Given the rising costs of imaging, there is increasing pressure to provide evidence for direct additive impact on clinical care. Appropriate use criteria (AUC) were developed to optimize test-patient selection and are increasingly used by payers to assess reimbursement. However, these criteria were created by expert consensus with limited systematic validation. The aims of this study were therefore to determine (1) rates of active clinical change resulting from stress cardiovascular magnetic resonance (CMR) imaging and (2) whether the AUC can predict these changes. We prospectively enrolled 350 consecutive outpatients referred for stress CMR. Categories of “active changes in clinical care” due to stress CMR were predefined. Appropriateness was classified according to the 2013 AUC. Multivariate logistic regression analysis was used to identify factors independently associated with active change. Overall, stress CMR led to an active change in clinical care in about 70% of patients. Rates of change in clinical care did not vary significantly across AUC categories (p = 0.767). In a multivariate model adjusting for clinical variables and AUC, only ischemia (odds ratio [OR] 6.896, 95% CI 2.637 to 18.032, p <0.001), known coronary artery disease (OR 0.300, 95% CI 0.161 to 0.559, p <0.001), and age (OR 0.977, 95% CI 0.954 to 1.000, p = 0.050) independently predicted significant clinical change. In conclusion, stress CMR made a significant impact on clinical management, resulting in active change in clinical care in about 70% of patients. AUC categories were not an independent predictor of clinical change. Clinical change was independently associated with the presence of ischemia, absence of known coronary artery disease, and younger age.
The appropriate use criteria (AUC) for stress cardiac magnetic resonance (CMR) have recently been published as part of a multimodality approach to detection and risk assessment of stable ischemic heart disease. These criteria were created by expert consensus with limited systematic validation –particularly for CMR. We recently reported downstream utilization rates of angiography and revascularization procedures after stress-CMR, based on the most recent AUC. However, stress CMR routinely provides significant information beyond ischemia assessment. We therefore hypothesized that the overall clinical impact of stress CMR may extend beyond just angiography and revascularization procedures to other aspects of patient management and care. Moreover, given that the purpose of the AUC is to optimize test-patient selection, one might expect it to predict active change in clinical management resulting from the test. The aims of this study were therefore (1) to determine overall rates of active clinical change resulting from stress CMR in the outpatient setting and (2) to determine whether the AUC can predict these rates of active change.
Methods
A total of 350 consecutive outpatients referred for CMR stress testing were prospectively enrolled in a single academic medical center. Patients were excluded if they had metallic implants incompatible with CMR, glomerular filtration rate <30 ml/min, high degree atrioventricular block, severe active wheezing from asthma or severe claustrophobia. Subjects were asked to abstain from caffeine-containing products for at least 12 hours before the test. Information on baseline demographic variables and previous laboratory testing was obtained from patient interviews and the electronic medical record. Patients gave informed written consent for the protocol, which was approved by the local institutional review board.
Images were acquired on a 3-T scanner (Philips Achieva, Philips Medical Systems, Best, the Netherlands) using a 6-element phased-array receiver coil as previously described. Steady-state free-precession cine images were acquired in multiple short-axis and 3 long-axis views (repetition time, 3.0 ms; echo time, 1.5 ms; flip angle, 40°; slice thickness 6 mm).
The patient table was then partially pulled outside the scanner bore to allow direct observation of the patient and full access. A 0.4-mg bolus of regadenoson (Lexiscan; Astellas Pharma Inc., Northbrook, Illinois) was infused under continuous electrocardiography and blood pressure monitoring. Approximately 1 minute after regadenoson administration, the perfusion sequence was applied and gadolinium contrast (0.075 mmol/kg gadoteridol; Bracco Diagnostics, Princeton, New Jersey) followed by a saline solution flush (30 ml) was infused (4.5 ml/s) through an antecubital vein. On the console, the perfusion images were observed as they were acquired, with breath-holding starting from the appearance of contrast in the right ventricular cavity. Imaging was completed 10 to 15 seconds after the gadolinium bolus had transited the left ventricular myocardium. Perfusion images consisted of 3 to 4 short-axis slices obtained every heartbeat with a saturation recovery, gradient echo sequence (repetition time 2.8 ms; echo time 1.1 ms; flip angle 20°; voxel size, 2.5 × 2.5 × 8 mm). Aminophylline (100 mg intravenous) was administered immediately after stress perfusion imaging. Rest perfusion images were acquired 15 minutes after stress imaging with an additional contrast bolus (0.075 mmol/kg gadoteridol) using identical sequence parameters. Five minutes after rest perfusion, late gadolinium enhancement (LGE) imaging was performed with a 2D segmented gradient echo phase-sensitive inversion recovery sequence in the identical views as cine CMR. Inversion delay times were typically 280 to 360 ms. Perfusion and LGE images were visually interpreted by standard methods.
Two general cardiologists reviewed all clinical information dated before the CMR stress test. These reviewers were blinded to the results of the CMR and to the clinical course subsequent to the test. The CMR stress tests were classified as “appropriate,” “maybe appropriate,” or “rarely appropriate” as defined by the 2013 AUC. A third blinded independent physician adjudicated any discrepancy between the interpreters.
Two general cardiologists blinded to AUC classification independently assessed the clinical impact of each stress CMR by review of the electronic medical records through to the next outpatient visit with the ordering provider. If referral for coronary angiography was made then occurrence of revascularization was noted. Clinical impact was defined a priori in 1 of the following 2 mutually exclusive categories: (1) active change in care and (2) no change in care. Categories of active change included referral to coronary angiography, revascularization, preoperative clearance, medication change, subspecialty referral, ordering of additional diagnostic testing, and discharge from cardiology clinic. Categorization strictly required the presence of a statement by the referring physician in the follow-up clinical note, stating that the change (e.g., discharge from clinic or medication change) was initiated as a result of the stress CMR results. Patients could be included in more than 1 category of the active change group.
Normally distributed data were expressed as mean ± SD. Continuous variables were compared by the Student t test or Wilcoxon rank-sum (depending on data normality). Comparisons of discrete variables were made using the chi-square test; Fisher’s exact test was used when the assumptions of the chi-square test were not met. To identify which clinical indexes were associated with active clinical change, we performed univariate (unadjusted) logistic regression analysis to estimate the unadjusted odds ratios (ORs) and the 95% CIs for baseline clinical variables and AUC categorization. For the multivariate model, covariates were chosen on the basis of established clinical risk factors and significant univariate predictors (at p <0.10) from the list of baseline characteristics. A p value of <0.05 was considered statistically significant.
Results
Table 1 summarizes baseline patient characteristics. In the overall cohort, 243 (69.5%) of stress CMRs resulted in an active change in care, and 107 (30.5%) led to no change ( Table 2 and Figure 1 ). The most common active changes were discharge from cardiology clinic (21.1%) or medication change (18.3%; Tables 2 and 3 ). Most active changes were noninvasive (65.5%) as opposed to invasive (8.6%) in nature ( Figure 1 ). A significant minority (4.6%) underwent both a noninvasive and invasive change in management.
| Characteristics | Total (N=350) |
|---|---|
| Age (years) | 59 (±13.7) |
| Body Mass Index (kg/m 2 ) | 30.6 (±5.9) |
| Male | 46.3% |
| Diabetes Mellitus | 34.9% |
| Hyperlipidemia | 53.4% |
| Smoker | 18.9% |
| Hypertension | 74.9% |
| Known Coronary Artery Disease | 31.4% |
| Left Ventricular Ejection Fraction | 59 (±11.5) |
| Late Gadolinium Enhancement present | 20.3% |
| Ischemia present | 13.7% |
| Category | Definition | N=350 (%) |
|---|---|---|
| Active Change in Clinical Care | Angiography with Revascularization | 19 (5.4%) |
| Angiography without Revascularization | 27 (7.7%) | |
| Preoperative Clearance | 39 (11.1%) | |
| Medication Changes | 64 (18.3%) | |
| Subspecialty Consultation | 33 (9.4%) | |
| Additional Diagnostic Test Ordered | 32 (9.1%) | |
| Discharge from Cardiology Clinic | 74 (21.1%) | |
| No Active Change in Clinical Care | Continuation of pre-CMR Care | 107 (30.5%) |
| Drug Class | Initiation or Increase | Discontinuation or Decrease |
|---|---|---|
| Beta-Blocker | 17 | 3 |
| ACE inhibitor/Angiotensin Receptor Blocker | 14 | 3 |
| Cholesterol Lowering | 13 | 0 |
| Antiplatelet | 12 | 5 |
| Diuretic | 8 | 2 |
| Nitrate | 6 | 1 |
| Calcium Channel Blocker | 3 | 1 |
| Hydralazine | 2 | 0 |
Based on the 2013 AUC, 52% of stress CMRs were classified as appropriate, 36% as maybe appropriate, and 12% as rarely appropriate. The most common rarely appropriate categories in this study are presented in Table 4 . The overall rates of active clinical change were similar between studies classified as appropriate (68%), may be appropriate (72%), and rarely appropriate (72%; p = 0.766; Figure 2 ). However, there were significantly more invasive changes in the appropriate group compared with the rarely appropriate group (16.5% vs 2.3%, p = 0.015).
| AUC Description | N |
|---|---|
| Symptomatic with low pre-test probability of CAD and interpretable electrocardiogram and able to exercise | 23 |
| Follow up testing (>90 Days) with asymptomatic or stable symptoms with an abnormal prior stress imaging study < 2 years ago | 3 |
| Follow up testing (>90 Days) with asymptomatic or stable symptoms with a normal prior stress imaging study or non-obstructive CAD on angiogram and low global CAD risk | 3 |
The presence of ischemia (OR 3.517, 95% CI 1.447 to 8.550, p = 0.006), known coronary artery disease (CAD; OR 0.374, 95% CI 0.232 to 0.603, p <0.001), hyperlipidemia (OR 0.520, 95% CI 0.325 to 0.831, p = 0.006), and age (OR 0.976, 95% CI 0.959 to 0.995, p = 0.010) were significant univariate predictors of significant clinical impact ( Table 5 ). In a multivariate model adjusting for clinical variables and AUC, only ischemia (OR 6.896, 95% CI 2.637 to 18.032, p <0.001), known CAD (OR 0.300, 95% CI 0.161 to 0.559, p <0.001), and age (OR 0.977, 95% CI 0.954 to 1.000, p = 0.050) independently predicted significant clinical change ( Table 5 ). AUC categories were not an independent predictor of clinical change (OR 0.936, 95% CI 0.637 to 1.376, p = 0.736). AUC was still not an independent predictor of clinical change even if surgical clearance was excluded as a category of change (OR 0.891, 95% CI 0.689 to 1.153, p = 0.380).
