Appropriate choice of stress modality in patients undergoing myocardial perfusion scintigraphy with a cardiac camera equipped with solid-state detectors: the role of diabetes mellitus

Appropriate choice of stress modality in patients undergoing myocardial perfusion scintigraphy... Abstract Aims To evaluate the impact of diabetes mellitus (DM) on the accuracy of myocardial perfusion scintigraphy (MPS) in detecting coronary artery disease (CAD). Methods and results Two hundred and sixteen patients with DM and 432 matched controls were submitted to MPS on a dedicated cardiac camera equipped with cadmium–zinc–telluride (CZT) detectors and coronary angiography. Exercise stress was performed in 442 (68%) patients, while the remainders underwent vasodilator stress. Exercise level was determined as the percentage of the predicted maximal workload that was attained (%Wattmax). The summed difference score was derived from CZT images. A coronary stenosis >70% was considered obstructive. The prevalence of obstructive CAD was 59.7% in patients with DM and 56.2% in controls (P = NS). The accuracy of MPS in detecting CAD was similar in patients with and without DM [area under the ROC curve (AUC) 0.77 vs. 0.78, P = NS]. An interaction between the accuracy of MPS and cardiac stress-protocol was revealed. In fact, in patients with DM exercise stress CZT had a lower accuracy than vasodilator-stress (AUC 0.70 vs. 0.89, P < 0.001), because of a lower specificity (45% vs. 69%), while in the control group the accuracy of MPS was similar regardless of the stress-protocol adopted. Patients with DM attained a significantly lower %Wattmax during exercise than controls (76 ± 27% vs. 82 ± 26%, P = 0.038), which resulted an independent predictor of reduced specificity (P = 0.026). Conclusion The accuracy of CZT imaging in patients with DM is elevated, and it is quite comparable to the one obtained in patients without DM. However, a reduced specificity can be expected in the case of exercise stress CZT, because of an impaired exercise capacity. myocardial perfusion scintigraphy, diabetes mellitus, diagnostic accuracy, exercise stress test, obstructive CAD Introduction Epidemiological studies have reported the association of diabetes mellitus (DM) and coronary artery disease (CAD).1,2 These evidences have led to the concept of DM as a coronary risk equivalent, requiring aggressive preventive, and therapeutic measures.3 Moreover, patients with DM have been generally considered at a higher risk of silent myocardial ischaemia, supporting the possible prognostic benefit of ischaemia testing in asymptomatic diabetic patients.4,5 However, recent appraisals have changed these concepts, showing that patients with DM have a substantially lower risk of future cardiac events than expected and questioning the real value of screening strategies in asymptomatic DM patients.6,7 Myocardial perfusion scintigraphy (MPS) on single-photon-emission computed tomography (SPECT) is used frequently in patients with DM to identify the presence of significant CAD.4 One common finding in patients with DM is that the extent and severity of perfusion abnormalities may not reflect the effective CAD burden8 and result in the frequent occurrence of ‘false-positive’ findings, possibly due to the presence of underlying microvascular dysfunction.9,10 An interaction between the specific stress-protocol adopted (i.e. exercise vs. vasodilator) and MPS accuracy has been also suggested, being exercise stress-MPS possibly characterized by a lower specificity than in normal patients.11 Despite some preliminary studies, an accurate evaluation of MPS diagnostic power in detecting obstructive CAD in a consistent population of patients with DM has not been performed and the impact of the specific stress-protocol adopted on MPS diagnostic power in patients with and without DM is still lacking. Accordingly, we aimed to evaluate the comparative accuracy of MPS performed on a dedicated cadmium–zinc–telluride (CZT) camera in patients with and without DM and to explore the impact of cardiac stress-protocol on MPS diagnostic power. Materials and methods Patient population Between 2010 and 2016, 6120 subjects with anginal chest pain and suspected were referred to our institution for a scintigraphic evaluation of myocardial perfusion at rest and after stress with a CZT camera. Among those, 216 consecutive patients with DM, also submitted to invasive coronary angiography (ICA), were enrolled. Those patients with an ascertained diagnosis of DM according to current guidelines,12 were referred to MPS mainly by the local community hospitals. From the same population of patients, a control group of subjects without DM, known coronary anatomy, and similar baseline clinical characteristics (particularly regarding age, sex, and chest pain characteristics) were also selected. In the control population, the absence of DM was documented according to the presence of a normal fasting plasma glucose in the absence of any antidiabetic drugs. In order to ensure an effective comparison of the two populations, DM and control patients were selected with a 1:2 ratio and also matched for CZT stress protocol (exercise vs. pharmacological). In the entire population, both patients with DM and controls were submitted to ICA within 3 months from the CZT study, as indicated by the referring physicians. Exclusion criteria were: previous acute coronary syndrome or coronary intervention, known CAD, haemodynamic instability, severely symptomatic heart failure, and previous cardiac infective/inflammatory disease. Moreover, to eliminate a possible bias in the analysis of the data, the four patients that underwent dobutamine MPS in the same study period were also excluded. The study was approved by the Local Ethical Committee and conformed to the Declaration of Helsinki on human research. Written informed consent was obtained from every patient. Patient preparation and stress protocols Patients discontinued beta-blockers, calcium-antagonists, and nitrates for 24 h before testing. Moreover, theophylline and caffeine containing products were also prohibited in the 24 h before MPS. Four hundred and thirty-two (67%) patients (149 DM vs. 243 controls) underwent bicycle exercise (stepwise increments of 25 W every 2 min), while 206 (33%) (67 DM vs. 139 controls) dipyridamole (0.56 mg/kg IV over 4 min) stress testing, depending on patients’ ability to exercise or according to clinical reasons. Of the patients undergoing exercise stress testing, 142 (49 DM vs. 91 controls, P = NS) reached 85% of the age-predicted maximum heart rate (%HR), 121 (40 DM vs. 81 controls, P = NS) reached a peak rate pressure product higher than 25 000, and 88 developed signs of myocardial ischaemia on electrocardiogram (ECG) (40 DM vs. 48 controls, P = 0.012). The remaining patients were injected because of symptoms (chest pain and/or dyspnoea) believed to be indicative of myocardial ischaemia. In each patient, the predicted maximal workload (Wattmax) was calculated13 and the percentage of the reached Wattmax (%Wattmax) determined. Acquisition protocol Patients underwent stress–rest CZT imaging with a single-day protocol (148–185 MBq of 99mTc-tetrofosmin during stress and 296–370 MBq at rest). In all patients, stress and rest CZT imaging were acquired as previously described.14,15 Stress acquisitions were started 10 (exercise) to 15 (dipyridamole) min after the completion of the stress protocols, while rest scans were started 30 min after injection. Patients were imaged in the supine position with arms placed over their head without any detector or collimator motion. All images were acquired with a 32 × 32 matrix and a 20% energy window centred at the 140 keV photopeak of 99mTc. List mode files were acquired and stored. Images were reconstructed on a standard workstation (Xeleris II; GE Healthcare, Haifa, Israel) using a dedicated iterative algorithm.14 All studies were reconstructed using a standard iterative algorithm with ordered-subset expectation maximization with 50 iterations, without resolution recovery or attenuation correction. A Butterworth post-processing filter (frequency 0.37, order 7) was applied to the reconstructed slices. The tomographic studies were also re-projected into 60 planar projections to emulate a standard SPECT layout. Semiquantitative analysis of perfusion images Stress and rest images were semiquantitatively scored according to the 17-segment left ventricular (LV) model and a five-point scale (0: normal, 1: equivocal, 2: moderate, 3: severe reduction in radioisotope uptake, and 4: absence of detectable tracer uptake).15,16 Accordingly, the summed stress score (SSS), summed rest score, and summed difference score (SDS) were calculated. Two experienced nuclear cardiologists performed the semiquantitative analysis independently and consensus was reached when necessary. A SDS >3 was identified as a measure of significant myocardial ischaemia.17 Moreover, in each patient, the regional perfusion scores of the three coronary territories [left-anterior descending (LAD), left circumflex artery (LCX), and right coronary artery (RCA)] were also determined by adding the defect scores (DS) of the pertinent myocardial segments and reported as follows: DS-LAD, DS-LCX, and DS-RCA. Analysis of gated images LV function analysis was performed from 16-frames reformatted images using the commercially available software (Corridor4DM, Invia, Ann Arbor, MI, USA). End-diastolic volume (EDV), end-systolic volume, ejection fraction, and peak filling rate (EDV*s−1) were automatically calculated.18,19 All functional measurements were obtained from rest and stress ECG-gated 99mTc-tetrofosmin images. ICA Coronary angiograms were quantified with a dedicated computer software (Advanced Vessel Analysis, Innova 3DXR for Advanced Workstations; GE Healthcare) using an automatic edge-contour detection algorithm to compute stenosis severity. Coronary stenosis >70% were considered obstructive, while luminal narrowings ≤70% were recorded as non-obstructive. Statistical analysis Continuous variables were expressed as mean ± 1 SD and categorical variables as percentages. Groups were compared for categorical data using Fisher’s exact test and for continuous variables using analysis of variance followed by Fisher’s protected least significant difference for multiple comparisons. All tests were two-sided; a P-value of <0.05 was considered to be significant. The accuracy of MPS in unmasking the presence of significant coronary stenoses was assessed by the receiving operating characteristic analysis. Accordingly, the pertinent areas under the curves (AUC) with the appropriate 95% confidence intervals (CIs) were determined. The predictors of a reduced diagnostic specificity (‘true-negative rate’) on MPS were assessed at multivariate logistic regression analysis and the odds ratios (ORs) with the pertinent 95% CI determined. Statistical analyses were performed using JMP statistical software (SAS Institute Inc., version 4.0.0) and Stata software (Stata Statistical Software: Release 10, StataCorp. 2007, College Station, TX, USA). Results Characterization of the study population The characteristics of the study population are summarized in Table 1. Patients with DM had a significantly higher cardiovascular risk profile than controls, with a higher prevalence of hypertension (P = 0.006) and obesity (P = 0.049). Table 1 Characteristics of patients Parameters Overall population (n = 648) No diabetes mellitus (n = 432) Diabetes mellitus (n = 216) P-value Demographics  Age (years) 70 ± 8 72 ± 9 72 ± 9 0.511  Male gender, n (%) 495 (76) 330 (76) 165 (76) >0.999  Pre-test probability of CAD (%) 66 ± 18 65 ± 18 67 ± 18 0.271 Cardiovascular risk factors  Family history of CAD, n (%) 195 (30) 110 (25) 85 (39) <0.001  Hypercholesterolemia, n (%) 246 (38) 137 (32) 109 (50) <0.001  Hypertension, n (%) 364 (56) 226 (52) 138 (59) 0.006  Smoking, n (%) 65 (10) 36 (8) 29 (13) 0.051  Body mass index 29 ± 5 28 ± 4 29 ± 5 0.049 Pharmacologic treatment  Beta-blockers, n (%) 26 (4) 21 (5) 5 (2) 0.140  ACE-inhibitors/ARB, n (%) 585 (90) 401 (93) 184 (85) 0.002  Statins, n (%) 241 (37) 131 (95) 109 (100) 0.002  Aspirin, n (%) 640 (99) 428 (99) 212 (99) 0.675  Oral anti-diabetic agents, n (%) 210 (32) 0 (0) 210 (97) <0.001  Insulin, n (%) 6 (9) 0 (0) 6 (3) 0.001 MPI protocol 0.789  Exercise stress test, n (%) 442 (68) 293 (68) 149 (69)  Dipyridamole stress, n (%) 206 (32) 139 (32) 67 (31) Parameters Overall population (n = 648) No diabetes mellitus (n = 432) Diabetes mellitus (n = 216) P-value Demographics  Age (years) 70 ± 8 72 ± 9 72 ± 9 0.511  Male gender, n (%) 495 (76) 330 (76) 165 (76) >0.999  Pre-test probability of CAD (%) 66 ± 18 65 ± 18 67 ± 18 0.271 Cardiovascular risk factors  Family history of CAD, n (%) 195 (30) 110 (25) 85 (39) <0.001  Hypercholesterolemia, n (%) 246 (38) 137 (32) 109 (50) <0.001  Hypertension, n (%) 364 (56) 226 (52) 138 (59) 0.006  Smoking, n (%) 65 (10) 36 (8) 29 (13) 0.051  Body mass index 29 ± 5 28 ± 4 29 ± 5 0.049 Pharmacologic treatment  Beta-blockers, n (%) 26 (4) 21 (5) 5 (2) 0.140  ACE-inhibitors/ARB, n (%) 585 (90) 401 (93) 184 (85) 0.002  Statins, n (%) 241 (37) 131 (95) 109 (100) 0.002  Aspirin, n (%) 640 (99) 428 (99) 212 (99) 0.675  Oral anti-diabetic agents, n (%) 210 (32) 0 (0) 210 (97) <0.001  Insulin, n (%) 6 (9) 0 (0) 6 (3) 0.001 MPI protocol 0.789  Exercise stress test, n (%) 442 (68) 293 (68) 149 (69)  Dipyridamole stress, n (%) 206 (32) 139 (32) 67 (31) Table 1 Characteristics of patients Parameters Overall population (n = 648) No diabetes mellitus (n = 432) Diabetes mellitus (n = 216) P-value Demographics  Age (years) 70 ± 8 72 ± 9 72 ± 9 0.511  Male gender, n (%) 495 (76) 330 (76) 165 (76) >0.999  Pre-test probability of CAD (%) 66 ± 18 65 ± 18 67 ± 18 0.271 Cardiovascular risk factors  Family history of CAD, n (%) 195 (30) 110 (25) 85 (39) <0.001  Hypercholesterolemia, n (%) 246 (38) 137 (32) 109 (50) <0.001  Hypertension, n (%) 364 (56) 226 (52) 138 (59) 0.006  Smoking, n (%) 65 (10) 36 (8) 29 (13) 0.051  Body mass index 29 ± 5 28 ± 4 29 ± 5 0.049 Pharmacologic treatment  Beta-blockers, n (%) 26 (4) 21 (5) 5 (2) 0.140  ACE-inhibitors/ARB, n (%) 585 (90) 401 (93) 184 (85) 0.002  Statins, n (%) 241 (37) 131 (95) 109 (100) 0.002  Aspirin, n (%) 640 (99) 428 (99) 212 (99) 0.675  Oral anti-diabetic agents, n (%) 210 (32) 0 (0) 210 (97) <0.001  Insulin, n (%) 6 (9) 0 (0) 6 (3) 0.001 MPI protocol 0.789  Exercise stress test, n (%) 442 (68) 293 (68) 149 (69)  Dipyridamole stress, n (%) 206 (32) 139 (32) 67 (31) Parameters Overall population (n = 648) No diabetes mellitus (n = 432) Diabetes mellitus (n = 216) P-value Demographics  Age (years) 70 ± 8 72 ± 9 72 ± 9 0.511  Male gender, n (%) 495 (76) 330 (76) 165 (76) >0.999  Pre-test probability of CAD (%) 66 ± 18 65 ± 18 67 ± 18 0.271 Cardiovascular risk factors  Family history of CAD, n (%) 195 (30) 110 (25) 85 (39) <0.001  Hypercholesterolemia, n (%) 246 (38) 137 (32) 109 (50) <0.001  Hypertension, n (%) 364 (56) 226 (52) 138 (59) 0.006  Smoking, n (%) 65 (10) 36 (8) 29 (13) 0.051  Body mass index 29 ± 5 28 ± 4 29 ± 5 0.049 Pharmacologic treatment  Beta-blockers, n (%) 26 (4) 21 (5) 5 (2) 0.140  ACE-inhibitors/ARB, n (%) 585 (90) 401 (93) 184 (85) 0.002  Statins, n (%) 241 (37) 131 (95) 109 (100) 0.002  Aspirin, n (%) 640 (99) 428 (99) 212 (99) 0.675  Oral anti-diabetic agents, n (%) 210 (32) 0 (0) 210 (97) <0.001  Insulin, n (%) 6 (9) 0 (0) 6 (3) 0.001 MPI protocol 0.789  Exercise stress test, n (%) 442 (68) 293 (68) 149 (69)  Dipyridamole stress, n (%) 206 (32) 139 (32) 67 (31) Interactions between DM, coronary anatomy, and myocardial perfusion: per-patient analysis Patients with DM and controls showed a similar prevalence and extent of obstructive CAD (Table 2). Similarly, no differences regarding major LV functional parameters (i.e. left ventricular ejection fraction and cavitary volumes) were observed between the two patients categories. On the other hand, despite a comparable scar burden, patients with DM showed a higher extent of myocardial ischaemia than controls (P = 0.028). This finding was explained by the presence of more extensive ischaemia in diabetic patients with obstructive CAD than in controls (Figure 1A), and was limited to subjects submitted to vasodilator stress test (Figure 1B). Table 2 Coronary anatomy and cardiac functional parameters Parameters Overall population (n = 648) No diabetes mellitus (n = 432) Diabetes mellitus (n = 216) P-value Coronary anatomy 0.285  Normal coronary arteries, n (%) 174 (27) 124 (29) 50 (23)  Non-obstructive atherosclerosis, n (%) 102 (16) 65 (15) 37 (17)  Single vessel CAD, n (%) 192 (29) 120 (28) 72 (33)  Multivessel CAD, n (%) 180 (28) 123 (28) 57 (27) Stress test results  Positive stress ECG, n (%) 118 (18) 71 (16) 47 (22) 0.106 Perfusion data  Summed rest score 3.4 ± 6.4 3.5 ± 6.9 3.2 ± 5.2 0.565  Summed stress score 7.9 ± 6.5 7.8 ± 6.8 8.2 ± 5.9 0.403  Summed difference score 5.0 ± 3.6 4.8 ± 3.2 5.5 ± 4.2 0.028 LV volumes and function at rest  Ejection fraction (%) 58 ± 14 59 ± 14 57 ± 13 0.180  End-diastolic volume (mL) 111 ± 50 109 ± 50 114 ± 49 0.211  End-systolic volume (mL) 52 ± 45 51 ± 46 54 ± 43 0.401  Peak filling rate (EDV/s) 2.5 ± 0.8 2.4 ± 0.8 2.5 ± 0.7 0.845 LV volumes and function after stress  Ejection fraction (%) 58 ± 13 58 ± 14 57 ± 12 0.148  End-diastolic volume (mL) 107 ± 44 106 ± 46 110 ± 40 0.326  End-systolic volume (mL) 50 ± 37 49 ± 39 51 ± 33 0.419  Peak filling rate (EDV/s) 2.4 ± 0.8 2.4 ± 0.8 2.5 ± 0.7 0.596 Parameters Overall population (n = 648) No diabetes mellitus (n = 432) Diabetes mellitus (n = 216) P-value Coronary anatomy 0.285  Normal coronary arteries, n (%) 174 (27) 124 (29) 50 (23)  Non-obstructive atherosclerosis, n (%) 102 (16) 65 (15) 37 (17)  Single vessel CAD, n (%) 192 (29) 120 (28) 72 (33)  Multivessel CAD, n (%) 180 (28) 123 (28) 57 (27) Stress test results  Positive stress ECG, n (%) 118 (18) 71 (16) 47 (22) 0.106 Perfusion data  Summed rest score 3.4 ± 6.4 3.5 ± 6.9 3.2 ± 5.2 0.565  Summed stress score 7.9 ± 6.5 7.8 ± 6.8 8.2 ± 5.9 0.403  Summed difference score 5.0 ± 3.6 4.8 ± 3.2 5.5 ± 4.2 0.028 LV volumes and function at rest  Ejection fraction (%) 58 ± 14 59 ± 14 57 ± 13 0.180  End-diastolic volume (mL) 111 ± 50 109 ± 50 114 ± 49 0.211  End-systolic volume (mL) 52 ± 45 51 ± 46 54 ± 43 0.401  Peak filling rate (EDV/s) 2.5 ± 0.8 2.4 ± 0.8 2.5 ± 0.7 0.845 LV volumes and function after stress  Ejection fraction (%) 58 ± 13 58 ± 14 57 ± 12 0.148  End-diastolic volume (mL) 107 ± 44 106 ± 46 110 ± 40 0.326  End-systolic volume (mL) 50 ± 37 49 ± 39 51 ± 33 0.419  Peak filling rate (EDV/s) 2.4 ± 0.8 2.4 ± 0.8 2.5 ± 0.7 0.596 Table 2 Coronary anatomy and cardiac functional parameters Parameters Overall population (n = 648) No diabetes mellitus (n = 432) Diabetes mellitus (n = 216) P-value Coronary anatomy 0.285  Normal coronary arteries, n (%) 174 (27) 124 (29) 50 (23)  Non-obstructive atherosclerosis, n (%) 102 (16) 65 (15) 37 (17)  Single vessel CAD, n (%) 192 (29) 120 (28) 72 (33)  Multivessel CAD, n (%) 180 (28) 123 (28) 57 (27) Stress test results  Positive stress ECG, n (%) 118 (18) 71 (16) 47 (22) 0.106 Perfusion data  Summed rest score 3.4 ± 6.4 3.5 ± 6.9 3.2 ± 5.2 0.565  Summed stress score 7.9 ± 6.5 7.8 ± 6.8 8.2 ± 5.9 0.403  Summed difference score 5.0 ± 3.6 4.8 ± 3.2 5.5 ± 4.2 0.028 LV volumes and function at rest  Ejection fraction (%) 58 ± 14 59 ± 14 57 ± 13 0.180  End-diastolic volume (mL) 111 ± 50 109 ± 50 114 ± 49 0.211  End-systolic volume (mL) 52 ± 45 51 ± 46 54 ± 43 0.401  Peak filling rate (EDV/s) 2.5 ± 0.8 2.4 ± 0.8 2.5 ± 0.7 0.845 LV volumes and function after stress  Ejection fraction (%) 58 ± 13 58 ± 14 57 ± 12 0.148  End-diastolic volume (mL) 107 ± 44 106 ± 46 110 ± 40 0.326  End-systolic volume (mL) 50 ± 37 49 ± 39 51 ± 33 0.419  Peak filling rate (EDV/s) 2.4 ± 0.8 2.4 ± 0.8 2.5 ± 0.7 0.596 Parameters Overall population (n = 648) No diabetes mellitus (n = 432) Diabetes mellitus (n = 216) P-value Coronary anatomy 0.285  Normal coronary arteries, n (%) 174 (27) 124 (29) 50 (23)  Non-obstructive atherosclerosis, n (%) 102 (16) 65 (15) 37 (17)  Single vessel CAD, n (%) 192 (29) 120 (28) 72 (33)  Multivessel CAD, n (%) 180 (28) 123 (28) 57 (27) Stress test results  Positive stress ECG, n (%) 118 (18) 71 (16) 47 (22) 0.106 Perfusion data  Summed rest score 3.4 ± 6.4 3.5 ± 6.9 3.2 ± 5.2 0.565  Summed stress score 7.9 ± 6.5 7.8 ± 6.8 8.2 ± 5.9 0.403  Summed difference score 5.0 ± 3.6 4.8 ± 3.2 5.5 ± 4.2 0.028 LV volumes and function at rest  Ejection fraction (%) 58 ± 14 59 ± 14 57 ± 13 0.180  End-diastolic volume (mL) 111 ± 50 109 ± 50 114 ± 49 0.211  End-systolic volume (mL) 52 ± 45 51 ± 46 54 ± 43 0.401  Peak filling rate (EDV/s) 2.5 ± 0.8 2.4 ± 0.8 2.5 ± 0.7 0.845 LV volumes and function after stress  Ejection fraction (%) 58 ± 13 58 ± 14 57 ± 12 0.148  End-diastolic volume (mL) 107 ± 44 106 ± 46 110 ± 40 0.326  End-systolic volume (mL) 50 ± 37 49 ± 39 51 ± 33 0.419  Peak filling rate (EDV/s) 2.4 ± 0.8 2.4 ± 0.8 2.5 ± 0.7 0.596 Figure 1 View largeDownload slide Impact of the presence of CAD on myocardial ischaemic burden in patients with and without DM both in the whole study population (A) and in patients submitted to the specific cardiac stress-protocol (exercise vs. vasodilator) (B). Figure 1 View largeDownload slide Impact of the presence of CAD on myocardial ischaemic burden in patients with and without DM both in the whole study population (A) and in patients submitted to the specific cardiac stress-protocol (exercise vs. vasodilator) (B). Interactions between DM, coronary anatomy, and myocardial perfusion: per-vessel analysis Of the 1944 total vessels, 232 (12%) and 624 (32%) showed obstructive and non-obstructive CAD, respectively. While patients with DM and controls had the same proportion of obstructive coronary lesions (32% vs. 32%, P = NS), the former showed a higher prevalence of non-obstructive CAD (14% in DM vs. 10% in controls, P = 0.04). The interaction between DM, CAD severity, and myocardial ischaemic burden was then evaluated. Despite regional myocardial ischaemic burden gradually increased in normal, non-obstructive, and obstructive CAD, patients with DM showed significantly more severe ischaemia downstream obstructive coronary lesions than controls (Figure 2A). However, this finding was limited to patients submitted to vasodilator stress test, while disappeared in those undergoing exercise stress (Figure 2B). Figure 2 View largeDownload slide Impact of CAD severity on the extent of regional myocardial ischaemia in patients with and without DM both in the whole study population (A) and in patients submitted to the specific cardiac stress-protocol (exercise vs. vasodilator) (B). Figure 2 View largeDownload slide Impact of CAD severity on the extent of regional myocardial ischaemia in patients with and without DM both in the whole study population (A) and in patients submitted to the specific cardiac stress-protocol (exercise vs. vasodilator) (B). MPS accuracy in patients with and without DM: the role of stress-protocol In the overall population, MPS showed a significant accuracy in unmasking the presence of obstructive CAD (AUC 0.78, 95% CI 0.74–0.81; P < 0.001), which was maintained in patients with and without DM (Figure 3A). However, a significant interaction between stress-protocol and MPS accuracy was revealed. In fact, in patients with DM exercise stress MPS had a significantly lower diagnostic power than vasodilator stress SPECT (P for difference <0.001), because of a lower specificity (45% vs. 69%), while in the control group the accuracy of MPS was similar regardless of the stress-protocol adopted (Figure 3B). Figure 3 View largeDownload slide Diagnostic accuracy of MPS in detecting obstructive CAD in patients with and without DM both in the whole study population (A) and in patients submitted to the specific cardiac stress-protocol (exercise vs. vasodilator) (B). Figure 3 View largeDownload slide Diagnostic accuracy of MPS in detecting obstructive CAD in patients with and without DM both in the whole study population (A) and in patients submitted to the specific cardiac stress-protocol (exercise vs. vasodilator) (B). The relationship between patients characteristics, stress variables and MPS accuracy in patients submitted to exercise stress test was explored. Specifically, patients with DM attained a significantly lower %Wattmax (76 ± 27% vs. 82 ± 26%, P = 0.038) than controls, despite similar values of %HR and of the other major exercise variables. In this respect, a significant correlation between %Wattmax and body mass index (BMI) values was revealed (R = −0.41; P < 0.001). After correction for major clinical, LV functional and other exercise variables, a lower %Wattmax (OR 0.98, 95% CI 0.97–0.99; P = 0.026) turned to be an independent predictor of a reduced diagnostic specificity on MPS. Representative CZT and angiographic images of patients with and without DM are further reported (see Supplementary data online, Figures S1–S4), outlining the better diagnostic accuracy of MPS in the non-diabetic cohort. Discussion This study shows that in patients with DM submitted to MPS the choice of the appropriate stress-modality is mandatory for the correct evaluation of the extent and severity of myocardial ischaemic burden. While our results confirm the generally elevated accuracy of MPS in detecting significant CAD, they also show the presence of a significantly lower specificity in patients with DM than controls when an exercise stress test is adopted. DM and MPS accuracy: more than just CAD Solid evidence shows that patients with DM are at high-risk for different cardiovascular disorders.1,3 Moreover, a significant association between the presence of DM and silent myocardial ischaemia has been suggested.8,20 In this respect, it has been also reported that diabetic patients may show an excess of inducible myocardial ischaemia with respect to the coronary involvement,8,21 as a possible result of underlying coronary endothelial/microvascular risk factors.9,10 Accordingly, while in the general population MPS is still among the most frequently performed stress cardiac imaging techniques, it may theoretically have some disadvantages in the evaluation of patients with DM.11 In particular, the presence of diffuse myocardial blood flow impairment due to coronary endothelial/microvascular dysfunction might decrease the accuracy of MPS in unmasking the presence of focal coronary luminal narrowings.9,10 On the other hand, the presence of diffuse coronary atherosclerosis, as frequently observed in patients with long-standing DM,8 might pose the conditions for a balanced reduction of myocardial perfusion, further decreasing the diagnostic power of MPS. Despite those theoretical drawbacks, the impact of DM on the diagnostic accuracy of MPS has been loosely investigated.11 This results show that MPS maintains a similarly elevated accuracy in unmasking the presence of obstructive CAD both in patients with and without DM. Interestingly, no significant difference in terms of prevalence of obstructive CAD was revealed between patients with DM and controls. On the other hand, patients with DM also showed the presence a significantly higher prevalence of by-standing non-obstructive coronary lesions, which were in turn associated with some degrees of downstream myocardial ischaemia. Regarding MPS evaluation, patients with DM were characterized by a higher myocardial ischaemic burden than controls. Nevertheless, in those patients myocardial ischaemia was mainly concentrated downstream obstructive coronary lesions, pointing against the presence of a diffuse impairment of myocardial perfusion. Finally, the lower diagnostic specificity of exercise stress SPECT in patients with DM than in control subjects has been already reported in a smaller cohort of patients,11 possibly indicating a higher prevalence of false-positive imaging findings in this cohort of subjects. In fact, the lower cardiac workload that was obtained in those with DM, coupled with a significantly higher BMI may suggest a possibly higher prevalence of image artefacts in these patients. Further, dedicated studies will be needed to better describe these correlations. Interaction between MPS accuracy and stress test protocol: the case of DM MPS is one of the most versatile cardiac imaging techniques that are available in clinical practice. However, despite the theoretical variety of possible stress protocols that can be performed, exercise stress still represents the cornerstone of cardiac functional evaluation.22 On the other hand, in patients that cannot exercise sufficiently, a pharmacological stress test is generally performed, typically by means of a coronary vasodilator (i.e. adenosine or dipyridamole). In this respect, while in the general population the diagnostic accuracy of MPS seems generally unaffected by the specific stress-protocol that is adopted,19 the prognostic value of the two main stress strategies appears somehow different.23 Moreover, it has been already reported that in some categories of patients, such as those with atrial fibrillation, an interaction between cardiac stress-protocol, and MPS accuracy can be present, particularly when exercise stress test is performed.24 Accordingly, while the data of this study, confirm that the excellent accuracy of MPS is maintained in patients with and without DM, they also show that in the former patients the diagnostic power of MPS can be significantly influenced by the particular stress-protocol that is adopted. In particular, in patients with DM exercise stress MPS had a lower diagnostic efficiency in detecting obstructive CAD than vasodilator-stress, because of a relatively impaired specificity. This finding could be explained by the fact that patients with DM submitted to exercise stress test were able to reach a significantly lower cardiac workload than controls, which ultimately resulted an independent predictor of impaired diagnostic specificity on MPS. Interestingly, while exercise stress MPS has been shown to risk-stratify accurately patients with DM,23,25 it has been also demonstrated that diabetic patients with a reduced exercise capacity are characterized by a significantly impaired prognosis.26,27 In this respect, our data show that in those patients the accuracy of MPS could be reduced, possibly leading to the misdiagnosis of CAD. Present and previous data confirm that, in patients submitted to MPS, the choice of the stress-protocol (i.e. exercise vs. vasodilator) should be also based on the presence of simple constitutional and exercise-related variables (i.e. obesity and exercise capacity). Accordingly, our data suggest that in patients with DM that have obtained an insufficient cardiac workload on exercise stress test the results of MPS might be inaccurate. Limitations The retrospective nature of the study should be acknowledged. Since patients were referred to clinically indicated ICA also according to MPS results, some sort of referral bias cannot be completely ruled-out. In these cases, the comparison of the normalcy rates of MPS in patients with low-likelihood of CAD has been proposed as a possible option to reduce the impact of referral bias.11 However, in our population, only two patients presented a low pre-test likelihood of CAD (<15%), prohibiting this kind of analysis. In this respect, the fact that the outmost majority of the study patients had an intermediate-to-high pre-test probability of CAD, further stresses the high level of appropriateness of patients selection. In this study, only a single anatomic cut-off value for the definition of significant CAD was used, namely >70% luminal narrowing. In this respect, since the relationship between CAD anatomic severity and myocardial ischaemic burden may be rather elusive, the use of a functional cut-off of CAD haemodynamic significance [i.e. using fraction flow reserve (FFR) in case of intermediate coronary stenosis] would have helped to better classify the patients studies and offered the chance to allow a more appropriate definition of CZT accuracy.28 However, since in this study FFR was not used systematically, its implementation in the diagnostic algorithm was impossible. Accordingly, in order to try to try to be more specific, a relatively more stringent cut-off value of CAD anatomic severity was used (70% rather than the classical 50% diemeter reduction threshold)29 reasonably reducing the occurrence of false-positive angiographic findings. The fact that a subgroup of patients was injected despite a theoretically submaximal (%HR <85%) exercise stress test might have limited MPS accuracy.14 However, considering that the proportion of those patients was similar in patients with DM and controls and that the majority of those had presented electrocardiographic or clinical signs of ischaemia during stress protocol, the impact of this variable should be considered limited. Accordingly, despite similar values of %HR, in control patients MPS accuracy after an exercise stress test remained high, while in those with DM the diagnostic value of exercise-MPS was reduced. The use of dipyridamole rather than the relatively more potent adenosine might have theoretically reduced the accuracy of MPS in detecting significant CAD. However, since several studies have reported an elevated accuracy of dipyridamole myocardial perfusion imaging in unmasking the presence of CAD,14,30 the impact of the chosen coronary stressor should be considered limited. While patients were matched for pre-test probability of CAD, those with DM presented a significantly higher cardiovascular risk profile (i.e. a higher prevalence of major cardiovascular risk factors), possibly impacting the diagnostic accuracy of MPS because of a higher prevalence of underlying microvascular dysfunction. However, since in this study, myocardial perfusion abnormalities were concentrated downstream vessels with CAD (Figure 2), the impact of microvascular dysfunction on MPS accuracy should be considered minor. While dobutamine MPS is also performed in our laboratory when both exercise and vasodilator stress test is not feasible, those patients were not included in the analyses because of their limited numerosity (four subjects). Regarding patients selection, while the presence of DM was ascertained according to current guidelines,12 its absence was solely base on fasting plasma glucose levels in the absence of any antidiabetic drugs. Accordingly, while the presence of some patients with early stages of DM in the control group cannot be completely excluded, a different screening strategy (i.e. with the use of glucose tolerance test) should be considered unrealistic. Conclusions MPS was accurate in detecting obstructive CAD both in patients with and without DM. However, in the former patients a significant interaction between MPS accuracy and the specific stress protocol was observed, with a lower diagnostic power of exercise stress MPS than vasodilator-stress one. This finding could be explained by the fact that patients with DM attained a significantly lower exercise-related cardiac workload than controls, possibly due to a higher prevalence of obesity, resulting in a lower diagnostic specificity in unmasking the presence of obstructive CAD. Conflict of interest: none declared. References 1 Bloomgarden ZT. Diabetes and cardiovascular disease . Diabetes Care 2011 ; 34 : e24 – 30 . Google Scholar Crossref Search ADS PubMed 2 Torp-Pedersen C , Jeppesen J. Diabetes and hypertension and atherosclerotic cardiovascular disease: related or separate entities often found together . Hypertension 2011 ; 57 : 887 – 8 . Google Scholar Crossref Search ADS PubMed 3 Kavousi M , Leening MJ , Nanchen D , Greenland P , Graham IM , Steyerberg EW et al. Comparison of application of the ACC/AHA guidelines, adult treatment panel III guidelines, and European Society of Cardiology guidelines for cardiovascular disease prevention in a European cohort . JAMA 2014 ; 311 : 1416 – 23 . Google Scholar Crossref Search ADS PubMed 4 Wackers FJ , Young LH , Inzucchi SE , Chyun DA , Davey JA , Barrett EJ et al. Detection of ischemia in asymptomatic diabetics investigators. detection of silent myocardial ischemia in asymptomatic diabetic subjects: the DIAD study . Diabetes Care 2004 ; 27 : 1954 – 61 . Google Scholar Crossref Search ADS PubMed 5 Muhlestein JB , Lappé DL , Lima JAC , Rosen BD , May HT , Knight S et al. Effect of screening for coronary artery disease using CT angiography on mortality and cardiac events in high-risk patients with diabetes: the FACTOR-64 randomized clinical trial . JAMA 2014 ; 312 : 2234 – 43 . Google Scholar Crossref Search ADS PubMed 6 Bourque JM , Patel CA , Ali MM , Perez M , Watson DD , Beller GA. Prevalence and predictors of ischemia and outcomes in outpatients with diabetes mellitus referred for single-photon emission computed tomography myocardial perfusion imaging . Circ Cardiovasc Imaging 2013 ; 6 : 466 – 77 . Google Scholar Crossref Search ADS PubMed 7 Park G-M , Lee J-H , Lee S-W , Yun S-C , Kim Y-H , Cho Y-R et al. Comparison of coronary computed tomographic angiographic findings in asymptomatic subjects with versus without diabetes mellitus . Am J Cardiol 2015 ; 116 : 372 – 8 . Google Scholar Crossref Search ADS PubMed 8 Di Carli MF , Hachamovitch R. Should we screen for occult coronary artery disease among asymptomatic patients with diabetes? J Am Coll Cardiol 2005 ; 45 : 50 – 3 . Google Scholar Crossref Search ADS PubMed 9 Di Carli MF , Janisse J , Grunberger G , Ager J. Role of chronic hyperglycemia in the pathogenesis of coronary microvascular dysfunction in diabetes . J Am Coll Cardiol 2003 ; 41 : 1387 – 93 . Google Scholar Crossref Search ADS PubMed 10 Di Carli MF , Bianco-Batlles D , Landa ME , Kazmers A , Groehn H , Muzik O et al. Effects of autonomic neuropathy on coronary blood flow in patients with diabetes mellitus . Circulation 1999 ; 100 : 813 – 9 . Google Scholar Crossref Search ADS PubMed 11 Kang X , Berman DS , Lewin H , Miranda R , Erel J , Friedman JD et al. Comparative ability of myocardial perfusion single-photon emission computed tomography to detect coronary artery disease in patients with and without diabetes mellitus . Am Heart J 1999 ; 137 : 949 – 57 . Google Scholar Crossref Search ADS PubMed 12 Rydén L , Grant PJ , Anker SD , Berne C , Cosentino F , Danchin N et al. ESC guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD: the task force on diabetes, pre-diabetes, and cardiovascular diseases of the European Society of Cardiology (ESC) and developed in collaboration with the European Association for the Study of Diabetes (EASD) . Eur Heart J 2013 ; 34 : 3035 – 87 . Google Scholar Crossref Search ADS PubMed 13 Wasserman K , Hansen JE , Sue DY , Stringer WW , Whipp BJ . Principles of Exercise Testing and Interpretation. Lippincott Williams & Wilkins; 4th Edition 2004 . 14 Gimelli A , Liga R , Pasanisi EM , Casagranda M , Coceani M , Marzullo P. Influence of cardiac stress protocol on myocardial perfusion imaging accuracy: the role of exercise level on the evaluation of ischemic burden . J Nucl Cardiol 2016 ; 23 : 1114 – 22 . Google Scholar Crossref Search ADS PubMed 15 Gimelli A , Liga R , Giorgetti A , Kusch A , Pasanisi EM , Marzullo P. Relationships between myocardial perfusion abnormalities and poststress left ventricular functional impairment on cadmium-zinc-telluride imaging . Eur J Nucl Med Mol Imaging 2015 ; 42 : 994 – 1003 . Google Scholar Crossref Search ADS PubMed 16 Gimelli A , Liga R , Duce V , Kusch A , Clemente A , Marzullo P. Accuracy of myocardial perfusion imaging in detecting multivessel coronary artery disease: a cardiac CZT study . J Nucl Cardiol 2017 ; 24 : 687 – 95 . Google Scholar Crossref Search ADS PubMed 17 Gutstein A , Navzorov R , Solodky A , Mats I , Kornowski R , Zafrir N. Angiographic correlation of myocardial perfusion imaging with half the radiation dose using orderedsubset expectation maximization with resolution recovery software . J Nucl Cardiol 2013 ; 20 : 539 – 44 . Google Scholar Crossref Search ADS PubMed 18 Giorgetti A , Masci PG , Marras G , Rustamova YK , Gimelli A , Genovesi D et al. Gated SPECT evaluation of left ventricular function using a CZT camera and a fast low-dose clinical protocol: comparison to cardiac magnetic resonance imaging . Eur J Nucl Med Mol Imaging 2013 ; 40 : 1869 – 75 . Google Scholar Crossref Search ADS PubMed 19 Gimelli A , Liga R , Bottai M , Pasanisi EM , Giorgetti A , Fucci S et al. Diastolic dysfunction assessed by ultra-fast cadmium-zinc-telluride cardiac imaging: impact on the evaluation of ischaemia . Eur Heart J Cardiovasc Imaging 2015 ; 16 : 68 – 73 . Google Scholar Crossref Search ADS PubMed 20 Scholte AJ , Schuijf JD , Kharagjitsingh AV , Dibbets-Schneider P , Stokkel MP , Jukema JW et al. Different manifestations of coronary artery disease by stress SPECT myocardial perfusion imaging, coronary calcium scoring, and multislice CT coronary angiography in asymptomatic patients with type 2 diabetes mellitus . J Nucl Cardiol 2008 ; 15 : 503 – 9 . Google Scholar Crossref Search ADS PubMed 21 Rajagopalan N , Miller TD , Hodge DO , Frye RL , Gibbons RJ. Identifying high-risk asymptomatic diabetic patients who are candidates for screening stress single-photon emission computed tomography imaging . J Am Coll Cardiol 2005 ; 45 : 4:43 – 9 . Google Scholar Crossref Search ADS 22 Shaw LJ , Mieres JH , Hendel RH , Boden WE , Gulati M , Veledar E et al. Comparative effectiveness of exercise electrocardiography with or without myocardial perfusion single photon emission computed tomography in women with suspected coronary artery disease: results from the what is the optimal method for ischemia evaluation in women (WOMEN) trial . Circulation 2011 ; 124 : 1239 – 49 . Google Scholar Crossref Search ADS PubMed 23 Padala SK , Ghatak A , Padala S , Katten DM , Polk DM , Heller GV. Cardiovascular risk stratification in diabetic patients following stress single-photon emission-computed tomography myocardial perfusion imaging: the impact of achieved exercise level . J Nucl Cardiol 2014 ; 21 : 1132 – 43 . Google Scholar Crossref Search ADS PubMed 24 Gimelli A , Liga R , Startari U , Giorgetti A , Pieraccini L , Marzullo P. Evaluation of ischaemia in patients with atrial fibrillation: impact of stress protocol on myocardial perfusion imaging accuracy . Eur Heart J Cardiovasc Imaging 2015 ; 16 : 781 – 7 . Google Scholar Crossref Search ADS PubMed 25 Ghatak A , Padala S , Katten DM , Polk DM , Heller GV. Risk stratification among diabetic patients undergoing stress myocardial perfusion imaging . J Nucl Cardiol 2013 ; 20 : 529 – 38 . Google Scholar Crossref Search ADS PubMed 26 Rozanski A , Gransar H , Min JK , Hayes SW , Friedman JD , Thomson LE et al. Long-term mortality following normal exercise myocardial perfusion SPECT according to coronary disease risk factors . J Nucl Cardiol 2014 ; 21 : 341 – 50 . Google Scholar Crossref Search ADS PubMed 27 Kokkinos P , Myers J , Nylen E , Panagiotakos DB , Manolis A , Pittaras A et al. Exercise capacity and all-cause mortality in African American and Caucasian men with type 2 diabetes . Diabetes Care 2009 ; 32 : 623 – 8 . Google Scholar Crossref Search ADS PubMed 28 De Bruyne B , Pijls NH , Kalesan B , Barbato E , Tonino PA , Piroth Z et al. Fractional flow reserve-guided PCI versus medical therapy in stable coronary disease . N Engl J Med 2012 ; 367 : 991 – 1001 . Google Scholar Crossref Search ADS PubMed 29 Budoff MJ , Nakazato R , Mancini GB , Gransar H , Leipsic J , Berman DS et al. CT angiography for the prediction of hemodynamic significance in intermediate and severe lesions: head-to-head comparison with quantitative coronary angiography using fractional flow reserve as the reference standard . JACC Cardiovasc Imaging 2016 ; 9 : 55964 . 30 Cramer MJ , Verzijlbergen JF , van der Wall EE , Vermeersch PH , Niemeyer MG , Zwinderman AH et al. Comparison of adenosine and high-dose dipyridamole both combined with low-level exercise stress for 99Tcm-MIBI SPET myocardial perfusion imaging . Nucl Med Commun 1996 ; 17 : 97 – 104 . Google Scholar Crossref Search ADS PubMed Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2017. For permissions, please email: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png European Heart Journal – Cardiovascular Imaging Oxford University Press

Appropriate choice of stress modality in patients undergoing myocardial perfusion scintigraphy with a cardiac camera equipped with solid-state detectors: the role of diabetes mellitus

Loading next page...
 
/lp/ou_press/appropriate-choice-of-stress-modality-in-patients-undergoing-wwGQj8ewUL
Publisher
Oxford University Press
Copyright
Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2017. For permissions, please email: journals.permissions@oup.com.
ISSN
2047-2404
D.O.I.
10.1093/ehjci/jex313
Publisher site
See Article on Publisher Site

Abstract

Abstract Aims To evaluate the impact of diabetes mellitus (DM) on the accuracy of myocardial perfusion scintigraphy (MPS) in detecting coronary artery disease (CAD). Methods and results Two hundred and sixteen patients with DM and 432 matched controls were submitted to MPS on a dedicated cardiac camera equipped with cadmium–zinc–telluride (CZT) detectors and coronary angiography. Exercise stress was performed in 442 (68%) patients, while the remainders underwent vasodilator stress. Exercise level was determined as the percentage of the predicted maximal workload that was attained (%Wattmax). The summed difference score was derived from CZT images. A coronary stenosis >70% was considered obstructive. The prevalence of obstructive CAD was 59.7% in patients with DM and 56.2% in controls (P = NS). The accuracy of MPS in detecting CAD was similar in patients with and without DM [area under the ROC curve (AUC) 0.77 vs. 0.78, P = NS]. An interaction between the accuracy of MPS and cardiac stress-protocol was revealed. In fact, in patients with DM exercise stress CZT had a lower accuracy than vasodilator-stress (AUC 0.70 vs. 0.89, P < 0.001), because of a lower specificity (45% vs. 69%), while in the control group the accuracy of MPS was similar regardless of the stress-protocol adopted. Patients with DM attained a significantly lower %Wattmax during exercise than controls (76 ± 27% vs. 82 ± 26%, P = 0.038), which resulted an independent predictor of reduced specificity (P = 0.026). Conclusion The accuracy of CZT imaging in patients with DM is elevated, and it is quite comparable to the one obtained in patients without DM. However, a reduced specificity can be expected in the case of exercise stress CZT, because of an impaired exercise capacity. myocardial perfusion scintigraphy, diabetes mellitus, diagnostic accuracy, exercise stress test, obstructive CAD Introduction Epidemiological studies have reported the association of diabetes mellitus (DM) and coronary artery disease (CAD).1,2 These evidences have led to the concept of DM as a coronary risk equivalent, requiring aggressive preventive, and therapeutic measures.3 Moreover, patients with DM have been generally considered at a higher risk of silent myocardial ischaemia, supporting the possible prognostic benefit of ischaemia testing in asymptomatic diabetic patients.4,5 However, recent appraisals have changed these concepts, showing that patients with DM have a substantially lower risk of future cardiac events than expected and questioning the real value of screening strategies in asymptomatic DM patients.6,7 Myocardial perfusion scintigraphy (MPS) on single-photon-emission computed tomography (SPECT) is used frequently in patients with DM to identify the presence of significant CAD.4 One common finding in patients with DM is that the extent and severity of perfusion abnormalities may not reflect the effective CAD burden8 and result in the frequent occurrence of ‘false-positive’ findings, possibly due to the presence of underlying microvascular dysfunction.9,10 An interaction between the specific stress-protocol adopted (i.e. exercise vs. vasodilator) and MPS accuracy has been also suggested, being exercise stress-MPS possibly characterized by a lower specificity than in normal patients.11 Despite some preliminary studies, an accurate evaluation of MPS diagnostic power in detecting obstructive CAD in a consistent population of patients with DM has not been performed and the impact of the specific stress-protocol adopted on MPS diagnostic power in patients with and without DM is still lacking. Accordingly, we aimed to evaluate the comparative accuracy of MPS performed on a dedicated cadmium–zinc–telluride (CZT) camera in patients with and without DM and to explore the impact of cardiac stress-protocol on MPS diagnostic power. Materials and methods Patient population Between 2010 and 2016, 6120 subjects with anginal chest pain and suspected were referred to our institution for a scintigraphic evaluation of myocardial perfusion at rest and after stress with a CZT camera. Among those, 216 consecutive patients with DM, also submitted to invasive coronary angiography (ICA), were enrolled. Those patients with an ascertained diagnosis of DM according to current guidelines,12 were referred to MPS mainly by the local community hospitals. From the same population of patients, a control group of subjects without DM, known coronary anatomy, and similar baseline clinical characteristics (particularly regarding age, sex, and chest pain characteristics) were also selected. In the control population, the absence of DM was documented according to the presence of a normal fasting plasma glucose in the absence of any antidiabetic drugs. In order to ensure an effective comparison of the two populations, DM and control patients were selected with a 1:2 ratio and also matched for CZT stress protocol (exercise vs. pharmacological). In the entire population, both patients with DM and controls were submitted to ICA within 3 months from the CZT study, as indicated by the referring physicians. Exclusion criteria were: previous acute coronary syndrome or coronary intervention, known CAD, haemodynamic instability, severely symptomatic heart failure, and previous cardiac infective/inflammatory disease. Moreover, to eliminate a possible bias in the analysis of the data, the four patients that underwent dobutamine MPS in the same study period were also excluded. The study was approved by the Local Ethical Committee and conformed to the Declaration of Helsinki on human research. Written informed consent was obtained from every patient. Patient preparation and stress protocols Patients discontinued beta-blockers, calcium-antagonists, and nitrates for 24 h before testing. Moreover, theophylline and caffeine containing products were also prohibited in the 24 h before MPS. Four hundred and thirty-two (67%) patients (149 DM vs. 243 controls) underwent bicycle exercise (stepwise increments of 25 W every 2 min), while 206 (33%) (67 DM vs. 139 controls) dipyridamole (0.56 mg/kg IV over 4 min) stress testing, depending on patients’ ability to exercise or according to clinical reasons. Of the patients undergoing exercise stress testing, 142 (49 DM vs. 91 controls, P = NS) reached 85% of the age-predicted maximum heart rate (%HR), 121 (40 DM vs. 81 controls, P = NS) reached a peak rate pressure product higher than 25 000, and 88 developed signs of myocardial ischaemia on electrocardiogram (ECG) (40 DM vs. 48 controls, P = 0.012). The remaining patients were injected because of symptoms (chest pain and/or dyspnoea) believed to be indicative of myocardial ischaemia. In each patient, the predicted maximal workload (Wattmax) was calculated13 and the percentage of the reached Wattmax (%Wattmax) determined. Acquisition protocol Patients underwent stress–rest CZT imaging with a single-day protocol (148–185 MBq of 99mTc-tetrofosmin during stress and 296–370 MBq at rest). In all patients, stress and rest CZT imaging were acquired as previously described.14,15 Stress acquisitions were started 10 (exercise) to 15 (dipyridamole) min after the completion of the stress protocols, while rest scans were started 30 min after injection. Patients were imaged in the supine position with arms placed over their head without any detector or collimator motion. All images were acquired with a 32 × 32 matrix and a 20% energy window centred at the 140 keV photopeak of 99mTc. List mode files were acquired and stored. Images were reconstructed on a standard workstation (Xeleris II; GE Healthcare, Haifa, Israel) using a dedicated iterative algorithm.14 All studies were reconstructed using a standard iterative algorithm with ordered-subset expectation maximization with 50 iterations, without resolution recovery or attenuation correction. A Butterworth post-processing filter (frequency 0.37, order 7) was applied to the reconstructed slices. The tomographic studies were also re-projected into 60 planar projections to emulate a standard SPECT layout. Semiquantitative analysis of perfusion images Stress and rest images were semiquantitatively scored according to the 17-segment left ventricular (LV) model and a five-point scale (0: normal, 1: equivocal, 2: moderate, 3: severe reduction in radioisotope uptake, and 4: absence of detectable tracer uptake).15,16 Accordingly, the summed stress score (SSS), summed rest score, and summed difference score (SDS) were calculated. Two experienced nuclear cardiologists performed the semiquantitative analysis independently and consensus was reached when necessary. A SDS >3 was identified as a measure of significant myocardial ischaemia.17 Moreover, in each patient, the regional perfusion scores of the three coronary territories [left-anterior descending (LAD), left circumflex artery (LCX), and right coronary artery (RCA)] were also determined by adding the defect scores (DS) of the pertinent myocardial segments and reported as follows: DS-LAD, DS-LCX, and DS-RCA. Analysis of gated images LV function analysis was performed from 16-frames reformatted images using the commercially available software (Corridor4DM, Invia, Ann Arbor, MI, USA). End-diastolic volume (EDV), end-systolic volume, ejection fraction, and peak filling rate (EDV*s−1) were automatically calculated.18,19 All functional measurements were obtained from rest and stress ECG-gated 99mTc-tetrofosmin images. ICA Coronary angiograms were quantified with a dedicated computer software (Advanced Vessel Analysis, Innova 3DXR for Advanced Workstations; GE Healthcare) using an automatic edge-contour detection algorithm to compute stenosis severity. Coronary stenosis >70% were considered obstructive, while luminal narrowings ≤70% were recorded as non-obstructive. Statistical analysis Continuous variables were expressed as mean ± 1 SD and categorical variables as percentages. Groups were compared for categorical data using Fisher’s exact test and for continuous variables using analysis of variance followed by Fisher’s protected least significant difference for multiple comparisons. All tests were two-sided; a P-value of <0.05 was considered to be significant. The accuracy of MPS in unmasking the presence of significant coronary stenoses was assessed by the receiving operating characteristic analysis. Accordingly, the pertinent areas under the curves (AUC) with the appropriate 95% confidence intervals (CIs) were determined. The predictors of a reduced diagnostic specificity (‘true-negative rate’) on MPS were assessed at multivariate logistic regression analysis and the odds ratios (ORs) with the pertinent 95% CI determined. Statistical analyses were performed using JMP statistical software (SAS Institute Inc., version 4.0.0) and Stata software (Stata Statistical Software: Release 10, StataCorp. 2007, College Station, TX, USA). Results Characterization of the study population The characteristics of the study population are summarized in Table 1. Patients with DM had a significantly higher cardiovascular risk profile than controls, with a higher prevalence of hypertension (P = 0.006) and obesity (P = 0.049). Table 1 Characteristics of patients Parameters Overall population (n = 648) No diabetes mellitus (n = 432) Diabetes mellitus (n = 216) P-value Demographics  Age (years) 70 ± 8 72 ± 9 72 ± 9 0.511  Male gender, n (%) 495 (76) 330 (76) 165 (76) >0.999  Pre-test probability of CAD (%) 66 ± 18 65 ± 18 67 ± 18 0.271 Cardiovascular risk factors  Family history of CAD, n (%) 195 (30) 110 (25) 85 (39) <0.001  Hypercholesterolemia, n (%) 246 (38) 137 (32) 109 (50) <0.001  Hypertension, n (%) 364 (56) 226 (52) 138 (59) 0.006  Smoking, n (%) 65 (10) 36 (8) 29 (13) 0.051  Body mass index 29 ± 5 28 ± 4 29 ± 5 0.049 Pharmacologic treatment  Beta-blockers, n (%) 26 (4) 21 (5) 5 (2) 0.140  ACE-inhibitors/ARB, n (%) 585 (90) 401 (93) 184 (85) 0.002  Statins, n (%) 241 (37) 131 (95) 109 (100) 0.002  Aspirin, n (%) 640 (99) 428 (99) 212 (99) 0.675  Oral anti-diabetic agents, n (%) 210 (32) 0 (0) 210 (97) <0.001  Insulin, n (%) 6 (9) 0 (0) 6 (3) 0.001 MPI protocol 0.789  Exercise stress test, n (%) 442 (68) 293 (68) 149 (69)  Dipyridamole stress, n (%) 206 (32) 139 (32) 67 (31) Parameters Overall population (n = 648) No diabetes mellitus (n = 432) Diabetes mellitus (n = 216) P-value Demographics  Age (years) 70 ± 8 72 ± 9 72 ± 9 0.511  Male gender, n (%) 495 (76) 330 (76) 165 (76) >0.999  Pre-test probability of CAD (%) 66 ± 18 65 ± 18 67 ± 18 0.271 Cardiovascular risk factors  Family history of CAD, n (%) 195 (30) 110 (25) 85 (39) <0.001  Hypercholesterolemia, n (%) 246 (38) 137 (32) 109 (50) <0.001  Hypertension, n (%) 364 (56) 226 (52) 138 (59) 0.006  Smoking, n (%) 65 (10) 36 (8) 29 (13) 0.051  Body mass index 29 ± 5 28 ± 4 29 ± 5 0.049 Pharmacologic treatment  Beta-blockers, n (%) 26 (4) 21 (5) 5 (2) 0.140  ACE-inhibitors/ARB, n (%) 585 (90) 401 (93) 184 (85) 0.002  Statins, n (%) 241 (37) 131 (95) 109 (100) 0.002  Aspirin, n (%) 640 (99) 428 (99) 212 (99) 0.675  Oral anti-diabetic agents, n (%) 210 (32) 0 (0) 210 (97) <0.001  Insulin, n (%) 6 (9) 0 (0) 6 (3) 0.001 MPI protocol 0.789  Exercise stress test, n (%) 442 (68) 293 (68) 149 (69)  Dipyridamole stress, n (%) 206 (32) 139 (32) 67 (31) Table 1 Characteristics of patients Parameters Overall population (n = 648) No diabetes mellitus (n = 432) Diabetes mellitus (n = 216) P-value Demographics  Age (years) 70 ± 8 72 ± 9 72 ± 9 0.511  Male gender, n (%) 495 (76) 330 (76) 165 (76) >0.999  Pre-test probability of CAD (%) 66 ± 18 65 ± 18 67 ± 18 0.271 Cardiovascular risk factors  Family history of CAD, n (%) 195 (30) 110 (25) 85 (39) <0.001  Hypercholesterolemia, n (%) 246 (38) 137 (32) 109 (50) <0.001  Hypertension, n (%) 364 (56) 226 (52) 138 (59) 0.006  Smoking, n (%) 65 (10) 36 (8) 29 (13) 0.051  Body mass index 29 ± 5 28 ± 4 29 ± 5 0.049 Pharmacologic treatment  Beta-blockers, n (%) 26 (4) 21 (5) 5 (2) 0.140  ACE-inhibitors/ARB, n (%) 585 (90) 401 (93) 184 (85) 0.002  Statins, n (%) 241 (37) 131 (95) 109 (100) 0.002  Aspirin, n (%) 640 (99) 428 (99) 212 (99) 0.675  Oral anti-diabetic agents, n (%) 210 (32) 0 (0) 210 (97) <0.001  Insulin, n (%) 6 (9) 0 (0) 6 (3) 0.001 MPI protocol 0.789  Exercise stress test, n (%) 442 (68) 293 (68) 149 (69)  Dipyridamole stress, n (%) 206 (32) 139 (32) 67 (31) Parameters Overall population (n = 648) No diabetes mellitus (n = 432) Diabetes mellitus (n = 216) P-value Demographics  Age (years) 70 ± 8 72 ± 9 72 ± 9 0.511  Male gender, n (%) 495 (76) 330 (76) 165 (76) >0.999  Pre-test probability of CAD (%) 66 ± 18 65 ± 18 67 ± 18 0.271 Cardiovascular risk factors  Family history of CAD, n (%) 195 (30) 110 (25) 85 (39) <0.001  Hypercholesterolemia, n (%) 246 (38) 137 (32) 109 (50) <0.001  Hypertension, n (%) 364 (56) 226 (52) 138 (59) 0.006  Smoking, n (%) 65 (10) 36 (8) 29 (13) 0.051  Body mass index 29 ± 5 28 ± 4 29 ± 5 0.049 Pharmacologic treatment  Beta-blockers, n (%) 26 (4) 21 (5) 5 (2) 0.140  ACE-inhibitors/ARB, n (%) 585 (90) 401 (93) 184 (85) 0.002  Statins, n (%) 241 (37) 131 (95) 109 (100) 0.002  Aspirin, n (%) 640 (99) 428 (99) 212 (99) 0.675  Oral anti-diabetic agents, n (%) 210 (32) 0 (0) 210 (97) <0.001  Insulin, n (%) 6 (9) 0 (0) 6 (3) 0.001 MPI protocol 0.789  Exercise stress test, n (%) 442 (68) 293 (68) 149 (69)  Dipyridamole stress, n (%) 206 (32) 139 (32) 67 (31) Interactions between DM, coronary anatomy, and myocardial perfusion: per-patient analysis Patients with DM and controls showed a similar prevalence and extent of obstructive CAD (Table 2). Similarly, no differences regarding major LV functional parameters (i.e. left ventricular ejection fraction and cavitary volumes) were observed between the two patients categories. On the other hand, despite a comparable scar burden, patients with DM showed a higher extent of myocardial ischaemia than controls (P = 0.028). This finding was explained by the presence of more extensive ischaemia in diabetic patients with obstructive CAD than in controls (Figure 1A), and was limited to subjects submitted to vasodilator stress test (Figure 1B). Table 2 Coronary anatomy and cardiac functional parameters Parameters Overall population (n = 648) No diabetes mellitus (n = 432) Diabetes mellitus (n = 216) P-value Coronary anatomy 0.285  Normal coronary arteries, n (%) 174 (27) 124 (29) 50 (23)  Non-obstructive atherosclerosis, n (%) 102 (16) 65 (15) 37 (17)  Single vessel CAD, n (%) 192 (29) 120 (28) 72 (33)  Multivessel CAD, n (%) 180 (28) 123 (28) 57 (27) Stress test results  Positive stress ECG, n (%) 118 (18) 71 (16) 47 (22) 0.106 Perfusion data  Summed rest score 3.4 ± 6.4 3.5 ± 6.9 3.2 ± 5.2 0.565  Summed stress score 7.9 ± 6.5 7.8 ± 6.8 8.2 ± 5.9 0.403  Summed difference score 5.0 ± 3.6 4.8 ± 3.2 5.5 ± 4.2 0.028 LV volumes and function at rest  Ejection fraction (%) 58 ± 14 59 ± 14 57 ± 13 0.180  End-diastolic volume (mL) 111 ± 50 109 ± 50 114 ± 49 0.211  End-systolic volume (mL) 52 ± 45 51 ± 46 54 ± 43 0.401  Peak filling rate (EDV/s) 2.5 ± 0.8 2.4 ± 0.8 2.5 ± 0.7 0.845 LV volumes and function after stress  Ejection fraction (%) 58 ± 13 58 ± 14 57 ± 12 0.148  End-diastolic volume (mL) 107 ± 44 106 ± 46 110 ± 40 0.326  End-systolic volume (mL) 50 ± 37 49 ± 39 51 ± 33 0.419  Peak filling rate (EDV/s) 2.4 ± 0.8 2.4 ± 0.8 2.5 ± 0.7 0.596 Parameters Overall population (n = 648) No diabetes mellitus (n = 432) Diabetes mellitus (n = 216) P-value Coronary anatomy 0.285  Normal coronary arteries, n (%) 174 (27) 124 (29) 50 (23)  Non-obstructive atherosclerosis, n (%) 102 (16) 65 (15) 37 (17)  Single vessel CAD, n (%) 192 (29) 120 (28) 72 (33)  Multivessel CAD, n (%) 180 (28) 123 (28) 57 (27) Stress test results  Positive stress ECG, n (%) 118 (18) 71 (16) 47 (22) 0.106 Perfusion data  Summed rest score 3.4 ± 6.4 3.5 ± 6.9 3.2 ± 5.2 0.565  Summed stress score 7.9 ± 6.5 7.8 ± 6.8 8.2 ± 5.9 0.403  Summed difference score 5.0 ± 3.6 4.8 ± 3.2 5.5 ± 4.2 0.028 LV volumes and function at rest  Ejection fraction (%) 58 ± 14 59 ± 14 57 ± 13 0.180  End-diastolic volume (mL) 111 ± 50 109 ± 50 114 ± 49 0.211  End-systolic volume (mL) 52 ± 45 51 ± 46 54 ± 43 0.401  Peak filling rate (EDV/s) 2.5 ± 0.8 2.4 ± 0.8 2.5 ± 0.7 0.845 LV volumes and function after stress  Ejection fraction (%) 58 ± 13 58 ± 14 57 ± 12 0.148  End-diastolic volume (mL) 107 ± 44 106 ± 46 110 ± 40 0.326  End-systolic volume (mL) 50 ± 37 49 ± 39 51 ± 33 0.419  Peak filling rate (EDV/s) 2.4 ± 0.8 2.4 ± 0.8 2.5 ± 0.7 0.596 Table 2 Coronary anatomy and cardiac functional parameters Parameters Overall population (n = 648) No diabetes mellitus (n = 432) Diabetes mellitus (n = 216) P-value Coronary anatomy 0.285  Normal coronary arteries, n (%) 174 (27) 124 (29) 50 (23)  Non-obstructive atherosclerosis, n (%) 102 (16) 65 (15) 37 (17)  Single vessel CAD, n (%) 192 (29) 120 (28) 72 (33)  Multivessel CAD, n (%) 180 (28) 123 (28) 57 (27) Stress test results  Positive stress ECG, n (%) 118 (18) 71 (16) 47 (22) 0.106 Perfusion data  Summed rest score 3.4 ± 6.4 3.5 ± 6.9 3.2 ± 5.2 0.565  Summed stress score 7.9 ± 6.5 7.8 ± 6.8 8.2 ± 5.9 0.403  Summed difference score 5.0 ± 3.6 4.8 ± 3.2 5.5 ± 4.2 0.028 LV volumes and function at rest  Ejection fraction (%) 58 ± 14 59 ± 14 57 ± 13 0.180  End-diastolic volume (mL) 111 ± 50 109 ± 50 114 ± 49 0.211  End-systolic volume (mL) 52 ± 45 51 ± 46 54 ± 43 0.401  Peak filling rate (EDV/s) 2.5 ± 0.8 2.4 ± 0.8 2.5 ± 0.7 0.845 LV volumes and function after stress  Ejection fraction (%) 58 ± 13 58 ± 14 57 ± 12 0.148  End-diastolic volume (mL) 107 ± 44 106 ± 46 110 ± 40 0.326  End-systolic volume (mL) 50 ± 37 49 ± 39 51 ± 33 0.419  Peak filling rate (EDV/s) 2.4 ± 0.8 2.4 ± 0.8 2.5 ± 0.7 0.596 Parameters Overall population (n = 648) No diabetes mellitus (n = 432) Diabetes mellitus (n = 216) P-value Coronary anatomy 0.285  Normal coronary arteries, n (%) 174 (27) 124 (29) 50 (23)  Non-obstructive atherosclerosis, n (%) 102 (16) 65 (15) 37 (17)  Single vessel CAD, n (%) 192 (29) 120 (28) 72 (33)  Multivessel CAD, n (%) 180 (28) 123 (28) 57 (27) Stress test results  Positive stress ECG, n (%) 118 (18) 71 (16) 47 (22) 0.106 Perfusion data  Summed rest score 3.4 ± 6.4 3.5 ± 6.9 3.2 ± 5.2 0.565  Summed stress score 7.9 ± 6.5 7.8 ± 6.8 8.2 ± 5.9 0.403  Summed difference score 5.0 ± 3.6 4.8 ± 3.2 5.5 ± 4.2 0.028 LV volumes and function at rest  Ejection fraction (%) 58 ± 14 59 ± 14 57 ± 13 0.180  End-diastolic volume (mL) 111 ± 50 109 ± 50 114 ± 49 0.211  End-systolic volume (mL) 52 ± 45 51 ± 46 54 ± 43 0.401  Peak filling rate (EDV/s) 2.5 ± 0.8 2.4 ± 0.8 2.5 ± 0.7 0.845 LV volumes and function after stress  Ejection fraction (%) 58 ± 13 58 ± 14 57 ± 12 0.148  End-diastolic volume (mL) 107 ± 44 106 ± 46 110 ± 40 0.326  End-systolic volume (mL) 50 ± 37 49 ± 39 51 ± 33 0.419  Peak filling rate (EDV/s) 2.4 ± 0.8 2.4 ± 0.8 2.5 ± 0.7 0.596 Figure 1 View largeDownload slide Impact of the presence of CAD on myocardial ischaemic burden in patients with and without DM both in the whole study population (A) and in patients submitted to the specific cardiac stress-protocol (exercise vs. vasodilator) (B). Figure 1 View largeDownload slide Impact of the presence of CAD on myocardial ischaemic burden in patients with and without DM both in the whole study population (A) and in patients submitted to the specific cardiac stress-protocol (exercise vs. vasodilator) (B). Interactions between DM, coronary anatomy, and myocardial perfusion: per-vessel analysis Of the 1944 total vessels, 232 (12%) and 624 (32%) showed obstructive and non-obstructive CAD, respectively. While patients with DM and controls had the same proportion of obstructive coronary lesions (32% vs. 32%, P = NS), the former showed a higher prevalence of non-obstructive CAD (14% in DM vs. 10% in controls, P = 0.04). The interaction between DM, CAD severity, and myocardial ischaemic burden was then evaluated. Despite regional myocardial ischaemic burden gradually increased in normal, non-obstructive, and obstructive CAD, patients with DM showed significantly more severe ischaemia downstream obstructive coronary lesions than controls (Figure 2A). However, this finding was limited to patients submitted to vasodilator stress test, while disappeared in those undergoing exercise stress (Figure 2B). Figure 2 View largeDownload slide Impact of CAD severity on the extent of regional myocardial ischaemia in patients with and without DM both in the whole study population (A) and in patients submitted to the specific cardiac stress-protocol (exercise vs. vasodilator) (B). Figure 2 View largeDownload slide Impact of CAD severity on the extent of regional myocardial ischaemia in patients with and without DM both in the whole study population (A) and in patients submitted to the specific cardiac stress-protocol (exercise vs. vasodilator) (B). MPS accuracy in patients with and without DM: the role of stress-protocol In the overall population, MPS showed a significant accuracy in unmasking the presence of obstructive CAD (AUC 0.78, 95% CI 0.74–0.81; P < 0.001), which was maintained in patients with and without DM (Figure 3A). However, a significant interaction between stress-protocol and MPS accuracy was revealed. In fact, in patients with DM exercise stress MPS had a significantly lower diagnostic power than vasodilator stress SPECT (P for difference <0.001), because of a lower specificity (45% vs. 69%), while in the control group the accuracy of MPS was similar regardless of the stress-protocol adopted (Figure 3B). Figure 3 View largeDownload slide Diagnostic accuracy of MPS in detecting obstructive CAD in patients with and without DM both in the whole study population (A) and in patients submitted to the specific cardiac stress-protocol (exercise vs. vasodilator) (B). Figure 3 View largeDownload slide Diagnostic accuracy of MPS in detecting obstructive CAD in patients with and without DM both in the whole study population (A) and in patients submitted to the specific cardiac stress-protocol (exercise vs. vasodilator) (B). The relationship between patients characteristics, stress variables and MPS accuracy in patients submitted to exercise stress test was explored. Specifically, patients with DM attained a significantly lower %Wattmax (76 ± 27% vs. 82 ± 26%, P = 0.038) than controls, despite similar values of %HR and of the other major exercise variables. In this respect, a significant correlation between %Wattmax and body mass index (BMI) values was revealed (R = −0.41; P < 0.001). After correction for major clinical, LV functional and other exercise variables, a lower %Wattmax (OR 0.98, 95% CI 0.97–0.99; P = 0.026) turned to be an independent predictor of a reduced diagnostic specificity on MPS. Representative CZT and angiographic images of patients with and without DM are further reported (see Supplementary data online, Figures S1–S4), outlining the better diagnostic accuracy of MPS in the non-diabetic cohort. Discussion This study shows that in patients with DM submitted to MPS the choice of the appropriate stress-modality is mandatory for the correct evaluation of the extent and severity of myocardial ischaemic burden. While our results confirm the generally elevated accuracy of MPS in detecting significant CAD, they also show the presence of a significantly lower specificity in patients with DM than controls when an exercise stress test is adopted. DM and MPS accuracy: more than just CAD Solid evidence shows that patients with DM are at high-risk for different cardiovascular disorders.1,3 Moreover, a significant association between the presence of DM and silent myocardial ischaemia has been suggested.8,20 In this respect, it has been also reported that diabetic patients may show an excess of inducible myocardial ischaemia with respect to the coronary involvement,8,21 as a possible result of underlying coronary endothelial/microvascular risk factors.9,10 Accordingly, while in the general population MPS is still among the most frequently performed stress cardiac imaging techniques, it may theoretically have some disadvantages in the evaluation of patients with DM.11 In particular, the presence of diffuse myocardial blood flow impairment due to coronary endothelial/microvascular dysfunction might decrease the accuracy of MPS in unmasking the presence of focal coronary luminal narrowings.9,10 On the other hand, the presence of diffuse coronary atherosclerosis, as frequently observed in patients with long-standing DM,8 might pose the conditions for a balanced reduction of myocardial perfusion, further decreasing the diagnostic power of MPS. Despite those theoretical drawbacks, the impact of DM on the diagnostic accuracy of MPS has been loosely investigated.11 This results show that MPS maintains a similarly elevated accuracy in unmasking the presence of obstructive CAD both in patients with and without DM. Interestingly, no significant difference in terms of prevalence of obstructive CAD was revealed between patients with DM and controls. On the other hand, patients with DM also showed the presence a significantly higher prevalence of by-standing non-obstructive coronary lesions, which were in turn associated with some degrees of downstream myocardial ischaemia. Regarding MPS evaluation, patients with DM were characterized by a higher myocardial ischaemic burden than controls. Nevertheless, in those patients myocardial ischaemia was mainly concentrated downstream obstructive coronary lesions, pointing against the presence of a diffuse impairment of myocardial perfusion. Finally, the lower diagnostic specificity of exercise stress SPECT in patients with DM than in control subjects has been already reported in a smaller cohort of patients,11 possibly indicating a higher prevalence of false-positive imaging findings in this cohort of subjects. In fact, the lower cardiac workload that was obtained in those with DM, coupled with a significantly higher BMI may suggest a possibly higher prevalence of image artefacts in these patients. Further, dedicated studies will be needed to better describe these correlations. Interaction between MPS accuracy and stress test protocol: the case of DM MPS is one of the most versatile cardiac imaging techniques that are available in clinical practice. However, despite the theoretical variety of possible stress protocols that can be performed, exercise stress still represents the cornerstone of cardiac functional evaluation.22 On the other hand, in patients that cannot exercise sufficiently, a pharmacological stress test is generally performed, typically by means of a coronary vasodilator (i.e. adenosine or dipyridamole). In this respect, while in the general population the diagnostic accuracy of MPS seems generally unaffected by the specific stress-protocol that is adopted,19 the prognostic value of the two main stress strategies appears somehow different.23 Moreover, it has been already reported that in some categories of patients, such as those with atrial fibrillation, an interaction between cardiac stress-protocol, and MPS accuracy can be present, particularly when exercise stress test is performed.24 Accordingly, while the data of this study, confirm that the excellent accuracy of MPS is maintained in patients with and without DM, they also show that in the former patients the diagnostic power of MPS can be significantly influenced by the particular stress-protocol that is adopted. In particular, in patients with DM exercise stress MPS had a lower diagnostic efficiency in detecting obstructive CAD than vasodilator-stress, because of a relatively impaired specificity. This finding could be explained by the fact that patients with DM submitted to exercise stress test were able to reach a significantly lower cardiac workload than controls, which ultimately resulted an independent predictor of impaired diagnostic specificity on MPS. Interestingly, while exercise stress MPS has been shown to risk-stratify accurately patients with DM,23,25 it has been also demonstrated that diabetic patients with a reduced exercise capacity are characterized by a significantly impaired prognosis.26,27 In this respect, our data show that in those patients the accuracy of MPS could be reduced, possibly leading to the misdiagnosis of CAD. Present and previous data confirm that, in patients submitted to MPS, the choice of the stress-protocol (i.e. exercise vs. vasodilator) should be also based on the presence of simple constitutional and exercise-related variables (i.e. obesity and exercise capacity). Accordingly, our data suggest that in patients with DM that have obtained an insufficient cardiac workload on exercise stress test the results of MPS might be inaccurate. Limitations The retrospective nature of the study should be acknowledged. Since patients were referred to clinically indicated ICA also according to MPS results, some sort of referral bias cannot be completely ruled-out. In these cases, the comparison of the normalcy rates of MPS in patients with low-likelihood of CAD has been proposed as a possible option to reduce the impact of referral bias.11 However, in our population, only two patients presented a low pre-test likelihood of CAD (<15%), prohibiting this kind of analysis. In this respect, the fact that the outmost majority of the study patients had an intermediate-to-high pre-test probability of CAD, further stresses the high level of appropriateness of patients selection. In this study, only a single anatomic cut-off value for the definition of significant CAD was used, namely >70% luminal narrowing. In this respect, since the relationship between CAD anatomic severity and myocardial ischaemic burden may be rather elusive, the use of a functional cut-off of CAD haemodynamic significance [i.e. using fraction flow reserve (FFR) in case of intermediate coronary stenosis] would have helped to better classify the patients studies and offered the chance to allow a more appropriate definition of CZT accuracy.28 However, since in this study FFR was not used systematically, its implementation in the diagnostic algorithm was impossible. Accordingly, in order to try to try to be more specific, a relatively more stringent cut-off value of CAD anatomic severity was used (70% rather than the classical 50% diemeter reduction threshold)29 reasonably reducing the occurrence of false-positive angiographic findings. The fact that a subgroup of patients was injected despite a theoretically submaximal (%HR <85%) exercise stress test might have limited MPS accuracy.14 However, considering that the proportion of those patients was similar in patients with DM and controls and that the majority of those had presented electrocardiographic or clinical signs of ischaemia during stress protocol, the impact of this variable should be considered limited. Accordingly, despite similar values of %HR, in control patients MPS accuracy after an exercise stress test remained high, while in those with DM the diagnostic value of exercise-MPS was reduced. The use of dipyridamole rather than the relatively more potent adenosine might have theoretically reduced the accuracy of MPS in detecting significant CAD. However, since several studies have reported an elevated accuracy of dipyridamole myocardial perfusion imaging in unmasking the presence of CAD,14,30 the impact of the chosen coronary stressor should be considered limited. While patients were matched for pre-test probability of CAD, those with DM presented a significantly higher cardiovascular risk profile (i.e. a higher prevalence of major cardiovascular risk factors), possibly impacting the diagnostic accuracy of MPS because of a higher prevalence of underlying microvascular dysfunction. However, since in this study, myocardial perfusion abnormalities were concentrated downstream vessels with CAD (Figure 2), the impact of microvascular dysfunction on MPS accuracy should be considered minor. While dobutamine MPS is also performed in our laboratory when both exercise and vasodilator stress test is not feasible, those patients were not included in the analyses because of their limited numerosity (four subjects). Regarding patients selection, while the presence of DM was ascertained according to current guidelines,12 its absence was solely base on fasting plasma glucose levels in the absence of any antidiabetic drugs. Accordingly, while the presence of some patients with early stages of DM in the control group cannot be completely excluded, a different screening strategy (i.e. with the use of glucose tolerance test) should be considered unrealistic. Conclusions MPS was accurate in detecting obstructive CAD both in patients with and without DM. However, in the former patients a significant interaction between MPS accuracy and the specific stress protocol was observed, with a lower diagnostic power of exercise stress MPS than vasodilator-stress one. This finding could be explained by the fact that patients with DM attained a significantly lower exercise-related cardiac workload than controls, possibly due to a higher prevalence of obesity, resulting in a lower diagnostic specificity in unmasking the presence of obstructive CAD. Conflict of interest: none declared. References 1 Bloomgarden ZT. Diabetes and cardiovascular disease . Diabetes Care 2011 ; 34 : e24 – 30 . Google Scholar Crossref Search ADS PubMed 2 Torp-Pedersen C , Jeppesen J. Diabetes and hypertension and atherosclerotic cardiovascular disease: related or separate entities often found together . Hypertension 2011 ; 57 : 887 – 8 . Google Scholar Crossref Search ADS PubMed 3 Kavousi M , Leening MJ , Nanchen D , Greenland P , Graham IM , Steyerberg EW et al. Comparison of application of the ACC/AHA guidelines, adult treatment panel III guidelines, and European Society of Cardiology guidelines for cardiovascular disease prevention in a European cohort . JAMA 2014 ; 311 : 1416 – 23 . Google Scholar Crossref Search ADS PubMed 4 Wackers FJ , Young LH , Inzucchi SE , Chyun DA , Davey JA , Barrett EJ et al. Detection of ischemia in asymptomatic diabetics investigators. detection of silent myocardial ischemia in asymptomatic diabetic subjects: the DIAD study . Diabetes Care 2004 ; 27 : 1954 – 61 . Google Scholar Crossref Search ADS PubMed 5 Muhlestein JB , Lappé DL , Lima JAC , Rosen BD , May HT , Knight S et al. Effect of screening for coronary artery disease using CT angiography on mortality and cardiac events in high-risk patients with diabetes: the FACTOR-64 randomized clinical trial . JAMA 2014 ; 312 : 2234 – 43 . Google Scholar Crossref Search ADS PubMed 6 Bourque JM , Patel CA , Ali MM , Perez M , Watson DD , Beller GA. Prevalence and predictors of ischemia and outcomes in outpatients with diabetes mellitus referred for single-photon emission computed tomography myocardial perfusion imaging . Circ Cardiovasc Imaging 2013 ; 6 : 466 – 77 . Google Scholar Crossref Search ADS PubMed 7 Park G-M , Lee J-H , Lee S-W , Yun S-C , Kim Y-H , Cho Y-R et al. Comparison of coronary computed tomographic angiographic findings in asymptomatic subjects with versus without diabetes mellitus . Am J Cardiol 2015 ; 116 : 372 – 8 . Google Scholar Crossref Search ADS PubMed 8 Di Carli MF , Hachamovitch R. Should we screen for occult coronary artery disease among asymptomatic patients with diabetes? J Am Coll Cardiol 2005 ; 45 : 50 – 3 . Google Scholar Crossref Search ADS PubMed 9 Di Carli MF , Janisse J , Grunberger G , Ager J. Role of chronic hyperglycemia in the pathogenesis of coronary microvascular dysfunction in diabetes . J Am Coll Cardiol 2003 ; 41 : 1387 – 93 . Google Scholar Crossref Search ADS PubMed 10 Di Carli MF , Bianco-Batlles D , Landa ME , Kazmers A , Groehn H , Muzik O et al. Effects of autonomic neuropathy on coronary blood flow in patients with diabetes mellitus . Circulation 1999 ; 100 : 813 – 9 . Google Scholar Crossref Search ADS PubMed 11 Kang X , Berman DS , Lewin H , Miranda R , Erel J , Friedman JD et al. Comparative ability of myocardial perfusion single-photon emission computed tomography to detect coronary artery disease in patients with and without diabetes mellitus . Am Heart J 1999 ; 137 : 949 – 57 . Google Scholar Crossref Search ADS PubMed 12 Rydén L , Grant PJ , Anker SD , Berne C , Cosentino F , Danchin N et al. ESC guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD: the task force on diabetes, pre-diabetes, and cardiovascular diseases of the European Society of Cardiology (ESC) and developed in collaboration with the European Association for the Study of Diabetes (EASD) . Eur Heart J 2013 ; 34 : 3035 – 87 . Google Scholar Crossref Search ADS PubMed 13 Wasserman K , Hansen JE , Sue DY , Stringer WW , Whipp BJ . Principles of Exercise Testing and Interpretation. Lippincott Williams & Wilkins; 4th Edition 2004 . 14 Gimelli A , Liga R , Pasanisi EM , Casagranda M , Coceani M , Marzullo P. Influence of cardiac stress protocol on myocardial perfusion imaging accuracy: the role of exercise level on the evaluation of ischemic burden . J Nucl Cardiol 2016 ; 23 : 1114 – 22 . Google Scholar Crossref Search ADS PubMed 15 Gimelli A , Liga R , Giorgetti A , Kusch A , Pasanisi EM , Marzullo P. Relationships between myocardial perfusion abnormalities and poststress left ventricular functional impairment on cadmium-zinc-telluride imaging . Eur J Nucl Med Mol Imaging 2015 ; 42 : 994 – 1003 . Google Scholar Crossref Search ADS PubMed 16 Gimelli A , Liga R , Duce V , Kusch A , Clemente A , Marzullo P. Accuracy of myocardial perfusion imaging in detecting multivessel coronary artery disease: a cardiac CZT study . J Nucl Cardiol 2017 ; 24 : 687 – 95 . Google Scholar Crossref Search ADS PubMed 17 Gutstein A , Navzorov R , Solodky A , Mats I , Kornowski R , Zafrir N. Angiographic correlation of myocardial perfusion imaging with half the radiation dose using orderedsubset expectation maximization with resolution recovery software . J Nucl Cardiol 2013 ; 20 : 539 – 44 . Google Scholar Crossref Search ADS PubMed 18 Giorgetti A , Masci PG , Marras G , Rustamova YK , Gimelli A , Genovesi D et al. Gated SPECT evaluation of left ventricular function using a CZT camera and a fast low-dose clinical protocol: comparison to cardiac magnetic resonance imaging . Eur J Nucl Med Mol Imaging 2013 ; 40 : 1869 – 75 . Google Scholar Crossref Search ADS PubMed 19 Gimelli A , Liga R , Bottai M , Pasanisi EM , Giorgetti A , Fucci S et al. Diastolic dysfunction assessed by ultra-fast cadmium-zinc-telluride cardiac imaging: impact on the evaluation of ischaemia . Eur Heart J Cardiovasc Imaging 2015 ; 16 : 68 – 73 . Google Scholar Crossref Search ADS PubMed 20 Scholte AJ , Schuijf JD , Kharagjitsingh AV , Dibbets-Schneider P , Stokkel MP , Jukema JW et al. Different manifestations of coronary artery disease by stress SPECT myocardial perfusion imaging, coronary calcium scoring, and multislice CT coronary angiography in asymptomatic patients with type 2 diabetes mellitus . J Nucl Cardiol 2008 ; 15 : 503 – 9 . Google Scholar Crossref Search ADS PubMed 21 Rajagopalan N , Miller TD , Hodge DO , Frye RL , Gibbons RJ. Identifying high-risk asymptomatic diabetic patients who are candidates for screening stress single-photon emission computed tomography imaging . J Am Coll Cardiol 2005 ; 45 : 4:43 – 9 . Google Scholar Crossref Search ADS 22 Shaw LJ , Mieres JH , Hendel RH , Boden WE , Gulati M , Veledar E et al. Comparative effectiveness of exercise electrocardiography with or without myocardial perfusion single photon emission computed tomography in women with suspected coronary artery disease: results from the what is the optimal method for ischemia evaluation in women (WOMEN) trial . Circulation 2011 ; 124 : 1239 – 49 . Google Scholar Crossref Search ADS PubMed 23 Padala SK , Ghatak A , Padala S , Katten DM , Polk DM , Heller GV. Cardiovascular risk stratification in diabetic patients following stress single-photon emission-computed tomography myocardial perfusion imaging: the impact of achieved exercise level . J Nucl Cardiol 2014 ; 21 : 1132 – 43 . Google Scholar Crossref Search ADS PubMed 24 Gimelli A , Liga R , Startari U , Giorgetti A , Pieraccini L , Marzullo P. Evaluation of ischaemia in patients with atrial fibrillation: impact of stress protocol on myocardial perfusion imaging accuracy . Eur Heart J Cardiovasc Imaging 2015 ; 16 : 781 – 7 . Google Scholar Crossref Search ADS PubMed 25 Ghatak A , Padala S , Katten DM , Polk DM , Heller GV. Risk stratification among diabetic patients undergoing stress myocardial perfusion imaging . J Nucl Cardiol 2013 ; 20 : 529 – 38 . Google Scholar Crossref Search ADS PubMed 26 Rozanski A , Gransar H , Min JK , Hayes SW , Friedman JD , Thomson LE et al. Long-term mortality following normal exercise myocardial perfusion SPECT according to coronary disease risk factors . J Nucl Cardiol 2014 ; 21 : 341 – 50 . Google Scholar Crossref Search ADS PubMed 27 Kokkinos P , Myers J , Nylen E , Panagiotakos DB , Manolis A , Pittaras A et al. Exercise capacity and all-cause mortality in African American and Caucasian men with type 2 diabetes . Diabetes Care 2009 ; 32 : 623 – 8 . Google Scholar Crossref Search ADS PubMed 28 De Bruyne B , Pijls NH , Kalesan B , Barbato E , Tonino PA , Piroth Z et al. Fractional flow reserve-guided PCI versus medical therapy in stable coronary disease . N Engl J Med 2012 ; 367 : 991 – 1001 . Google Scholar Crossref Search ADS PubMed 29 Budoff MJ , Nakazato R , Mancini GB , Gransar H , Leipsic J , Berman DS et al. CT angiography for the prediction of hemodynamic significance in intermediate and severe lesions: head-to-head comparison with quantitative coronary angiography using fractional flow reserve as the reference standard . JACC Cardiovasc Imaging 2016 ; 9 : 55964 . 30 Cramer MJ , Verzijlbergen JF , van der Wall EE , Vermeersch PH , Niemeyer MG , Zwinderman AH et al. Comparison of adenosine and high-dose dipyridamole both combined with low-level exercise stress for 99Tcm-MIBI SPET myocardial perfusion imaging . Nucl Med Commun 1996 ; 17 : 97 – 104 . Google Scholar Crossref Search ADS PubMed Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2017. For permissions, please email: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Journal

European Heart Journal – Cardiovascular ImagingOxford University Press

Published: Nov 1, 2018

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off