1741-7015-5-35 1741-7015 Research article <p>Validity of electron beam computed tomography for coronary artery disease: asystematic review and meta-analysis</p> Dendukuri Nandini nandini.dendukuri@mcgill.ca Chiu Keith keith_chiu@hotmail.com Brophy M James james.brophy@mcgill.ca

Technology Assessment Unit, McGill University Health Center, 687 Pine Avenue West R4.09, Montreal, PQ, H3A 1A1 Canada

Queen Elizabeth Hospital, University Hospital Birmingham NHS Foundation Trust, Birmingham, West Midlands B15 2TH, UK

 BMC Medicine 1741-7015 2007 5 1 35 http://www.biomedcentral.com/1741-7015/5/35 18036252 10.1186/1741-7015-5-35 13 7 2007 25 11 2007 25 11 2007 2007 Dendukuri et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

Electron beam computed tomography (EBCT) is a method for measuring coronary calcification and has been promoted as a possible non-invasive screening/diagnostic tool for coronary artery disease (CAD). Our objective was to carry out a systematic review and meta-analysis of EBCT for the screening of asymptomatic patients and the diagnosis of symptomatic patients for CAD.

Methods

Studies were identified from the PUBMED, MEDLINE, EMBASE, Current Contents, INAHTA and Cochrane Collaboration databases. We identified studies published in English evaluating EBCT using: (1) a prospective design among asymptomatic patients where CAD was measured in terms of clinical outcomes (e.g. myocardial infarction, death, revascularization); and (2)a cross-sectional design among symptomatic patients where CAD was measured by coronary angiography. We compared the risk of CAD in EBCT score categories defined as low (0–10), moderate (11–400) and high (>400). A hierarchical meta-analysis was used to pool risk ratios comparing categories across studies.

Results

We identified 9 studies of asymptomatic patients and 10 studies of symptomatic patients. In both types of studies, we found variability in EBCT category distribution and risk of CAD within categories. For studies of asymptomatic patients we estimated the following risk ratios (95% credible intervals): moderate versus low 3.5 (2.4, 5.1) and high versus low 9.9 (5.3, 17.6). Similar results were obtained for studies of symptomatic patients. Ratios comparing the risk of no CAD among symptomatic patients were as follows: moderate versus low 0.5 (0.3, 0.8) and high versus low 0.12 (0.05, 0.2).

Conclusion

Increasing EBCT scores indicate higher risk for CAD in both asymptomatic and symptomatic patients. In general, asymptomatic patients with EBCT scores in the high category can perhaps be considered for preventive medical therapy and risk factor modification. Symptomatic patients with EBCT scores in the low category can perhaps, at least temporarily, avoid invasive coronary angiography. However, the non-uniform quality of studies and the lack of availability of individual-level data preclude the extension of our results to individual patients.

Background

Despite tremendous advances in the prevention and treatment of coronary artery disease (CAD), diagnosis and prognosis remain difficult issues. The measurement of coronary calcium deposits has been proposed as a new non-invasive diagnostic tool. Calcium deposition can be quantified non-invasively at a very early stage by electron beam computed tomography (EBCT) using the Agatston method 1. EBCT scanners are not as versatile as multidetector slice computed tomography (MDCT), but their technological simplicity without moving parts permits more rapid examinations at lower costs 2. While MDCT is also widely used for the assessment of coronary calcium, the current article focuses on evaluating EBCT. A more detailed examination of EBCT versus MDCT technology appears in a recent scientific statement from the American Heart Association 2.

While the measurement of coronary calcification using EBCT has emerged as a promising screening and diagnostic tool for CAD, there is concern about widespread dissemination of this technology into routine clinical practice before adequate evaluation. Herein we provide a systematic review of the literature on the efficacy of EBCT with separate analyses for both asymptomatic and symptomatic patients. We improve upon previous meta-analyses by: (1) updating previous conclusions with results of recent articles; (2)providing quantitative support for guidelines defining low, moderate and high EBCT scores 3 for both asymptomatic and symptomatic patients; and (3) providing risk ratios for comparing both positive and negative predictive values between low, moderate and high EBCT score categories.

Methods

Data sources and searches

We searched the following electronic literature databases: PUBMED, MEDLINE, EMBASE, Current Contents, INAHTA and Cochrane Collaboration. Search terms were 'electron beam tomography' OR 'electron beam' OR 'EBT' OR 'EBCT' OR 'ultrafast' AND 'coronary artery disease' OR 'coronary blood vessel' OR 'coronary' AND 'calcification' OR 'calcium'. Bibliographies of identified articles were searched further. We included studies that were published before 31 July 2006.

Study selection, data extraction and quality assessment

We required that studies: (1) were published in English; (2) recruited consecutive patients; (3) followed a prospective design for studies of asymptomatic patients; and (4)were designed such that both EBCT and coronary angiography were carried out within 3 months in studies of symptomatic patients. From each study we extracted details of the method of recruitment, inclusion/exclusion criteria, length of follow-up and percentage of completed follow-up (for prospective studies), details of the EBCT protocol, EBCT categories, distribution of patients across EBCT categories, outcome definition, percentage of patients with the outcome of interest in each EBCT category and aggregate results on covariates such as age, sex, smoking, diabetes, hypertension, hypercholesterolemia and history of CAD. Data were extracted by two of the authors (KC and ND). Relevant items from the Standards for Reporting of Diagnostic Accuracy (STARD) guidelines were used to evaluate the quality of individual articles 4.

Data synthesis and analysis

Defining categories on the EBCT scale

There are no standardized EBCT cut-offs, which makes comparative analyses difficult. One guideline 3 has suggested the following cut-offs: 0 (very low), 1–10 (low), 11–100 (moderate), 101–400 (moderately high), >400 (high). For each selected study, we elected to transform the reported categories as closely as possible into one of three standardized categories: low (0–10), moderate (11–400) and high (>400). The lowest and highest categories in a given study were always classified as low and high, respectively. When there was uncertainty about the EBCT classification we carried out sensitivity analyses by placing the category into the adjacent group.

Hierarchical meta-analysis comparing predictive values between EBCT categories

For each category we calculated the probability of the outcome (positive predictive value) and the probability of the absence of the outcome (negative predictive value). Given the variability in predictive values across studies, we decided that only the ratios comparing predictive values between categories could reasonably be pooled across studies. This was done by means of a hierarchical meta-analysis 5. A separate model was fit for each pair of categories that were compared. We chose to compare categories using risk ratios, rather than the commonly used odds ratio, for greater interpretability 5. Non-informative prior distributions were used for all parameters, thus the results reflect the information in the observed data. We reported the posterior median and 95% credible intervals for the parameters of interest. The models were implemented using WinBUGS software. A copy of the program is available from the first author upon request.

Results

The results of the literature search are summarized in Figure 1. From 745 initial studies, we identified 9 prospective studies of asymptomatic patients 67891011121314 and 10 cross-sectional studies of symptomatic patients 15161718192021222324.

<p>Figure 1</p>

Flowchart summarizing study selection

Flowchart summarizing study selection.

Quality of individual studies

Tables 1 and 2 summarize the quality of each study according to relevant criteria from the STARD guidelines 4. Most studies of asymptomatic patients excluded patients with a history of heart disease or with a suspected myocardial infarction (MI). However, there was variability in the source population: in some studies patients were self-referred, while in others they were referred by a physician or identified during a routine annual examination. These studies reported the risk of hard outcomes (unstable angina, MI, stroke, coronary death, all-cause mortality) and sometimes also included the softer cardiac outcome of coronary revascularization (Table 3) 1411. Most studies of symptomatic patients recruited patients with MI or suspected MI, who would normally be considered candidates for angiography. All of these studies defined coronary disease as the presence of at least 50% coronary stenosis based on angiographic findings. A summary of the demographic and clinical characteristics from the selected studies is given in Table 4. There was no clear difference in the distribution of age, sex, smoking and history of CAD among studies of asymptomatic and symptomatic patients based on the reported aggregate data. The percentage of patients with hypertension and diabetes was somewhat higher in studies of symptomatic patients compared with asymptomatic patients.

<p>Table 1</p>

Quality of studies of asymptomatic subjects

Item

Value

Wong et al. [6]

Arad et al. [11]

Raggi et al. [7]

Kondos et al. [8]

Shaw et al. [9]

Greenland et al. [10]

Taylor et al. [12]

Vliegenthart et al. [13]

LaMonte et al. [14]

Patient recruitment

1: Self referred

2

NR

2

1

2

1

3

3

2

2: Clinician referred

3: Population sample

Exclusion criteria

1: Did not exclude history of heart disease

2

2

NR

2

2

2

2

2

2

2: Excluded history of heart disease

Outcome defined

1: All cause mortality

2

2

3

3

1

3

3

2

2

2: Coronary outcomes* and revascularization

3: Coronary outcomes* only

EBCT categories

1: Quartiles or pre-selected cut-offs

1

1

2

1

1

1

1

1

1

2: Age – sex adjusted

Outcome extraction blinded to EBCT score

1: No

2

2

2

2

2

2

2

2

2

2: Yes

Demographic characteristics, risk factors

NR: Not reported

1

1

1

1

1

1

1

1

1

1: Reported (see Table 3)

Percentage follow-up

NR: Not reported

NR

1

1

1

1

1

1

1

1

1: Yes (see Table 4)

Adjusted results available

1: Yes but cannot be used in meta-analysis

1

1

3

2

3

3

2

3

3

2: Yes at single cut-off and can be used in meta-analysis.

3: Yes at multiple cut-offs and can be used in meta-analysis

Total

Maximum: 17

11

11

14

13

13

14

15

15

14

*Coronary outcomes: angina, myocardial infarction, coronary death.

 <p>Table 2</p>

Quality of studies of symptomatic subjects

Item

Value

Budoff et al. [15]

Baumgart et al. [16]

Seese et al. [17]

Yao et al. [18]

Chen et al. [19]

Bielak et al. [20]

Hosoi et al. [21]

Budoff et al. [22]

Almeda et al. [23]

Knez et al. [24]

Patient recruitment

NR: Not reported

1

1

1

1

1

1

1

1

NR

1

1: Undergoing CAG for clinical indications

Exclusion criteria

NR: Not reported

NR

1

NR

NR

1

1

NR

1

NR

NR

1: History of CAD/revascularization

Outcome defined

1: No

2

2

2

2

2

2

2

2

2

2

2: Yes

EBCT categories

1: Quartiles or pre-selected cut-offs

1

1

1

1

1

1

1

1

1

1

2: Age – sex adjusted

Outcome measurement blinded to EBCT score

1: No

NR

2

NR

NR

2

NR

2

2

NR

2

2: Yes

Demographic characteristics

NR: Not reported

1

1

1

NR

1

1

1

1

1

1

1: Reported

Risk factors

NR : Not reported

NR

NR

NR

NR

1

1

1

NR

1

1

1: Reported (see Table 3)

Adjusted results available

1: Yes but cannot be used in meta-analysis

1

NR

NR

NR

2

3

1

2

2

NR

2: Yes at single cut-off and can be used in meta-analysis.

3: Yes at multiple cut-offs and can be used in meta-analysis

Total

Maximum: 13

6

8

5

4

11

10

9

10

7

8

 <p>Table 3</p>

Definition of outcomes and length of follow-up in studies of asymptomatic subjects

Study

Definition of outcome*

Mean length of follow-up (years)

Follow-up completed (%)

1. Wong et al. [6]

MI, stroke, revascularization

3.3

---

2. Raggi et al. [7]

MI, sudden cardiac death or death due to MI

2.7

100%

3. Kondos et al. [8]

Death (owing to CHD or unknown cause), MI

3.1

64%

4. Shaw et al. [9]

All-cause mortality

5

100%

5. Greenland et al. [10]

MI or CHD death

7.0

87.5%

6. Arad et al. [11]

Coronary death, non-fatal MI, CABG, PTCA

4.3

94%

7. Taylor et al. [12]

Sudden cardiac death, MI, unstable angina

3.0

99.2%

8. Vliegenthart et al. [13]

Incident MI, CHD mortality, revascularization

3.3

~100%

9. LaMonte et al. [14]

Non-fatal MI or death from coronary causes, coronary revascularization

3.5

70%

*CABG, coronary artery bypass graft; CHD, coronary heart disease; MI, myocardial infarction; PTCA, percutaneous transluminal coronary angioplasty.

 <p>Table 4</p>

Summary of demographic characteristics and risk factors

Study

Age mean (SD) (years)

Male (%)

Smokers (%)

Hypertension (%)

Diabetes (%)

History of CAD* (%)

Studies of asymptomatic subjects

1. Wong et al. [6]

64 (--)

76

24

26

5

---

2. Raggi et al. [7]

52 (9)

50

65

52

15

47

3. Kondos et al. [8]

51 (9)

74

48

20

3

---

4. Shaw et al. [9]

53 (0.1)

60

40

44

9

69

5. Greenland et al. [10]

66 (8)

90

18

41

-

---

6. Arad et al. [11]

53 (11)

71

10

34

6

21

7. Taylor et al. [12]

43 (3)

82

8

29

1

32

8. Vliegenthart et al. [13]

71 (6)

43

16

60

12

19

9. LaMonte et al. [14]

54 (10)

64

9

18

3

---

Studies of symptomatic subjects

1. Budoff et al. [15]

56 (12)

64

---

---

---

---

2. Baumgart et al. [16]

54 (9)

79

---

---

---

---

3. Seese et al. [17]

55 (8)

87

---

---

---

---

4. Yao et al. [18]

---

---

---

---

---

---

5. Chen et al. [19]

66 (10)

85

53

59

16

32

6. Bielak et al. [20]

56 (11)

76

62

45

13

51

7. Hosoi et al. [21]

63 (-)

67

---

56

36

---

8. Budoff et al. [22]

58 (11)

63

---

---

---

---

9. Almeda et al. [23]

60

78

14

51

18

57

10. Knez et al. [24]

62 (10)

78

23

66

22

---

*CAD, coronary artery disease.

EBCT protocol

Eighteen studies followed the Agatston method for scoring; only one study followed the Erlanger method for scoring 17. While all studies used a threshold of greater than 130 Hounsfield units to identify a calcified lesion, there was wide variability in the minimum area in which the signal had to be observed ranging from 0.44 to 1.02 mm2. The width of each slice was 3 mm in all studies except one, which used 6 mm slices10. The scan time per rotation was 100 ms in all studies. The percentage of the R-R interval to which the scan acquisition trigger was set was typically 80%.

Results from prospective studies of asymptomatic patients

Distribution of EBCT categories and risk of outcome

The distributions of EBCT scores as reported in each study are given in Table 5 along with the summary categorization from low to high. The cut-offs reported by individual studies varied greatly, although most studies reported the number of patients with a calcium score of zero. The probability of the outcome increased with increasing EBCT score in all studies (Figure 2 and Table 5). Given the considerable variation between studies in the risk of developing CAD (see Table 5), we concluded that it was not clinically meaningful to pool predictive values across studies. We concentrated instead on pooling the ratios of moderate- or high-risk categories to the baseline low category from each study.

<p>Table 5</p>

EBCT score distribution and positive predicted values in studies of asymptomatic patients

Reported classification

Simplified classification

Study

EBCT category

Number (%) of patients

Number (%) with outcome

EBCT Category

Number (%) of patients

Number (%) with outcome

1. Wong et al. [6]

0

398 (42.9)

4 (1.0)

Low

1–15

133 (14.3)

1 (0.8)

Low

531 (57.2)

5 (0.1)

16–80

134 (14.4)

5 (3.7)

Moderate

81–270

131 (14.1)

7 (5.3)

Moderate

265 (28.6)

12 (4.5)

≥271

132 (14.2)

11 (8.3)

High

132 (14.2)

11 (8.3)

Total

928

28

2. Raggi et al. [7]

0

292 (46.2)

1 (0.3)

Low

292 (46.2)

1 (0.3)

1–99

219 (34.7)

12 (5.5)

Moderate

100–400

74 (11.7)

8 (10.8)

Moderate

293 (46.3)

20 (6.8)

>400

47 (7.4)

6 (12.8)

High

47 (7.4)

6 (7.4)

Total

632

27

3. Kondos et al. [8]

0

1816 (32.2)

5 (0.3)

Low

1816 (32.2)

5 (0.3)

>0

3819 (67.8)

53 (1.4)

Moderate/high

3819 (67.8)

53 (1.4)

Total

5635

58

4. Shaw et al. [9]

≤10

5946 (57.3)

62 (1.0)

Low

5946 (57.3)

62 (1.0)

11–100

2044 (19.7)

53 (3.0)

Moderate

101–400

1432 (13.8)

54 (3.8)

Moderate

3476 (33.5)

107 (2.9)

401–1000

623 (6.0)

39 (6.3)

High

>1000

332 (3.2)

41 (12.4)

High

955 (9.2)

80 (8.4)

Total

10377

249

5. Greenland et al. [10]

0

316 (30.7)

14 (4.4)

Low

316 (30.7)

14 (4.4)

1–100

321 (31.2)

21 (6.5)

Moderate

101–300

171 (16.6)

15 (8.8)

Moderate

492 (47.8)

36 (7.3)

≥301

221 (21.5)

34 (15.4)

High

221 (21.5)

34 (15.4)

Total

1029

84

6. Arad et al. [11]

0

1504 (25)

8 (0.5)

Low

1504 (25.0)

8 (0.5)

1–99

1973 (25)

20 (1)

Moderate

100–399

686 (25)

38 (5.5)

Moderate

2659 (50.0)

58 (2.2)

≥400

450 (25)

63 (14)

High

450 (25.0)

63 (14.0)

Total

4613

129

7. Taylor et al. [12]

0

1261 (77.6)

7 (0.6)

Low

1381 (85.0)

7 (0.6)

1–9

120 (7.4)

0 (0.0)

Moderate

10–44

120 (7.4)

2 (1.7)

Moderate

120 (7.4)

2 (1.7)

>44

124 (7.6)

5 (4.0)

Moderate/high

124 (7.6)

5 (4.0)

Total

1625

14

8. Vliegenthart et al. [13]

0–100

905 (50.4)

7 (0.8)

Low

905 (50.4)

7 (0.8)

101–400

425 (23.7)

13 (3.1)

Moderate

425 (23.7)

13 (3.1)

401–1000

269 (15.0)

13 (4.5)

High

>1000

196 (10.9)

17 (8.7)

High

365 (25.9)

30 (8.2)

Total

1795

50

9. LaMonte et al. [14]

0

5472 (50.9)

15 (0.3)

Low

5472 (50.9)

15 (0.3)

1–38 (Men), 1–16 (Women)

1760 (16.4)

19 (1.1)

Moderate

39–249 (Men), 17–112 (Women)

1758 (16.4)

62 (3.5)

Moderate

3518 (32.8)

81 (2.3)

≥250(Men), ≥113 (Women)

1756 (16.3)

191 (10.9)

High

1756 (16.3)

191 (10.9)

Total

10746

287

*CAD, coronary artery disease.

 <p>Figure 2</p>

Distribution of positive predictive values across EBCT score categories

Distribution of positive predictive values across EBCT score categories.

Meta-analysis

A forest plot of the individual and overall risk ratios is given in Figure 3. The pooled risk ratios comparing positive predictive values across EBCT categories were statistically significant. A forest plot of the risk ratios of the negative predicted values is given in Figure 4. The overall risk ratios comparing the three categories were all close to 1, indicating that despite the fact that the negative predictive values were high the EBCT categorization was not very useful for identifying patients unlikely to have CAD.

<p>Figure 3</p>

Forest plots from meta-analyses of ratios of positive predictive values among asymptomatic subjects

Forest plots from meta-analyses of ratios of positive predictive values among asymptomatic subjects.

 <p>Figure 4</p>

Forest plots from meta-analyses of ratios of negative predictive values among asymptomatic subjects

Forest plots from meta-analyses of ratios of negative predictive values among asymptomatic subjects.

Results from cross-sectional studies of symptomatic patients

Distribution of EBCT categories and risk of outcome

The distribution of EBCT scores in each study is given in Table 6 along with the summary categorization from low to high. As in the case of studies of asymptomatic patients, there was variation in the reported cut-offs, the distribution of EBCT scores and the risk of CAD in each EBCT category. The negative predictive value (probability of no CAD) corresponding to a calcium score of zero ranged from 0.58 to 0.92 in the individual studies.

<p>Table 6</p>

EBCT score distribution and positive predictive values in studies of symptomatic subjects

Reported classification

Simplified classification

Study

EBCT category

Number (%) of patients

Number (%) with CAD

EBCT category

Number (%) of patients

Number (%) with CAD

Budoff et al. [15]

0

147 (20.7)

23 (15.6)

Low

147 (20.7)

23 (15.6)

>0

563 (79.3)

404 (71.8)

Moderate/high

563 (79.3)

404 (71.8)

Total

710

427

Baumgart et al. [16]

0

32 (56.1)

10 (31.3)

Low

32 (56.1)

10 (31.3)

>0

25 (43.9)

19 (76.0)

Moderate/high

25 (43.9)

19 (76.0)

Total

57

29

Seese et al. [17]

0

22 (20.6)

4 (18.2)

Low

22 (20.6)

4 (18.2)

>0

85 (79.4)

83 (97.7)

Moderate/high

85 (79.4)

83 (97.7)

Total

107

87

Yao et al. [18]

0

26 (40.1)

11 (42.3)

Low

26 (40.1)

11 (42.3)

>0

38 (59.9)

34 (89.5)

Moderate/high

38 (59.9)

34 (89.5)

Total

64

45

Bielak et al. [20]

0

40 (18.8)

1 (2.5)

Low

1–9

31 (14.6)

8 (25.8)

Low

71 (33.3)

9 (12.7)

10–49

25 (11.7)

6 (24.0)

Moderate

50–99

13 (6.1)

7 (53.8)

Moderate

100–199

24 (11.3)

17 (70.8)

Moderate

200–499

25 (11.7)

21 (84.0)

Moderate?

87 (40.8)

51 (58.6)

≥500

55 (25.8)

52 (94.5)

High

55 (25.8)

52 (94.5)

Total

213

112

Chen et al. [19]

0–5

22 (19.0)

2 (9.1)

Low

22 (19.0)

2 (9.1)

6–75

17 (14.7)

3 (17.7)

Moderate

76–500

29 (15.0)

24 (82.8)

Moderate?

46 (29.7)

27 (58.7)

>500

48 (41.4)

45 (93.8)

High

48 (41.4)

45 (93.8)

Total

116

74

Hosoi et al. [21]

0

36 (12.8)

7 (19.4)

Low

74 (26.3)

29 (39.2)

1–10

38 (13.5)

22 (57.9)

Low

11–100

59 (20.9)

39 (66.1)

Moderate

101–399

53 (18.8)

41 (77.4)

Moderate

112 (39.7)

80 (71.4)

>399

96 (34)

93 (96.9)

High

96 (34)

93 (96.9)

Total

282

202

Budoff et al. [22]

0

386 (20.9)

39 (10.1)

Low

386 (20.9)

39 (10.1)

1–20

216 (11.7)

60 (27.8)

Moderate?

21–80

230 (12.4)

108 (47.0)

Moderate

81–100

55 (3.0)

29 (52.7)

Moderate

501 (27.1)

197 (39.3)

>100

964 (52.1)

747 (77.5)

High

964 (52.1)

747 (77.5)

Total

1851

983

Almeda et al. [23]

0

26 (10.6)

2 (7.7)

Low

26 (10.6)

2 (7.7)

<100

28 (11.4)

14 (50.0)

Moderate

100–399

71 (28.9)

39 (54.9)

Moderate

99 (40.3)

53 (53.5)

≥400

121 (49.2)

105 (86.8)

High

121 (49.2)

105 (86.8)

Total

246

160

Knez et al. [24]

0

254 (12.0)

13 (5.1)

Low

1–9

427 (20.2)

75 (17.6)

Low

681 (32.2)

88 (12.9)

10–99

211 (10.0)

150 (71.1)

Moderate

211 (10.0)

150 (71.1)

>99

1223 (57.8)

1017 (83.2)

High

1223 (57.8)

1017 (83.2)

Total

2115

1255

*CAD, coronary artery disease; EBCT, electron beam computed tomography.

**Here '?' denotes that the simplified EBCT category could be the adjacent category.

Meta-analysis

The results of the meta-analyses are presented in Figures 5 and 6. Once again there was a statistically significant difference in the positive predictive values in the low category compared with the other two categories. In particular, the pooled risk ratios suggest that a symptomatic subject with a low score has approximately one-quarter of the risk of having significant angiographic coronary stenosis than the moderate/high categories. This would mean that if the average patient in the moderate/high category had an 80% risk of CAD, the average patient in the low category would have a 20% risk of CAD. The overall risk ratios comparing negative predictive values were significantly different from 1. In particular, negative predictive values in the low category were much higher than in the other two categories suggesting that a symptomatic subject with a low score has a very small likelihood of having coronary stenosis. For three studies 1922 there was ambiguity in determining the simplified classification of the EBCT score category. However, repeating the meta-analysis following a reclassification of these categories did not affect our final results.

<p>Figure 5</p>

Forest plots from meta-analyses of ratios of positive predictive values among symptomatic subjects

Forest plots from meta-analyses of ratios of positive predictive values among symptomatic subjects.

 <p>Figure 6</p>

Forest plots from meta-analyses of ratios of negative predictive values among symptomatic subjects

Forest plots from meta-analyses of ratios of negative predictive values among symptomatic subjects.

Discussion

We have carried out a systematic review and meta-analysis of the published literature on the screening and diagnosis of CAD using EBCT. We identified two different types of studies: (1) 9 prospective studies of asymptomatic patients evaluating the predictive validity of EBCT for a mixture of hard outcomes (unstable angina, MI, stroke, coronary death, all-cause mortality) and revascularization; and (2) 10 cross-sectional studies of symptomatic patients evaluating the concurrent validity of EBCT for coronary stenosis as measured by coronary angiography. The risk of coronary events or angiographically confirmed CAD increased consistently with increasing EBCT scores in both types of studies.

Our results provide quantitative support for the cut-offs proposed by Rumberger et al. 3 to determine low-, moderate- and high-risk categories of EBCT scores. These cut-offs have since been used by a number of studies for reporting the validity of EBCT 911132123. Similar cut-offs have been used in two earlier meta-analyses of studies of asymptomatic patients, including the recently published ACCF/AHA Clinical Expert Consensus Document 2526. These cut-offs were determined based on a qualitative review of the literature. For example, an EBCT score of 0 was assumed to be associated with a risk of 5% or less of CAD, while a score of 400 or more was assumed to be associated with a risk of 90% or more of CAD. Based on the studies we have identified, these assumptions seem to be reasonable estimates of the risk of coronary stenosis in symptomatic patients, but not of prognostic risk in asymptomatic patients. Furthermore, we have found that even among studies of symptomatic patients there is considerable variability in these values. Nonetheless, our analyses have shown that the cut-offs proposed by Rumberger et al. 3 are clinically meaningful for classifying the aggregate patient into categories associated with a monotonically increasing risk of an adverse cardiac outcome. For asymptomatic patients this difference in risk is statistically significantly different between all categories, while in symptomatic patients the difference between moderate and high categories is only of borderline statistical significance. We found that among asymptomatic patients, negative predictive values in these same categories were very high independent of the category threshold, and thus did not help us to distinguish between categories. Among symptomatic patients, where the disease prevalence is naturally much higher, negative predictive values were lower. However, they were significantly higher in the low EBCT category compared with moderate or high categories.

We cannot comment on the utility of EBCT scores for individual risk stratification as our results were not adjusted for age, sex and other known risk factors. Therefore, while the proposed cut-offs 3 may be useful for making conclusions about the average patient they should not be used for individual risk stratification in light of our knowledge of the age-sex variation in coronary calcium scores 27. For the average patient, our results suggest that an asymptomatic patient with a high EBCT score may benefit from extensive follow-up possibly supported by intensive medical therapy. Similar conclusions apply to asymptomatic patients classified as having a moderate EBCT score. In symptomatic patients, it appears that the average patient with a low EBCT score may have only a small chance of having coronary stenosis compared with those with high or moderate scores. Thus, they could be further evaluated with non-invasive tests possibly avoiding angiography.

Compared with earlier meta-analyses of asymptomatic subjects 262825, we found four new articles 8131412 and cohorts with longer follow-up for three previously included articles 10911. A previous meta-analysis 25 extrapolated data for individual studies in order to make them comparable when the reported EBCT cut-offs were different from the values identified to define low, moderate and high categories. Instead, we chose to use categories that were actually reported and classified them as closely as possible into low, moderate and high. Also, in contrast to this earlier meta-analysis 25, we chose to combine the ratios of predictive values that had not been adjusted for CAD risk factors in order to permit a larger sample size for analysis. Although our ratios of positive predictive values are slightly higher than the values from this meta-analysis 25, they lead to similar conclusions. Similar results were also obtained in a meta-analysis of six studies of asymptomatic patients published recently in the ACCF/AHA Clinical Expert Consensus Document 26. Compared with the earlier meta-analysis of symptomatic subjects 29, we have identified one additional article 17 and added additional information in two studies 221. Unlike the previous analysis, we treated the results from one multi-centre trial as coming from a single study 15. Furthermore, we divided the 'calcium positive' scores into moderate and high categories. In our analyses of both types of studies we reported ratios comparing negative predictive values in addition to those comparing positive predictive values.

The large number of potentially relevant studies identified by our literature search reduced to 19 unique studies providing quantitative information that could be used to evaluate the utility of EBCT. Thus, despite the large number of publications about this technology, there is clearly a paucity of useful information for evaluating it. The main limitation of our study relates to the quality of the original publications and their deficiencies in following definitive standards for diagnostic publications. Combining information across the identified studies proved challenging given the lack of a standardized approach for reporting EBCT scores and the variability in recruitment methods/population across studies. Very few studies met more than 80% of the criteria identified by the STARD guidelines for measuring quality of reporting in diagnostic studies.

We were unable to separate results based on age or gender, both of which have been shown to have an important impact on the interpretation of EBCT scores. We were also unable to estimate the incremental value of EBCT beyond established risk factors. Both of these limitations were the result of the lack of access to individual-level data. For studies of asymptomatic patients we were unable to evaluate the change in risk over time in each category, as information on the time of occurrence of outcomes was not always available. Consequently, we were limited to a crude cross-sectional type analysis based only on the observed outcomes in each category at the end of each study. Also incomplete study follow-up may have also introduced bias. Finally, another possible limitation of our work may relate to our literature search where unpublished, grey literature and non-English articles were not considered. On the other hand, our search of the conventional electronic databases may be seen as thorough and systematic.

Conclusion

We conclude that increasing calcium scores are associated with increasing risk of CAD among both asymptomatic and symptomatic patients. In general, it appears that asymptomatic patients with a high EBCT score may benefit from preventive interventions such as medical therapy and risk factor modification. Similar conclusions apply to asymptomatic patients classified as having a moderate EBCT score. Among symptomatic patients, those with a low EBCT score could perhaps be further evaluated with non-invasive tests possibly avoiding angiography. However, the evidence in the literature does not allow us to draw conclusions about the value of this technology for individual patients, and therefore to justify its routine use. It is especially unclear what additional value EBCT scores provide to patients in different age-sex groups. We recommend that future studies of EBCT need to: (1) use standardized cut-offs to allow for comparability; (2) adjust for the age-sex distribution of EBCT scores in classifying individuals into risk categories; and (3) use survival analysis techniques while reporting data from prospective studies.

Abbreviations

AHA : American Heart Association;

CAD : Coronary artery disease;

CABG : Coronary artery bypass graft;

CHD : Coronary heart disease;

EBCT : Electron beam computed tomography;

MI : Myocardial infarction;

NR : Not reported;

PTCA : Percutaneous transluminal coronary angiography;

STARD : Standards for Reporting of Diagnostic Accuracy.

Authors' contributions

ND participated in the study design, literature search, extraction of data, data analysis, interpretation of results and writing of the manuscript. KC participated in the literature search, extraction of data and writing of the manuscript. JMB participated in the study design, interpretation of results and writing of the manuscript. All authors read and approved the final manuscript.

Acknowledgements

Supported by a research grant from the Canadian Association for Drugs and Technologies in Health (CADTH). ND and JMB are funded scholars of le Fonds de Recherché en Santé du Québec (FRSQ).

 <p>Long-term prognostic value of coronary calcification detected by electron-beam computed tomography in patients undergoing coronary angiography</p> Keelan PC Bielak LF Ashai K Jamjoum LS Denktas AE Rumberger JA Sheedy PF II Peyser PA Schwartz RS Circulation 2001 104 4 412 7 10.1161/hc2901.093112 11468202 <p>Assessment of coronary artery disease by cardiac computed tomography: a scientific statement from the American Heart Association Committee on Cardiovascular Imaging and Intervention, Council on Cardiovascular Radiology and Intervention, and Committee on Cardiac Imaging, Council on Clinical Cardiology</p> Budoff MJ Achenbach S Blumenthal RS Carr JJ Goldin JG Greenland P Guerci AD Lima JA Rader DJ Rubin GD Shaw LJ Wiegers SE Circulation 2006 114 16 1761 91 10.1161/CIRCULATIONAHA.106.178458 17015792 <p>Electron bearn computed tomography and coronary artery disease: scanning for coronary artery calcification</p> Rumberger JA Sheedy PF 2nd Breen JF Fitzpatrick LA Schwartz RS Mayo Clin Proc 1996 71 369 77 8637260 <p>Coronary calcification improves cardiovascular risk prediction in the elderly</p> Vliegenthart R Oudkerk M Hofman A Oei HH van Dijck W van Rooij FJ Witteman JC Circulation 2005 112 4 572 7 10.1161/CIRCULATIONAHA.104.488916 16009800 <p>Effect of eicosapentaenoic acid on restenosis rate, clinical course and blood lipids in patients after percutaneous transluminal coronary angioplasty</p> Nye ER Ablett MB Robertson MC Ilsley CD Sutherland WH Aust N Z J Med 1990 20 4 549 52 2222347 <p>Coronary artery calcium evaluation by electron beam computed tomography and its relation to new cardiovascular events</p> Wong ND Hsu JC Detrano RC Diamond G Eisenberg H Gardin JM Am J Cardiol 2000 86 5 495 8 10.1016/S0002-9149(00)01000-6 11009264 <p>Identification of patients at increased risk of first unheralded acute myocardial infarction by electron-beam computed tomography</p> Raggi P Callister TQ Cooil B He ZX Lippolis NJ Russo DJ Zelinger A Mahmarian JJ Circulation 2000 101 8 850 5 10694523 <p>Electron-beam tomography coronary artery calcium and cardiac events: a 37-month follow-up of 5635 initially asymptomatic low- to intermediate-risk adults</p> Kondos GT Hoff JA Sevrukov A Daviglus ML Garside DB Devries SS Chomka EV Liu K Circulation 2003 107 20 2571 6 10.1161/01.CIR.0000068341.61180.55 12743005 <p>Prognostic value of cardiac risk factors and coronary artery calcium screening for all-cause mortality</p> Shaw LJ Raggi P Schisterman E Berman DS Callister TQ Radiology 2003 228 3 826 33 10.1148/radiol.2283021006 12869688 <p>Coronary artery calcium score combined with Framingham score for risk prediction in asymptomatic individuals</p> Greenland P LaBree L Azen SP Doherty TM Detrano RC JAMA 2004 291 2 210 5 10.1001/jama.291.2.210 14722147 <p>Coronary calcification, coronary disease risk factors, C-reactive protein, and atherosclerotic cardiovascular disease events: the St. Francis Heart Study</p> Arad Y Goodman KJ Roth M Newstein D Guerci AD J Am Coll Cardiol 2005 46 1 158 65 10.1016/j.jacc.2005.02.088 15992651 <p>Coronary calcium independently predicts incident premature coronary heart disease over measured cardiovascular risk factors: mean three-year outcomes in the Prospective Army Coronary Calcium (PACC) project</p> Taylor AJ Bindeman J Feuerstein I Cao F Brazaitis M O'Malley PG J Am Coll Cardiol 2005 46 5 807 14 10.1016/j.jacc.2005.05.049 16139129 <p>Bayesian random effects meta-analysis of trials with binary outcomes: methods for the absolute risk difference and relative risk scales</p> Warn DE Thompson SG Spiegelhalter DJ Stat Med 2002 21 11 1601 23 10.1002/sim.1189 12111922 <p>Coronary artery calcium score and coronary heart disease events in a large cohort of asymptomatic men and women</p> LaMonte MJ FitzGerald SJ Church TS Barlow CE Radford NB Levine BD Pippin JJ Gibbons LW Blair SN Nichaman MZ Am J Epidemiol 2005 162 5 421 9 10.1093/aje/kwi228 16076829 <p>Ultrafast computed tomography as a diagnostic modality in the detection of coronary artery disease: a multicenter study</p> Budoff MJ Georgiou D Brody A Agatston AS Kennedy J Wolfkiel C Stanford W Shields P Lewis RJ Janowitz WR Rich S Brundage BH Circulation 1996 93 5 898 904 8598080 <p>Comparison of electron beam computed tomography with intracoronary ultrasound and coronary angiography for detection of coronary atherosclerosis</p> Baumgart D Schmermund A Goerge G Haude M Ge J Adamzik M Sehnert C Altmaier K Groenemeyer D Seibel R Erbel R J Am Coll Cardiol 1997 30 1 57 64 10.1016/S0735-1097(97)00147-2 9207621 <p>Possibilities of Electron Beam Tomography in Noninvasive Diagnosis of Coronary Artery Disease: A Comparison between Quantity of Coronary Calcification and Angiographic Findings</p> Seese Bernd Moshage Werner Achenbach Stephan Bachmann Kurt International Journal of Angiology 1997 6 124 9 10.1007/BF01616682 <p>A comparison of 99Tcm-MIBI myocardial SPET and electron beam computed tomography in the assessment of coronary artery disease in two different age groups</p> Yao Z Liu XJ Shi RF Dai R Zhang S Liu YZ Tian YQ Zhang XL Nucl Med Commun 2000 21 1 43 8 10.1097/00006231-200001000-00008 10717901 <p>Coronary artery calcium determined by electron beam computed tomography for predicting angiographic coronary artery disease in moderate- to high-risk Chinese patients</p> Chen LC Ding PY Chen JW Wu MH Liu JC Lan GY Chern MS Chang CY Chang MS Cardiology 2001 95 4 183 9 10.1159/000047370 11585993 <p>Probabilistic model for prediction of angiographically defined obstructive coronary artery disease using electron beam computed tomography calcium score strata</p> Bielak LF Rumberger JA Sheedy PF 2nd Schwartz RS Peyser PA Circulation 2000 102 4 380 5 10908208 <p>Impact of diabetes on coronary stenosis and coronary artery calcification detected by electron-beam computed tomography in symptomatic patients</p> Hosoi M Sato T Yamagami K Hasegawa T Yamakita T Miyamoto M Yoshioka K Yamamoto T Ishii T Tanaka S Itoh A Haze K Fujii S Diabetes Care 2002 25 4 696 701 10.2337/diacare.25.4.696 11919127 <p>Continuous probabilistic prediction of angiographically significant coronary artery disease using electron beam tomography</p> Budoff MJ Diamond GA Raggi P Arad Y Guerci AD Callister TQ Berman D Circulation 2002 105 15 1791 6 10.1161/01.CIR.0000014483.43921.8C 11956121 <p>Clinical and angiographic profile of patients with markedly elevated coronary calcium scores (> or = 1000) detected by electron beam computed tomography</p> Almeda FQ Shah R Senter S Kason TT Haynie J Calvin JE Kavinsky CJ Snell RJ Schaer GL McLaughlin VV Cardiovasc Radiat Med 2004 5 3 109 12 10.1016/j.carrad.2004.07.001 15721844 <p>Relation of coronary calcium scores by electron beam tomography to obstructive disease in 2,115 symptomatic patients</p> Knez A Becker A Leber A White C Becker CR Reiser MF Steinbeck G Boekstegers P Am J Cardiol 2004 93 9 1150 2 10.1016/j.amjcard.2004.01.044 15110209 <p>Using the coronary artery calcium score to predict coronary heart disease events: a systematic review and meta-analysis</p> Pletcher MJ Tice JA Pignone M Browner WS Arch Intern Med 2004 164 12 1285 92 10.1001/archinte.164.12.1285 15226161 <p>ACCF/AHA 2007 clinical expert consensus document on coronary artery calcium scoring by computed tomography in global cardiovascular risk assessment and in evaluation of patients with chest pain: a report of the American College of Cardiology Foundation Clinical Expert Consensus Task Force (ACCF/AHA Writing Committee to Update the 2000 Expert Consensus Document on Electron Beam Computed Tomography) developed in collaboration with the Society of Atherosclerosis Imaging and Prevention and the Society of Cardiovascular Computed Tomography</p> Greenland P Bonow RO Brundage BH Budoff MJ Eisenberg MJ Grundy SM Lauer MS Post WS Raggi P Redberg RF Rodgers GP Shaw LJ Taylor AJ Weintraub WS Harrington RA Abrams J Anderson JL Bates ER Eisenberg MJ Grines CL Hlatky MA Lichtenberg RC Lindner JR Pohost GM Schofield RS Shubrooks SJ Jr Stein JH Tracy CM Vogel RA Wesley DJ J Am Coll Cardiol 2007 49 3 378 402 10.1016/j.jacc.2006.10.001 17239724 <p>Age and gender distributions of coronary artery calcium detected by electron beam tomography in 35,246 adults</p> Hoff JA Chomka EV Krainik AJ Daviglus M Rich S Kondos GT Am J Cardiol 2001 87 12 1335 9 10.1016/S0002-9149(01)01548-X 11397349 <p>Prognostic value of coronary electron-beam computed tomography for coronary heart disease events in asymptomatic populations</p> O'Malley PG Taylor AJ Jackson JL Doherty TM Detrano RC Am J Cardiol 2000 85 8 945 8 10.1016/S0002-9149(99)00906-6 10760331 <p>Electron-beam computed tomography in the diagnosis of coronary artery disease: a meta-analysis</p> Nallamothu BK Saint S Bielak LF Sonnad SS Peyser PA Rubenfire M Fendrick AM Arch Intern Med 2001 161 6 833 8 10.1001/archinte.161.6.833 11268225

Pre-publication history

The pre-publication history for this paper can be accessed here:

http://www.biomedcentral.com/1741-7015/5/35/prepub