Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

The primary focus of this section is the economics of the test; however, it is useful to discuss the aspects of the assessment of pre-test probability for CAD first. As described in Protocol steps 2 and 3, the estimation of the pre-test probability of CAD is a critical step in contextualizing the CAD Score and determining the post-test probability of CAD. We have used the ESC 2013 guidelines, which base the probability of CAD on three characteristics: gender, age, and symptoms (within groups of typical angina, atypical, and non-anginal pain)⁷. However, it is important to note that the physician may apply their clinical judgment in the selection of any quantitative or qualitative method. Further, there are other published quantitative methods available for such estimation, including Diamond-Forrester¹³, which requires the same input characteristics as ESC 2013. More comprehensive variations have also been published, such as which extends¹⁴ Diamond-Forrester with estrogen status for females (to compensate for age variation in menopause, as well as any hysterectomy), as well as diabetes, hypertension, hyperlipidemia, smoking, family history of coronary disease, and obesity. However, be aware that some methodologies may define CAD at 50% luminal narrowing, whereas 70% represents the current ACC guidelines (barring the left main artery at 50%).

The economics of POC-CAD and how the test interacts with the care pathway represent crucial characteristics in determining whether the test is truly suitable for clinical use. For the diagnosis of CAD, the most often used test is MPI, often known as a nuclear stress test or SPECT. As of 2019, MPI was used in approximately 85% of assessments for CAD¹⁵. An estimated 7-8 million SPECT-MPI studies are performed in the USA annually¹⁶. In approximately 5% of patient cases, CCTA is used to diagnose CAD. CCTA has been recognized as having superior diagnostic performance in ruling out significant CAD. Two other imaging modalities, PET and cardiac MRI, make up the bulk of the remaining assessments. For all of these tests, an imaging camera and a qualified team are required to run and interpret the test. Such cameras are expensive pieces of capital equipment. Consequently, these tests are often limited in availability to urban healthcare settings, where there is sufficient volume to justify both the initial expense plus operating costs.

In 2021, the American College of Cardiology (ACC) published its guidelines for patients with no known CAD or former tests and with intermediate risk of CAD^2,17where both MPI and CCTA were recognized as front-line diagnostic options. In contrast to CCTA and MPI, POC-CAD may be used in any point-of-care setting, such as a physician's office, critical access hospital, tribal care center, and tertiary care centers, rather than being restricted, as CCTA and MPI are, to only those centers with expensive imaging equipment and the teams required to operate them.

Typically, patients who are suspected of CAD following a front-line test will be referred for ICA; as described, this is considered the gold standard diagnostic for CAD. ICA is an invasive procedure undertaken in an outpatient hospital setting by invasive cardiology specialists.

A recent study of an appointment attendance rate for radiological examinations at a large tertiary academic center showed that 24% of scheduled appointments were not attended by the patient due to either cancellation or no-show¹⁸. Radiological modalities studied included CAD diagnostics. The figure reported in that study aligns with a discussion with the financial officer of a healthcare system, who assumes a no-show rate of 20%. Further, in multiple discussions with rural physicians, where significant travel time may be required to reach the test, up to 50% of patients might not proceed to both make an appointment and attend the appointment. In the following model, conservatively for the rural market, this is assumed to be 30% (i.e., 70% attend the appointment).

There is a direct impact on the diagnostic yield of downstream tests where significant numbers of patients do not proceed to the recommended test. Assuming that the rate is consistent across individuals with and without significant coronary artery disease, the sensitivity of detecting the condition across the referred population would be 70% of the expected sensitivity. In the case of SPECT, which has a reported sensitivity for detecting functionally significant CAD of 73%, the effective sensitivity is just 53% (= 0.7 x 73%)¹², i.e., almost half of those patients in the referred population with functionally significant CAD would be missed. These missed patients do not cost the healthcare system zero. The positive patients with functionally significant CAD will likely be seen later and incur significant additional costs due to the progression of their disease. For example, if the patient subsequently presents at the emergency department, the average cost of the visit for a patient with signs of acute coronary syndrome has been calculated as $30,000¹⁹.

Furthermore, even for those patients without significant CAD, if they do not follow their physician's referral for imaging, they are less likely to return to that physician, and thus, there is limited opportunity to determine what else might be causing their symptoms. This can also be costly to both the patient, in terms of the progression of the underlying condition, and the healthcare system. For example, a delay in diagnosis of heart failure has been shown to add an incremental $8,000 per year to the cost of treating that patient²⁰.

The cost economic model presented here aims to capture these impacts, comparing the scenario where POC-CAD is used as the front-line test for significant CAD to a scenario in which SPECT is used. As POC-CAD is a rule-out test, a larger number of false positive results are expected than for SPECT. It is unreasonable to expect that all positives from the test would proceed to ICA. In many cases, it would be ideal to employ a second test. When analyzing the rule-in and rule-out performances of various non-invasive tests for functionally significant CAD, Knuuti et al. recommend following CCTA with a functional test, such as SPECT¹². However, this again runs the risk of failing to identify a relatively large proportion of the positive patients. A better functional test would be cardiac PET, which has a sensitivity of 89% and specificity of 85%¹². Cardiac PET is increasingly being used in the USA, is currently most often prescribed by cardiologists, and is already used more often than CCTA^15,21. In this model, positives from PET are then assumed to proceed to ICA. As the sensitivity of PET is similar to the sensitivity of POC-CAD, all positives in the incoming PET population are assumed to be correctly classified as positive by PET. In the model in which SPECT is used as the front-line test, positives are assumed to proceed to ICA. Assuming an incoming prevalence of 12%, this results in a positive yield at ICA of 41%, which is close to that observed in the PROMISE study¹⁰.

Figure 3 depicts the model and results using a hypothetical incoming population of 200 patients with symptoms of cardiovascular disease. Each of SPECT, PET, and POC-CAD is assumed to be reimbursed at the same rate: $2,500 per test. ICA is assumed to be reimbursed at $3,000. The cost of any treatment during ICA, or subsequently, is not included in the model as this is beyond the diagnostic pathway that the model represents. The costs of lost negative and lost positive patients are more difficult to estimate. Given that these patients are defined as having not followed their physician's direction to proceed with an imaging test, it is assumed that their next interaction with the healthcare system is to present at an emergency department with symptoms of cardiovascular disease. In a study of emergency department presentations with such symptoms, Pope et al. determined that 17% met the criteria for acute cardiac ischemia, 6% had stable angina, 21% had nonischemic cardiac problems, and 56% had noncardiac problems²². The costs for initial presentation at the emergency department have been calculated by O'Sullivan et al. as $34,200 for non-fatal myocardial ischemia and $17,300 for angina without coronary revascularization²³. Using solely the costs at the initial presentation is a conservative approach as the acute phase (3-year) costs incurred for the event are substantially higher, at $73,300 and $36,000, respectively. The cost of an emergency department visit for 53% of visits for chest pain, but with no resultant cardiac problem identified, has been estimated as $2,988 using information reported by United Healthcare²⁴. Finally, for the 21% with a nonischemic cardiac issue, the cost has been estimated as $2,988, plus an incremental cost caused by delay in treating the cardiac condition. As stated above, for a patient with heart failure, the incremental annual cost of delayed diagnosis has been calculated at $8,000²⁰. Therefore, the cost for the 21% has been assumed to be $10,988, assuming that the majority of these patients will have either heart failure or pulmonary hypertension and a similar delayed treatment cost for both. Once again, using just one year of incremental cost is a conservative approach as this would, in fact, apply to each subsequent year. Overall, the weighted average of these costs, as used in the model, is $10,833.

The cost economic model demonstrates a potential average saving of $1,172 per patient to the health care system due, in summary, to the cost of delayed treatment resulting in disease progression requiring more advanced care. Additionally, adopting this pathway would increase the yield at ICA to 62%, compared to the 41% currently observed and modeled in the SPECT pathway.

If extrapolated out to the incoming rural population of 2.25 million patients presenting per year with symptoms of cardiovascular disease in the USA, the total potential savings of implementing POC-CAD as a front-line test for significant CAD would be approximately $2.64 billion. As discussed above, a large component of these savings is derived from not missing patients with functionally significant CAD due to a higher sensitivity and higher diagnostic yield than SPECT. Additionally, the higher yield that could be achieved at ICA is a crucial benefit as more ICAs would be performed under the POC-CAD>PET pathway than the SPECT pathway. A higher yield at ICA would be beneficial to patients, who are often currently frustrated by negative ICA results²⁵, the interventional cardiologist, and the healthcare system.

One possible objection to this model is that everyone should be referred directly to PET rather than using the POC-CAD test first. This approach, especially when considering the rural population, could be fraught with the same issue that is currently observed with all imaging tests – that of loss to follow-up. If the same assumptions for the model were maintained, the effective yield of PET would have a sensitivity of just 62% (= 0.7 x 89%), thereby still missing many of the truly positive patients in this population. Conversely, it is observed that having a positive test from POC-CAD can encourage patients to follow up with their referral for subsequent diagnostic tests (such as ICA), and hence, this is not expected to be an issue in the POC-CAD>PET pathway. One might also argue that SPECT is the predominant pathway and, hence, is most likely to be the test used in practice following POC-CAD. In this case, the savings of the resultant POC-CAD>SPECT pathway would be slightly higher than those shown above, but the effective yield at ICA would be slightly lower (58%) as there would be an additional three missed positive patients (false negatives) of two hundred in the flow.

In conclusion, POC-CAD provides a promising addition to the diagnostic pathway. The primary advantage of this test is that it can be used at point-of-care, without the use of radiation, stress, or contrast, and yet delivers results comparable to those of CCTA for ruling out functionally significant CAD²⁶. Further, results are available within the same visit, allowing the physician to discuss the next steps in care with the patient without scheduling another visit. Since the test can be administered at point-of-care, and the results are rapidly available, POC-CAD addresses the significant problem of loss to follow-up observed, especially in rural areas, representing approximately one-fourth of the US population. Given the robust rule-out profile of the test, with a negative result, the physician can rapidly explore other explanations of the patient's symptoms. When the costs of the resultant delay in diagnosis of the patient are modeled, the use of POC-CAD as a front-line test would provide significant advantages to patients, physicians, and the payor. Furthermore, the existing imaging modalities would not see either a significant increase or decrease in volume, which is a concern for many imaging centers. The cost savings are realized through more timely diagnosis and treatment of the patient's condition.