What is included in the initial evaluation of myocardial ischemia?

Updated: Aug 07, 2019
  • Author: Thomas F Heston, MD, FAAFP, FASNC, FACNM; Chief Editor: Eugene C Lin, MD  more...
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Myocardial ischemia is a major cause of morbidity and mortality worldwide. Because the condition is often treatable, the early diagnosis of CAD is of great importance.

To determine the proper workup for patients with chest pain or other signs indicative of myocardial ischemia, an understanding of risk stratification is necessary. A rapid way to stratify patients with chest pain is to classify their pain as nonanginal, atypical, or typical on the basis of the Diamond and Forrester criteria. Patients with a low risk of myocardial ischemia may simply be treated with medical therapies or an exercise treadmill test. Patients with an intermediate-to-high risk need an imaging study in addition to the stress test. Nuclear myocardial scanning is usually the preferred imaging study because of its proven and powerful risk-stratification capability. In patients with normal findings on nuclear myocardial scans, the risk of myocardial infarction or cardiac death is less than 1% per year for as long as 5 years.

Indications for nuclear myocardial scanning include diagnosing CAD, risk stratification, and the evaluation of response to therapy. Patients unable to perform an exercise stress test because of medical conditions, such as severe osteoarthritis, are also excellent candidates for scanning.

Myocardial perfusion imaging has been standardized worldwide, and established protocols must be followed rigorously. Protocols are readily available online from the Society of Nuclear Medicine and the ASNC. [4, 45, 6, 47] SPECT and gated imaging should always be included whenever feasible.

Quality control is essential in nuclear scanning because of the highly technical nature of the studies. This ranges from proper camera selection to standardized report generation. Daily, weekly, and monthly quality control must be performed on the camera, as recommended by the manufacturer. Viewing raw cinematic data and reconstructed sections on the computer monitor by the interpreting clinician is essential, as is customizing the report to answer the specific question asked by the referring clinician.

New technologies, protocols, and quantifying methods have significantly improved the diagnostic performance and prognostic value of nuclear cardiology imaging. Automation, artificial intelligence, and machine learning will increase performance even further. [8]  Machine learning algorithms can complement nuclear cardiology analyses packages and reporting software, with data being derived to calculate risk estimates factored in decision support tools (DSTs). [6, 48]

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