Abstract
Aims
Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) remains one of the most widely used imaging modalities for the diagnosis and prognostication of coronary artery disease (CAD). Despite the extensive prognostic information provided by MPI, little is known about how this influences the prescription of medical therapy for CAD. We evaluated the relationship between MPI with computed tomography (CT) attenuation correction and prescription of acetylsalicylic acid (ASA) and statins.
Methods and results
We performed a retrospective analysis of consecutive patients who underwent SPECT MPI at a single centre between 2015 and 2021. Myocardial perfusion abnormalities and coronary calcium burden were assessed, with attenuation correction imaging 77.8% of patients. Medication prescriptions before and within 180 days after the test were compared. Associations between abnormal perfusion and calcium burden with ASA and statin prescription were assessed using multivariable logistic regression. In total, 9908 patients were included, with a mean age 66.8 ± 11.7 years and 5337 (53.9%) males. The prescription of statins increased more in patients with abnormal perfusion (increase of 19.2 vs. 12.0%, P < 0.001). Similarly, the presence of extensive CAC led to a greater increase in statin prescription compared with no calcium (increase 12.1 vs. 7.8%, P < 0.001). In multivariable analyses, ischaemia and coronary artery calcium were independently associated with ASA and statin prescription.
Conclusion
Abnormal MPI testing was associated with significant changes in medical therapy. Both calcium burden and perfusion abnormalities were associated with increased prescriptions of medical therapy for CAD.
Keywords: coronary artery disease, myocardial perfusion imaging, medical therapy
Graphical Abstract
Graphical Abstract.
Overview of study design. Perfusion and coronary artery calcium (CAC) were assessed for patients undergoing myocardial perfusion imaging (MPI). We evaluated associations between imaging findings and prescriptions of medical therapy including acetylsalicylic acid and statins.
Introduction
With wide availability and strong diagnostic performance, myocardial perfusion imaging (MPI) continues to be highly utilized for the diagnosis and risk stratification of coronary artery disease (CAD).1 Abnormal perfusion on MPI is associated with a 2–5-fold increase in major adverse cardiovascular events (MACE).2–4 Left ventricular volumes and left ventricular function also carry prognostic value.5 The use of computed tomographic (CT) attenuation correction affords information about coronary artery calcium (CAC),6 which could help guide decisions regarding statin therapy.7–9 Combining perfusion and functional imaging findings with established clinical risk factors can further improve risk prediction.10,11
Despite the robust clinical and imaging-based data afforded by MPI, less is known about how the results influence medical management. Aggressive medical therapy for CAD forms the foundation of therapy in the absence of refractory symptoms or prognostically significant disease.12,13 Yet, a high proportion of patients with CAD and even high-risk disease do not achieve optimal medical therapy. In a 10-year follow-up of the SYNTAX trial investigating the role of optimal medical therapy following revascularization for surgical disease, only 46% of patients were on optimal medical therapy at 5 years despite a supervised clinical trial setting.14,15 Importantly, targeted adjustments to medical therapy are the most likely reason that specific testing strategies improve clinical outcomes.16 The complementary functional and anatomic information from MPI with CAC assessment could potentially be leveraged by physicians to accurately target medical therapies such as beta-blockers, acetylsalicylic acid (ASA), statins, and renin–angiotensin–aldosterone system inhibitors. Despite this promise, there is limited evidence that MPI findings lead to more optimal medical therapy in routine care.
The primary objective of this study was to determine prescription rates for ASA and statins in a real-world and contemporary setting before and after MPI. The secondary objective was to describe changes in other medications and evaluate associations with imaging findings to determine which findings may influence prescription patterns.
Methods
Study population
We included 10 052 consecutive patients who underwent single-photon emission computed tomography (SPECT) MPI as part of clinical care between 1 January 2015 and 31 December 2021, in Calgary, Alberta, Canada. After excluding patients missing age, sex, or perfusion information (n = 144), 9908 patients were included. We did not exclude patients with known CAD, since these patients may not be optimally treated in real-world clinical practice and MPI may still influence their medical therapy. The study was approved by the University of Calgary Research Ethics Board (REB-22–0833), including a waiver of consent.
Clinical information
The Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH) database was used to prospectively collect medical and family history.17 CAD was defined as either a prior myocardial infarction (MI) or revascularization procedure, including percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG).18,19 Body mass index (BMI), height, and weight were collected at the time of imaging.
Prescription information
Prescription information was retrieved from the Pharmaceutical Information Network (PIN) using Anatomical Therapeutic Chemical codes. Prescriptions filled in the 180 days before MPI were used to determine baseline therapies. Prescriptions filled in the 180 days after MPI were used to determine post-MPI therapies. Previous studies have demonstrated that PIN captures >95% of prescriptions in Alberta.20 At least one prescription before or after MPI was identified in 8750 (88.3%) patients.
MPI acquisition and interpretation
Patients underwent a 99mTc-Sestamibi SPECT MPI with a Ventri (GE, Boston, USA, n = 4404), GE Discovery 570 (GE, Boston, USA, n = 4451), or GE Discovery 870 (GE, Boston, USA, n = 1053) camera SPECT/CT system. Patients underwent 1-day rest–stress (n = 9675, 97.7%) or 2-day protocols (n = 233, 2.4%) as previously described.6 Stress testing was performed using symptom-limited exercise stress (n = 5989) or pharmacological stress (n = 3919).
SPECT MPI interpretation was performed at the time of clinical interpretation with access to medical history, indication for testing, coronary calcium scoring results, and method of stressing. For each perfusion study, the summed stress score (SSS), summed rest score (SRS), and summed difference score (SDS) were determined using the 17-segment model.21 Abnormal myocardial perfusion was defined as SSS > 3, and ischaemia was defined as SDS > 2.22 Each perfusion defect was described in terms of its anatomic size, extent of defect (mild, moderate, severe to absent uptake), and reversibility. A comprehensive final report is provided to the referring physician including a comment on overall burden of myocardial ischaemia and infarction. Stress left ventricular ejection fraction (LVEF) and end-systolic volume (LVESV) were quantified at the time of reporting.
CT attenuation correction image acquisition and interpretation
CT attenuation correction (CTAC) was performed using an integrated CT scanner for all camera systems (LightSpeed VCT 64, GE, Boston, USA). Each study took place after rest scintigraphy acquisition during an end-expiratory breath hold without ECG gating. A helical mode with a slice thickness of 5 mm, tube voltage of 120 kVp, and 20 mA was utilized, with a 512 × 512 matrix. CAC was visually estimated from coronary CTAC images and graded as absent, equivocal, present, or extensive. Extensive calcification was defined as an estimated CAC score >400 Hounsfield units by expert visual estimation.23,24 Examples of each grade are shown in Supplementary data online, Figure S1. Coronary calcium visual estimates, using the same four categories, were included in the reports to the referring physicians. In total, 2186 (22.1%) patients did not have CTAC imaging. Therefore, coronary artery calcium estimates are not available for this group of patients. We did not assess interobserver agreement for visual coronary calcium estimates, but prior studies have demonstrated excellent interobserver reliability.25
Statistical analyses
We performed a descriptive analysis by tabulating the percentage of patients with a cardiovascular medication prescription before and after SPECT MPI and compared them with Student’s t-test. Mixed effects multivariable logistic regression models were used to assess associations with post-MPI ASA and statin prescriptions, adjusting for age, sex, coronary calcium and myocardial perfusion abnormalities and medical history of dyslipidaemia, hypertension, diabetes, family history of CAD, chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), smoking history, and CAD. A separate model was developed for each medication, with random effects for individual patients and pre-test medication prescriptions included as a clustering feature. Odds ratios (OR) and 95% confidence intervals (CIs) were reported. All statistical tests were two-sided; a P value < 0.05 was considered statistically significant. A formal correction for multiple testing was not included; however, the results were consistent and highly statistically significant which makes a type 1 error unlikely. All analyses were performed using Stata/IC version 13.1 (StataCorp, College Station, Texas, USA) and R (version 4.1.2).
Results
Patient characteristics
In total, 9908 patients were included with an evaluation of CAC available in 7587. The mean age was 66.8 ± 11.7, and 5337 (53.9%) patients were male. Patient characteristics stratified by perfusion findings are shown in Table 1. Stratified by perfusion, patients with abnormal SPECT MPI studies were older (70.0 vs 66.8 years) and more often male (73% male vs. 49%). Patients with normal perfusion were more likely to have absent CAC (32.6 vs. 6.5%), whereas those with abnormal perfusion were more likely to have extensive CAC (49.5% vs 17.9%).
Table 1.
Patient characteristics stratified by presence of abnormal myocardial perfusion
| Factor | Normal Perfusion N = 7890 |
Abnormal Perfusion N = 2018 |
P value |
|---|---|---|---|
| Age, median (IQR) | 66.8 (58.3–74.8) | 70.0 (62.5–77.5) | <0.001 |
| Female, n (%) | 4026 (51.0%) | 545 (27.0%) | <0.001 |
| BMI, median (IQR) | 29.7 (27.0, 32.0) | 29.1 (25.3, 31.7) | <0.001 |
| Hypertension, n (%) | 4698 (59.5%) | 1414 (70.1%) | <0.001 |
| Diabetes mellitus, n (%) | 1913 (24.2%) | 730 (36.2%) | <0.001 |
| Dyslipidaemia, n (%) | 3715 (47.1%) | 1240 (61.4%) | <0.001 |
| Family history of CAD, n (%) | 3123 (39.6%) | 669 (33.2%) | <0.001 |
| COPD, n (%) | 449 (5.7%) | 117 (5.8%) | 0.951 |
| CKD, n (%) | 113 (1.4%) | 66 (3.3%) | <0.001 |
| Prior CAD, n (%) | 1171 (14.8%) | 935 (46.3%) | <0.001 |
| Smoking history, n (%) | 818 (10.4%) | 229 (11.3%) | 0.204 |
| LVEF post, median (IQR) | 69 (60, 74) | 53 (41, 65) | <0.001 |
| LVESV post, median (IQR) | 25 (16, 39) | 51 (30, 84) | <0.001 |
| Summed stress score, median (IQR) | 0 (0, 0) | 8 (7, 11) | <0.001 |
| Summed rest score, median (IQR) | 0 (0, 0) | 3 (0, 6) | <0.001 |
| Summed difference score (IQR) | 0 (0, 0) | 7 (2, 8) | <0.001 |
| CAC not assessed, n (%) | 1573 (19.9%) | 613 (30.4%) | <0.001 |
| CAC assessed, n (%) | 6317 (80.1%) | 1405 (69.9%) | <0.001 |
| CAC absent, n (%) | 2062 (32.6%) | 91 (6.5%) | |
| Equivocal for CAC, n (%) | 133 (2.1%) | 16 (1.1%) | |
| CAC present, n (%) | 2991 (47.3%) | 602 (42.8%) | |
| Extensive CAC, n (%) | 1131 (17.9%) | 696 (49.5%) |
BMI, body mass index; CAC, coronary artery calcium; CAD, coronary artery disease; COPD, chronic obstructive pulmonary disease; CKD, chronic kidney disease; LVEF, left ventricular ejection fraction; LVESV, left ventricular end-systolic volume.
Patient characteristics stratified by CAC findings are in Table 2. Patients without CAC were younger than those with CAC present (59.1 vs. 68.3 years) or with extensive CAC (72.9 years). Female patients were more likely to have absent calcium or an equivocal burden. Although most patients with CAC had normal perfusion, those with extensive calcification had higher SDS.
Table 2.
Patient characteristics stratified by calcium findings
| Factor | CAC absent N = 2153 |
Equivocal CAC N = 149 |
CAC present N = 3593 |
Extensive CAC N = 1827 |
CAC not assessed N = 2186 |
P value |
|---|---|---|---|---|---|---|
| Age, median (IQR) | 59.1 (51.4, 66.9) | 64.0 (55.2, 74.4) | 68.3 (60.7, 75.5) | 72.9 (66.7, 79.2) | 68.8 (60.8, 76.9) | <0.001 |
| Female | 1329 (61.7%) | 74 (79.7%) | 1539 (42.8%) | 617 (33.8%) | 1012 (46.3%) | <0.001 |
| BMI, median (IQR) | 29.7 (27.0, 32.0) | 29.7 (27.0, 29.7) | 29.7 (26.0, 32.0) | 29 (25.0, 31.0) | 29.7 (28, 29.7) | <0.001 |
| Hypertension | 968 (45.0%) | 69 (46.3%) | 2291 (63.8%) | 1354 (74.1%) | 1430 (65.4%) | <0.001 |
| Diabetes mellitus | 340 (15.8%) | 27 (18.1%) | 974 (27.1%) | 639 (35.0%) | 663 (30.3%) | <0.001 |
| Dyslipidaemia | 694 (32.2%) | 59 (39.6%) | 1940 (54.0%) | 1166 (63.8%) | 1096 (50.1%) | <0.001 |
| Family history | 929 (43.1%) | 31 (20.8%) | 1521 (42.3%) | 702 (38.4%) | 609 (27.9%) | <0.001 |
| COPD | 44 (2.0%) | 5 (3.4%) | 186 (5.2%) | 132 (7.2%) | 199 (9.1%) | <0.001 |
| CKD | 11 (0.5%) | 1 (0.7%) | 43 (2.6%) | 47 (2.6%) | 77 (3.5%) | <0.001 |
| Prior CAD | 39 (1.8%) | 17 (11.4%) | 805 (22.4%) | 696 (31.0%) | 549 (25.1%) | <0.001 |
| Smoking | 167 (7.8%) | 11 (7.4%) | 329 (9.2%) | 235 (12.9%) | 306 (14.0%) | <0.001 |
| Stress LVEF, median (IQR) | 69 (61, 74) | 68.5 (61, 74) | 66 (56, 72) | 62 (50, 70) | 68 (57, 75) | <0.001 |
| Summed stress score, median (IQR) | 0 (0, 0) | 0 (0, 3) | 0 (0, 3) | 3 (0, 7) | 0 (0, 6) | <0.001 |
| Summed rest score, median (IQR) | 0 (0, 0) | 0 (0, 0) | 0 (0, 0) | 0 (0, 3) | 0 (0, 0) | <0.001 |
| Summed difference score (IQR) | 0 (0, 0) | 0 (0, 2) | 0 (0, 3) | 1 (0, 3) | 0 (0, 3) | <0.001 |
BMI, body mass index; CAC; coronary artery calcium; CAD, coronary artery disease; COPD, chronic obstructive pulmonary disease; CKD, chronic kidney disease; LVEF, left ventricular ejection fraction; LVESV, left ventricular end-systolic volume.
Initiation of medications
Prescriptions before and after SPECT MPI stratified by perfusion findings are outlined in Figure 1. Patients with abnormal perfusion had a greater increase in all prescriptions compared with patients with normal perfusion, with increases ranging from 10.5 to 24.6% compared with increases of 3.7–12.0% for patients with normal perfusion (P < 0.001 for all comparisons). For example, patients with abnormal perfusion were more likely to be receiving ASA (33.0 vs. 15.5%, P < 0.001) and statins (67.0 vs. 47.7%, P < 0.001) at baseline. After MPI, these rates increased to 57.6 vs. 21.7% and 86.2 vs. 59.7% (P < 0.001 for both). Additional details are shown in Table 3, where we also demonstrate an increase in the prescriptions of angiotensin-converting enzyme (ACE) inhibitors (3.7% increase in normal perfusion vs. 10.5% in abnormal), beta-blockers (8.3% increase in normal perfusion vs. 22.0% increase in abnormal), and oral anticoagulants.
Figure 1.
Prescriptions before and after myocardial perfusion imaging, stratified by perfusion findings. Baseline medical therapy was used more frequently in patients with abnormal perfusion but also increased to a greater extent after study for all medication classes.
Table 3.
Medication changes before and after SPECT MPI by perfusion findings
| Medication class | Normal perfusion N = 7890 |
Abnormal perfusion N = 2018 |
P value |
|---|---|---|---|
| ASA pre-study | 1227 (15.5%) | 666 (33.0%) | <0.001 |
| ASA post-study | 1708 (21.7%) | 1162 (57.6%) | <0.001 |
| Statin pre-study | 3767 (47.7%) | 1351 (67.0%) | <0.001 |
| Statin post-study | 4713 (59.7%) | 1740 (86.2%) | <0.001 |
| ACEi pre-study | 2107 (26.7%) | 853 (42.3%) | <0.001 |
| ACEi post-study | 2399 (30.4%) | 1066 (52.8%) | <0.001 |
| ARB pre-study | 2066 (26.2%) | 552 (27.4%) | 0.295 |
| ARB post-study | 2096 (26.6%) | 566 (28.1%) | 0.186 |
| Beta-blocker pre-study | 2567 (32.5%) | 1119 (55.5%) | <0.001 |
| Beta-blocker post-study | 3218 (40.8%) | 1564 (77.5%) | <0.001 |
| OAC pre-study | 1095 (13.9%) | 342 (17.0%) | <0.001 |
| OAC post-study | 1385 (17.6%) | 491 (24.3%) | <0.001 |
ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; ASA, acetylsalicylic acid; BB, beta-blocker; OAC, oral anticoagulant.
Among patients with available CTAC imaging, increasing coronary calcium was associated with higher post-MPI prescription rates in all patients as well as those with normal perfusion (Figure 2). Patients without CAC had the lowest prescription rates for ASA (9.3%) and statins (36.0%), with the highest rates among patients with extensive CAC (45.9 and 82.9%, respectively). Similar findings were observed in the subset of patients with normal perfusion, with ASA prescription rates increasing from 7.9 to 34.2% in those with absent and extensive coronary calcium, respectively, and from 34.2 to 79.9% for statins. Additional details are shown in Table 4, where we show that extensive coronary calcium burden was associated with significant increases across all medication classes. Prescription rates for patients who did not have a CTAC assessment (N = 2186) are also tabulated.
Figure 2.
Medication prescriptions after myocardial perfusion imaging stratified by visually estimated coronary artery calcium (CAC).
Table 4.
Medications before and after SPECT MPI by coronary artery calcium (CAC) findings
| Factor | CAC absent N = 2153 |
Equivocal CAC N = 149 |
CAC present N = 3593 |
Extensive CAC N = 1827 |
CAC not assessed N = 2186 |
P value |
|---|---|---|---|---|---|---|
| ASA pre-study | 140 (6.5%) | 23 (15.4%) | 695 (19.3%) | 564 (30.9%) | 471 (21.6%) | <0.001 |
| ASA post-study | 201 (9.3%) | 45 (30.2%) | 1037 (28.9%) | 838 (45.9%) | 749 (34.3%) | <0.001 |
| Statin pre-study | 606 (28.2%) | 67 (45.0%) | 1990 (55.4%) | 1294 (70.8%) | 1161 (53.1%) | <0.001 |
| Statin post-study | 775 (36.0%) | 91 (61.1%) | 2608 (72.6%) | 1514 (82.9%) | 1465 (67.0%) | <0.001 |
| ACEi pre-study | 390 (18.1%) | 39 (26.2%) | 1123 (31.3%) | 712 (39.0%) | 696 (31.8%) | <0.001 |
| ACEi post-study | 489 (22.7%) | 52 (34.9%) | 1306 (36.4%) | 801 (43.8%) | 817 (37.4%) | <0.001 |
| ARB pre-study | 412 (19.1%) | 34 (22.8%) | 971 (27.0%) | 586 (32.1%) | 615 (28.1%) | <0.001 |
| ARB post-study | 430 (20.0%) | 33 (22.2%) | 1007 (28.0%) | 585 (32.0%) | 607 (27.8%) | <0.001 |
| BB pre-study | 456 (21.2%) | 46 (30.9%) | 1389 (38.7%) | 927 (50.7%) | 868 (39.7%) | <0.001 |
| BB post-study | 605 (28.1%) | 70 (47.0%) | 1793 (49.9%) | 1179 (64.5%) | 1135 (51.9%) | <0.001 |
| OAC pre-study | 210 (9.8%) | 18 (12.1%) | 576 (16.0%) | 339 (18.6%) | 294 (13.5%) | <0.001 |
| OAC post-study | 273 (12.7%) | 21 (14.1%) | 746 (20.8%) | 436 (23.9%) | 400 (18.3%) | <0.001 |
ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; ASA, acetylsalicylic acid; BB; beta-blocker, OAC; oral anticoagulant.
Prescriptions of medications after MPI, stratified by ischaemia and CAC findings, are shown in Figure 3. Patients with extensive coronary calcification had the highest prescription rates for ASA and statins in patients with and without ischaemia. The presence of ischaemia and extensive coronary calcium led to the highest prescription rates of ASA and statins (54.8% for ASA and 85.9% for statins). Patients without CAC had the lowest prescription rates. The absence of CTAC imaging was associated with higher rates of ASA in patients without ischaemia (25.0 vs. 21.0%, P = 0.002), with no other significant differences. However, reporting visually estimated CAC may lead to more appropriate targeting of therapies (with higher rates in patients with extensive CAC and lower rates in patients without CAC).
Figure 3.
Medication prescriptions after myocardial perfusion imaging stratified by visually estimated coronary artery calcium (CAC) and presence of ischaemia. CAC was assessed on computed tomography attenuation correction (CTAC) imaging.
Multivariable analysis
Multivariable analyses evaluating associations with ASA and statin prescription are shown in Table 5. Myocardial ischaemia (OR 2.27, 95% CI 2.05–2.52, P < 0.001) and extensive CAC (OR 3.53, 95% CI 2.90–4.30, P < 0.001) were strongly associated with ASA prescriptions. For new statin prescriptions, the strongest association, even adjusting for dyslipidaemia and prior CAD, was with extensive coronary calcium (OR 3.14, 95% CI 2.54–3.89, P < 0.001) and the presence of non-extensive coronary calcium (OR 3.08, 95% CI 2.63–3.62, P < 0.001) followed by myocardial ischaemia and dyslipidaemia. Male sex was independently associated with the prescription of statins relative to female sex (OR 1.13, 95% CI 1.00–1.27, P = 0.042). Patients who did not have any assessment of coronary calcium had an OR of 1.92 (95% CI 1.61–2.29, P < 0.001) for statin prescription compared with an OR of 2.21 (95% CI 1.43–3.42, P < 0.001) for equivocal calcium with patients without evidence of calcium as the reference group. A model incorporating abnormal perfusion instead of ischaemia is shown in Supplementary data online, Table S1. We performed a sensitivity analysis excluding patients who had undergone early coronary invasive coronary angiography (8.4% of patients within the first 180 days) and included the results in Supplementary data online, Table S2. In this population, ischaemia and coronary calcium were independent predictors of aspirin and statin prescription. In Supplementary data online, Table S3, we show that similar associations between calcium and ischaemia with prescription of ASA and statin therapy remained after excluding patients with prior CAD (n = 2106, 21.3%) from the analysis.
Table 5.
Associations with medication prescription after myocardial perfusion imaging
| Variable | ASA | P value | Statin | P value |
|---|---|---|---|---|
| Odds ratio (95% CI) | Odds ratio (95% CI) | |||
| Age (per 10 years) | 0.98 (0.94–1.03) | 0.428 | 1.05 (1.00–1.11) | 0.06 |
| Male | 1.06 (0.96–1.18) | 0.228 | 1.13 (1.00–1.27) | 0.042 |
| CAC absent | Reference | — | Reference | — |
| Equivocal CAC | 3.24 (2.18–4.82) | <0.001 | 2.21 (1.43–3.42) | <0.001 |
| CAC present | 2.35 (1.97–2.81) | <0.001 | 3.08 (2.63–3.62) | <0.001 |
| Extensive CAC | 3.53 (2.90–4.30) | <0.001 | 3.14 (2.54–3.89) | <0.001 |
| CAC not assessed | 2.50 (2.07–3.01) | <0.001 | 1.92 (1.61–2.29) | <0.001 |
| Ischaemia | 2.27 (2.05–2.52) | <0.001 | 2.60 (2.27–2.97) | <0.001 |
| Hypertension | 1.10 (0.99–1.24) | 0.085 | 1.10 (0.97–1.24) | 0.13 |
| Diabetes mellitus | 1.21 (1.08–1.35) | 0.001 | 1.08 (0.93–1.25) | 0.319 |
| Dyslipidaemia | 1.20 (1.08–1.34) | 0.001 | 1.99 (1.74–2.27) | <0.001 |
| Family history | 0.81 (0.73–0.90) | <0.001 | 0.86 (0.76–0.97) | 0.012 |
| COPD | 0.86 (0.70–1.06) | 0.165 | 1.06 (0.82–1.37) | 0.662 |
| CKD | 1.38 (0.99–1.93) | 0.057 | 0.99 (0.62–1.56) | 0.951 |
| Smoking | 1.21 (1.04–1.42) | 0.017 | 1.30 (1.08–1.57) | 0.006 |
| Prior CAD | 2.81 (2.50–3.14) | <0.001 | 0.93 (0.78–1.11) | 0.434 |
| Exercise stress | 0.75 (0.68–0.83) | <0.001 | 1.01 (0.89–1.14) | 0.911 |
CAC, coronary artery calcium; CAD, coronary artery disease; CI, confidence interval; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease.
Subgroup analyses
Female patients were less likely to be prescribed statins in patients with normal perfusion (55.5 vs. 64.2%, P < 0.001) and patients with abnormal perfusion (82.4 vs. 87.6%, P = 0.002), with full results in Supplementary data online, Table S4. Prescriptions in patients undergoing exercise or pharmacologic stress are shown in Supplementary data online, Table S5 and stratified by prior CAD in Supplementary data online, Table S6.
Discussion
We evaluated how MPI findings influence the medical management of patients with CAD, affording valuable insights into prescriber practice. We demonstrated that the presence of abnormal myocardial perfusion was associated with higher rates of medication prescriptions. Similarly, there was a stepwise increase in prescription rates across categories of visually estimated CAC. While there were many predictors of statin prescriptions, we noted that female patients were less likely to be prescribed these therapies after adjusting for relevant confounders. This association persisted after excluding patients with prior CAD or revascularization within 180 days. Lastly, it is worth noting that both abnormal perfusion and increasing CAC burden were independently associated with prescription rates, highlighting the importance of reporting perfusion and calcium findings when available.
Extensive evidence suggests that perfusion and CAC findings on SPECT MPI can identify patients at the highest risk of cardiovascular events.6,26 Ischaemic burden and left ventricular dysfunction are strong predictors of cardiac events, which are highly actionable items for medical therapy.27 Similarly, CAC can identify high-risk patients, and knowledge of CAC can improve patient adherence to medications and lifestyle advice.7 Abnormal perfusion and coronary calcium were associated with higher prescribing rates, suggesting that physicians are targeting medications to higher-risk patients. Suggestions for medical therapy based on CAC scores are a well-established paradigm. However, the magnitude of the increase was consequently most significant for patients with abnormal perfusion, demonstrating potential value added by imaging to guide cardiovascular care.28
While we noted increased utilization of medical therapy post-MPI, there is still potential for improvement, given the foundational role of medications in the management of CAD. Suboptimal medical management has been long identified as a barrier to high-quality coronary care. For example, in one study, only 10% of patients were identified as having optimal medical therapy despite follow-up for known CAD.29 By comparison, 95% of patients in the ISCHEMIA trial were on a statin, and all were on an antiplatelet or anticoagulant therapy.13 Prescription of guideline-directed medical therapy could be enhanced using longer prescription durations or direct communication with primary care providers about prescription requests.30 One study utilized deep learning to detect coronary calcium on chest CT scans performed for non-cardiac reasons. Documentation of CAC was associated with increased prescription of statin medications primarily in the arm of patients whose primary care providers were notified compared with those patients who received standard care (51.2 vs. 6.9%).31 Similar interventions could readily be implemented into MPI reporting.
Our study also suggested that male sex was an independent predictor of statin prescription following SPECT MPI. Although male sex is a risk factor for CAD and may warrant more aggressive therapy, gender biases in the prescription of lipid therapy have been reported in the past. In an analysis from a nationwide lipid registry of patients with or at risk of developing cardiovascular disease, women eligible for lipid treatment were less likely to be offered the same or underdosed if offered. They also reported higher discontinuation rates due to side effects.32 Similar sex differences have been observed following a MI, with women less likely to receive high-intensity statin therapy.33 Our analysis reported that male patients were 13% more likely than women to receive a statin prescription after adjusting for medical history, MPI, and CAC findings. Therefore, more research is needed to elucidate the underlying reasons and measures to provide automated suggestions to consider medical therapy in female patients should be considered.
Study limitations
Our study has several limitations. We assessed medication prescriptions 180 days following SPECT MPI, but we do not have data regarding the long-term medication adherence or prescription rate beyond this. Prior data have suggested significant decreases in long-term adherence, with worse cardiovascular outcomes.34 Our observations also inherently assume that prescriptions were made as a direct consequence of abnormal coronary calcium or perfusion findings. However, this is only a correlation and prescriptions within 180 days of the study may have been due to other reasons. Finally, because the vast majority of the patients in our study received CTAC and visual coronary artery calcium estimation, it is difficult to know the extent to which medication changes were predominately guided by perfusion results or calcium burden. A recent study of 281 patients undergoing SPECT MPI (ICCAMPA trial) used medical questionnaires to establish that coronary artery calcium estimation was associated with alterations in the medical management in 47% of patients, including patients with both a positive and negative MPI.35 Another limitation is that our study likely underestimates the prescription rate of ASA. Because ASA is often purchased over the counter, it is often not reflected in provincial medication databases. Nonetheless, trends in the prescription rate of ASA post-MPI demonstrated in our analysis remain instructive.
Conclusions
Abnormal MPI testing was associated with significant medication changes after study completion. Both calcium burden and perfusion abnormalities were associated with increased prescription of guideline-directed medical therapy. More research is needed to optimize treatment uptake and sex-based differences to minimize cardiovascular morbidity and mortality.
Supplementary data
Supplementary data are available at European Heart Journal - Cardiovascular Imaging online.
Supplementary Material
Contributor Information
Waseem Hijazi, Department of Cardiac Sciences, University of Calgary, 1403 - 29th St. NW, Calgary, AB, T2N 2T9, Canada.
Yuanchao Feng, Department of Cardiac Sciences, University of Calgary, 1403 - 29th St. NW, Calgary, AB, T2N 2T9, Canada.
Danielle A Southern, Department of Medicine, Cumming School of Medicine, University of Calgary, 1403 - 29th St. NW, Calgary, AB, T2N 2T9, Canada; Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, 1403 - 29th St. NW, Calgary, AB, T2N 2T9, Canada; O’Brien Institute for Public Health, University of Calgary, 1403 - 29th St. NW, Calgary, AB, T2N 2T9, Canada; Libin Cardiovascular Institute, University of Calgary, 1403 - 29th St. NW, Calgary, AB, T2N 2T9, Canada.
Derek Chew, Department of Cardiac Sciences, University of Calgary, 1403 - 29th St. NW, Calgary, AB, T2N 2T9, Canada; Department of Medicine, Cumming School of Medicine, University of Calgary, 1403 - 29th St. NW, Calgary, AB, T2N 2T9, Canada; Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, 1403 - 29th St. NW, Calgary, AB, T2N 2T9, Canada; O’Brien Institute for Public Health, University of Calgary, 1403 - 29th St. NW, Calgary, AB, T2N 2T9, Canada; Libin Cardiovascular Institute, University of Calgary, 1403 - 29th St. NW, Calgary, AB, T2N 2T9, Canada.
Neil Filipchuk, Department of Cardiac Sciences, University of Calgary, 1403 - 29th St. NW, Calgary, AB, T2N 2T9, Canada.
Bryan Har, Department of Cardiac Sciences, University of Calgary, 1403 - 29th St. NW, Calgary, AB, T2N 2T9, Canada.
Matthew James, Department of Medicine, Cumming School of Medicine, University of Calgary, 1403 - 29th St. NW, Calgary, AB, T2N 2T9, Canada; Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, 1403 - 29th St. NW, Calgary, AB, T2N 2T9, Canada; O’Brien Institute for Public Health, University of Calgary, 1403 - 29th St. NW, Calgary, AB, T2N 2T9, Canada; Libin Cardiovascular Institute, University of Calgary, 1403 - 29th St. NW, Calgary, AB, T2N 2T9, Canada.
Stephen Wilton, Department of Cardiac Sciences, University of Calgary, 1403 - 29th St. NW, Calgary, AB, T2N 2T9, Canada.
Piotr J Slomka, Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA; Department of Imaging, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA; Department of Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA.
Daniel Berman, Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA; Department of Imaging, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA; Department of Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA.
Robert J H Miller, Department of Cardiac Sciences, University of Calgary, 1403 - 29th St. NW, Calgary, AB, T2N 2T9, Canada.
Funding
P.J.S. receives funding from the National Heart, Lung, and Blood Institute at the National Institutes of Health under grants R01HL089765 and R35HL161195.
Data availability
The data underlying this article will be shared on reasonable request to the corresponding author.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data underlying this article will be shared on reasonable request to the corresponding author.