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Myocardial perfusion scintigraphy and cost effectiveness of diagnosis and management of coronary heart disease
  1. S R Underwood1,
  2. L J Shaw2
  1. 1Imperial College, Royal Brompton Hospital, London, UK
  2. 2Atlanta Cardiovascular Research Institute, Atlanta, Georgia, USA
  1. Correspondence to:
    Professor S Richard Underwood
    Imperial College London, Royal Brompton Hospital, Sydney Street, London SW3 6NP, UK;

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Myocardial perfusion scintigraphy (MPS) is a well validated, non-invasive imaging technique that has a valuable role in the diagnosis, management, and assessment of prognosis of coronary heart disease (CHD). The diagnostic accuracy of MPS allows reliable risk stratification and guides the selection of patients for further interventions, such as revascularisation. MPS also has particular advantages over alternative techniques in the management of a number of patient subgroups, including women, the elderly, and those with diabetes. Increased use of MPS will improve patient outcomes and be associated with greater cost effectiveness of treatment, in terms of life-years saved, particularly in these special patient groups. Currently, however, access to MPS in the UK is limited, with inappropriately long waiting times, and MPS activity levels fall short of estimated need.


Principles of cost effectiveness

Several principles underlie why a more accurate diagnostic test with additional prognostic information, such as MPS, can be more cost effective even if it is more expensive than an alternative test such as the exercise ECG (table 1).1

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Table 1

 Principles of cost effective diagnosis and management of coronary heart disease using myocardial perfusion scintigraphy

For example, fig 1 shows that when a patient with presenting likelihood of CHD of 50% has an abnormal exercise ECG the post-test likelihood of disease is 73%, which is not sufficiently high to be confident of the diagnosis. A subsequent abnormal MPS gives a likelihood of 96%, but if the same patient had gone directly to MPS the post-test likelihood would have been 90%, which should be sufficiently high to diagnose the presence of CHD depending upon the clinical circumstances.

Figure 1

 Pre-and post-test likelihood of CHD calculated using Bayesian principles for the exercise ECG and MPS, using sensitivities of 68% and 92%, respectively, and specificities of 77% and 88%, respectively. The curved lines from top to bottom represent MPS+, …

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