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Conditional probability in the diagnosis of coronary artery disease: a future tool for eliminating unnecessary
Insights
Predicting normal coronary arteriograms using conditional probability can reduce unnecessary procedures. A 20% probability cutoff effectively identified patients unlikely to have coronary artery disease (CAD), avoiding invasive tests.
Area of Science:
- Cardiology
- Medical Diagnostics
- Health Informatics
Background:
- Coronary arteriography is a common diagnostic tool for coronary artery disease (CAD).
- A significant percentage (approximately 30%) of annual coronary arteriograms in the US yield normal results, indicating potential for unnecessary procedures.
Purpose of the Study:
- To evaluate the utility of conditional probability in predicting normal coronary arteriogram results.
- To assess if a computer program (CADENZA) could aid in identifying patients unlikely to have CAD, thereby optimizing diagnostic strategies.
Main Methods:
- Retrospective assessment of 96 patients presenting with chest pain but no prior myocardial infarction.
- Calculation of coronary artery disease (CAD) probability using the CADENZA computer program, integrating data from patient history, exercise electrocardiography, and thallium-201 scintigraphy.
- Comparison of pre-angiogram probabilities with definitive coronary arteriography results, testing various probability cutoff points.
Main Results:
- A pre-angiogram probability cutoff of 20% demonstrated the best performance in distinguishing between normal and abnormal angiograms.
- Applying this 20% cutoff could have advised against angiography in 38 of 42 patients with normal angiograms.
- This approach would have missed only two of the 54 patients subsequently diagnosed with CAD.
Conclusions:
- Conditional probability, as calculated by the CADENZA program, can effectively predict normal coronary arteriograms.
- Utilizing this predictive capability can help avoid unnecessary invasive procedures and redundant noninvasive testing.
- This strategy has the potential to improve the cost-effectiveness and safety of the coronary artery disease diagnostic process.
Abstract:
Approximately 30% of the 300,000 coronary arteriograms done annually in the United States yield normal results. To see if the use of conditional probability in the diagnosis of coronary artery disease (CAD) could aid in predicting these normal results, we retrospectively assessed 96 patients with chest pain but without prior myocardial infarction. The probability of CAD was calculated by a computer program (CADENZA) using data from the history, exercise electrocardiography, and thallium-201 scintigraphy. All patients had coronary arteriography for definitive diagnosis. Based on preangiogram probabilities, different cutoff points were used to separate patients with angiograms likely to be normal from those likely to be abnormal. A preangiogram probability of 20% appeared to be the best separator. If the computer program, with this cutoff point, had been used to influence the decision to perform angiography, 38 of the 42 patients with negative angiograms would have been advised against it, and only two of the 54 patients who later were found to have CAD would have been missed. In view of the costs and risks of the CAD diagnostic process, the ability to identify the likelihood of positive angiograms could be useful in planning testing strategy, both in terms of avoiding "unnecessary" angiograms and avoiding redundant noninvasive procedures.