Open Access

Meta-analysis of the diagnostic performance of stress perfusion cardiovascular magnetic resonance for detection of coronary artery disease

  • Michèle Hamon1, 2Email author,
  • Georges Fau1,
  • Guillaume Née3,
  • Javed Ehtisham4,
  • Rémy Morello5 and
  • Martial Hamon4, 6
Journal of Cardiovascular Magnetic Resonance201012:29

DOI: 10.1186/1532-429X-12-29

Received: 18 September 2009

Accepted: 19 May 2010

Published: 19 May 2010

Abstract

Aim

Evaluation of the diagnostic accuracy of stress perfusion cardiovascular magnetic resonance for the diagnosis of significant obstructive coronary artery disease (CAD) through meta-analysis of the available data.

Methodology

Original articles in any language published before July 2009 were selected from available databases (MEDLINE, Cochrane Library and BioMedCentral) using the combined search terms of magnetic resonance, perfusion, and coronary angiography; with the exploded term coronary artery disease. Statistical analysis was only performed on studies that: (1) used a [greater than or equal to] 1.5 Tesla MR scanner; (2) employed invasive coronary angiography as the reference standard for diagnosing significant obstructive CAD, defined as a [greater than or equal to] 50% diameter stenosis; and (3) provided sufficient data to permit analysis.

Results

From the 263 citations identified, 55 relevant original articles were selected. Only 35 fulfilled all of the inclusion criteria, and of these 26 presented data on patient-based analysis. The overall patient-based analysis demonstrated a sensitivity of 89% (95% CI: 88-91%), and a specificity of 80% (95% CI: 78-83%). Adenosine stress perfusion CMR had better sensitivity than with dipyridamole (90% (88-92%) versus 86% (80-90%), P = 0.022), and a tendency to a better specificity (81% (78-84%) versus 77% (71-82%), P = 0.065).

Conclusion

Stress perfusion CMR is highly sensitive for detection of CAD but its specificity remains moderate.

Introduction

Perfusion cardiovascular magnetic resonance (CMR) is an emerging technique for the detection of coronary artery disease (CAD). The technique is attractive because of its non-invasive nature and safe characteristics, and might potentially play a major role in future diagnosis and risk stratification guidelines for patients with suspected CAD. Several small studies have evaluated the diagnostic performance of stress perfusion CMR and some of those have been included in a previous meta-analysis [1]. In the current study we provide a comprehensive and contemporary meta-analysis of its diagnostic accuracy compared with an invasive coronary angiography (CA) used as a reference standard.

Methods

Search strategy

Using the combined medical subject headings (MeSH) of magnetic resonance, perfusion, and coronary angiography, with the exploded terms coronary artery disease; the MEDLINE, Cochrane Library and BioMedCentral databases were searched independently by two investigators (MH, GF) for all publications, in any language, before July 2009. In addition, the published reference lists of these articles were systematically searched.

Study eligibility

The search results were collated by the same two investigators (MH, GF), and duplicate or overlapping papers removed. Studies were eligible if: [1] stress perfusion CMR was used as a diagnostic test for significant obstructive CAD; [2] conventional invasive CA was used as the reference standard for diagnosing significant obstructive CAD, defined as a ≥50% diameter stenosis; and [3] the absolute numbers of true positive (TP), false positive (FP), true negative (TN), and false negative (FN) were reported, or could be derived. Studies were excluded if they were performed with a 0.5 or 1 Tesla MR scanner, if they included less than 10 patients, and if only abstracts from scientific meetings were published as the data provided may either be not sufficiently detailed or finalized. Any disagreements on eligibility were resolved by discussion and consensus between the two investigators.

Data extraction and quality assessment

Data extraction was performed independently by the two investigators (MH, GF) for each study. The following fields were recorded: study population size; gender distribution; mean age and standard deviation; number of patients with documented CAD; prevalence of CAD; relative timing of the two imaging procedures; the degree of blinding in interpretation of test results (both to the patient's clinical context and the results of the other imaging modality); type and brand of MR machine used; the type of perfusion stressor (adenosine, nicorandil, dipyridamole), and the number of side effects; the dose and injection rate of Gadolinium administrated; and the modality of MR image analysis (visual, or semi-quantitative). Any discrepancies were resolved by discussion and consensus between the two investigators. Where available, data was recorded separately at the level of coronary territories and coronary arteries. The study quality conformed to the Quality Assessment of Studies of Diagnostic Accuracy included in Systematic Reviews guidelines [2]. In one study, for which patients were evaluated both with 1.5 and 3T CMR, we used 1.5 T data in the meta-analysis. For the studies where analysis was performed with both 50% and 70% coronary stenosis definitions, we included results with the 70% definition in the pooled reported sensitivity and specificity.

Data synthesis and statistical analysis

Data analysis was performed at the level of the patient, the coronary territory and the coronary artery. Sensitivity and specificity were calculated using the TP, TN, FP, and FN rates [3, 4]. From these were calculated the likelihood ratios, which express how much the odds of significant obstructive CAD change in the presence of either an abnormal stress perfusion CMR (positive likelihood ratio: PLR = sensitivity/(1- specificity)), or a normal stress perfusion CMR (negative likelihood ratio: NLR = (1- sensitivity)/specificity). Finally, the ratio of the PLR to the NLR was used to calculate the diagnostic odds ratio (DOR), which estimates how much greater the odds of having significant obstructive CAD are for patients with a positive test result compared with a negative one.

All these measures of diagnostic accuracy were calculated for each individual study and reported as point estimates with 95% confidence intervals. They were then combined using a random-effects model and each point estimate weighted by the inverse of the sum of its variance and the between-study variance. We also assessed between-study statistical heterogeneity using the Cochran Q chi-square tests (cut off for statistical significance P ≤ .10). Since diagnostic parameters are, by definition, interdependent, independent weighting may sometimes give spurious results and provide biased estimates; to overcome the interdependence problem, we computed the weighted symmetric summary receiver operating characteristic curve, with pertinent areas under the curve, using the Moses-Shapiro-Littenberg method [57]. All statistical calculations were performed with SPSS 14.0 (SPSS, Chicago, IL) and Meta-DiSc [8], and significance testing was at the two-tailed 0.05 level [9].

Results

Database and literature searches retrieved 263 citations, amongst which 55 relevant publications were identified (Figure 1). Further scrutiny led 20 papers to be rejected either because of overlapping data, or exclusion criteria were met (employed 0.5 or 1 T CMR, or inclusion criteria were absent (impossible to find or calculate absolute figures from presented data). Therefore, 35 studies were finally included in the meta-analysis [1044], all of which had been published between 2000 and 2009. Study and population characteristics are summarized in Table 1, and the results of the pooled analyses are summarized in Table 2. Dose of contrast Gadolinium administrated range from 0.025 to 0.15 mmol/kg, with an injection rate varying from 3 to 10 mL/s. Quality assessments for all included studies are shown in Table 3. The 35 papers eligible for the analyses comprised 2,456 patients, and of the 2,154 patients for whom gender and the age were specified, 1,481 were males (68.7%) and the mean age was 61.3 years.
https://static-content.springer.com/image/art%3A10.1186%2F1532-429X-12-29/MediaObjects/12968_2009_Article_960_Fig1_HTML.jpg
Figure 1

Flow diagram of the reviewing process.

Table 1

Characteristics of included studies

Authors

Year

Brand

Tesla

Patients (n)

Excluded (n)

Male (%)

Mean Age (SD)

Prevalence (% per patient)

Coronary Stenosis (%)

Stressor*

Side Effects ** (n)

Data assessment

Al Saadi, (10)

2000

Philips

1.5

40

6

-

-

100

≥ 75

D

0

1/2 Quantitative

Schwitter (11)

2001

GE

1.5

48

1

83

59(-)

79

≥ 50

D

0

1/2 Quantitative

Ibrahim, (12)

2002

Philips

1.5

25

0

76

63(13)

100

> 75

A

-

1/2 Quantitative

Sensky (13)

2002

Siemens

1.5

30

0

90

62(-)

100

> 50

A

0

Visual

Chiu, (14)

2003

Siemens

1.5

13

0

54

68(-)

92

> 50

A

0

Visual

Doyle (15)

2003

Philips

1.5

229

45

0

59(11)

14

≥ 70

D

-

1/2 Quantitative

Ishida (16)

2003

GE

1.5

104

0

78

66(12)

74

> 70

D

0

Visual

Nagel (17)

2003

Philips

1.5

90

6

81

63(8)

51

≥ 75

A

2

1/2 Quantitative

Bunce (18)

2004

Picker

1.5

35

0

77

56(12)

49

≥ 50

A

0

1/2 Quantitative

Giang (19)

2004

GE

1.5

94

14

69

58(-)

65

≥ 50

A

0

1/2 Quantitative

Kawase (20)

2004

Philips

1.5

50

0

58

66(12)

66

≥ 70

N

0

Visual

Paetsch (21)

2004

Philips

1.5

79

0

66

61(9)

67

> 50

A

0

Visual

Plein (22)

2004

Philips

1.5

72

4

79

57(11)

82

≥ 70

A

1

Visual

Takase (23)

2004

GE

1.5

102

-

83

66(9)

74

> 50

D

-

Visual

Thiele (24)

2004

Philips

1.5

20

0

-

64(8)

90

≥ 70

A

0

1/2 Quantitative

Okuda (25)

2005

GE

1.5

33

0

88

60(-)

97

≥ 75

D

0

Visual

Plein (26)

2005

Philips

1.5

92

10

74

58(-)

72

> 70

A

0

1/2 Quantitative

Sakuma (27)

2005

Siemens

1.5

40

0

70

65(9)

52

> 70

D

0

Visual

Cury (28)

2006

GE

1.5

47

1

81

63(5)

65

≥ 70

D

-

Visual

Klem (29)

2006

Siemens

1.5

100

8

49

58(11)

40

>50/≥ 70

A

1

Visual

Pilz (30)

2006

GE

1.5

176

5

63

62(12)

66

> 70

A

2

Visual

Rieber (31)

2006

Siemens

1.5

50

7

88

61(8)

67

> 50

A

0

1/2 Quantitative

Cheng (32)

2007

Siemens

1.5/3

65

4

75

64(8)

66

≥ 50

A

1

Visual

Costa (33)

2007

Siemens

1.5

37

7

53

65(11)

97

> 50/> 70

A

0

1/2 Quantitative

Greenwood (34)

2007

Philips

1.5

35

0

89

55(-)

83

≥ 70

A

0

Visual

Kühl (35)

2007

Philips

1.5

20

1

68

64(13)

100

≥ 50

A

0

1/2 Quantitative

Merkle (36)

2007

Philips

1.5

228

0

79

61(11)

75

> 50/> 70

A

0

Visual

Seeger (37)

2007

Siemens

1.5

51

0

86

65(9)

74

> 70

A

0

1/2 Quantitative

Gebker (38)

2008

Philips

3

101

3

70

62(8)

69

≥ 50

A

2

Visual

Meyer (39)

2008

Philips

3

60

0

63

59(10)

60

≥ 70

A

0

Visual

Pilz (40)

2008

GE

1.5

22

0

64

66(12)

33

≥ 70

A

0

Visual

Klein (41)

2008

Philips

1.5

54

5

65

60(10)

47

≥ 50

A

2

Visual

Klem (42)

2008

Siemens

1.5

147

11

0

63(11)

27

≥ 70

A

0

Visual

Thomas (43)

2008

Philips

3

60

0

68

-

47

≥ 50

A

0

Visual

Burgstahler (44)

2008

Philips

1.5

23

3

65

68(12)

40

≥ 70

A

0

Visual

* Stressor: A (Adenosine); D (Dypirydamole); N (Nicorandil) ** n: significant side effects, which led to stop the MR exam.

Table 2

Pooled summary results

Studies

N studies

N

Sensitivity

Specificity

Positive Likelihood ratio

Negative likelihood ratio

Diagnostic odds ratio

Per Patient analysis (all)

26

2125 Patients

89% (88-91)

80% (78-83)

4.18 (3.31-5.27)

0.15 (0.11-0.20)

33.65 (22.09-51.27)

   Adenosine stressor

20

1658 Patients

90% (88-92)

81% (78-84)

4.47 (3.39-5.88)

0.14 (0.11-0.18)

37.17 (25.16-54.91)

   Dipyridamole stressor

5

417 Patients

86% (80-90)

77% (71-82)

2.97 (2.16-4.09)

0.20 (0.09-0.45)

17.03 (5.56 - 52.18)

   Visual assessment

20

1624 Patients

91% (89-93)

79% (76-83)

4.08 (3.15-5.29)

0.13 (0.10-0.17)

36.79 (23.90-56.63)

   Semi-quant. assessment

6

501 Patients

82%(77-87)

82% (77-86)

4.88 (2.62-9.09)

0.22 (0.13-0.37)

25.44 (8.90-72.70)

Per Territory analysis

17

2709 Territories

82% (79-84)

84% (82-85)

4.90(3.66-6.55)

0.23 (0.20-0.27)

23.23 (18.33-29.45)

Per Artery analysis

       

   LAD

8

662 Arteries

83%(78-88)

83%(79-86)

4.37(2.96-6.44)

0.22 (0.16-0.31)

21.42 (10.94-41.94)

   CX

8

672 Arteries

76%(70-82)

87%(84-90)

5.74(3.94-8.35)

0.30 (0.23-0.38)

22.25 (14.09-35.10)

   RCA

8

657 Arteries

78%(71-84)

87% (83-90)

5.58 (3.74-8.32)

0.29 (0.21-0.38)

23.07 (14.55-36.57)

Table 3

Quality assessment (QUADAS)

Study

Item 1

Item 2

Item 3

Item 4

Item 5

Item 6

Item 7

Item 8

Item 9

Item 10

Item 11

Item 12

Item 13

Item 14

Al Saadi, 2000 (10)

no

yes

yes

unclear

yes

yes

yes

no

no

unclear

unclear

no

yes

yes

Schwitter, 2001 (11)

yes

yes

yes

yes

yes

yes

yes

yes

Yes

yes

yes

yes

yes

yes

Ibrahim, 2002 (12)

yes

yes

yes

unclear

yes

yes

yes

yes

Yes

unclear

unclear

yes

yes

yes

Sensky, 2002 (13)

yes

yes

yes

unclear

yes

yes

yes

yes

Yes

yes

yes

yes

yes

yes

Chiu, 2003 (14)

yes

yes

yes

yes

yes

yes

yes

yes

Yes

yes

yes

yes

yes

yes

Doyle, 2003 (15)

yes

yes

yes

unclear

yes

yes

yes

yes

Yes

no

no

yes

yes

yes

Ishida,2003 (16)

yes

yes

yes

yes

yes

yes

yes

yes

Yes

yes

yes

yes

yes

yes

Nagel, 2003 (17)

yes

yes

yes

yes

yes

yes

yes

yes

Yes

yes

yes

yes

yes

yes

Bunce, 2004 (18)

yes

yes

yes

yes

yes

yes

yes

yes

Yes

yes

yes

yes

no

yes

Giang, 2004 (19)

yes

yes

yes

yes

yes

yes

yes

yes

Yes

yes

yes

yes

yes

yes

Kawase, 2004 (20)

yes

yes

yes

yes

yes

yes

yes

yes

Yes

yes

yes

yes

yes

yes

Paetsch, 2004 (21)

yes

yes

yes

unclear

yes

yes

yes

yes

Yes

yes

yes

yes

unclear

unclear

Plein, 2004 (22)

yes

yes

yes

yes

yes

yes

yes

yes

Yes

yes

yes

yes

yes

yes

Takase, 2004 (23)

yes

yes

yes

yes

yes

yes

yes

yes

Yes

yes

yes

yes

no

unclear

Thiele,2004 (24)

yes

yes

yes

no

yes

yes

yes

yes

Yes

yes

yes

yes

yes

yes

Okuda,2005 (25)

yes

yes

yes

yes

yes

yes

yes

yes

Yes

yes

yes

yes

yes

yes

Plein, 2005 (26)

yes

yes

yes

yes

yes

yes

yes

yes

Yes

yes

yes

yes

yes

yes

Sakuma,2005 (27)

yes

yes

yes

yes

yes

yes

yes

yes

Yes

yes

yes

yes

yes

yes

Cury, 2006 (28)

yes

yes

yes

yes

yes

yes

yes

yes

Yes

yes

yes

yes

yes

yes

Klem, 2006 (29)

yes

yes

yes

yes

yes

yes

yes

yes

Yes

yes

yes

yes

yes

yes

Pliz, 2006 (30)

yes

yes

yes

unclear

yes

yes

yes

yes

Yes

yes

yes

yes

yes

yes

Rieber, 2006 (31)

yes

yes

yes

yes

yes

yes

yes

yes

Yes

yes

yes

yes

yes

yes

Cheng, 2007 (32)

yes

yes

yes

yes

yes

yes

yes

yes

Yes

yes

yes

yes

yes

yes

Costa,2007 (33)

yes

yes

yes

yes

yes

yes

yes

yes

Yes

yes

yes

yes

yes

yes

Greenwood, 2007 (34)

no

yes

yes

yes

yes

yes

yes

yes

Yes

yes

yes

yes

yes

yes

Kuhl, 2007 (35)

yes

yes

yes

yes

yes

yes

yes

yes

Yes

yes

yes

yes

yes

yes

Merkle, 2007 (36)

yes

yes

yes

yes

yes

yes

yes

yes

Yes

yes

yes

yes

yes

yes

Seeger, 2007 (37)

yes

yes

yes

yes

yes

yes

yes

yes

Yes

yes

yes

yes

yes

yes

Gebker, 2008 (38)

yes

yes

yes

yes

yes

yes

yes

yes

Yes

yes

yes

yes

yes

yes

Meyer, 2008 (39)

yes

yes

yes

yes

yes

yes

yes

yes

Yes

yes

yes

yes

unclear

unclear

Pilz, 2008 (40)

yes

yes

yes

yes

yes

yes

yes

yes

Yes

yes

yes

yes

yes

yes

Klein,2008 (41)

yes

yes

yes

yes

yes

yes

yes

yes

Yes

yes

yes

yes

yes

yes

Klem, 2008 (42)

no

yes

yes

yes

yes

yes

yes

yes

Yes

yes

yes

yes

yes

yes

Thomas, 2008 (43)

yes

yes

yes

unclear

yes

yes

yes

yes

Yes

yes

yes

yes

yes

unclear

Burgstahler,2008 (44)

yes

yes

yes

unclear

yes

yes

yes

yes

Yes

unclear

unclear

yes

yes

yes

Item 1: was the spectrum of patients representative of the patients who will receive the test in practice?; Item 2: were selection criteria clearly described?; Item 3: is the reference standard likely to correctly classify the target condition?; Item 4: is the time period between reference and standard and index test short enough to be reasonably sure that the target condition did not change between the two tests?; Item 5: did the whole sample or a random selection of the sample, receive verification using a reference standard of diagnosis?; Item 6: did patients receive the same reference standard regardless of the index test results?; Item 7: was the reference standard independent of the index test (i.e. the index test did not form part of the reference standard); Item 8: was the execution of the index test described in the sufficient detail to permit replication of the test; Item 9: was the execution of the reference standard described in the sufficient detail to permit its replication?; Item 10: were the index test results interpreted without knowledge of the results of the reference standard?; Item 11: were the reference standard results interpreted without knowledge of the results of the index test?; Item 12: were the same clinical data available when test results were interpreted as would be available when the test is used in practice?; Item 13: were uninterpretable/intermediate test results reported?; Item 14: were withdrawals from the study explained.

Diagnostic performance of stress perfusion CMR: Patient-based analysis

Overall per-patient analysis results pooled from 26 studies (2,125 patients) demonstrated a sensitivity of 89% (95% CI: 88-91%), a specificity of 80% (95% CI: 78-83%), a PLR of 4.18 (3.31-5.27), a NLR of 0.15 (95% CI: 0.11-0.20), a DOR of 33.65 (95% CI: 22.09-51.27), and an AUC of 0.92 (Figures 2, 3, 4, 5, 6). Statistical heterogeneity was observed for all relevant diagnostic performance measures. The per-patient prevalence of CAD was 57% (1,205 of 2,125 patients).
https://static-content.springer.com/image/art%3A10.1186%2F1532-429X-12-29/MediaObjects/12968_2009_Article_960_Fig2_HTML.jpg
Figure 2

Forest plot of patient-level sensitivity of stress perfusion CMR, compared with coronary angiography..

https://static-content.springer.com/image/art%3A10.1186%2F1532-429X-12-29/MediaObjects/12968_2009_Article_960_Fig3_HTML.jpg
Figure 3

Forest plot of patient-level specificity of stress perfusion CMR, compared with coronary angiography.

https://static-content.springer.com/image/art%3A10.1186%2F1532-429X-12-29/MediaObjects/12968_2009_Article_960_Fig4_HTML.jpg
Figure 4

Forest plot of patient-level positive likelihood ratio of stress perfusion CMR, compared with coronary angiography.

https://static-content.springer.com/image/art%3A10.1186%2F1532-429X-12-29/MediaObjects/12968_2009_Article_960_Fig5_HTML.jpg
Figure 5

Forest plot of patient-level negative likelihood ratio of stress perfusion CMR, compared with coronary angiography.

https://static-content.springer.com/image/art%3A10.1186%2F1532-429X-12-29/MediaObjects/12968_2009_Article_960_Fig6_HTML.jpg
Figure 6

Plot of symmetric summary receiver operating curve characteristic of stress perfusion CMR, compared with coronary angiography. The receiver operator characteristic curve provides a graphical display of diagnostic accuracy by plotting 1-specificity in the horizontal axis and sensitivity in the vertical axis. The pertinent area under the curve (AUC) and the Q* statistic (the point where sensitivity and specificity are maximized), both with standard errors (SE), are also included.

With adenosine as the stressor (20 studies, 1,658 patients) the results were: a sensitivity of 90% (88-92%), a specificity of 81% (78-84%), a PLR of 4.47 (3.39-5.88), a NLR of 0.14 (0.11-0.18), a DOR of 37.17 (25.16-54.91), and an AUC of 0.93. Statistical heterogeneity was observed for all relevant diagnostic performance measures. With dipyridamole as the stressor (5 studies, 417 patients), the results were: a sensitivity of 86% (80-90%), a specificity of 77% (71-82%), a PLR of 2.97 (2.16-4.09), a NLR of 0.20 (0.09-0.45), a DOR of 17.03 (5.56-52.18), and an AUC of 0.84. Statistical heterogeneity was observed for all relevant diagnostic performance measures, except specificity and positive likelihood ratio. ROC curves for stress perfusion CMR performed with adenosine or dipyridamole are shown in Figure 7.
https://static-content.springer.com/image/art%3A10.1186%2F1532-429X-12-29/MediaObjects/12968_2009_Article_960_Fig7_HTML.jpg
Figure 7

Plots of symmetric summary receiver operating curve characteristic of stress perfusion CMR, compared with coronary angiography for adenosine and dipyridamole stressors.

A sensitivity analysis was carried out based on the equipment used (3 Tesla, and 1.5 Tesla MRI). For 3 Tesla (4 studies, 282 patients), results were: a sensitivity of 92% (87-95%), a specificity of 78% (69-85%), a PLR of 3.96 (2.78-5.63), a NLR of 0.12 (0.07-0.20), and a DOR of 35.74 (17.13-74.53). For 1.5 Tesla (23 studies, 1,904 patients), results were: a sensitivity 89% (87-91%), a specificity of 80% (78-83%), a PLR of 4.26 (3.26-5.55), a NLR of 0.15 (0.11-0.20), and a DOR of 34.25 (21.26-55.17).

Diagnostic performance of stress perfusion CMR: Coronary territory and coronary artery-based analysis

Per-territory results, pooled from 17 studies corresponding to 2,709 coronary territories, demonstrated a sensitivity of 82% (79-84%), a specificity of 84% (82-85%), a PLR of 4.90 (3.66-6.55), a NLR of 0.23 (0.20-0.27), and a DOR of 23.23 (18.33-29.45). At the territory level heterogeneity was significant for all relevant diagnostic performance measures except sensitivity, negative likelihood ratio and diagnostic odd ratios.

Per-artery analysis pooled 8 datasets and demonstrated for left anterior descending artery (LAD), circumflex artery (CX) and right coronary artery (RCA), respectively, sensitivities of 83%, 76% and 78% and specificities of 83%, 87%, and 87%. Statistical heterogeneity was observed for all the performance measurements except sensitivity and negative likelihood ratio for LAD and CX, and diagnostic odds ratio for CX.

Discussion

This meta-analysis showed stress perfusion CMR to have a high sensitivity (89%) and a moderate specificity (80%) at patient level for the diagnosis of significant obstructive CAD in patients with high prevalence of CAD (57%). We included twelve more studies (on stress perfusion CMR) than the previous meta-analysis by Nandalur et al. [1], which showed a similar diagnostic performance with a pooled sensitivity and specificity of respectively 90% and 81% from 14 perfusion studies. A high false positive rate could have driven the relatively low specificity, and may be due to perfusion defects caused by: [1] dark rim artefacts, the hypo-intensities along the endocardial border of the left ventricular myocardium seen during first-pass transit of a MR contrast medium, thought to be due to a combination of the gadolinium bolus, motion and resolution [45]; [2] the presence of microvascular disease; and [3] spontaneous or therapeutic re-opening of a coronary artery supplying an area of myocardial infarction that has persistent microvascular obstruction [28, 32].

Alternatively, because CA detects luminal morphology rather than the functional significance of a stenosis, a false positive CMR results may in fact represent a 'false negative' angiogram in the context of angiographically 'invisible' small vessel disease capable of inducing subendocardial ischemia [40]. This potential source of error could be minimised if the hemodynamic significance of an epicardial coronary artery stenosis were to be determined by the measurement of the fractional flow reserve (FFR) during CA. If validated, this may represent a better reference standard than CA alone. However, although three studies found there to be a good correlation between the performance of stress perfusion CMR and CA with FFR measurement [31, 33, 35], sufficient data was not present to evaluate its accuracy in this study.

Another point to outline is that for some studies [11, 17, 19], different decision thresholds to diagnose perfusion CMR as abnormal were appraised: for these studies, the reported sensitivity and specificity could be considered as optimistic because the end points was chosen retrospectively.

In addition, there was a large range of contrast doses used in the individual studies, with the dose of gadolinium administered in the included studies varying by 6-fold, with dose ranging from 0.025 to 0.15 mmole/kg. Although currently there is no consensus regarding the optimal dose and injection rates for perfusion CMR, two multicenter dose-ranging studies have evaluated the impact of contrast dose on the performance of perfusion CMR using a visual analysis [46, 47]. In the first, Wolff et al. considered a low dose of 0.05 mmol/kg to be at least as efficacious as any higher dose, and hypothesized that higher doses preformed less well because of the increased likelihood and intensity of artefacts at these doses [46]. However, in the MR-Impact study, Schwitter et al. found better results were obtained using 0.1 mmol/kg [47].

In this meta-analysis, 18 studies were based on stress perfusion CMR alone [1013, 15, 17, 1921, 24, 26, 3133, 35, 37, 39, 44], whilst the other 17 included a multi-component examination (cine and/or late gadolinium enhancement (LGE) and/or coronary angiography and/or stress tagging) [14, 16, 18, 22, 23, 25, 2730, 34, 36, 38, 4043]. In their studies, Plein [22], Cury [28] and Klem [29] evaluated the differences in accuracy based on the sequences evaluated and found that all studies increased accuracy when using a combined analysis. In his study, Klem reported increased specificity (moving from 58% to 87%) when using an algorithm interpretation (including perfusion, cine and LGE).

Having access to data from different sequences (cine, perfusion, and LGE) is especially useful when one component shows a borderline result or is affected by image artefacts. Most of the authors have argued that rest perfusion is an important component because, in combination with late enhancement CMR, it can help distinguish true defects from artefacts on the stress perfusion images.

The fact that the meta-analysis demonstrated a low NLR for stress perfusion CMR suggests that a negative test result may in fact be more clinically useful. This is in keeping with several reports, in different clinical settings, of improved prognosis associated with a normal adenosine stress perfusion CMR scan [4850]. This meta-analysis also demonstrated adenosine to be superior to dipyridamole as the vasodilating stressor agent. Adenosine may also be safer, with minor side effects of flushing and headache being reported to occur more frequently that any severe adverse effects [51]. Its shorter half life (< 10 s) is an added advantage. Moreover, adenosine has documented safety in the context of non-ST elevation acute coronary syndromes (in a study of 72 patients only one demonstrated intolerance), and in recent ST elevation myocardial infarction [14, 22, 34].

From this analysis, visual assessment of stress perfusion CMR provided a higher sensitivity but a lower specificity than semi-quantitative assessment. Currently there is no consensus on the superiority of visual over semi-quantitative assessment, or on which method of semi-quantitative assessment should be used. However, the drawbacks of semi-quantitative assessment are that it is more time-consuming, hence not ideal for day-to-day clinical purposes, and the lack of any homogeneous post-processing protocols. Therefore, visual assessment is currently the method most often used in routine clinical practice.

Only 4 studies were performed using 3T CMR, which provides improved resolution [32, 38, 39, 43]. Enhanced sensitivity has been reported [32] and attributed to the higher signal-to-noise and contrast-to-noise ratios permitting improved detection of endocardial perfusion defects. Although most authors argue that the increased prevalence of dark rim artefacts at these higher field strengths (ranging from 8 up to 82%) does not hamper myocardial perfusion analysis [32, 39, 43], Gebker disagrees and suggests they could limit specificity by increasing false positive rates [38]. In this analysis, 3T CMR was also found to have a decreased specificity, indicating that higher false positive rates may be a real problem. Further studies will be necessary if this controversy is to be resolved.

The results of the per- territory-based analysis showed the anticipated decrease in sensitivity and increase in specificity seen when moving from the level of the patient to that of the coronary territory. Among the 8 studies that performed a coronary-artery level analysis, stress perfusion CMR had a higher sensitivity for detection of significant coronary disease in the LAD artery, compared with the CX and RCA. A possible explanation for this finding may have been the use of a surface radiofrequency coil, which led to lower signal intensities in the more distant inferior and lateral segments.

Study limitations

Although conventional CA is the established technique for diagnosing significant CAD in routine clinical practice, it remains an imperfect reference standard due to its inability to evaluate the hemodynamic significance of a stenosis.

Substantial inter-study heterogeneity in multiple performance characteristics were observed. Therefore, the pooled performance indices and their interpretation have to be treated with a degree of caution, even though the random-effects model used throughout the analysis should have compensated for this. The observed heterogeneity may have been due to variations in: (i) the image acquisition technique (MR scanner manufacturer, 1.5T or 3T field strengths, pulse sequence, number of slices, contrast dose and rate of infusion); (ii) the interpretation method (visual or semi-quantitative, post-processing techniques); (iii) the patient selection criteria (exclusion or inclusion of patients with prior myocardial infarction, patient populations with differing prevalence of CAD); and (iv) in the definition of significant obstructive CAD (50% or 70%). We noticed, as expected, that studies which performed analysis for 50% and for 70% coronary artery stenosis thresholds, reported an increased sensitivity and a decreased specificity when moving thresholds from 50% to 70% [29, 33, 36].

These general limitations of stress perfusion CMR could be addressed in future multi-centre studies if standardized imaging protocols, post-processing techniques and patient selection criteria are employed.

Conclusion

Stress Perfusion CMR has a high sensitivity and moderate specificity for the diagnosis of significant obstructive CAD compared with CA in patients with a high prevalence of the disease.

Future technical developments that increase spatial and temporal resolution whilst reducing artefacts may further improve the diagnostic performance of stress perfusion CMR, and in particular improve its specificity [32]. Currently, however, the low NLR makes stress perfusion CMR particularly accurate and useful in ruling out significant CAD.

Declarations

Authors’ Affiliations

(1)
Department of Radiology, University Hospital of Caen
(2)
INSERM 919, Cyceron
(3)
CNRS UMR 6072, Greyc Laboratory
(4)
Department of Cardiology, University Hospital of Caen
(5)
Department of Statistics, University Hospital of Caen
(6)
INSERM 744, Institut Pasteur de Lille

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