Highlights
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Visually estimated coronary artery calcium was an independent predictor of MACE.
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VECAC provides complementary anatomic information with other SPECT-MPI markers.
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Physicians should consider routinely evaluating and reporting VECACS.
Keywords: Atherosclerosis, Coronary artery calcium, Myocardial perfusion imaging, Risk stratification
Abstract
Aims
Computed tomographic attenuation correction (CTAC) scans for single photon emission computed tomography myocardial perfusion imaging (SPECT-MPI) may reveal coronary artery calcification. The independent prognostic value of a visually estimated coronary artery calcium score (VECACS) from these low-dose, non-gated scans is not established.
Methods & Results
VECACS was evaluated in 4,720 patients undergoing SPECT-MPI with CTAC using a 4-point scale. Major adverse cardiac events (MACE) were defined as all-cause mortality, acute coronary syndrome, or revascularization > 90 days after SPECT-MPI. Independent associations with MACE were determined with multivariable Cox proportional hazards analyses adjusted for age, sex, past medical history, perfusion findings, and left ventricular ejection fraction. During a median follow up of 2.9 years (interquartile range 1.8 – 4.2), 494 (10.5%) patients experienced MACE. Compared to absent VECACS, patients with increased VECACS were more likely to experience MACE (all log-rank p < 0.001), and findings were similar when stratified by normal or abnormal perfusion. Multivariable analysis showed an increased MACE risk associated with VECACS categories of equivocal (adjusted hazard ratio [HR] 2.54, 95% CI 1.45–4.45, p = 0.001), present (adjusted HR 2.44, 95% CI 1.74–3.42, p < 0.001) and extensive (adjusted HR 3.47, 95% CI 2.41–5.00, p < 0.001) compared to absent. Addition of VECACS to the multivariable model improved risk classification (continuous net reclassification index 0.207, 95% CI 0.131 – 0.310).
Conclusion
VECACS was an independent predictor of MACE in this large SPECT-MPI patient cohort. VECACS from CTAC can be used to improve risk stratification with SPECT-MPI without additional radiation.
1. Introduction
Single photon emission computed tomography myocardial perfusion imaging (SPECT-MPI) is a well-established and widely utilized non-invasive imaging modality for the diagnosis and prognostication of coronary artery disease (CAD) [1], [2], [3], [4]. SPECT-MPI provides a functional assessment of CAD through evaluation of stress-induced perfusion abnormalities [1], [5]. Traditional prognostic findings on SPECT-MPI include the extent and severity of ischemia, scar burden, left ventricular systolic function and volume [6], [7], [8]. Contemporary SPECT-MPI systems incorporate non-gated low-dose computed tomography imaging for attenuation correction. This innovation allows for correction of soft tissue attenuation artifacts inherent to myocardial nuclear imaging and has become the standard of care. Computed tomography attenuation correction (CTAC) improves diagnostic accuracy [9], and can decrease the number of patients who require rest imaging after a stress-first protocol[10].
CTAC imaging with SPECT-MPI also allows for visualization of coronary artery calcium (CAC) [11], but this is not routinely evaluated or reported. In contrast, the quantitative Agatston coronary artery calcium score (CACS) is a well-established prognostic marker for CAD[2], [3], [12], [13], [14], [15]. Agatston CACS imaging requires a dedicated, ECG-gated non-contrast CT-scan. CTAC imaging contains thicker slices, which is known to influence CACS [16], and is not ECG-gated. SPECT-MPI and Agatston CACS provide complementary functional and anatomic information and yield independent risk stratification [2], [13]. Since CTAC is embedded within the SPECT-MPI workflow, with no additional cost or radiation, it may be beneficial to extract similar anatomic information. Visual CAC estimates derived from CTAC imaging correlate well with the Agatston CACS [17], [18]. However, it is unclear whether a visually estimated coronary artery calcium score (VECACS) from CTAC imaging can provide incremental prognostic value. The aim of this study was to determine if a VECACS derived from CTAC has independent prognostic value in SPECT-MPI.
2. Methods
2.1. Study population
This was a retrospective study of consecutive patients who underwent SPECT-MPI with CTAC between September 1, 2014 to December 31, 2018 at a single tertiary academic hospital system. SPECT-MPI was performed in two affiliated nuclear testing laboratories and the population included both outpatients and inpatients with suspected or known CAD. Patients who underwent early revascularization (revascularization within 90 days of SPECT MPI) were excluded (n = 304) because SPECT MPI results may influence the decision to pursue revascularization [1], which may alter long-term outcomes [19], [20]. The study was approved by University of Calgary Research Ethics Board (REB-ID ASD-7564), including waiver of consent. Data will be shared upon receipt of reasonable written request.
Past medical history and family history were prospectively collected in the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH) database [21]. History of CAD was classified as history of previous myocardial infarction or revascularization with either percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG)[22]. Information about resting ECG and stress induced ECG changes were also recorded prospectively.
2.2. Myocardial perfusion imaging protocol/scoring
Patients underwent a 99mTc-Sestamibi rest-stress acquisition protocol with CTAC [23]. All studies were reported by experienced cardiologists with at least 5 years of experience in CACS and SPECT-MPI. Stress testing was conducted with symptom-limited exercise stress (n = 2,954) or pharmacological stress using dipyridamole (n = 1,534), adenosine (n = 17) or dobutamine (n = 91).
Weight-adjusted stress and rest dosages of 99mTc-Sestamibi were used. The resting dosages were: standard-one day, 259 MBq (7 mCi); 350 MBq (9.5 mCi) for patients > 100 kg; and two-day each at 777 MBq (21 mCi) > for patients > 136 kg. The stress dosages corresponded to: standard-one day, 777 MBq (21 mCi); 1050 MBq (28.4 mCi) for patients > 100 kg. For pharmacological stress testing, both stress and rest SPECT images were obtained at least 45 min after tracer injection. For patients who underwent treadmill stress testing the SPECT images was acquired 15–30 min after stress. In total, 2,951 (61.9%) patients were scanned using the GE Discovery 570 CZT scanner (GE, Boston, USA). The remaining patients (n = 1,817, 38.1%) were imaged with a Ventri camera system (GE, Boston, USA).
The interpretation of SPECT-MPI was performed unblinded to clinical history and VECACS, using a 17-segment model [24]. Summed stress score (SSS), summed rest score (SRS), and summed difference score (SDS) were calculated for each patient as previously described [24]. Normal myocardial perfusion was defined as SSS < 4 [25]. Left ventricular ejection fraction was calculated from gated images.
2.3. CTAC image acquisition and interpretation
CTAC was performed using a built in CT scanner (Lightspeed VCT 64, GE, Boston, USA). CTAC study was performed after the rest acquisition during end-expiratory breath hold with no ECG-gating, in helical mode with a slice thickness of 5-mm, tube voltage of 120 kVp and 30 mA, using a 512x512 matrix. CTAC images were reviewed at the time of SPECT-MPI reporting and graded as: absent, equivocal, present or extensive. Extensive calcification was defined as estimated Agatston CACS>400 [26], [27]. Fig. 1 shows a representative case from each group. We did not assess interobserver agreement, but previous studies have demonstrated excellent interobserver agreement for similar estimates (kappa 0.89 to 0.94) [17], [18].
Fig. 1.
Examples of visually estimated coronary artery calcium groups. A) absent, no coronary artery calcification B) equivocal, possible calcification in proximal right coronary artery C) present, calcification in the left anterior descending and right coronary artery ostium D) extensive, calcification in the left main, left anterior descending, and left circumflex arteries.
2.4. Clinical outcomes
Follow up for major adverse cardiovascular events was obtained through the Discharge Abstracts/National Ambulatory Care Reporting system and Alberta Vital Statistics. MACE was defined as late revascularization (PCI or CABG, >90 days after SPECT-MPI), non-fatal acute coronary syndrome (non-fatal myocardial infarction or admission for unstable angina), or all-cause mortality. Follow-up was established up until December 31, 2019. However, these databases due not capture emigration and it’s possible that some patients were lost to follow-up on this basis. Patients undergoing early revascularization were excluded. Event rates for VECACS score categories (absent, equivocal, present, and extensive) were determined. SPECT-MPI and VECACS findings were combined to assess whether VECACS yielded incremental prognostic information and resulted in risk re-classification beyond SPECT-MPI findings alone.
2.5. Statistical analysis
Continuous variables were summarized as mean (standard deviation [SD]) if normally distributed and compared using a Student’s t-test or analysis of variance. Continuous variables that were not normally distributed were summarized as median (interquartile range [IQR]) and compared using a Mann-Whitney U test or Kruskal-Wallis test. Associations with MACE were assessed for categories of perfusion abnormality and VECACS in bivariable and multivariable Cox proportional hazards analyses. There was minimal missing data (0.04%). Missing variables were imputed with the population mean value for continuous variables and a distinct missing category for categorical variables.
The multivariable model included VECACS in addition to age, sex, past medical history (hypertension, diabetes, dyslipidemia, smoker, CHF, stroke, CKD, or prior CAD), mode of stress, inpatient status, SRS, SDS and LVEF similar to previous studies [2], [8], [28], [29]. We assessed for interactions between perfusion and VECACS with all other variables included in the multivariable model.
The analysis was repeated to assess associations with each of the components of the composite outcome. The proportional hazards assumption was assessed for all models using Schoenfeld residuals and was found to be valid in all analyses. Lastly, we assessed the net reclassification index of adding VECACS to the full multivariables model. Net re-classification index (NRI) was used to assess the additive prognostic utility of VECACS when added to the other components of the multivariable model.[30]. Bootstrapping was used to calculate 95% confidence intervals (CI) for event, non-event, and continuous NRI.[31] Categorical NRI was also assessed. Model goodness-of-fit was compared with a likelihood ratio test and calibration was assessed using Brier scores.
All statistical tests were two-sided and a p-value < 0.05 was considered statistically significant. All analyses were performed using Stata/IC version 13.1 (StataCorp, College Station, Texas, USA).
3. Results
3.1. Patient characteristics
A total of 4,720 patients who underwent SPECT MPI between September 1, 2014 and December 31, 2018 were included. Population characteristics are shown in Table 1. The patients who experienced MACE were older (69.9 ± 11.1 vs 64.9 ± 11.7, p < 0.001) and more likely to be male (64.0 %vs 52.4%, p < 0.001) or have a history of diabetes (35.8% vs 23.8%, p < 0.001). Patients experiencing MACE were more likely to have extensive VECACS (42.7% vs.18.8%, p < 0.001). Patients without MACE were more likely to have absent VECACS at baseline (32.7% vs 8.9%, p < 0.001). Characteristics of patients with and without MACE are shown in Table S1.
Table 1.
Baseline Population Characteristics that stratified by VECACS score.
| Absent (n = 1,427) |
Equivocal (n = 128) |
Present (n = 2,181) |
Present-extensive (n = 1,032) | P-Value | |
|---|---|---|---|---|---|
| Age, mean ± SD | 68.5 ± 11.3 | 63.4 ± 12.6 | 66.9 ± 10.6 | 72.1 ± 9.2 | <0.001 |
| Male, n(%) | 571 (40.0) | 66 (51.6) | 1,254 (57.5) | 673 (65.2) | <0.001 |
| Past Medical History | |||||
| Hypertension, n(%) | 646 (45.3) | 54 (42.2) | 1,332 (61.1) | 712 (69.0) | <0.001 |
| Diabetes, n(%) | 229 (16.1) | 28 (21.9) | 597 (27.4) | 344 (33.3) | <0.001 |
| Dyslipidemia, n(%) | 446 (31.3) | 43 (33.6) | 1,093 (50.1) | 621 (60.2) | <0.001 |
| Current Smoker, n(%) | 143 (10.0) | 11 (8.6) | 224 (10.3) | 133 (12.9) | 0.077 |
| History of CAD, n(%) | 41 (2.9) | 22 (17.2) | 258 (11.8) | 272 (26.4) | <0.001 |
| CHF, n(%) | 28 (2.0) | 6 (4.7) | 98 (4.5) | 81 (7.9) | <0.001 |
| Stroke, n(%) | 9 (0.6) | 4 (3.1) | 19 (0.9) | 11 (1.1) | 0.035 |
| CKD, n(%) | 6 (0.4) | 2 (1.6) | 32 (1.5) | 19 (1.8) | 0.008 |
| Family History, n(%) | 613 (43.0) | 24 (18.8) | 963 (44.2) | 422 (40.9) | <0.001 |
| Exercise Stress, n(%) | 1,017 (71.3) | 82 (64.1) | 1364 (62.5) | 525 (50.9) | <0.001 |
| Inpatient, n(%) | 470 (32.9) | 54 (42.2) | 829 (38.0) | 418 (40.5) | 0.001 |
| SSS | 0 (0 – 0) | 0 (0 – 2) | 0 (0 – 2) | 2 (0 – 6) | <0.001 |
| SRS | 0 (0 – 0) | 0 (0 – 0) | 0 (0 – 0) | 0 (0 – 3) | <0.001 |
| SDS | 0 (0 – 0) | 0 (0 – 1) | 0 (0 – 1) | 0 (0 – 2) | <0.001 |
| LVEF | 69 (61 – 74) | 69 (60 – 75) | 66 (57 – 73) | 63 (49 – 71) | <0.001 |
Categorical variables presented as n (%), continuous variables presented as mean ± SD or median (interquartile range). MACE - major adverse cardiac event, CAD-coronary artery disease, CKD-chronic kidney disease, SSS-summed stress score, SRS-summed rest score, SDS- summed difference score, LVEF-left ventricular ejection fraction, LVESV-left ventricular end systolic volume, VECACS- Visually estimated coronary artery calcium score
3.2. Associations with MACE
During median follow-up of 2.9 years (IQR 1.8 – 4.2), at least one MACE occurred in 494 (10.5%) patients including a total of 322 (6.8%) deaths, 155 (3.3%) acute coronary syndromes, and 111 (2.4%) late revascularizations. Kaplan-Meir survival estimate curves stratified by VECACS in the overall population are demonstrated in Fig. 2. Patients with equivocal, present, or extensive VECACS were more likely to experience MACE during follow-up compared to patients without VECACS (all log-rank p < 0.001). Patients with extensive VECACS were more likely to experience MACE than patients with equivocal or present VECACS (log rank p < 0.001). Annualized MACE rates across categories of perfusion and VECACS are shown in Fig. 3.
Fig. 2.
MACE-free survival based on visually estimated coronary artery calcium score in the overall patient population. Significance assessed using log-rank p-values.
Fig. 3.
Incidence of major adverse cardiovascular events (MACE), stratified by stress perfusion and visually estimated coronary artery calcium (VECACS). SSS – summed stress score.
Kaplan-Meier survival curves stratified by VECACS in patients with normal (SSS < 4) and abnormal (SSS ≥ 4) stress perfusion are shown in Fig. 4a and 4b respectively. Differences between groups of VECACS were similar to the overall results in patients with normal SPECT perfusion. However, in patients with abnormal perfusion, only extensive VECACS was associated with increased MACE compared to patients with absent VECACS (log-rank p < 0.001) or compared to patients with equivocal or present VECACS (both log-rank p < 0.03). We also assessed results in patients without a history of CAD (Fig. 4c) and with known CAD (Fig. 4d). In patients without CAD, patients with equivocal, present, or extensive VECACS were more likely to experience MACE during follow-up compared to patients with absent VECACS (all log-rank p < 0.001). There were no significant differences in patients with a history of CAD (all log-rank p > 0.05).
Fig. 4.
MACE-free survival based on visually estimated coronary artery calcium score in patients with a) normal SPECT-MPI b) abnormal SPECT-MPI c) no history of coronary artery disease (CAD) d) known CAD. Significance assessed using log-rank p-values.
Results of univariable and multivariable Cox proportional hazard analysis for the primary outcome are shown in Table 2. VECACS categories of equivocal (adjusted HR 2.54, 95% CI 1.45–4.45, p = 0.001), present (adjusted HR 2.44, 95% CI 1.74–3.42, p < 0.001) and extensive (adjusted HR 3.47, 95% CI 2.41–5.00, p < 0.001) were all independently associated with increased MACE events compared to absent. Additionally, increasing SDS was independently associated with increased risk of MACE (adjusted HR 1.05 per point, 95% CI 1.02–1.07, p < 0.001).
Table 2.
Results of Cox proportional hazard analysis for primary outcome.
| Variable | Unadjusted HR (95% CI) |
p-value | Adjusted HR (95% CI) |
p-value |
|---|---|---|---|---|
| Age (per 10 years) | 1.46(1.35–1.58) | <0.001 | 1.16 (1.05 – 1.27) | 0.002 |
| Male | 1.60 (1.33–1.92) | <0.001 | 1.15 (0.94 – 1.40) | 0.179 |
| Hypertension | 1.24(1.03–1.48) | 0.020 | 0.95 (0.77 – 1.16) | 0.587 |
| Diabetes | 1.82 (1.51–2.19) | <0.001 | 1.43 (1.17 – 1.74) | <0.001 |
| Dyslipidemia | 1.11(0.93–1.33) | 0.247 | 0.78 (0.64 – 0.95) | 0.012 |
| Current Smoker | 1.01(0.76–1.33) | 0.953 | 1.00 (0.75 – 1.33) | 0.982 |
| History of CAD | 3.52 (2.91–4.27) | <0.001 | 2.06 (1.67 – 2.53) | <0.001 |
| CHF | 2.72 (2.04, 3.64) | <0.001 | 1.33 (0.62 – 2.86) | 0.461 |
| Stroke | 2.14 (1.15 – 4.01) | 0.017 | 1.42 (0.75 – 2.68) | 0.282 |
| CKD | 1.94 (1.07 – 3.53) | 0.029 | 1.12 (0.61 – 2.06) | 0.707 |
| Exercise Stress | 0.41 (0.34 – 0.49) | <0.001 | 0.56 (0.47 – 0.68) | <0.001 |
| Inpatient | 1.69 (1.41 – 2.01) | <0.001 | 1.34 (1.11 – 1.60) | 0.002 |
| SSS | 1.07 (1.06 – 1.08) | <0.001 | – | – |
| SRS | 1.07 (1.06 – 1.08) | <0.001 | 1.02 (1.00 – 1.03) | 0.049 |
| SDS | 1.10 (1.08 – 1.13) | <0.001 | 1.05 (1.02 – 1.07) | <0.001 |
| LVEF | 0.97 (0.96–0.97) | <0.001 | 0.99 (0.98 – 1.00) | 0.003 |
| VECACS | ||||
| Absent | Reference | Reference | Reference | Reference |
| Equivocal | 3.81 (2.20 – 6.59) | <0.001 | 2.54 (1.45 – 4.45) | 0.001 |
| Present | 3.51 (2.54 – 4.85) | <0.001 | 2.44 (1.74 – 3.42) | <0.001 |
| Extensive | 7.61 (5.50 – 10.5) | <0.001 | 3.47 (2.41 – 5.00) | <0.001 |
CAD: coronary artery disease, CHF: congestive heart failure, CKD: chronic kidney disease, SSS: summed stress score, SRS: summed rest score, SDS: summed difference score, LVEF: left ventricular ejection fraction, VECACS: visually estimated coronary artery calcium score.
The multivariable analysis was repeated for each of the components of the MACE outcome with results in Table 3. Associations with non-fatal outcomes were assessed using a Fine Gray competing risk analysis and death as the competing risk. There was an independent association with increased all-cause mortality associated with VECACS categories of equivocal (adjusted HR 2.39, p = 0.007), present (adjusted HR 1.75, p = 0.004) and extensive (adjusted HR 2.44, p < 0.001) compared to a VECACS category of absent. For the non-fatal outcomes there was a more stepwise increase in risk associated with increasing VECACS categories. Associations with VECACS in patients with and without history of CAD are shown in Table S2.
Table 3.
Results of multivariable Cox proportional hazard analysis MACE components.
| Unadjusted HR (95% CI) | p-value | Adjusted HR (95% CI) | p-value | |
|---|---|---|---|---|
| All-cause mortality | ||||
| Absent | Reference | Reference | Reference | Reference |
| Equivocal | 3.50 (1.89 – 6.50) | <0.001 | 2.39 (1.26 – 4.54) | 0.007 |
| Present | 2.65 (1.84 – 3.83) | <0.001 | 1.75 (1.19 – 2.56) | 0.004 |
| Extensive | 5.57 (3.85 – 8.05) | <0.001 | 2.44 (1.61 – 3.69) | <0.001 |
| Acute coronary Syndrome | ||||
| Absent | Reference | Reference | Reference | Reference |
| Equivocal | 3.84 | 0.054 | 2.51 (0.62 – 10.2) | 0.196 |
| Present | 6.18 (2.83 – 13.5) | <0.001 | 4.90 (2.12 – 11.3) | <0.001 |
| Extensive | 13.2 (6.04 – 28.8) | <0.001 | 7.37 (2.95 – 18.4) | <0.001 |
| Late revascularization | ||||
| Absent | Reference | Reference | Reference | Reference |
| Equivocal | 6.88(1.52 – 31.2) | 0.012 | 4.13 (0.86 – 19.8) | 0.076 |
| Present | 8.56 (3.09–23.7) | <0.001 | 6.80 (2.27 – 20.4) | <0.001 |
| Extensive | 18.8 (6.82 – 52.1) | <0.001 | 11.6 (3.50 – 38.5) | <0.001 |
Results of univariable and multivariable Cox proportional hazard analysis for each of the components of MACE. Association with non-fatal outcomes modeled with a competing hazard of death and values representing sub-hazard ratio estimates. CI – confidence interval, HR- hazard ratio.
3.3. Net reclassification
We assessed the net risk reclassification when VECACS was added to the remainder of the multivariable model (as shown in Table 2). Addition of VECACS using the four-group system improved overall reclassification (continuous NRI 0.207, 95% CI 0.131– 0.310), with improvement in model fit (increase LR chi2 44.9, p < 0.001). This was driven by improved reclassification of the proportion of patients with events to higher predicted risks (event NRI 0.219, 95% CI 0.165– 0.297) but not patients without events to lower predicted risks (non-event NRI −0.012, 95% CI −0.052 to 0.038). Model calibration was similar with (Brier score 0.102) and without VECACS (Brier score 0.108). VECACS with 4 groups resulted in better model fit compared to the presence/absence of coronary calcification alone (increase LR chi2 11.4, likelihood ratio test p-value = 0.003). Results of the categorical NRI analysis are shown in Table S3.
4. Discussion
In patients who underwent SPECT-MPI, VECACS was a significant and independent predictor of MACE, which persisted after adjusting for traditional SPECT-MPI results. We found an independent increase in death, ACS, and late revascularization that occurred in a stepwise fashion with progressively increased VECACS category. VECACS stratified risk of MACE in patients with normal and abnormal regional perfusion. Additionally, reporting of extensive VECACS significantly improved risk estimation when added to other SPECT MPI variables. Our findings suggest that a VECACS should be reported on SPECT-MPI studies using CTAC.
There is growing evidence that physicians should incorporate both anatomical and functional information when assessing patients with known or suspected CAD. Although a sensitive marker for coronary atherosclerosis, the Agatston CACS has modest predictive value for obstructive CAD on cardiac catheterization [32]. In contrast, SPECT-MPI identifies flow limiting CAD but is insensitive to subclinical atherosclerosis [33]. When combined, SPECT-MPI and quantitative CACS provide both anatomical and functional assessment of CAD, which may improve sensitivity and specificity compared with each test alone [3]. Chang et al. (2009) demonstrated that quantitative CACS combined with SPECT-MPI findings provided independent and complementary information among a cohort of 1,126 patients without prior CAD [13]. The prevalence of abnormal perfusion increased with increasing CACS, and CACS score predicted cardiovascular risk among patients with both normal and abnormal perfusion. Engbers et al. evaluated combined Agatston CACS and SPECT-MPI in 4,897 symptomatic patients without prior CAD[2], demonstrating a stepwise increase in MACE with increasing CACS among patients with both normal and abnormal perfusion. Our study is in line with these findings and confirms that anatomical data from the VECACS has a added prognostic value when combined with SPECT-MPI.
The strong prognostic value of VECACS on CTAC has implications for reporting of CAC detected through other non-dedicated CT scans. Coronary calcification has significant prognostic value on lung cancer screening CT scans [34]. Recent Society of Cardiovascular CT guidelines recommend the routine reporting of incidental CAC detected on non-cardiac chest CT [35]. The suggested visually estimated Coronary Artery Calcium Data and Reporting System (CAC-DRS) parallels the VECACS score used in our study [36]. Our study highlights the significance of incidental CAC detected during clinical practice and supports recommendations for reporting VECACS.
VECACS from low-dose CTAC scans have shown good correlation with the quantitative Agatston CACS [17], [18]. Einstein et al. compared these values in 492 patients who underwent both SPECT-MPI and quantitative CACS imaging [18]. Experienced readers scored VECACS using a six-level scale (0, 1–9, 10–99, 100–399, 400–999 and > 1,000). The CACS score was within the visually estimated range in 63% of cases and within one category in 93% of cases (weighted kappa 0.89, p < 0.0001). Mylonas et al. assessed VECACS and CACS in 91 patients who underwent positron emission tomography MPI and CACS within 6 months [17]. VECACS from CTAC images had good intraclass correlation with CACS (0.844), and demonstrated excellent interobserver agreement using a 4-point scale (kappa 0.941) [17]. Therefore, while VECACS is inherently subjective there seems to be reasonable accuracy and interobserver agreement. The radiation exposure from CTAC (<1mSv) is lower compared to dedicated CAC scanning which average ~ 1 mSv [37], [38]. However, both are associated with relatively low radiation exposure compared to a 1-day myocardial rest stress study using Tc-99 m sestamibi (~9 mSv) [39].
In addition to improving risk estimation, reporting VECACS may influence patient management decisions. Current guidelines recommenced use of CACS to identify intermediate risk patients who may benefit from lipid lowering therapy [40]. In a randomized trial use of the Agatston CACS to guide therapy improved vascular risk profile versus conventional management alone [41]. It is likely that patient knowledge of CACS improved compliance with medications and lifestyle changes. CACS also identifies patients most likely to benefit from statin therapy, with an NNT of 12 to prevent 10-year MACE with CACS > 100 [42]. Detecting coronary calcium may facilitate preventative interventions, facilitating appropriate therapy intensification but also enabling de-escalation of low-yield strategies. In our study, patients with absent VECACS had a low risk of MACE independent of SPECT-MPI findings. Additionally, even equivocal VECACS, which represents patients with a small burden of coronary calcification, was associated with increased risk. Reporting VECACS could help physicians target medical therapies, but also be used to engage patients in their care.
Our study has a few important limitations in addition to its retrospective design. Readers were not blinded to VECACS and this likely influenced interpretation of perfusion findings. In spite of this, VECACS was an independent predictor of MACE. Some patients may have been lost to follow-up due to emigration; however, this is less common in the older age groups represented in our study [43]. VECACS was reported by individual readers experienced in MPI and quantitative CACS imaging. While there was inherent interobserver variability, previous studies of VECACS have shown this variability is low. The simple visual VECACS classification scale utilized in this study was readily integrated into a clinical workflow and provided meaningful risk stratification. However, the development of automated methods to quantify CAC, for example using artificial intelligence techniques [34], [44], may provide more precise estimates of CACS and remove the need for visual estimation. Future studies could investigate novel risk scores incorporating VECACS, with dedicated derivation and validation cohorts. Finally, VECACS was reported clinically which could have influenced patient management and decisions to pursue revascularization. However, increasing VECACS was also associated with increased risk for all-cause mortality and ACS.
5. Conclusion
The VECACS is an independent predictor of MACE, complementary to traditional clinical and SPECT-MPI risk-markers. SPECT-MPI readers should consider routinely evaluating and reporting VECACS based on CTAC. Future SPECT-MPI reporting guidelines may consider recommendations for VECACS assessment as well as standardized reporting criteria.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
Acknowledgments
None.
Funding.
None
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.ijcha.2021.100827.
Appendix A. Supplementary data
The following are the Supplementary data to this article:
References
- 1.Berman D.S., Hachamovitch R., Kiat H., Cohen I., Cabico J.A., Wang F.P., Friedman J.D., Germano G., Van Train K., Diamond G.A. Incremental value of prognostic testing in patients with known or suspected ischemic heart disease: a basis for optimal utilization of exercise technetium-99m sestamibi myocardial perfusion single-photon emission computed tomography. J. Am. Coll. Cardiol. 1995;26(3):639–647. doi: 10.1016/0735-1097(95)00218-S. [DOI] [PubMed] [Google Scholar]
- 2.Engbers E.M., Timmer J.R., Ottervanger J.P., Mouden M., Knollema S., Jager P.L. Prognostic Value of Coronary Artery Calcium Scoring in Addition to Single-Photon Emission Computed Tomographic Myocardial Perfusion Imaging in Symptomatic Patients. Circ Cardiovasc Imaging. 2016;9(5) doi: 10.1161/CIRCIMAGING.115.003966. [DOI] [PubMed] [Google Scholar]
- 3.Schepis T., Gaemperli O., Koepfli P., Namdar M., Valenta I., Scheffel H., Leschka S., Husmann L., Eberli F.R., Luscher T.F., Alkadhi H., Kaufmann P.A. Added value of coronary artery calcium score as an adjunct to gated SPECT for the evaluation of coronary artery disease in an intermediate-risk population. J. Nucl. Med. 2007;48(9):1424–1430. doi: 10.2967/jnumed.107.040758. [DOI] [PubMed] [Google Scholar]
- 4.Fihn S.D., Gardin J.M., Abrams J. 2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS Guideline for the diagnosis and management of patients with stable ischemic heart disease: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines, and the American College of Physicians, American Association for Thoracic Surgery, Preventive Cardiovascular Nurses Association, Society for Cardiovascular Angiography and Interventions, and Society of Thoracic Surgeons. J. Am. Coll. Cardiol. 2012;60:e44–e164. doi: 10.1016/j.jacc.2012.07.013. [DOI] [PubMed] [Google Scholar]
- 5.Otaki Y., Betancur J., Sharir T. 5-Year Prognostic Value of Quantitative Versus Visual MPI in Subtle Perfusion Defects: Results From REFINE SPECT. JACC Cardiovasc Imaging. 2020;13:774–785. doi: 10.1016/j.jcmg.2019.02.028. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Bourque J.M., Beller G.A. Stress myocardial perfusion imaging for assessing prognosis: an update. JACC Cardiovasc Imaging. 2011;4(12):1305–1319. doi: 10.1016/j.jcmg.2011.10.003. [DOI] [PubMed] [Google Scholar]
- 7.Miller R.J.H., Hu L.-H., Gransar H., Betancur J., Eisenberg E., Otaki Y., Sharir T., Fish M.B., Ruddy T.D., Dorbala S., Carli M.D., Einstein A.J., Kaufmann P.A., Sinusas A.J., Miller E.J., Bateman T., Germano G., Tamarappoo B.K., Dey D., Berman D.S., Slomka P.J. Transient ischaemic dilation and post-stress wall motion abnormality increase risk in patients with less than moderate ischaemia: analysis of the REFINE SPECT registry. Eur Heart J Cardiovasc Imaging. 2020;21(5):567–575. doi: 10.1093/ehjci/jez172. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Hachamovitch R., Rozanski A., Shaw L.J., Stone G.W., Thomson L.E.J., Friedman J.D., Hayes S.W., Cohen I., Germano G., Berman D.S. Impact of ischaemia and scar on the therapeutic benefit derived from myocardial revascularization vs. medical therapy among patients undergoing stress-rest myocardial perfusion scintigraphy. Eur. Heart J. 2011;32(8):1012–1024. doi: 10.1093/eurheartj/ehq500. [DOI] [PubMed] [Google Scholar]
- 9.Huang J.-Y., Huang C.-K., Yen R.-F., Wu H.-Y., Tu Y.-K., Cheng M.-F., Lu C.-C., Tzen K.-Y., Chien K.-L., Wu Y.-W. Diagnostic Performance of Attenuation-Corrected Myocardial Perfusion Imaging for Coronary Artery Disease: A Systematic Review and Meta-Analysis. J. Nucl. Med. 2016;57(12):1893–1898. doi: 10.2967/jnumed.115.171462. [DOI] [PubMed] [Google Scholar]
- 10.van Dijk J.D., Mouden M., Ottervanger J.P., van Dalen J.A., Knollema S., Slump C.H., Jager P.L. Value of attenuation correction in stress-only myocardial perfusion imaging using CZT-SPECT. J Nucl Cardiol. 2017;24(2):395–401. doi: 10.1007/s12350-015-0374-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Patchett N.D., Pawar S., Miller E.J. Visual identification of coronary calcifications on attenuation correction CT improves diagnostic accuracy of SPECT/CT myocardial perfusion imaging. J Nucl Cardiol. 2017;24(2):711–720. doi: 10.1007/s12350-016-0395-5. [DOI] [PubMed] [Google Scholar]
- 12.Agatston A.S., Janowitz W.R., Hildner F.J., Zusmer N.R., Viamonte M., Detrano R. Quantification of Coronary-Artery Calcium Using Ultrafast Computed-Tomography. J. Am. Coll. Cardiol. 1990;15(4):827–832. doi: 10.1016/0735-1097(90)90282-t. [DOI] [PubMed] [Google Scholar]
- 13.Chang S.M., Nabi F., Xu J., Peterson L.E., Achari A., Pratt C.M., Mahmarian J.J. The coronary artery calcium score and stress myocardial perfusion imaging provide independent and complementary prediction of cardiac risk. J. Am. Coll. Cardiol. 2009;54(20):1872–1882. doi: 10.1016/j.jacc.2009.05.071. [DOI] [PubMed] [Google Scholar]
- 14.Peng A.W., Mirbolouk M., Orimoloye O.A. Long-Term All-Cause and Cause-Specific Mortality in Asymptomatic Patients With CAC >/=1,000: Results From the CAC Consortium. JACC Cardiovasc Imaging. 2020;13:83–93. doi: 10.1016/j.jcmg.2019.02.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Blaha M.J., Blankstein R., Nasir K. Coronary Artery Calcium Scores of Zero and Establishing the Concept of Negative Risk Factors. J. Am. Coll. Cardiol. 2019;74:12–14. doi: 10.1016/j.jacc.2019.05.032. [DOI] [PubMed] [Google Scholar]
- 16.Mühlenbruch G., Thomas C., Wildberger J.E., Koos R., Das M., Hohl C., Katoh M., Günther R.W., Mahnken A.H. Effect of varying slice thickness on coronary calcium scoring with multislice computed tomography in vitro and in vivo. Invest. Radiol. 2005;40(11):695–699. doi: 10.1097/01.rli.0000179523.07907.a6. [DOI] [PubMed] [Google Scholar]
- 17.Mylonas I., Kazmi M., Fuller L., deKemp R.A., Yam Y., Chen L., Beanlands R.S., Chow B.J.W. Measuring coronary artery calcification using positron emission tomography-computed tomography attenuation correction images. Eur Heart J Cardiovasc Imaging. 2012;13(9):786–792. doi: 10.1093/ehjci/jes079. [DOI] [PubMed] [Google Scholar]
- 18.Einstein A.J., Johnson L.L., Bokhari S., Son J., Thompson R.C., Bateman T.M., Hayes S.W., Berman D.S. Agreement of visual estimation of coronary artery calcium from low-dose CT attenuation correction scans in hybrid PET/CT and SPECT/CT with standard Agatston score. J. Am. Coll. Cardiol. 2010;56(23):1914–1921. doi: 10.1016/j.jacc.2010.05.057. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Miller R.J.H., Bonow R.O., Gransar H., Park R., Slomka P.J., Friedman J.D., Hayes S., Thomson L., Tamarappoo B., Rozanski A., Doenst T., Berman D.S. Percutaneous or surgical revascularization is associated with survival benefit in stable coronary artery disease. Eur Heart J Cardiovasc Imaging. 2020;21(9):961–970. doi: 10.1093/ehjci/jeaa083. [DOI] [PubMed] [Google Scholar]
- 20.Azadani P.N., Miller R.J.H., Sharir T. Impact of Early Revascularization on Major Adverse Cardiovascular Events in Relation to Automatically Quantified Ischemia. JACC Cardiovasc Imaging. 2021;14(3):644–663. doi: 10.1016/j.jcmg.2020.05.039. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Ghali W.A., Knudtson M.L. Overview of the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease. On behalf of the APPROACH investigators. Can. J. Cardiol. 2000;16:1225–1230. [PubMed] [Google Scholar]
- 22.Miller R.J.H., Klein E., Gransar H., Slomka P.J., Friedman J.D., Hayes S., Thomson L., Tamarappoo B., Rozanski A., Berman D.S. Prognostic significance of previous myocardial infarction and previous revascularization in patients undergoing SPECT MPI. Int. J. Cardiol. 2020;313:9–15. doi: 10.1016/j.ijcard.2020.04.012. [DOI] [PubMed] [Google Scholar]
- 23.Henzlova M.J., Duvall W.L., Einstein A.J., Travin M.I., Verberne H.J. ASNC imaging guidelines for SPECT nuclear cardiology procedures: Stress, protocols, and tracers. J Nucl Cardiol. 2016;23(3):606–639. doi: 10.1007/s12350-015-0387-x. [DOI] [PubMed] [Google Scholar]
- 24.Berman D.S., Abidov A., Kang X. Prognostic validation of a 17-segment score derived from a 20-segment score for myocardial perfusion SPECT interpretation. J Nucl Cardiol. 2004;11:414–423. doi: 10.1016/j.nuclcard.2004.03.033. [DOI] [PubMed] [Google Scholar]
- 25.Hachamovitch R., Berman D.S., Kiat H., Cohen I., Cabico J.A., Friedman J., Diamond G.A. Exercise myocardial perfusion SPECT in patients without known coronary artery disease: incremental prognostic value and use in risk stratification. Circulation. 1996;93(5):905–914. doi: 10.1161/01.cir.93.5.905. [DOI] [PubMed] [Google Scholar]
- 26.Greenland P., Bonow R.O., Brundage B.H., Budoff M.J., Eisenberg M.J., Grundy S.M., Lauer M.S., Post W.S., Raggi P., Redberg R.F., Rodgers G.P., Shaw L.J., Taylor A.J., Weintraub W.S., Harrington R.A., Abrams J., Anderson J.L., Bates E.R., Eisenberg M.J., Grines C.L., Hlatky M.A., Lichtenberg R.C., Lindner J.R., Pohost G.M., Schofield R.S., Shubrooks S.J., Stein J.H., Tracy C.M., Vogel R.A., Wesley D.J. ACCF/AHA 2007 clinical expert consensus document on coronary artery calcium scoring by computed tomography in global cardiovascular risk assessment and in evaluation of patients with chest pain. J. Am. Coll. Cardiol. 2007;49(3):378–402. doi: 10.1016/j.jacc.2006.10.001. [DOI] [PubMed] [Google Scholar]
- 27.Perrone-Filardi P, Achenbach S, Mohlenkamp S, et al. Cardiac computed tomography and myocardial perfusion scintigraphy for risk stratification in asymptomatic individuals without known cardiovascular disease. Eur Heart J. 2011;32:1986-93, 93a, 93b. [DOI] [PubMed]
- 28.Kattoor AJ, Kolkailah AA, Iskander F, et al. The prognostic value of regadenoson SPECT myocardial perfusion imaging: The largest cohort to date. J Nucl Cardiol. 2020; Epub ahead of print. [DOI] [PubMed]
- 29.Miller RJH, Sharir T, Otaki Y, et al. Quantitation of Post-Stress Change in Ventricular Morphology Improves Risk Stratification. J Nucl Med. 2021; Epub ahead of print. [DOI] [PMC free article] [PubMed]
- 30.Pencina M.J., D' Agostino R.B., D' Agostino R.B., Vasan R.S. Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond. Stat. Med. 2008;27(2):157–172. doi: 10.1002/sim.2929. [DOI] [PubMed] [Google Scholar]
- 31.Efron B. 1977 Rietz Lecture - Bootstrap Methods - Another Look at the Jackknife. Ann. Stat. 1979;7:1–26. [Google Scholar]
- 32.Yerramasu A., Lahiri A., Venuraju S., Dumo A., Lipkin D., Underwood S.R., Rakhit R.D., Patel D.J. Diagnostic role of coronary calcium scoring in the rapid access chest pain clinic: prospective evaluation of NICE guidance. Eur Heart J Cardiovasc Imaging. 2014;15(8):886–892. doi: 10.1093/ehjci/jeu011. [DOI] [PubMed] [Google Scholar]
- 33.Cremer P., Hachamovitch R., Tamarappoo B. Clinical decision making with myocardial perfusion imaging in patients with known or suspected coronary artery disease. Semin. Nucl. Med. 2014;44(4):320–329. doi: 10.1053/j.semnuclmed.2014.04.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Isgum I., Prokop M., Niemeijer M., Viergever M.A., van Ginneken B. Automatic coronary calcium scoring in low-dose chest computed tomography. IEEE Trans. Med. Imaging. 2012;31(12):2322–2334. doi: 10.1109/TMI.2012.2216889. [DOI] [PubMed] [Google Scholar]
- 35.Hecht H.S., Cronin P., Blaha M.J. 2016 SCCT/STR guidelines for coronary artery calcium scoring of noncontrast noncardiac chest CT scans. J. Cardiovasc. Comput. Tomogr. 2017;11:74–84. doi: 10.1016/j.jcct.2016.11.003. [DOI] [PubMed] [Google Scholar]
- 36.Hecht H.S., Blaha M.J., Kazerooni E.A., Cury R.C., Budoff M., Leipsic J., Shaw L. CAC-DRS: Coronary Artery Calcium Data and Reporting System. An expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT) J. Cardiovasc. Comput. Tomogr. 2018;12(3):185–191. doi: 10.1016/j.jcct.2018.03.008. [DOI] [PubMed] [Google Scholar]
- 37.Messenger B., Li D., Nasir K., Carr J.J., Blankstein R., Budoff M.J. Coronary calcium scans and radiation exposure in the multi-ethnic study of atherosclerosis. Int. J. Cardiovasc. Imaging. 2016;32(3):525–529. doi: 10.1007/s10554-015-0799-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Case J.A. Minimizing the radiation dose of CT attenuation correction while improving image quality: The case for innovation. J Nucl Cardiol. 2016;23(5):1080–1085. doi: 10.1007/s12350-015-0182-8. [DOI] [PubMed] [Google Scholar]
- 39.Gerber T.C., Carr J.J., Arai A.E., Dixon R.L., Ferrari V.A., Gomes A.S., Heller G.V., McCollough C.H., McNitt-Gray M.F., Mettler F.A., Mieres J.H., Morin R.L., Yester M.V. Ionizing radiation in cardiac imaging: a science advisory from the American Heart Association Committee on Cardiac Imaging of the Council on Clinical Cardiology and Committee on Cardiovascular Imaging and Intervention of the Council on Cardiovascular Radiology and Intervention. Circulation. 2009;119(7):1056–1065. doi: 10.1161/CIRCULATIONAHA.108.191650. [DOI] [PubMed] [Google Scholar]
- 40.Grundy S.M., Stone N.J., Bailey A.L. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2019;139:e1082–e1143. doi: 10.1161/CIR.0000000000000625. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Rozanski A., Gransar H., Shaw L.J., Kim J., Miranda-Peats L., Wong N.D., Rana J.S., Orakzai R., Hayes S.W., Friedman J.D., Thomson L.E.J., Polk D., Min J., Budoff M.J., Berman D.S. Impact of coronary artery calcium scanning on coronary risk factors and downstream testing the EISNER (Early Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) prospective randomized trial. J. Am. Coll. Cardiol. 2011;57(15):1622–1632. doi: 10.1016/j.jacc.2011.01.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Mitchell J.D., Fergestrom N., Gage B.F., Paisley R., Moon P., Novak E., Cheezum M., Shaw L.J., Villines T.C. Impact of Statins on Cardiovascular Outcomes Following Coronary Artery Calcium Scoring. J. Am. Coll. Cardiol. 2018;72(25):3233–3242. doi: 10.1016/j.jacc.2018.09.051. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Statistics Canada. Table 17-10-0015-01 Estimates of the components of interprovincial migration, by age and sex, annual. DOI: https://doi.org/10.25318/1710001501-eng
- 44.Commandeur F., Slomka P.J., Goeller M., Chen X.i., Cadet S., Razipour A., McElhinney P., Gransar H., Cantu S., Miller R.J.H., Rozanski A., Achenbach S., Tamarappoo B.K., Berman D.S., Dey D. Machine learning to predict the long-term risk of myocardial infarction and cardiac death based on clinical risk, coronary calcium, and epicardial adipose tissue: a prospective study. Cardiovasc. Res. 2020;116(14):2216–2225. doi: 10.1093/cvr/cvz321. [DOI] [PMC free article] [PubMed] [Google Scholar]
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