CLINICAL RESEARCH STUDY

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1 CLINICAL RESEARCH STUDY Prevalence, Extent, and Independent Predictors of Silent Myocardial Infarction Nisha Arenja, MD, Christian Mueller, MD, Niklas F. Ehl, MD, Miriam Brinkert, MD, Katharina Roost, MD, Tobias Reichlin, MD, Seoung Mann Sou, MD, Thomas Hochgruber, MD, Stefan Osswald, MD, Michael J. Zellweger, MD Department of Cardiology, University Hospital Basel, Switzerland. ABSTRACT BACKGROUND: The phenomenon of silent myocardial infarction is poorly understood. METHODS: We aimed to evaluate the prevalence, extent, and independent predictors of silent myocardial infarction in 2 large independent cohorts of consecutive patients without a history of myocardial infarction referred for rest/stress myocardial perfusion single photon emission computed tomography. There were 1621 patients enrolled in the derivation cohort and 338 patients in the validation cohort. Silent myocardial infarction was diagnosed in patients with a myocardial scar 5% of the left ventricle. RESULTS: In the derivation cohort, the prevalence of silent myocardial infarction was 23.3% (n ¼ 377). The median infarct size was 10% (interquartile range [IQR] 5%-15%) of the left ventricle. The prevalence of silent myocardial infarction was 28.5% in diabetics and 21.5% in nondiabetics (P ¼.004). Diabetes mellitus was an independent predictor for the presence of silent myocardial infarction (odds ratio 1.5; 95% confidence interval, ; P ¼.004). These findings were confirmed in the independent validation cohort. In the validation cohort, the prevalence of silent myocardial infarction was 26.3% (n ¼ 89), while the prevalence was higher in diabetics (35.8%) than in nondiabetics (24%; P ¼.049). The median infarct size was 11.8% (IQR 5.9%-17.6%) of the left ventricle. Again, in logistic regression analysis, diabetes mellitus was a significant predictor of the presence of silent myocardial infarction. CONCLUSION: Silent myocardial infarctions are more common than previously thought. One of 4 patients with suspected coronary artery disease had experienced a silent myocardial infarction; the extent in average is 10% of the left ventricle, and it is more common in diabetics. Ó 2013 Elsevier Inc. All rights reserved. The American Journal of Medicine (2013) 126, KEYWORDS: Diabetes mellitus; Myocardial perfusion single photon emission computed tomography Prevalence silent myocardial infarction Acute myocardial infarction is the major cause of death and disability worldwide. After 3 decades of unparalleled research, multiple highly effective treatments for acute myocardial infarction have been documented in largescale clinical trials, including early coronary revascularization, antithrombotic therapy, and secondary prevention. 1 Funding: The study was supported by the University Hospital Basel, the University Basel, and the Swiss National Science Foundation. Conflict of Interest: All authors declare that they have no conflict of interest related to this study. Authorship: All authors had access to the data and a role in writing the manuscript. Requests for reprints should be addressed to Michael J. Zellweger, MD, Department of Cardiology, Non-Invasive Cardiology and Cardiac Imaging, University Hospital Basel, Petersgraben 4, Basel CH-4031, Switzerland. address: mzellweger@uhbs.ch These therapies have led to a substantial reduction in mortality. 2,3 Two groups of acute myocardial infarction patients have not, however, been able to benefit from these advances: patients in whom acute myocardial infarction results in immediate sudden death and patients with silent myocardial infarction, in whom the patient or his environment do not recognize the occurring event in the acute phase. Little is known about the phenomenon of silent myocardial infarction. Silent myocardial infarction is characterized by lack of unequivocal objective signs of myocardial infarction and minimal, atypical, or no symptoms at all. Compared with the prevalence of silent myocardial ischemia, the prevalence of silent myocardial infarction is less well known. Silent myocardial infarctions represent missed opportunities for the early initiation of the highly effective and /$ -see front matter Ó 2013 Elsevier Inc. All rights reserved.

2 516 The American Journal of Medicine, Vol 126, No 6, June 2013 extensively documented secondary preventive measures such as antiplatelet therapy and high-dose statins. 4 Currently, silent myocardial infarction is most often diagnosed using the 12-lead electrocardiogram (ECG) for screening purposes. 5 However, the ECG has a very low sensitivity. Newer imaging techniques such as myocardial perfusion single photon emission computed tomography (myocardial perfusion SPECT [MPS]) or cardiac magnetic resonance offer better diagnostic capability, particularly test sensitivity. 6 Therefore, the purpose of this study was to evaluate the prevalence, extent, and independent predictors of silent myocardial infarction in consecutive patients referred for MPS. METHODS CLINICAL SIGNIFICANCE Setting and Study Population This retrospective study used data from 2 well-defined cohorts of unselected consecutive patients referred for coronary artery disease evaluation by rest/stress MPS at the University Hospital Basel, Switzerland The derivation cohort is based on 2400 consecutive patients who underwent rest thallium 201/stress technetium 99m sestamibi MPS study from April 2006 to March Of these, we excluded patients with history of known prior myocardial infarction (n ¼ 773) and patients with an incomplete MPS study (n ¼ 6). To assess the history of prior myocardial infarction, all clinical records of the patients were studied in detail. In addition to the detailed patient history, any prior hospital admission or cardiac intervention was analyzed carefully. The final patient s population of the derivation cohort consisted of 1621 patients: 1210 nondiabetic and 411 diabetic patients (Figure 1A). The independent validation cohort consisted of 522 consecutive patients referred for coronary artery disease evaluation from January 2010 until August The same exclusion criteria as in the derivation cohort were used. These left 338 patients in the validation cohort: 271 nondiabetic and 67 diabetic patients (Figure 1B). The prevalence of silent myocardial infarction (MI) is higher than previously thought. In particular, it is 50% higher in diabetics. Classical electrocardiogram criteria missed 2 of 3 silent MIs. The median infarct size of silent MIs was 10% to 12% of the left ventricle. With a median left ventricular ejection fraction (LVEF) of 47% on myocardial perfusion single photon emission computed tomography, diabetics with silent MI had significantly lower LVEF than diabetics without MI. exercise stress test was not possible or sufficient, a pharmacologic one (adenosine 140 mg/kg per minute for 6 minutes) was used either combined or pharmacologically only. Image acquisition was performed using standard protocols Image Interpretation Perfusion images were scored semiquantitatively with the use of 20-segment model in the derivation cohort and 17-segment model in the validation cohort. Each segment was scored using a 5-point scoring system: 0 ¼ normal, 1 ¼ equivocal, 2 ¼ moderate, 3 ¼ severe reduction of radioisotope uptake, and 4 ¼ apparent absence of detectable tracer uptake in a segment. 16 The number of nonreversible segments (stress-rest score combinations of 4-4, 4-3, 3-3, 3-2, or 2-2) was summed and used as an index of infarct size (with each nonreversible segment representing approximately 5% of the myocardium). Small myocardial infarction was defined as <4 nonreversible segments, while large myocardial infarction was characterized as 4 nonreversible segments. Statistic Analysis Continuous variables are described as the mean SD for the equally distributed and as median for the non-normally distributed variables. Comparisons were made using the unpaired t test for normally distributed continuous variables, and Mann-Whitney U test for non-normally distributed continuous variables. Fisher exact test was used for categorical variables with any field including <6 patients, and chi-squared test for the other categorical variables. A binominal logistic regression analysis was used to identify the independent predictors for myocardial scar. Established cardiovascular risk factors (diabetes mellitus, hypertension, smoking status, hyperlipidemia, positive family history for coronary artery disease) were used for the analyses. A P value <.05 was considered statistically significant. The statistical analyses were performed using the SPSS/PC (version 20.0, SPSS Inc., Chicago, Ill) software package. Rest/Stress MPS Protocol and Image Acquisition MPS was performed using a single-day gated rest/stress dual isotope protocol (TI-201 rest/tc-99m sestamibi stress as described previously). 11,12 Whenever possible, a symptomlimited exercise treadmill test using standard protocols was performed with 12-lead ECG recorded each minute. If RESULTS Baseline Characteristics Baseline characteristics were similar in the derivation and the validation cohort (Tables 1, 2). The mean age was versus years, and 37% versus 38% of patients were women. Patients with silent myocardial infarction were

3 Arenja et al Clinical Aspects of Silent Myocardial Infarction 517 Figure 1 (A) Composition of the derivation cohort / including incomplete myocardial perfusion single photon emission computed tomography scan. (B) Composition of the validation group. MI ¼ myocardial infarction; MPS ¼ myocardial perfusion single photon emission computed tomography. more often male, and more often had a history of diabetes mellitus and positive family history of coronary artery disease. In both cohorts, they were more often treated with aspirin, beta-blocker, and statins. The comparison of ECG parameters between patients with and without silent myocardial infarction indicated a higher number of Q waves present in patients with silent myocardial infarction of both cohorts. Similarly, in both groups the number of exercise stress tests was higher in patients without myocardial infarction, while patients with silent myocardial infarction frequently underwent pharmacologic or combined stress tests. Detection of Silent Myocardial Infarction Derivation cohort. In the derivation cohort, a scar on MPS (1 nonreversible segments) was detected in 377 patients (23.3%). Among these, Q-wave consistent with myocardial infarction was present in the ECGs of 123 patients (7.6%). The median infarct size was 10% (interquartile range [IQR]) 5%-15%) of the left ventricle. Among the 1621 patients, 1210 (74.6%) were nondiabetics and 411 (25.4%) were diabetics. Silent myocardial infarction was more often in the population of diabetics (117 patients, 28.5%) than in those without diabetes (260 patients, 21.5%; P ¼.004; Figure 2). The median of nonreversible segments was higher in diabetics 0 (IQR 0-1) than in nondiabetics 0 (IQR 0-0) (P ¼.003). Validation cohort. The validation cohort consists of 338 patients, with 271 nondiabetics and 67 diabetics. The prevalence of silent myocardial infarction on MPS was 26.3% (n ¼ 89). Among these, Q-waves consistent with myocardial infarction were present in the ECGs of 38 patients (11%). The median infarct size was 11.8% (IQR 5.9%-17.6%) of the

4 518 The American Journal of Medicine, Vol 126, No 6, June 2013 Table 1 Baseline Characteristics in the Derivation Group Overall (n ¼ 1621) No MI (n ¼ 1244) Silent MI (n ¼ 377) P Value Age, mean SD Sex female, n (%) 601 (37.1) 548 (44.1) 53 (14.1) <.001 BMI (kg/m 2 ), mean SD Cardiovascular risk factors, n (%) Diabetes mellitus 411 (25.4) 294 (23.6) 117 (31.0).004 Dyslipidemia 842 (51.9) 638 (51.3) 204 (54.1).343 Arterial hypertension 1076 (66.4) 832 (66.9) 244 (64.7).437 Family history of CAD 429 (26.5) 274 (22) 106 (28.1).024 Smoker 671 (41.4) 501 (40.3) 170 (45.1).098 Medication, n (%) Anticoagulation 123 (7.6) 84 (6.8) 39 (10.3).021 Aspirin 1054 (65) 782 (62.9) 272 (72.1).001 Beta-blockers 889 (54.8) 659 (53.0) 230 (61.0).006 ACE inhibitors 271 (16.7) 193 (15.5) 78 (20.7) <.001 ATII antagonists 282 (17.4) 227 (18.2) 55 (14.6).454 Calcium antagonists 316 (19.5) 232 (18.6) 84 (22.3).124 Nitrates 196 (12.1) 142 (11.4) 54 (14.3).136 Statins 641 (39.5) 475 (38.2) 166 (44.0).042 ECG parameters, n (%) Sinus rhythm 1545 (97.1) 1192 (95.8) 353 (93.6).079 Atrial fibrillation 47 (2.9) 31 (2.5) 16 (4.2).076 Pacemaker-rhythm 12 (0.7) 7 (0.6) 5 (1.3).13 Q-waves 123 (7.6) 74 (5.9) 49 (13) <.001 LBBB 61 (3.8) 38 (3.1) 23 (6.1).006 RBBB 206 (12.7) 146 (11.7) 60 (16.9).033 Stress test, n (%) Exercise 1078 (66.5) 860 (69.1) 218 (57.8) <.001 Pharmacologic 211 (13) 149 (12.0) 62 (16.4).024 Combined 332 (20.5) 235 (18.9) 97 (25.7).004 ACE ¼ angiotensin-converting enzyme; ATII ¼ angiotensin II receptor; BMI ¼ body mass index; CAD ¼ coronary artery disease; ECG ¼ electrocardiogram; LBBB ¼ left bundle branch block; MI ¼ myocardial infarction; RBBB ¼ right bundle branch block. left ventricle. As in the derivation group, the prevalence of myocardial infarction was higher in diabetics (24 patients, 35.8%) compared with nondiabetics (65 patients, 24%; P ¼.049, Figure 2). The same results as in the derivation cohort were evident in the comparison of median levels of nonreversible segments, however, there was no statistically significant difference (diabetics: median 0 [IQR 0-1]; nondiabetics: median 0 [IQR 0-0]; P ¼.068). Diabetic population with silent myocardial infarction. Of the 411 diabetics in the derivation cohort, 117 patients (28.5%) had evidence of a scar on MPS (Figure 1A). In the derivation cohort, diabetic patients with silent myocardial infarction were older, more often male, and less often had a history of arterial hypertension than diabetics without silent myocardial infarction. They also were more frequently treated with nitrates. Overall, left ventricular ejection fraction (LVEF) was significantly lower in diabetics with silent myocardial infarction (P <.001). In the validation cohort, the prevalence of silent myocardial infarction in diabetics was 35.8% (n ¼ 24; Figure 1B). Within this cohort, diabetics with silent myocardial infarction more often had a positive family history of coronary artery disease and were frequently treated with aspirin. As in the derivation cohort, the LVEF was significantly lower in diabetics with silent myocardial infarction compared with diabetics without myocardial infarction (P <.001; Table 3). Significant predictors of myocardial infarction. In the logistic regression analysis, of all known cardiovascular risk factors, diabetes mellitus and positive family history for coronary artery disease were independent predictors for the presence of silent myocardial infarction. The predictive role of diabetes mellitus remained statistically significant in both groups. Similarly, the positive family history was a significant predictor in the model (Tables 4 and 5). DISCUSSION This study used rest/stress MPS to evaluate the prevalence, the extent, and the predictors of silent myocardial infarction, one of the poorly understood phenomena in cardiology. We report 5 major findings. First, the prevalence of silent myocardial infarction in consecutive patients referred for coronary artery disease evaluation was higher than previously thought. 6,17-19 Nearly

5 Arenja et al Clinical Aspects of Silent Myocardial Infarction 519 Table 2 Baseline Characteristics in the Validation Cohort Overall (n ¼ 338) No MI (n ¼ 249) Silent MI (n ¼ 89) P Value Age, mean SD Sex female, n (%) 129 (38.2) 111 (44.6) 18 (20.2) <.001 BMI (kg/m 2 ), mean SD Cardiovascular risk factors, n (%) Diabetes mellitus 67 (19.8) 43 (17.3) 24 (27.0).049 Dyslipidemia 168 (49.7) 123 (29.4) 45 (50.6).85 Arterial hypertension 241 (71.3) 178 (71.5) 63 (70.8).9 Family history of CAD 83 (24.6) 54 (21.7) 29 (32.6).04 Smoker 178 (52.7) 132 (53.0) 46 (51.7).83 Medication, n (%) Anticoagulation 40 (11.8) 28 (11.2) 12 (13.5).575 Aspirin 167 (49.4) 112 (45.0) 55 (61.8).006 Beta-blockers 163 (48.2) 111 (44.6) 52 (58.4).025 ACE inhibitors 70 (20.7) 49 (19.7) 21 (23.6).434 ATII antagonists 113 (33.4) 82 (32.9) 31 (34.8).744 Calcium antagonists 73 (21.6) 47 (18.9) 26 (29.2).042 Nitrates 41 (12.1) 26 (10.4) 15 (16.9).112 Statins 148 (43.8) 102 (41.0) 46 (51.7).08 ECG parameters, n (%) Sinus rhythm 309 (91.4) 232 (93.2) 77 (86.5).054 Atrial fibrillation 15 (4.4) 11 (4.4) 4 (4.5).976 Pacemaker-rhythm 14 (4.1) 6 (2.4) 8 (9.0).008 Q-waves 38 (11.2) 18 (7.2) 20 (22.5).001 LBBB 14 (4.1) 12 (4.8) 2 (2.2).296 RBBB 12 (3.6) 9 (3.6) 3 (3.4).915 Stress test, n (%) Exercise 208 (61.5) 158 (63.5) 50 (56.2).226 Pharmacologic 49 (14.5) 28 (11.2) 21 (23.6).005 Combined 81 (24) 63 (25.3) 18 (20.2).336 ACE ¼ angiotensin-converting enzyme; ATII ¼ angiotensin II receptor; BMI ¼ body mass index; CAD ¼ coronary artery disease; ECG ¼ electrocardiogram; LBBB ¼ left bundle branch block; MI ¼ myocardial infarction; RBBB ¼ right bundle branch block. Figure 2 Percentage of silent myocardial infarction in diabetics and nondiabetics in the derivation and the validation cohort.

6 520 The American Journal of Medicine, Vol 126, No 6, June 2013 Table 3 Characteristics of Diabetic Patients in Both Cohorts Derivation Cohort (n ¼ 411) Validation Cohort (n ¼ 67) Variable Diabetics without MI (n ¼ 294) Diabetics with Silent MI (n ¼ 117) P Value Diabetics without MI (n ¼ 43) Diabetics with Silent MI (n ¼ 24) Age Sex female 113 (38.4) 19 (16.2) < (39.5) 6 (25).289 BMI (kg/m 2 ) Dyslipidemia 193 (65.6) 70 (59.8) (55.8) 13 (54.2) 1.0 Arterial hypertension 241 (82) 81 (69.2) (81.4) 21 (87.5).734 Family history of CAD 76 (25.9) 25 (21.4) (9.3) 10 (41.7).004 Smoker 113 (38.4) 53 (45.3) (48.8) 10 (41.7).617 Anticoagulation 22 (7.5) 16 (13.7).06 4 (9.3) 2 (8.3) Aspirin 190 (64.6) 80 (68.4) (51.2) 20 (83.3).010 Beta-blockers 144 (49) 63 (53.8) (44.2) 16 (66.7).125 ACE inhibitors 82 (27.9) 34 (29.1) (23.3) 6 (25) 1.0 ATII antagonists 85 (28.9) 21 (17.9) (41.9) 11 (45.8).801 Calcium antagonists 69 (23.5) 32 (27.4) (34.9) 13 (54.2).196 Nitrates 24 (8.2) 21 (17.9) (16.3) 5 (20.8).743 Statins 144 (49) 54 (46.2) (58.1) 18 (75).194 MPS - end diastolic volume 86 (68-107) 123 (97-157) < (65-105) 115 (99-173) <.001 MPS - end systolic volume 35 (24-47) 65 (44-89) < (17-48) 56 (42-110) <.001 MPS - ejection fraction 59 (53-65) 47 (37-53) < (52-70) 47 (37-59) <.001 Resting heart rate 82 (71-93) 78 (67-90) (66-86) 75 (63-86).974 Systolic blood pressure 130 ( ) 131 ( ) ( ) 132 ( ).643 Diastolic blood pressure 79 (70-89) 79 (65-87) (76-93) 81 (72-90).193 Sinus rhythm 283 (96.3) 107 (91.5) (90.7) 22 (91.7) 1.0 LBBB 11 (3.7) 8 (6.8) (4.7) 1 (4.2) 1.0 Q-waves 26 (8.8) 17 (14.5) (9.3) 5 (20.8).086 ST changes 39 (13.3) 30 (25.6) (14) 7 (29.2).197 BMI ¼ body mass index; CAD ¼ coronary artery disease; LBBB ¼ left bundle branch block; MI ¼ myocardial infarction; MPS ¼ myocardial perfusion single photon emission computed tomography. Data are presented in number (%), mean SD or median (interquartile range). P Value one fourth of patients had experienced a silent myocardial infarction that apparently was unrecognized by the patient or his caregivers. Second, median infarct size of these silent myocardial infarctions was 10% to 12% of the left ventricle. Thereby, the size of silent myocardial infarctions is smaller than reported in a study of consecutive patients undergoing a similar imaging protocol after a first myocardial infarction treated with a contemporary revascularization strategy. 20 Third, the prevalence of silent myocardial infarction was 50% higher in patients with diabetes mellitus. This finding was confirmed by multivariable analysis demonstrating the strong and independent association of the 2 conditions. Fourth, with a median LVEF of 47% on MPS, diabetics with silent myocardial infarction had significantly lower LVEF than diabetics without myocardial infarction. Fifth, classical ECG criteria missed 2 of 3 silent myocardial infarctions. Our findings extend and corroborate previous work on silent myocardial infarction. Three rather small previous studies that relied exclusively on ECG data had provided first insights into the prevalence of silent myocardial infarction in the general population in Europe, North Table 4 Derivation Cohort: Binominal Logistic Analysis to Predict Presence of Silent Myocardial Infarction Variables 95% CI OR P Value Diabetes mellitus Arterial hypertension Hyperlipidemia Smoking Positive family history for CAD CAD ¼ coronary artery disease; CI ¼ confidence interval; OR ¼ odds ratio. Table 5 Validation Cohort: Binomial Logistic Analysis to Predict Presence of Silent Myocardial Infarction Variables 95% CI OR P Value Diabetes mellitus Arterial hypertension Hyperlipidemia Smoking Positive family history for CAD CAD ¼ coronaryartery disease; CI ¼ confidenceinterval; OR ¼ oddsratio.

7 Arenja et al Clinical Aspects of Silent Myocardial Infarction 521 America, and India. 17,19,21 The observed prevalence varied greatly, with the prevalence found to depend on age, origin, and on the cardiovascular risk profile. It ranged from 0.7%-6% for subjects <65 years and 10%-12 % for those older than 65 years. 5 Q waves are an important marker of unrecognized cardiac disease, but there are limitations of using only a resting ECG for detection of silent myocardial scars. First, ECG has a low sensitivity, as silent myocardial infarction ECG findings may not persist in the long term due to regression of Q waves with time. 22 Second, the infarction size greatly influences its delectability by ECG, 23 so that a relatively large proportion of small silent myocardial infarctions are not associated with characteristic Q waves. MPS improved the diagnostic capability, particularly the test sensitivity, of myocardial scare that is missed by ECG. 6 This also is confirmed by our results, which clearly demonstrate that myocardial scars have been identified more often by MPS than by Q waves. The prognosis of silent myocardial infarction detected in imaging studies like MPS has not been well defined. This important aspect should be evaluated in future studies. Kwong et al 24 reported a high prevalence of silent myocardial infarctions as assessed by cardiac magnetic resonance among 107 patients with diabetes mellitus. Thereby, there is now increasing evidence that diabetes mellitus is associated not only with a high prevalence (about 20%) of reversible silent myocardial ischemia, but also with a high incidence of irreversible previous events. 25,26 In asymptomatic diabetic patients, the rates of objective evidence of coronary artery disease and annual critical events are similar to those found in diabetics with angina. In addition, diabetic patients with evidence of coronary artery disease have a worse outcome. 27 This highlights the need for specific interdisciplinary strategies to optimize clinical care, including prevention and early detection of cardiovascular disease. 28 Overall, the high prevalence of silent myocardial infarctions detected in these cohorts of patients with suspected coronary artery disease like patients with diabetes mellitus seems to support the rather liberal use of imaging modalities capable of detecting myocardial scars as well as the stringent control of cardiovascular risk factors accompanied by a rather liberal use of established secondary prevention therapies such as aspirin and highdose statins. 29 Further studies on the early detection and early treatment of silent myocardial infarction are clearly warranted. Study Limitations Several potential limitations merit consideration. First, we excluded all patients with a history of known prior myocardial infarction as it would have been impossible to reliably differentiate between the clinical apparent and silent myocardial infarctions. We assume that our finding also can be extrapolated to patients with prior clinical myocardial infarction. Of course, this assumption needs to be verified in future studies. Second, we may have misclassified some patients with very large myocardial ischemia but no definite myocardial scar. This might have led to an overestimation of the prevalence of silent myocardial infarction. Third, we may have missed some very small scars in MPS. This might have led to an underestimation of the prevalence of silent myocardial infarction, because the spatial resolution of MPS is around 8-10 mm. 30 CONCLUSION Silent myocardial infarctions are more common than previously thought. One of 4 patients with suspected coronary artery disease had experienced a silent myocardial infarction, the extent in average is 10% of the left ventricle and it is more frequent in diabetics. References 1. Van de Werf F, Bax J, Betriu A, et al. Management of acute myocardial infarction in patients presenting with persistent ST-segment elevation: the Task Force on the Management of ST-Segment Elevation Acute Myocardial Infarction of the European Society of Cardiology. Eur Heart J. 2008;29: Armstrong PW, Granger CB, Adams PX, et al. Pexelizumab for acute ST-elevation myocardial infarction in patients undergoing primary percutaneous coronary intervention: a randomized controlled trial. JAMA. 2007;297: Assessment of the Safety and Efficacy of a New Treatment Strategy with Percutaneous Coronary Intervention (ASSENT-4 PCI) investigators. Primary versus tenecteplase-facilitated percutaneous coronary intervention in patients with ST-segment elevation acute myocardial infarction (ASSENT-4 PCI): randomised trial. Lancet. 2006;367: Stiles MC, Seaquist ER, Yale JF, et al. Is silent myocardial infarction more common in women with type 2 diabetes than in men? J Diabetes Complications. 2012;26(2): Valensi P, Lorgis L, Cottin Y. Prevalence, incidence, predictive factors and prognosis of silent myocardial infarction: a review of the literature. Arch Cardiovasc Dis. 2011;104(3): Loong CY, Anagnostopoulos C. Diagnosis of coronary artery disease by radionuclide myocardial perfusion imaging. Heart. 2004;90(Suppl 5): v2-v9. 7. Staub D, Nusbaumer C, Zellweger MJ, et al. Use of B-type natriuretic peptide in the detection of myocardial ischemia. Am Heart J. 2006;151: Staub D, Morgenthaler NG, Buser C, et al. Use of copeptin in the detection of myocardial ischemia. Clin Chim Acta. 2009;399: Staub D, Jonas N, Zellweger MJ, et al. Use of N-terminal pro-b-type natriuretic peptide to detect myocardial ischemia. Am J Med. 2005;118: Ehl NF, Kuhne M, Brinkert M, et al. Diabetes reduces left ventricular ejection fractioneirrespective of presence and extent of coronary artery disease. Eur J Endocrinol. 2011;165: Berman DS, Kiat H, Friedman JD, et al. Separate acquisition rest thallium-201/stress technetium-99m sestamibi dual-isotope myocardial perfusion single-photon emission computed tomography: a clinical validation study. J Am Coll Cardiol. 1993;22: Zellweger MJ, Hachamovitch R, Kang X, et al. Threshold, incidence, and predictors of prognostically high-risk silent ischemia in asymptomatic patients without prior diagnosis of coronary artery disease. J Nucl Cardiol. 2009;16: Gibbons RJ, Balady GJ, Bricker JT, et al. ACC/AHA 2002 guideline update for exercise testing: summary article: a report of the American

8 522 The American Journal of Medicine, Vol 126, No 6, June 2013 College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee to Update the 1997 Exercise Testing Guidelines). Circulation. 2002;106: Zellweger MJ, Kaiser C, Jeger R, et al. Coronary artery disease progression late after successful stent implantation. J Am Coll Cardiol. 2012;59(9): Diamond GA, Staniloff HM, Forrester JS, et al. Computer-assisted diagnosis in the noninvasive evaluation of patients with suspected coronary artery disease. J Am Coll Cardiol. 1983;1: Zellweger MJ, Dubois EA, Lai S, et al. Risk stratification in patients with remote prior myocardial infarction using rest-stress myocardial perfusion SPECT: prognostic value and impact on referral to early catheterization. J Nucl Cardiol. 2002;9: Ammar KA, Samee S, Makwana R, et al. Echocardiographic characteristics of electrocardiographically unrecognized myocardial infarctions in a community population. Am J Cardiol. 2005;96: Lundblad D, Eliasson M. Silent myocardial infarction in women with impaired glucose tolerance: the Northern Sweden MONICA study. Cardiovasc Diabetol. 2003;2: Gopinath N, Kaul U, Chadha SL, et al. Asymptomatic coronary heart disease detected on epidemiological survey of urban population of Delhi. Indian Heart J. 1992;44: Schomig A, Mehilli J, Antoniucci D, et al. Mechanical reperfusion in patients with acute myocardial infarction presenting more than 12 hours from symptom onset: a randomized controlled trial. JAMA. 2005;293: Nadelmann J, Frishman WH, Ooi WL, et al. Prevalence, incidence and prognosis of recognized and unrecognized myocardial infarction in persons aged 75 years or older: the Bronx Aging Study. Am J Cardiol. 1990;66: Yano K, MacLean CJ. The incidence and prognosis of unrecognized myocardial infarction in the Honolulu, Hawaii, Heart Program. Arch Intern Med. 1989;149: Kaandorp TA, Bax JJ, Lamb HJ, et al. Which parameters on magnetic resonance imaging determine Q waves on the electrocardiogram? Am J Cardiol. 2005;95: Kwong RY, Sattar H, Wu H, et al. Incidence and prognostic implication of unrecognized myocardial scar characterized by cardiac magnetic resonance in diabetic patients without clinical evidence of myocardial infarction. Circulation. 2008;118: Milan Study on Atherosclerosis and Diabetes (MiSAD) Group. Prevalence of unrecognized silent myocardial ischemia and its association with atherosclerotic risk factors in noninsulin-dependent diabetes mellitus. Am J Cardiol. 1997;79: Vanzetto G, Halimi S, Hammoud T, et al. Prediction of cardiovascular events in clinically selected high-risk NIDDM patients. Prognostic value of exercise stress test and thallium-201 single-photon emission computed tomography. Diabetes Care. 1999;22: Zellweger MJ, Hachamovitch R, Kang X, et al. Prognostic relevance of symptoms versus objective evidence of coronary artery disease in diabetic patients. Eur Heart J. 2004;25: Jeger RV, Bonetti PO, Zellweger MJ, et al. Influence of revascularization on long-term outcome in patients > or ¼75 years of age with diabetes mellitus and angina pectoris. Am J Cardiol. 2005;96: Mueller C, Mc Hodgson JB, Brutsche M, et al. Impact of intracoronary ultrasound guidance on long-term outcome of percutaneous coronary interventions in diabeticseinsights from the randomized SIPS trial. Swiss Med Wkly. 2002;132: King MA, Long DT, Brill AB. SPECT volume quantitation: influence of spatial resolution, source size and shape, and voxel size. Med Phys. 1991;18:

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