9262.6 (1.3–4.3)2.1 (1.0–3.9)0.480
 MLA <4 mm2275 (76.6)11 9 (78.2)49 (79.0)0.53163 (71.6)44 (77.2)0.291
 Plaque burden, %68.0 (58.2–75.7)63.5 (54.3–69.8)62.8 (53.9–69.1)0.98974.3 (68.2–78.7)74.9 (70.1–79.6)0.772
 Plaque burden ≥70%154 (42.9)35 (23.0)12 (19.4)0.34764 (72.7)43 (75.4)0.435
 Diameter stenosis, %62.1 (41.0–77.1)63.8 (46.2–76.1)57.1 (32.4–69.9)0.08861.3 (38.4–81.8)67.2 (34.9–79.4)0.680
 Lesion length, mm30.5 (22.0–42.3)27.5 (18.7–37.9)31.1 (21.9–41.0)0.12531.9 (24.6–46.1)36.2 (25.0–46.5)0.398
 Remodelling index0.86 (0.58–1.22)0.76 (0.49–0.95)0.71 (0.54–1.07)0.4411.18 (0.74–1.32)1.16 (0.76–1.54)0.250
 TPV, mm3307.5 (180.1–495.8)242.5 (146.9–393.5)231.4 (151.0–336.9)0.598448.3 (255.7–721.2)438.6 (289.3–638.1)0.650
 Percent TPV, %68.0 (58.1–75.7)63.5 (54.3–69.8)62.8 (53.9–69.1)0.98974.3 (68.2–78.7)74.9 (70.1–79.6)0.772
 Non-calcified volume, mm3222.9 (135.8–356.0)192.1 (110.5–302.6)169.9 (98.5–247.1)0.374310.0 (185.2–454.9)299.4 (186.4–480.7)0.922
 Non-calcified volume, %79.0 (63.7–89.5)81.5 (71.0–90.4)80.7 (64.5–88.1)0.69772.3 (56.8–84.6)79.9 (61.0–89.4)0.071
 Fibrous volume, mm3207.0 (128.2–339.0)184.7 (108.4–265.0)154.3 (98.0–237.1)0.143285.5 (168.6–430.9)288.2 (167.7–443.4)0.761
 Fibrous volume, %74.4 (58.9–83.5)76.8 (65.0–84.5)76.7 (64.1–84.7)0.57967.9 (54.6–78.4)73.1 (53.9–83.4)0.577
 Lipid volume, mm313.3 (2.3–25.3)9.7 (2.1–23.7)5.3 (0.6–20.2)0.05419.3 (7.4–31.6)20.3 (5.5–34.0)0.991
 Lipid volume, %3.9 (1.1–6.7)3.9 (1.0–7.8)3.2 (0.4–6.5)0.1563.9 (1.8–6.3)4.1 (1.3–7.3)0.221
 Calcified plaque volume, mm354.6 (22.4–142.7)37.4 (20.2–82.8)35.0 (17.2–110.9)0.306127.5 (45.3–234.5)80.5 (41.2–169.2)0.044
 Calcified plaque volume, %20.7 (10.5–36.3)18.5 (9.6–29.3)18.9 (10.8–30.4)0.83027.7 (13.9–43.2)20.1 (10.6–39.0)0.235
Qualitative parameters
 Low-attenuation plaque129 (35.9)32 (21.1)22 (35.5)0.02340 (45.5)35 (61.4)0.044
 Positive remodelling119 (33.1)25 (16.4)14 (22.6)0.19449 (55.7)31 (54.4)0.507
 Spotty calcification58 (16.2)7 (4.6)3 (4.8)0.59330 (34.1)18 (31.6)0.449
 Napkin-ring sign34 (9.5)5 (3.3)0 (0)0.17716 (18.2)13 (22.8)0.318
Physiologic index
 FFRCT0.69 (0.55–0.79)0.72 (0.59–0.84)0.66 (0.63–0.72)0.0290.69 (0.52–0.84)0.64 (0.50–0.70)0.017
 FFRCT PPG0.69 (0.58–0.80)0.78 (0.69–0.85)0.55 (0.51–0.58)<0.0010.73 (0.68–0.80)0.51 (0.47–0.58)<0.001
Total N = 359<3 HRPC≥3 HRPC
Group 1, N = 152 Focal diseaseGroup 2, N = 62 Diffused diseaseP valueaGroup 3, N = 88 Focal diseaseGroup 4, N = 57 Diffused diseaseP valuea
Medications
 Aspirin359 (100.0)152 (100.0)62 (100.0)NA88 (100.0)57 (100.0)NA
 P2Y12 Inhibitors359 (100.0)152 (100.0)62 (100.0)NA88 (100.0)57 (100.0)NA
 Statin354 (98.6)149 (98.0)61 (98.4)0.86088 (100.0)56 (98.2)0.826
 Peri-procedural GP IIb/IIIa inhibitors64 (17.8)23 (15.1)14 (22.6)0.26817 (19.3)10 (17.5)0.960
Target vessel location
 LAD194 (54.0)98 (64.5)28 (45.2)0.00748 (54.5)20 (35.1)0.017
 LCX81 (22.6)23 (15.1)18 (29.0)0.01822 (25.0)18 (31.6)0.249
 RCA84 (23.4)31 (20.4)16 (25.8)0.24418 (20.5)19 (33.3)0.062
Target lesion location
 Proximal215 (59.9)94 (61.8)36 (58.1)0.60849 (55.7)36 (63.2)0.371
 Middle108 (30.1)43 (28.3)20 (32.3)0.56329 (33.0)16 (28.1)0.535
 Distal36 (10.0)15 (9.9)6 (8.7)0.96610 (11.4)5 (8.8)0.617
 Pre-PCI hs-cTnT, ng/mL0.010 (0.007–0.010)0.010 (0.006–0.010)0.010 (0.009–0.010)0.3480.010 (0.007–0.010)0.010 (0.007–0.011)0.973
 Post-PCI hs-cTnT, ng/mL0.030 (0.018–0.070)0.026 (0.013–0.050)0.028 (0.018–0.069)0.8620.038 (0.020–0.072)0.058 (0.020–0.160)0.049
 Proportion of post-PCI hs-cTnT >5 UNL86 (24.0)22 (14.5)16 (25.8)0.04123 (26.1)27 (47.4)0.007
Procedural characteristics
 Number of stents2 (1–2)1 (1–2)2 (1–3)0.0042 (1–3)2 (1–3)0.478
 Total stent length, mm43 (27–70)36 (24–54)56 (34–75)0.00150 (28–77)56 (33–96)0.051
 Intravascular physiologic guidance29 (8.1)12 (7.9)5 (8.1)0.9677 (8.0)5 (8.8)0.861
 Intravascular imaging guidance103 (28.7)42 (27.6)22 (35.5)0.38229 (33.0)16 (28.1)0.535
 Pre-dilation79 (22.0)28 (18.4)12 (19.4)0.87425 (28.4)14 (24.6)0.750
 Post-dilation253 (70.5)102 (67.1)45 (72.6)0.53567 (76.1)39 (68.4)0.406
 Rotablator7 (1.9)3 (2.0%)1 (1.6%)0.8602 (2.3%)1 (1.8%)0.831
 Bifurcation stenting63 (17.5%)22 (14.5%)8 (12.9%)0.76418 (20.5%)15 (26.3%)0.411
 Small vessel disease4 (1.1%)2 (1.3%)0 (0%)0.3642 (2.3%)0 (0%)0.252
 Side branch loss1 (0.3%)0 (0%)1 (1.6%)0.1170 (0%)0 (0%)NA
 Slow flow3 (0.8%)0 (0%)1 (1.6%)0.1171 (1.1%)1 (1.8%)0.755
 Non-flow-limiting dissention3 (0.8%)1 (0.7%)1 (1.6%)0.5101 (1.1%)0 (0%)0.419
Computed tomography parametersb
Quantitative parameters
 Minimum lumen area, mm22.5 (1.4–3.9)2.7 (1.7–3.6)2.5 (1.6–3.8)0.9262.6 (1.3–4.3)2.1 (1.0–3.9)0.480
 MLA <4 mm2275 (76.6)11 9 (78.2)49 (79.0)0.53163 (71.6)44 (77.2)0.291
 Plaque burden, %68.0 (58.2–75.7)63.5 (54.3–69.8)62.8 (53.9–69.1)0.98974.3 (68.2–78.7)74.9 (70.1–79.6)0.772
 Plaque burden ≥70%154 (42.9)35 (23.0)12 (19.4)0.34764 (72.7)43 (75.4)0.435
 Diameter stenosis, %62.1 (41.0–77.1)63.8 (46.2–76.1)57.1 (32.4–69.9)0.08861.3 (38.4–81.8)67.2 (34.9–79.4)0.680
 Lesion length, mm30.5 (22.0–42.3)27.5 (18.7–37.9)31.1 (21.9–41.0)0.12531.9 (24.6–46.1)36.2 (25.0–46.5)0.398
 Remodelling index0.86 (0.58–1.22)0.76 (0.49–0.95)0.71 (0.54–1.07)0.4411.18 (0.74–1.32)1.16 (0.76–1.54)0.250
 TPV, mm3307.5 (180.1–495.8)242.5 (146.9–393.5)231.4 (151.0–336.9)0.598448.3 (255.7–721.2)438.6 (289.3–638.1)0.650
 Percent TPV, %68.0 (58.1–75.7)63.5 (54.3–69.8)62.8 (53.9–69.1)0.98974.3 (68.2–78.7)74.9 (70.1–79.6)0.772
 Non-calcified volume, mm3222.9 (135.8–356.0)192.1 (110.5–302.6)169.9 (98.5–247.1)0.374310.0 (185.2–454.9)299.4 (186.4–480.7)0.922
 Non-calcified volume, %79.0 (63.7–89.5)81.5 (71.0–90.4)80.7 (64.5–88.1)0.69772.3 (56.8–84.6)79.9 (61.0–89.4)0.071
 Fibrous volume, mm3207.0 (128.2–339.0)184.7 (108.4–265.0)154.3 (98.0–237.1)0.143285.5 (168.6–430.9)288.2 (167.7–443.4)0.761
 Fibrous volume, %74.4 (58.9–83.5)76.8 (65.0–84.5)76.7 (64.1–84.7)0.57967.9 (54.6–78.4)73.1 (53.9–83.4)0.577
 Lipid volume, mm313.3 (2.3–25.3)9.7 (2.1–23.7)5.3 (0.6–20.2)0.05419.3 (7.4–31.6)20.3 (5.5–34.0)0.991
 Lipid volume, %3.9 (1.1–6.7)3.9 (1.0–7.8)3.2 (0.4–6.5)0.1563.9 (1.8–6.3)4.1 (1.3–7.3)0.221
 Calcified plaque volume, mm354.6 (22.4–142.7)37.4 (20.2–82.8)35.0 (17.2–110.9)0.306127.5 (45.3–234.5)80.5 (41.2–169.2)0.044
 Calcified plaque volume, %20.7 (10.5–36.3)18.5 (9.6–29.3)18.9 (10.8–30.4)0.83027.7 (13.9–43.2)20.1 (10.6–39.0)0.235
Qualitative parameters
 Low-attenuation plaque129 (35.9)32 (21.1)22 (35.5)0.02340 (45.5)35 (61.4)0.044
 Positive remodelling119 (33.1)25 (16.4)14 (22.6)0.19449 (55.7)31 (54.4)0.507
 Spotty calcification58 (16.2)7 (4.6)3 (4.8)0.59330 (34.1)18 (31.6)0.449
 Napkin-ring sign34 (9.5)5 (3.3)0 (0)0.17716 (18.2)13 (22.8)0.318
Physiologic index
 FFRCT0.69 (0.55–0.79)0.72 (0.59–0.84)0.66 (0.63–0.72)0.0290.69 (0.52–0.84)0.64 (0.50–0.70)0.017
 FFRCT PPG0.69 (0.58–0.80)0.78 (0.69–0.85)0.55 (0.51–0.58)<0.0010.73 (0.68–0.80)0.51 (0.47–0.58)<0.001

Values are presented as n (%) or median (IQR).

Generalized estimating equation model or maximum likelihood χ2 tests were used for overall and between-group comparison in per-patient analysis.

FFRCT: fractional flow reserve by computed tomography angiography; GP IIb/IIIa: glycoprotein IIb/IIIa; HRPC: high-risk plaque characteristics; LAD: left anterior descending coronary artery; LCX: left circumflex branch coronary artery; MLA: minimal lumen area; N: number of patients; PPG: pullback pressure gradient; RCA: right coronary artery; TPV: total plaque volume; UNL: upper reference limit; other abbreviations as in Table 1.

P values for the comparison of variables between focal and diffused disease groups.

Quantitative or qualitative plaque analysis was performed for target lesions that received PCI.

Plaque characteristics and physiologic disease patterns for PMI

Representative images for the adverse plaque characteristic and physiologic disease patterns in patients with and without PMI are presented in Figure 1. Amongst patients with only one HRPC in the target lesions, the mean post-PCI hs-cTnT level and the proportion of PMI were significantly lower than those with two or more HRPCs; conversely, amongst patients with diffused disease, the post-PCI hs-cTnT level was significantly higher and more of them experienced PMI (Figure 2).

Case examples of coronary CTA-assessed plaque characteristics and FFRCT PPG. Representative cases of patients with coronary CTA plaque characteristics and FFRCT PPG analysed. (A) A patient with PMI and target vessel revascularization at 9 months follow-up. (B) A patient without PMI or subsequent adverse events during follow-up. Abbreviations: CTA: computed tomography angiography; FFRCT: fractional flow reserve by coronary computed tomography angiography; HRPC: high-risk plaque characteristics; LAP: low-attenuation plaque; MLA: minimal lumen area; PB: plaque burden; PMI: peri-procedural myocardial infarction; PPG: pullback pressure gradient.
Figure 1

Case examples of coronary CTA-assessed plaque characteristics and FFRCT PPG. Representative cases of patients with coronary CTA plaque characteristics and FFRCT PPG analysed. (A) A patient with PMI and target vessel revascularization at 9 months follow-up. (B) A patient without PMI or subsequent adverse events during follow-up. Abbreviations: CTA: computed tomography angiography; FFRCT: fractional flow reserve by coronary computed tomography angiography; HRPC: high-risk plaque characteristics; LAP: low-attenuation plaque; MLA: minimal lumen area; PB: plaque burden; PMI: peri-procedural myocardial infarction; PPG: pullback pressure gradient.

Post-PCI hs-cTnT levels and proportions of PMI according to HRPC and FFRCT PPG. Both (A) number of HRPC and (B) FFRCT PPG showed significant association with post-PCI hs-cTnT levels and proportion of PMI. Abbreviations: hs-cTnT: high-sensitivity cardiac troponin T; other abbreviations as in Figure 1.
Figure 2

Post-PCI hs-cTnT levels and proportions of PMI according to HRPC and FFRCT PPG. Both (A) number of HRPC and (B) FFRCT PPG showed significant association with post-PCI hs-cTnT levels and proportion of PMI. Abbreviations: hs-cTnT: high-sensitivity cardiac troponin T; other abbreviations as in Figure 1.

In those with <3 HRPCs, patients with low FFRCT PPG showed similar PB (62.8% vs. 63.5%, P = 0.989), total plaque volume (231.4 mm3 vs. 242.5 mm3, P = 0.598), and lesion length (31.1 mm vs. 27.5 mm, P = 0.125), but the former ones had more (two vs. one, P = 0.004) and longer stents (56 mm vs. 36 mm, P = 0.001) implanted. Despite the number of HRPC in the target lesions, patients with diffused disease are more likely to suffer from PMI (Table 2). Linear relationship of plaque volume, PB, and FFRCT PPG with post-PCI hs-cTnT was observed (see Supplementary material online, Figure S4). In a multivariable model, both ≥3 HRPC (OR: 2.21, 95% CI: 1.29–3.80, P = 0.004) and low FFRCT PPG (OR: 1.23, 95% CI: 1.02–1.52, P = 0.028) were independent predictors of PMI (Table 3).

Table 3

Independent predictors of PMI

VariableUnivariable analysisMultivariable analysis
OR (95% CI)P valueOR (95% CI)P value
≥3 High-risk plaque characteristicsa2.57 (1.53–4.30)<0.0011.82 (1.19–2.80)0.006
FFRCT PPG (per 0.1 decrease)1.33 (1.11–1.60)0.0021.18 (1.02–1.82)0.035
Age (per 10 years)1.61 (1.20–2.16)0.0021.35 (1.09–1.98)0.025
Male1.06 (0.61–1.82)0.8391.02 (0.55–1.87)0.696
Hypertension1.20 (0.70–2.07)0.5101.08 (0.67–1.73)0.752
Diabetes mellitus1.15 (0.66–1.99)0.6241.07 (0.65–1.77)0.797
eGRF0.99 (0.97–1.01)0.2821.00 (0.98–1.02)0.863
Prior PCI1.22 (0.46–3.23)0.6921.07 (0.15–2.57)0.962
Left ventricular ejection fraction (per 10% increase)0.99 (0.95–1.03)0.5701.05 (0.65–1.68)0.871
Statin0.38 (0.05–2.78)0.3390.51 (0.15–1.70)0.275
Peri-procedural GP IIb/IIIa inhibitors0.40 (0.22–0.74)0.0030.54 (0.34–0.87)0.011
VariableUnivariable analysisMultivariable analysis
OR (95% CI)P valueOR (95% CI)P value
≥3 High-risk plaque characteristicsa2.57 (1.53–4.30)<0.0011.82 (1.19–2.80)0.006
FFRCT PPG (per 0.1 decrease)1.33 (1.11–1.60)0.0021.18 (1.02–1.82)0.035
Age (per 10 years)1.61 (1.20–2.16)0.0021.35 (1.09–1.98)0.025
Male1.06 (0.61–1.82)0.8391.02 (0.55–1.87)0.696
Hypertension1.20 (0.70–2.07)0.5101.08 (0.67–1.73)0.752
Diabetes mellitus1.15 (0.66–1.99)0.6241.07 (0.65–1.77)0.797
eGRF0.99 (0.97–1.01)0.2821.00 (0.98–1.02)0.863
Prior PCI1.22 (0.46–3.23)0.6921.07 (0.15–2.57)0.962
Left ventricular ejection fraction (per 10% increase)0.99 (0.95–1.03)0.5701.05 (0.65–1.68)0.871
Statin0.38 (0.05–2.78)0.3390.51 (0.15–1.70)0.275
Peri-procedural GP IIb/IIIa inhibitors0.40 (0.22–0.74)0.0030.54 (0.34–0.87)0.011

CI: confidence interval; FFRCT: fractional flow reserve by coronary computed tomography angiography; GP IIb/IIIa: glycoprotein IIb/IIIa; OR: odds ratio; other abbreviations as in Tables 1 and 2.

High-risk plaque characteristics: (1) plaque burden ≥70%, (2) MLA <4 mm2, (3) positive remodelling, (4) low-attenuation plaque, (5) napkin-ring sign, and (6) spotty calcification.

Table 3

Independent predictors of PMI

VariableUnivariable analysisMultivariable analysis
OR (95% CI)P valueOR (95% CI)P value
≥3 High-risk plaque characteristicsa2.57 (1.53–4.30)<0.0011.82 (1.19–2.80)0.006
FFRCT PPG (per 0.1 decrease)1.33 (1.11–1.60)0.0021.18 (1.02–1.82)0.035
Age (per 10 years)1.61 (1.20–2.16)0.0021.35 (1.09–1.98)0.025
Male1.06 (0.61–1.82)0.8391.02 (0.55–1.87)0.696
Hypertension1.20 (0.70–2.07)0.5101.08 (0.67–1.73)0.752
Diabetes mellitus1.15 (0.66–1.99)0.6241.07 (0.65–1.77)0.797
eGRF0.99 (0.97–1.01)0.2821.00 (0.98–1.02)0.863
Prior PCI1.22 (0.46–3.23)0.6921.07 (0.15–2.57)0.962
Left ventricular ejection fraction (per 10% increase)0.99 (0.95–1.03)0.5701.05 (0.65–1.68)0.871
Statin0.38 (0.05–2.78)0.3390.51 (0.15–1.70)0.275
Peri-procedural GP IIb/IIIa inhibitors0.40 (0.22–0.74)0.0030.54 (0.34–0.87)0.011
VariableUnivariable analysisMultivariable analysis
OR (95% CI)P valueOR (95% CI)P value
≥3 High-risk plaque characteristicsa2.57 (1.53–4.30)<0.0011.82 (1.19–2.80)0.006
FFRCT PPG (per 0.1 decrease)1.33 (1.11–1.60)0.0021.18 (1.02–1.82)0.035
Age (per 10 years)1.61 (1.20–2.16)0.0021.35 (1.09–1.98)0.025
Male1.06 (0.61–1.82)0.8391.02 (0.55–1.87)0.696
Hypertension1.20 (0.70–2.07)0.5101.08 (0.67–1.73)0.752
Diabetes mellitus1.15 (0.66–1.99)0.6241.07 (0.65–1.77)0.797
eGRF0.99 (0.97–1.01)0.2821.00 (0.98–1.02)0.863
Prior PCI1.22 (0.46–3.23)0.6921.07 (0.15–2.57)0.962
Left ventricular ejection fraction (per 10% increase)0.99 (0.95–1.03)0.5701.05 (0.65–1.68)0.871
Statin0.38 (0.05–2.78)0.3390.51 (0.15–1.70)0.275
Peri-procedural GP IIb/IIIa inhibitors0.40 (0.22–0.74)0.0030.54 (0.34–0.87)0.011

CI: confidence interval; FFRCT: fractional flow reserve by coronary computed tomography angiography; GP IIb/IIIa: glycoprotein IIb/IIIa; OR: odds ratio; other abbreviations as in Tables 1 and 2.

High-risk plaque characteristics: (1) plaque burden ≥70%, (2) MLA <4 mm2, (3) positive remodelling, (4) low-attenuation plaque, (5) napkin-ring sign, and (6) spotty calcification.

As shown in Supplementary material online, Figure S5, integrated high-risk plaque characteristics and functional disease patterns significantly improved the discriminant ability compared with a model with clinical risk factors (C index = 0.70 vs. 0.63, P = 0.035) as well as that with angiographic characteristics (C index = 0.74 vs. 0.68, P = 0.047) (see Supplementary material online, Figure S6).

Plaque characteristics and physiologic disease patterns for clinical outcomes

Patients with target lesions having ≥3 HRPC showed a higher risk of MACE than those with <3 HRPC (13.1% vs. 3.7%; HRadj: 3.34; 95% CI: 1.44–7.71; P = 0.005). Similarly, patients with diffused disease (low FFRCT PPG) had a higher MACE incidence than those with focal disease (high FFRCT PPG) (12.6% vs. 5.0%; HRadj: 2.28; 95% CI: 1.05–4.98; P = 0.038) (Figure 3). The components of MACE were shown in Supplementary material online, Tables S1 and S2. When the patients were classified into four groups according to HRPC and FFRCT PPG, the cumulative incidences of MACE were 2.6, 6.5, 9.1, and 19.3% for groups 1 (<3 HRPC and FFRCT PPG > 0.61), 2 (<3 HRPC and FFRCT PPG ≤ 0.61), 3 (≥3 HRPC and FFRCT PPG > 0.61), and 4 (≥3 HRPC and FFRCT PPG ≤ 0.61), respectively (Figure 4). Group 4 showed the highest risk of MACE (overall comparison P = 0.001). In addition, per-vessel analysis (see Supplementary material online, Figures S7 and S8) showed similar findings. Furthermore, when HRPC numbers of all lesions were added up on a per-patient basis, patient-level HRPC number > 3 was the best cut-off value to predict MACE (see Supplementary material online, Figure S9). Patients with total number of HRPC (including all lesions) >3 showed worse clinical outcomes (see Supplementary material online, Figure S10). A multivariable marginal Cox model revealed that ≥3 HRPC (HR: 2.41, 95% CI: 1.01–5.76, P = 0.047) and low FFRCT PPG (HR: 1.44, 95% CI: 1.08–1.93, P = 0.014) were independently associated with the occurrence of MACE (Table 4). When optimal cut-off value of FFRCT PPG (see Supplementary material online, Figure S11) for predicting MACE was applied to define diffuse or focal disease, our main results remained consistent (see Supplementary material online, Figures S12 and S13). The model incorporating HRPC and FFRCT PPG has a better performance than the clinical model to discriminate those with MACE from those without MACE (C-statistics: 0.78 vs. 0.60, P = 0.005). The NRI and IDI for predicting MACE using HRPC and FFRCT PPG plus clinical model were 0.210 (95% CI: 0.038–0.485), P = 0.020, and 0.209 (95% CI: 0.027–0.391), P = 0.020, respectively, suggesting that the model with HRPC and FFRCT PPG had an improvement in prediction accuracy compared with clinical model (Figure 5). The model with angiographic factors, HRPC, and FFRCT PPG showed a C-statistic of 0.77, IDI of 0.085 (95% CI: 0.006–0.164), P = 0.030, and NRI of 0.106 (95% CI: 0.008–0.204) (see Supplementary material online, Figure S14).

Clinical outcomes according to HRPC or FFRCT PPG. Cumulative incidence of the major adverse cardiovascular events (MACE) was compared between patients with (A) ≥ 3 HRPC and <3 HRPC and (B) low and high FFRCT PPG. Abbreviations: CI: confidence interval; HRadj: adjusted hazard ratio; others abbreviations as in Figure 1.
Figure 3

Clinical outcomes according to HRPC or FFRCT PPG. Cumulative incidence of the major adverse cardiovascular events (MACE) was compared between patients with (A) ≥ 3 HRPC and <3 HRPC and (B) low and high FFRCT PPG. Abbreviations: CI: confidence interval; HRadj: adjusted hazard ratio; others abbreviations as in Figure 1.

Clinical outcomes according to HRPC and FFRCT PPG. Cumulative incidence of the major adverse cardiovascular events (MACE) was compared. Abbreviations as in Figures 1 and 3.
Figure 4

Clinical outcomes according to HRPC and FFRCT PPG. Cumulative incidence of the major adverse cardiovascular events (MACE) was compared. Abbreviations as in Figures 1 and 3.

Comparison of discrimination and reclassification ability of predictive models for MACE. Prognostic values of models predicting MACE were compared. Model 1 included the clinical variables of age, sex, hypertension, diabetes mellitus, smoking, estimated glomerular filtration rate, and left ventricular ejection fraction. Model 2 included model 1 with addition of HRPC and FFRCT PPG. Abbreviations: AUC: area under curve; NRI: net reclassification index; IDI: integrated discrimination index; other abbreviations as in Figure 1.
Figure 5

Comparison of discrimination and reclassification ability of predictive models for MACE. Prognostic values of models predicting MACE were compared. Model 1 included the clinical variables of age, sex, hypertension, diabetes mellitus, smoking, estimated glomerular filtration rate, and left ventricular ejection fraction. Model 2 included model 1 with addition of HRPC and FFRCT PPG. Abbreviations: AUC: area under curve; NRI: net reclassification index; IDI: integrated discrimination index; other abbreviations as in Figure 1.

Table 4

Independent predictors of major adverse cardiovascular events

VariableUnivariable analysisMultivariable analysis
HR (95% CI)P valueHR (95% CI)P value
≥3 High-risk plaque characteristicsa3.59 (1.57–8.21)0.0022.41 (1.01–5.76)0.047
FFRCT PPG (per 0.1 decrease)1.71 (1.30–2.26)<0.0011.44 (1.08–1.93)0.014
PMI3.63 (1.70–7.72)0.0012.15 (0.90–5.11)0.085
Age (per 10 years)1.53 (0.99–2.36)0.0531.33 (0.80–2.24)0.275
Male1.12 (0.50–2.49)0.7831.29 (0.56–3.00)0.554
Hypertension1.01 (0.46–2.26)0.9730.98 (0.41–2.34)0.963
Diabetes mellitus1.12 (0.49–2.55)0.7951.06 (0.41–2.75)0.898
eGRF1.00 (0.97–1.02)0.8611.01 (0.98–1.05)0.429
Prior PCI2.54 (0.88–7.33)0.0861.91 (0.64–5.67)0.243
Left ventricular ejection fraction (per 10% increase)0.73 (0.44–1.23)0.2390.87 (0.49–1.53)0.629
SYNTAX score (per 1 increase)1.03 (0.98–1.09)0.2391.02 (0.98–1.06)0.347
Statin0.26 (0.04–1.91)0.1850.25 (0.03–2.10)0.201
Peri-procedural GP IIb/IIIa inhibitors0.75 (0.30–1.85)0.5270.84 (0.32–2.20)0.726
VariableUnivariable analysisMultivariable analysis
HR (95% CI)P valueHR (95% CI)P value
≥3 High-risk plaque characteristicsa3.59 (1.57–8.21)0.0022.41 (1.01–5.76)0.047
FFRCT PPG (per 0.1 decrease)1.71 (1.30–2.26)<0.0011.44 (1.08–1.93)0.014
PMI3.63 (1.70–7.72)0.0012.15 (0.90–5.11)0.085
Age (per 10 years)1.53 (0.99–2.36)0.0531.33 (0.80–2.24)0.275
Male1.12 (0.50–2.49)0.7831.29 (0.56–3.00)0.554
Hypertension1.01 (0.46–2.26)0.9730.98 (0.41–2.34)0.963
Diabetes mellitus1.12 (0.49–2.55)0.7951.06 (0.41–2.75)0.898
eGRF1.00 (0.97–1.02)0.8611.01 (0.98–1.05)0.429
Prior PCI2.54 (0.88–7.33)0.0861.91 (0.64–5.67)0.243
Left ventricular ejection fraction (per 10% increase)0.73 (0.44–1.23)0.2390.87 (0.49–1.53)0.629
SYNTAX score (per 1 increase)1.03 (0.98–1.09)0.2391.02 (0.98–1.06)0.347
Statin0.26 (0.04–1.91)0.1850.25 (0.03–2.10)0.201
Peri-procedural GP IIb/IIIa inhibitors0.75 (0.30–1.85)0.5270.84 (0.32–2.20)0.726

High-risk plaque characteristics: (1) plaque burden ≥70%, (2) MLA <4 mm2, (3) positive remodelling, (4) low-attenuation plaque, (5) napkin-ring sign, and (6) spotty calcification. PMI: peri-procedural myocardial infarction; HR: hazard ratio; other abbreviations as in Tables 1 and 3.

Table 4

Independent predictors of major adverse cardiovascular events

VariableUnivariable analysisMultivariable analysis
HR (95% CI)P valueHR (95% CI)P value
≥3 High-risk plaque characteristicsa3.59 (1.57–8.21)0.0022.41 (1.01–5.76)0.047
FFRCT PPG (per 0.1 decrease)1.71 (1.30–2.26)<0.0011.44 (1.08–1.93)0.014
PMI3.63 (1.70–7.72)0.0012.15 (0.90–5.11)0.085
Age (per 10 years)1.53 (0.99–2.36)0.0531.33 (0.80–2.24)0.275
Male1.12 (0.50–2.49)0.7831.29 (0.56–3.00)0.554
Hypertension1.01 (0.46–2.26)0.9730.98 (0.41–2.34)0.963
Diabetes mellitus1.12 (0.49–2.55)0.7951.06 (0.41–2.75)0.898
eGRF1.00 (0.97–1.02)0.8611.01 (0.98–1.05)0.429
Prior PCI2.54 (0.88–7.33)0.0861.91 (0.64–5.67)0.243
Left ventricular ejection fraction (per 10% increase)0.73 (0.44–1.23)0.2390.87 (0.49–1.53)0.629
SYNTAX score (per 1 increase)1.03 (0.98–1.09)0.2391.02 (0.98–1.06)0.347
Statin0.26 (0.04–1.91)0.1850.25 (0.03–2.10)0.201
Peri-procedural GP IIb/IIIa inhibitors0.75 (0.30–1.85)0.5270.84 (0.32–2.20)0.726
VariableUnivariable analysisMultivariable analysis
HR (95% CI)P valueHR (95% CI)P value
≥3 High-risk plaque characteristicsa3.59 (1.57–8.21)0.0022.41 (1.01–5.76)0.047
FFRCT PPG (per 0.1 decrease)1.71 (1.30–2.26)<0.0011.44 (1.08–1.93)0.014
PMI3.63 (1.70–7.72)0.0012.15 (0.90–5.11)0.085
Age (per 10 years)1.53 (0.99–2.36)0.0531.33 (0.80–2.24)0.275
Male1.12 (0.50–2.49)0.7831.29 (0.56–3.00)0.554
Hypertension1.01 (0.46–2.26)0.9730.98 (0.41–2.34)0.963
Diabetes mellitus1.12 (0.49–2.55)0.7951.06 (0.41–2.75)0.898
eGRF1.00 (0.97–1.02)0.8611.01 (0.98–1.05)0.429
Prior PCI2.54 (0.88–7.33)0.0861.91 (0.64–5.67)0.243
Left ventricular ejection fraction (per 10% increase)0.73 (0.44–1.23)0.2390.87 (0.49–1.53)0.629
SYNTAX score (per 1 increase)1.03 (0.98–1.09)0.2391.02 (0.98–1.06)0.347
Statin0.26 (0.04–1.91)0.1850.25 (0.03–2.10)0.201
Peri-procedural GP IIb/IIIa inhibitors0.75 (0.30–1.85)0.5270.84 (0.32–2.20)0.726

High-risk plaque characteristics: (1) plaque burden ≥70%, (2) MLA <4 mm2, (3) positive remodelling, (4) low-attenuation plaque, (5) napkin-ring sign, and (6) spotty calcification. PMI: peri-procedural myocardial infarction; HR: hazard ratio; other abbreviations as in Tables 1 and 3.

Discussion

The major findings are as follows: first, in stable CAD patients who underwent selective PCI, the presence of ≥3 HRPC in the target lesions and low FFRCT PPG were independent predictors of PMI and integration of adverse plaques and functional disease patterns with clinical factors increased the efficiency in predicting the occurrence of PMI after PCI. Second, in a four-group classification according to HRPC and FFRCT PPG, patients with ≥3 HRPC in the target lesions and low FFRCT PPG had the highest risk of MACE. Third, the presence of ≥3 HRPC in the target lesions and low FFRCT PPG were independent predictors of adverse clinical outcomes and a model with integrated plaque characteristics and functional disease patterns showed incremental prognostic value compared with a model with clinical risk factors alone.

Prognostic relevance of post-PCI myocardial infarction

Numerous studies have documented the occurrence of distal embolization during elective PCI correlates directly with the extent to plaque volume reduction by target lesion dilation, which sent more debris downstream.20 In addition, elevated troponin levels post-PCI correlated with new myocardial enhancement in magnetic resonance imaging because of side branch occlusion in 43% of cases. Kini et al.21 have reported that one of the pre-PCI predictors of creatine kinase–MB elevation was diffuse coronary disease, which is associated with the use of a greater number of and longer stents, increasing the risk of side branch occlusion. Moreover, stent implantation in diseased atherosclerotic segments can result in edge dissections, potentially leading to peri-procedural myocardial infarction.5 Consistent with these findings, our results demonstrated that patients with diffused disease are more likely to have longer stents implanted and higher incidence of PMI. Therefore, the detailed plaque characteristics and disease pattern (diffuse vs. focal) assessments may be indispensable before PCI, which will benefit to screen higher-risk patients of PMI, thus allowing preventive intervention.

Coronary CTA in predicting PMI and clinical outcomes

Modern CT scanners allow detection of high-risk features such as positive remodelling, low computed tomography attenuation, or napkin-ring sign12,13 and showed good agreement with intravascular imaging.22 Lesions with low-attenuation plaques, positive remodelling, and spotty calcification have been shown to be related with post-PCI troponin elevation.23

Furthermore, several studies have indicated that adverse plaque characteristics were related to worse outcomes.14,24,25 In addition to the high-risk plaque components, the PROSPECT trial,14 and ATHEROREMO-IVUS24 study both demonstrated the importance of MLA and PB derived by IVUS in predicting major cardiovascular events. Importantly, the PROSPECT trial14 has reported that the adverse event rate originated from lesions with MLA ≤ 4.0 mm2, PB ≥70%, and thin-cap fibroatheroma (TCFA) 18.2%, which was significantly higher than that with one or two of these characteristics; the ATHEROREMO-IVUS study24 showed that TCFA lacks of prognostic value in patients without a significant amount of PB (<70%). Similarly, our results demonstrated that each component of the high-risk plaque components had C-statistics in the range between 0.50 and 0.58 (see Supplementary material online, Figure S15), while the combined model of HRPC showed a higher C-statistic of 0.61 (see Supplementary material online, Figure S16). This is not strange as plaque progression is a necessary step before plaque rupture,26 while each of the HRPC components is not sufficient to predict which atheroma will undergo plaque progression14 and has low PPV for clinical outcomes.27 Of note, coronary CTA has been shown to be an accurate non-invasive tool to assess and quantify coronary plaque volume compared with IVUS.28 Using CTA-derived MLA <4 mm2, PB ≥ 70%, low-attenuation plaque, positive remodelling, napkin-ring sign, or spotty calcification to define HRPC, Lee et al.15 also revealed the prognostic value of HRPC. In this regard, we considered both quantitative and qualitative plaque components, and our results echo prior findings, suggesting that CTA-assessed HRPC might have a powerful impact on PMI after PCI as well as future adverse events. However, we need to bear in mind that these assessments are time-consuming and not yet applied in clinical practice. Coronary artery calcium (CAC) score measured through non-contrast cardiac computed tomography scanning has been shown to be a marker of the total burden of atherosclerosis;29 it may be reasonable to consider use of CAC measurement to predict PMI and future coronary events, but further investigation is needed. The current findings should be deemed as a proof-of-concept one, and hopefully, advancement in artificial intelligence might help physicians in applying these parameters in clinical practice.

A study has shown that FFRCT virtual pullback curves were accurate in assessing the distribution of epicardial resistance.6 Moreover, a quantitative index called PPG index derived from the pressure wire pullback manoeuver has been proposed to discriminate focal from diffused disease.5 Our group recently demonstrated the feasibility of PPG derived from quantitative flow reserve (QFR) virtual pullback curve.17,18 With the same method, PPG can also be derived from FFRCT virtual pullback curve to characterize the pathophysiologic patterns of CAD. Lower tertile value was selected to define the physiologic diffused disease, which is same as that derived from the ROC analysis. Nevertheless, this value might be varied in different populations and a prospective study with larger sample size is needed to validate this cut-off value. The use of the isolated value of FFRCT PPG for diffused disease definition might bias the results; nevertheless, when it was deemed as a continuous value in the multivariable analysis, its value in predicting PMI and MACE retained. On the contrary, quantifying the anatomic severity of cumulative, diffused disease is difficult, if even possible, especially in terms or units directly comparable with focal stenosis.30 The present analysis extends current knowledge by analysing the high-risk plaque characteristic and physiologic disease patterns simultaneously. More importantly, we investigated their value in predicting PMI and clinical outcomes.

In our study, patients with both adverse plaques and physiologic diffused disease had the worst outcome during follow-up and integration of adverse plaque characteristics and functional disease patterns into the model with clinical risk factors showed significantly increased discrimination ability for PMI and adverse clinical outcomes. Together with previous studies, our data suggest that it would be worthwhile to consider the identification of these vulnerable plaques and functional disease patterns by coronary CTA before PCI. Pre-procedural CTA might have its value in risk prediction and guiding therapeutic interventions in the catheterization laboratory. The ongoing precise procedural and PCI plan study will provide further answers on the usefulness of coronary CTA-guided PCI (NCT05253677).

Limitations

This study has several limitations. First, this is a single-centre experience with a relatively small number of patients. We could only include some of the clinical parameters in the multivariable model. Additionally, only 19 MACE events were recorded during follow-up, which might affect the statistical power. Results must therefore be interpreted as hypothesis generating. Prospective studies with larger sample size are needed to confirm our findings. Second, only relatively low-risk patients in stable conditions and with normal pre-PCI hs-cTnT values were considered. Therefore, our findings should be interpreted in a low- to medium-risk population in the setting of elective PCI and be cautiously extended to other populations. Third, our study used a single technique to analyse plaque. Further investigation and external validation are required for this to become accepted and part of clinical practice, and these should also incorporate analysis from more than one type of scanner or reconstruction algorithm. Fourth, as a new index derived from FFRCT, whether the lower spatial resolution of CTA compared to invasive angiography will affect its accuracy remains unknown. Prospective validation studies using invasive pressure wire-derived PPG as the reference should be anticipated. Besides, definite cut-off value for FFRCT PPG has not been defined yet, we adopted the low tertile (≤0.61) as the cut-off to define functional diffused disease, and further studies are warranted to validate the most reasonable cut-off value for the FFRCT PPG. Fifth, comprehensive analysis of coronary CTA including plaque characteristics and FFRCT PPG is time- and effort-consuming. Further software incorporating automation and machine learning techniques would therefore help facilitate the widespread clinical adoption of the concept. Furthermore, other quantified parameters such as pericoronary adipose tissue attenuation or radiomic features may provide addition value for risk stratification.

Conclusion

Coronary CTA offers the possibility of identifying vulnerable atherosclerotic plaques and physiologic disease patterns non-invasively, playing an important role in detecting patients at high risk for PMI and subsequent adverse outcomes.

Supplementary data

Supplementary data are available at European Heart Journal - Cardiovascular Imaging online.

Acknowledgements

The authors thank Dr. Yongfu Yu from Fudan University, Shanghai, China, for the statistical method consultation.

Funding

This work was supported by the National Key Research and Development Program of China (2020YFC1316700 and 2021YFC2500500), Grant of Shanghai Shenkang on Key Clinical Research Project (SHDC2020CR2015A), Shanghai Clinical Research Center for Interventional Medicine (Grant No: 19MC1910300), and China Cardiovascular Health Alliance-Access Research Fund (2020-CCA-ACCESS-124).

Data availability

The data underlying this article will be shared on reasonable request to the corresponding author.

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Author notes

Neng Dai and Zhangwei Chen contributed equally.

Conflict of interest: None declared.

This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://dbpia.nl.go.kr/pages/standard-publication-reuse-rights)

Supplementary data