The authors would like to thank Cody Wackerman and Hugh Dubberly of Dubberly Design Office (San Francisco) for their help in visualizing the pathways shown in Figure 3.

The study was approved by the Western Institutional Review Board and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all subjects for participation. Briefly, to be included, subjects must have been at least 18 years of age with cardiovascular symptoms but without known coronary artery disease and scheduled to undergo ICA or CCTA. Exclusion criteria included prior documented history of myocardial infarction, prior coronary artery bypass grafting, and prior heart valve replacement, among others. Complete study inclusion and exclusion criteria can be found on clinicaltrials.gov, under National Clinical Trial (NCT) number NCT03864081. The equipment and the software used are listed in the Table of Materials.
1. Signal acquisition
<ol>
	<li>Prepare the skin by shaving any hair from the electrode site. Ensure that there is no remaining residue on the skin by cleaning with soap and water and employing a clean and dry gauze or non-alcoholic wipe if needed.</li>
	<li>Connect the leads to electrodes and place them in accordance with Table 1. 
	NOTE: Electrode quality may affect the signal quality; it is recommended to use diagnostic quality or high-performance electrodes.</li>
	<li>Place the hemodynamic (photoplethysmogram, PPG) sensor on the subject&#39;s finger, ensuring that the finger is as far into the sensor as possible, with the sensor cable positioned along the top of the hand.
	<ol>
		<li>Utilize the index finger unless a wound or&#160;other clinically relevant reason precludes its use. Advise the patient not to move the sensor once it is placed.</li>
	</ol>
	</li>
	<li>Position the patient flat on their back in a comfortable resting position. Ensure that the PPG sensor cable and electrode cables do not overlap.
	<ol>
		<li>Use cushioning (e.g., a rolled blanket or towel, along with a pillow) if needed to prevent the patient from placing direct pressure on the blue electrode on the back.</li>
	</ol>
	</li>
	<li>To limit signal noise and maximize signal quality, to the degree possible, turn off all electronic devices (monitors, televisions, cell phones, etc.) or remove them from the room. Turn off the room lighting.</li>
	<li>Enter the patient information and confirm correctness: patient&#39;s first name, patient&#39;s last name, gender at birth, height in inches, weight in pounds, date of birth, and medical record number (all required, may be entered in any order).</li>
	<li>Ensure that the patient is comfortable, and remind them to be still and quiet (e.g., no talking during the acquisition).</li>
	<li>Place the device on a flat surface and confirm the placement in the application. Press Start to commence the signal acquisition.</li>
	<li>Monitor the device for any error messages, and if they appear, follow the directions on the screen to resolve them.</li>
	<li>After the signal acquisition is complete, requiring 3 min and 35 s, the device will automatically transmit the data to the cloud for signal quality assessment. Keep the patient attached to the device until confirmation of passing signal quality is received.</li>
	<li>Detach the electrodes from the patient and remove the PPG sensor.</li>
	<li>Access the portal to view the test results (will be available about 10 min after the signal has been uploaded to the cloud).</li>
</ol>
Table 1: Lead placement. Description of the anatomical locations at which each of the seven electrodes should be placed. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/66933/Table 1.xlsx" target="_blank">Please click here to download this Table.</a>
2. Interpreting the test results given test-positive vs . test-negative
<ol>
	<li>Determine the patient&#39;s pre-test probability of disease (e.g., using the European Society of Cardiology (ESC) guidelines7).</li>
	<li>Determine whether the patient&#39;s test is positive or negative by comparing the CAD score to 0: if greater than or equal to zero, then the patient&#39;s result is test-positive, or if less than zero, then the result is test-negative.</li>
	<li>Read the CAD Score value from the report and compare it to the distributions of negative and positive patients in the validation cohort shown at the bottom of the report.</li>
	<li>Calculate the sensitivity and specificity8, then the likelihood ratios (LR) using equation 1 and equation 29. 
	NOTE: A worked example is shown, given that the test sensitivity is 88% (calculated using subjects positive from ICA, which is the sole group of subjects with disease) and specificity is 51% (calculated using subjects negative from CCTA at a 94% weight and subjects negative from ICA at a 6% weight, to represent the overall population via the PROMISE study10). 
	<img alt="Equation 1" src="/files/ftp_upload/66933/66933eq01.jpg" style="margin: auto;" />&#160; &#160; (eqn 1) 
	<img alt="Equation 2" src="/files/ftp_upload/66933/66933eq02.jpg" style="margin: auto;" />&#160; &#160; (eqn 2)</li>
	<li>Calculate the patient&#39;s post-test probability given the following equations 3-511, where LR+ is used in the case of a positive test and LR- is used in the case of a negative test: 
	<img alt="Equation 3" src="/files/ftp_upload/66933/66933eq03.jpg" style="margin: auto;" />&#160; &#160; (eqn 3) 
	<img alt="Equation 4" src="/files/ftp_upload/66933/66933eq04.jpg" style="margin: auto;" />&#160; &#160; (eqn 4) 
	<img alt="Equation 5" src="/files/ftp_upload/66933/66933eq05.jpg" style="margin: auto;" />&#160; &#160; (eqn 5)</li>
	<li>With the updated probability of CAD, given the results of the test, determine the appropriate next step in patient care (e.g., using the ESC guideline7 recommendations shown in Table 2).</li>
</ol>
Table 2: ESC guidelines by probability of CAD. For four levels of probability of CAD, the European Society of Cardiology guidelines are for the appropriate interpretation of that probability, including the next test&#160;(if necessary). <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/66933/Table 2.xlsx" target="_blank">Please click here to download this Table.</a>
3. Interpreting the test results given at finer granularity
<ol>
	<li>Increase the granularity of the test-positive and test-negative results by considering ranges of the CAD score observed in the population used to validate the test, specifically division into tertiles (equal number of subjects in three bins) in the test-positive and test-negative ranges, as shown in Table 3. 
	NOTE: To simplify the calculations, the 6% contribution of the subjects negative from catheterization is excluded from this analysis, the inclusion of which will not change the outcomes significantly.</li>
	<li>Within each tertile in the test-negative range, calculate the number of True Negatives (TN), which are subjects that truly do not have the disease, and the number of False Negatives (FN), subjects who truly do have the disease.
	<ol>
		<li>Apply the following equations 6-811 to determine the LR- of each test-negative tertile (LR+ from negative tertiles not relevant). 
		NOTE: The equations assume that the performance profile of the tertile represents the full test-negative range to calculate performance statistics (necessitating the correction factor of three, corresponding to the number of tertiles, to achieve the original test-negativity frequency with respect to test positivity). 
		<img alt="Equation 6" src="/files/ftp_upload/66933/66933eq06.jpg" style="margin: auto;" />&#160; &#160; (eqn 6) 
		<img alt="Equation 7" src="/files/ftp_upload/66933/66933eq07.jpg" style="margin: auto;" />&#160; &#160; (eqn 7) 
		<img alt="Equation 8" src="/files/ftp_upload/66933/66933eq08.jpg" style="margin: auto;" />&#160; &#160;&#160;(eqn 8)</li>
	</ol>
	</li>
	<li>Within each tertile in the test-positive range, calculate the number of True Positives (TP), which are subjects that truly have the disease, and the number of False Positives (FP), subjects who truly do not have the disease.
	<ol>
		<li>Apply equations 9-11 to determine the LR+ of each test-positive tertile (LR- from positive tertiles not relevant)11. 
		NOTE: The equations assume that the performance profile of the tertile represents the full test-positive range for the purpose of calculating performance statistics (necessitating the correction factor of three, corresponding to the number of tertiles, to achieve the original test-positivity frequency with respect to test negativity). 
		<img alt="Equation 9" src="/files/ftp_upload/66933/66933eq09.jpg" style="margin: auto;" />&#160; &#160; (eqn 9) 
		<img alt="Equation 10" src="/files/ftp_upload/66933/66933eq10.jpg" style="margin: auto;" />&#160; &#160; (eqn 10) 
		<img alt="Equation 11" src="/files/ftp_upload/66933/66933eq11.jpg" style="margin: auto;" />&#160; &#160;&#160;(eqn 11)</li>
	</ol>
	</li>
	<li>Determine which tertile the patient&#39;s CAD score lies within, and given the LRs in Table 3, calculate the post-test probability given the procedure shown in step 2. 
	NOTE: As may be expected, the LR- values decrease with more negative CAD scores, and the LR+ values increase with more positive CAD scores, meaning that a more negative CAD score more effectively reduces the pre-test probability, and a more positive CAD score more effectively increases the pre-test probability.</li>
	<li>As an alternative to performing the calculations listed in steps 2.4-3.4, use Table 4 to translate the pre-test probability into a post-test probability using the patient&#39;s CAD score7.</li>
</ol>
Table 3: Likelihood ratios. For three ranges of scores within test-positive and test-negative, the corresponding positive likelihood ratios and negative likelihood ratios, respectively. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/66933/Table 3.xlsx" target="_blank">Please click here to download this Table.</a>
Table 4: Pre-test to post-test probability mapping. For 10 levels of pre-test probability, the corresponding post-test probability given a CAD score in each of the three negative tertiles, and each of the positive tertiles. Green coloring corresponds to &#60;15% disease probability (ESC guideline states that CAD can be ruled out); yellow coloring corresponds to 15%-65% disease probability (ESC guidelines state&#160;that imaging-based non-invasive testing is preferable); orange corresponds to 66%-85% disease probability (ESC guidelines states that imaging-based non-invasive testing should be performed); red corresponds to &#62;85% disease probability (ESC guideline states that CAD can be ruled in). See Table 2 for further guideline information. Note that a CAD score of 0 is considered test-positive. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/66933/Table 4.xlsx" target="_blank">Please click here to download this Table.</a>

Non-Invasive Point-of-Care Test for Coronary Artery Disease Assessment 

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D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Clinical+validation+of+a+machine-learned,+point-of-care+system+to+IDENTIFY+functionally+significant+coronary+artery+disease.">Clinical validation of a machine-learned, point-of-care system to IDENTIFY functionally significant coronary artery disease.</a> Diagnostics. 14 (10), 987 (2024).</li><li>Frank, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+accurate,+clinically+practical+system+for+spatial+vectorcardiography.">An accurate, clinically practical system for spatial vectorcardiography.</a> Circulation. 13 (5), 737-749 (1956).</li><li>Park, J., Seok, H. S., Kim, S. 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S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Outcomes+of+anatomical+versus+functional+testing+for+coronary+artery+disease.">Outcomes of anatomical versus functional testing for coronary artery disease.</a> New Eng J Med. 372 (14), 1291-1300 (2015).</li><li>Safari, S., Baratloo, A., Elfil, M., Negida, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evidence+based+emergency+medicine;+part+4:+Pre-test+and+post-test+probabilities+and+Fagan's+nomogram.">Evidence based emergency medicine; part 4: Pre-test and post-test probabilities and Fagan's nomogram.</a> Emergency. 4 (1), 48 (2016).</li><li>Knuuti, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+performance+of+non-invasive+tests+to+rule-in+and+rule-out+significant+coronary+artery+stenosis+in+patients+with+stable+angina:+a+meta-analysis+focused+on+post-test+disease+probability.">The performance of non-invasive tests to rule-in and rule-out significant coronary artery stenosis in patients with stable angina: a meta-analysis focused on post-test disease probability.</a> Eur Heart J. 39 (35), 3322-3330 (2018).</li><li>Diamond, G. A., Forrester, J. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+of+probability+as+an+aid+in+the+clinical+diagnosis+of+coronary-artery+disease.">Analysis of probability as an aid in the clinical diagnosis of coronary-artery disease.</a> New Eng J Med. 300 (24), 1350-1358 (1979).</li><li>Morise, A. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+the+Diamond-Forrester+method+and+a+new+score+to+estimate+the+pre-test+probability+of+coronary+disease+before+exercise+testing.">Comparison of the Diamond-Forrester method and a new score to estimate the pre-test probability of coronary disease before exercise testing.</a> Am Heart J. 138 (4), 740-745 (1999).</li><li>Pelletier-Galarneau, M., Vandenbroucke, E., Lu, M., Li, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Characteristics+and+key+differences+between+patient+populations+receiving+imaging+modalities+for+coronary+artery+disease+diagnosis+in+the+US.">Characteristics and key differences between patient populations receiving imaging modalities for coronary artery disease diagnosis in the US.</a> BMC Cardiovasc Disord. 23 (1), 251 (2023).</li><li>Li, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Factors+that+impact+a+patient's+experience+when+undergoing+single-photon+emission+computed+tomography+myocardial+perfusion+imaging+(SPECT-MPI)+in+the+US:+A+survey+of+patients,+imaging+center+staff,+and+physicians.">Factors that impact a patient's experience when undergoing single-photon emission computed tomography myocardial perfusion imaging (SPECT-MPI) in the US: A survey of patients, imaging center staff, and physicians.</a> J Nuclear Cardiol. 28 (4), 1507-1518 (2021).</li><li>Lawton, J. 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A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Acute+and+1-year+hospitalization+costs+for+acute+myocardial+infarction+treated+with+percutaneous+coronary+intervention:+Results+From+the+TRANSLATE-ACS+registry.">Acute and 1-year hospitalization costs for acute myocardial infarction treated with percutaneous coronary intervention: Results From the TRANSLATE-ACS registry.</a> J Am Heart Assoc. 16 (8), 8 (2019).</li><li>Thomas, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Forgone+medical+care+associated+with+increased+health+care+costs+among+the+U.S.+heart+failure+population.">Forgone medical care associated with increased health care costs among the U.S. heart failure population.</a> JACC Heart Fail. 9 (10), 710-719 (2021).</li><li>Reeves, R. A., Halpern, E. J., Rao, V. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cardiac+imaging+trends+from+2010+to+2019+in+the+Medicare+population.">Cardiac imaging trends from 2010 to 2019 in the Medicare population.</a> Radiol Cardiothorac Imaging. 3 (5), (2021).</li><li>Pope, J. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Missed+diagnoses+of+acute+cardiac+ischemia+in+the+Emergency+Department.">Missed diagnoses of acute cardiac ischemia in the Emergency Department.</a> New Eng J Med. 342 (16), 1163-1170 (2000).</li><li>O&#700;Sullivan, A. K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cost+estimation+of+cardiovascular+disease+events+in+the+US.">Cost estimation of cardiovascular disease events in the US.</a> Pharmacoeconomics. 29 (8), 693-704 (2011).</li><li>Emergency visit cost with and without insurance in 2024. Mira Available from: <a target="_blank" href="https://www.talktomira.com/post/how-much-does-an-er-visit-cost">https://www.talktomira.com/post/how-much-does-an-er-visit-cost</a> (2024)</li><li>Patel, M. 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T.B., N.N., F.F., H.G., C.B., S.R., and I.S. are employees of Analytics for Life.
Methodologies regarding the CorVista CAD score interpretation represent current assumptions based on a subset of the validated population and require further validation. Please refer to the product instructions for use.

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 CAD2,17where 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&#39;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&#39;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 CCTA15,21. 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&#39;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&#39;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&#62;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&#160;is not expected to be an issue in the POC-CAD&#62;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&#62;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&#39;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&#39;s condition.

Ischemic heart disease (IHD) is currently the leading cause of death globally1. Coronary artery disease (CAD) is the anatomical precursor to IHD, characterized by the presence of significant lesions within the coronary arteries. Significance can be defined anatomically by an occlusion of at least 70%, or 50% in the left main artery2. Significance can also be defined based on the functional impact of the lesion by measuring its effect on blood flow. Specifically, fractional flow reserve (FFR) and instantaneous flow reserve (iFR) assess the change in flow caused by the lesion, and lesions are defined as significant at FFR &#60; 0.80 or iFR &#60; 0.892. The gold standard for confirming the presence of significant CAD is invasive coronary angiography (ICA) by left heart catheterization (LHC). Significant CAD warrants revascularization through the placement of a stent or coronary artery bypass grafting, which alleviates myocardial ischemia and corresponding anginal symptoms and stabilizes the plaque (in the case of stenting).
Current non-invasive testing for CAD often requires radiation exposure, stress, and contrast injection (with renal impacts)1. The most commonly used diagnostic tests are myocardial perfusion imaging (MPI), also known as single photon emission computed tomography (SPECT), and coronary computed tomographic angiography (CCTA). SPECT involves the injection of a radioisotope to track perfusion during rest and stress (exercise or pharmaceutical). CCTA images the coronary arteries using multiple X-rays. Positron Emission Tomography (PET) and cardiac magnetic resonance imaging (CMR) are also occasionally used for CAD testing1. All these modalities require significant capital investment in equipment, trained personnel to perform the tests, and, in some cases, the availability of medical isotopes. Due to these requirements, these modalities are often not broadly available, especially in rural settings, which introduces delays in patient care and escalates associated risks.
Therefore, the goal was to design a non-invasive, point-of-care test for CAD (POC-CAD): the CorVista system3. This system is an FDA-cleared test for functionally significant CAD, with performance comparable to CCTA4. The test can be used without patient stress and without the need for capital investment in equipment or specialized staff. It is ideally suited for use in rural and resource-constrained environments where access to current imaging technologies is often not available.
The development3 and validation4 of POC-CAD have been published previously. Briefly, POC-CAD begins with the simultaneous acquisition of orthogonal voltage gradient and photoplethysmogram signals using the Capture device, as described in Protocol step 1. The orthogonal voltage gradient is a biopotential signal acquired using a lead placement similar to the Frank lead configuration5, comprising six bipolar electrodes and a reference electrode attached to the torso. The photoplethysmogram is obtained using the standard transmission modality, where red and infrared light is transmitted through the fingertip and strikes a sensor6. Once the signal is acquired, it is automatically uploaded to the cloud-based system, where engineered features are extracted from the signal and supplied to a machine-learned algorithm to yield the CAD score, which is presented in the web portal. In Protocol steps 2 and 3, the methodology to interpret the CAD Score returned by the test is described. Specifically, either published data or other sources relevant to the physician can be used to estimate the probability of CAD prior to the patient taking the test. Likelihood ratios can be calculated from the test performance and used to update the probability of CAD from pre-test to post-test, using either the binary test result (step 2) or more granular values of the CAD score (step 3). Finally, the modeling of the economics of POC-CAD in comparison to the current standard of care is discussed.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>CorVista CAD&#160;</td>
			<td>CorVista</td>
			<td>n/a</td>
			<td>System comprising signal acquisition, cloud processing, and delivery of a report containing the CAD Score via a web portal&#160;</td>
		</tr>
		<tr>
			<td>Electrodes</td>
			<td>Various (see comments)</td>
			<td></td>
			<td>Diagnostic quality or high performance electrodes are recommended. 
			For example: 
			Conmed ClearTrace 1700-007 
			Skintact FS-TB1 
			Vermed A10005</td>
		</tr>
	</tbody>
</table>

signal acquisition, score interpretation, and economics of a non-invasive point-of-care test for coronary artery disease

A point-of-care non-invasive test for Coronary Artery Disease (CAD) (POC-CAD) has been previously developed and validated. The test requires the simultaneous acquisition of orthogonal voltage gradient (OVG) and photoplethysmogram signals, which is the primary methodology described in this paper. The acquisition of the OVG, a biopotential signal, necessitates the placement of electrodes on the prepared skin of the patient&#39;s thorax (arranged similarly to the Frank lead configuration, comprising six bipolar electrodes and a reference electrode) and a hemodynamic sensor on the finger (using a standard transmission modality). The signal is uploaded to a cloud-based system, where engineered features are extracted from the signal and supplied to a machine-learned algorithm to yield the CAD Score. The physician must then interpret the value of the CAD Score in the context of their patient&#39;s pre-test probability of CAD, resulting in a post-test probability of CAD. This interpretation can be performed at the level of test positivity and test negativity or at a finer level of granularity; methodologies for each are proposed here based on likelihood ratios. Using the post-test probability, the physician must determine the appropriate next step in the treatment of their patient; several scenarios are used to illustrate this process. Test adoption is only feasible if economically viable; a discussion of the integration of the test into the CAD diagnostic flow and the resultant cost savings to the healthcare system is provided. The economic model demonstrates that cost savings to the healthcare system can be achieved by preventing delayed treatment, which, if left unaddressed, results in disease progression requiring more advanced (and expensive) care.

An example signal can be seen in Figure 1, and report in Figure 2, as would appear to a physician using the test. Below are several patient cases provided to demonstrate the application of the methodology in interpreting the CAD score&#39;s significance.
Case 1 
A 67-year-old man presented complaining of symptoms of chest pain at rest, with radiating arm pain, along with diaphoresis and nausea. Based on the ESC guidelines7, his age, sex, and symptoms are consistent&#160;with an initial probability of disease of 84%. The ESC guidelines suggest that such a patient requires non-invasive testing to make a diagnosis of CAD to warrant referral to cardiac catheterization and any needed intervention (Table 2). POC-CAD was performed, returning a CAD score of 0.32, which is a test-positive result and falls in the highest tertile of positive CAD Scores, with an LR+ of 2.888 (Table 3). Therefore, the patient&#39;s post-test probability is 94%, and per the ESC guidelines (Table 2), the patient can safely be assumed to have CAD, and invasive cardiac catheterization is an appropriate course of action.
Case 2 
A 39-year-old woman presents with chest pain at rest, radiating to both the left and right side. Based on the ESC guidelines7, her age, sex, and symptoms are consistent with an initial probability of disease of 28%, and the guidelines suggest that a non-invasive test would be the preferable next step to gain a greater understanding of the probability of CAD. However, the guidelines also state that in young patients (such as this 39-year-old woman), the physician should be conscious of radiation exposure, which is necessary to administer a test such as SPECT, requiring an injected radioisotope. Another option for such a test is CCTA, but that requires many X-rays. Therefore, POC-CAD is a logical choice, as it doesn&#39;t require radiation. The test was performed, returning a value of -0.12, within the bounds of the first tertile of the negative range, with an LR- of 0.129 (Table 3). The resultant post-test probability is 4%, which falls to the level where CAD can be safely ruled out per the ESC guidelines, with no further testing required for CAD.
Case 3 
A 74-year-old female presents with non-anginal chest pain and dyspnea. Based on the ESC guidelines7, her age, sex, and symptoms are consistent with an initial probability of disease of 24%, and the guidelines suggest that a non-invasive test would be the preferable next step to gain a greater understanding of the probability of CAD. The patient is hesitant to travel from the rural clinic where she is being treated to the large urban center where all the testing modalities (PET, SPECT, CCTA, CMR) are available, so the physician offers POC-CAD. The CAD score is 0.07, falling into the first tertile of the positive range with an LR+ of 1.446, resulting in a post-test probability of 33%. Based on the positive result and the increase in the probability of disease, the patient agrees to travel to the city for a PET scan. The PET was also indicative of disease, with an LR+ of 6.0412, and augments the 33% to 75% post-PET.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/66933/66933fig01.jpg" /> 
Figure 1: Example signals. Exemplar POC-CAD report showing visualizations of the orthogonal voltage gradient, also referred to as the electrical signal (A), and the photoplethysmogram signal focused on a 20 s interval (B) and full duration (C). <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/66933/66933fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/66933/66933fig02.jpg" /> 
Figure 2: Example CAD report. Exemplar POC-CAD report, showing a CAD Score of -0.19 (A), which falls into the test-negative range (&#60;0). The test performance is shown in (B). The distribution of CAD scores is displayed for the subjects on which POC-CAD was validated in (C). <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/66933/66933fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/66933/66933fig03.jpg" /> 
Figure 3: Cost economic model flow. A hypothetical incoming population of 200 patients is run through the POC-CAD&#62;PET pathway (A) and the SPECT pathway (B). The costs to the healthcare system are shown for each branch of each pathway, along with the average saving per patient when the POC-CAD&#62;PET pathway is implemented. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/66933/66933fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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

The methodology to acquire the physiological signal for a Coronary Artery Disease (CAD) test is presented. A method is proposed to interpret the CAD score concerning test positivity and negativity, including the granularities of each. The economics of the test are discussed in the context of the current standard of care.

A point-of-care non-invasive test for Coronary Artery Disease (CAD) (POC-CAD) has been previously developed and validated. The test requires the ...

signal-acquisition-score-interpretation-economics-non-invasive-point

Universidad de Cartagena UDEC

Research

JoVE Journal

Medicine

312 Views.  Analytics for Life, Toronto. The methodology to acquire the physiological signal for a Coronary Artery Disease (CAD) test is presented. A method is proposed to interpret the CAD score concerning test positivity and negativity, including the granularities of each. The economics of the test are discussed in the context of the current standard of care.

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

Rapid Point-of-Care Assay of Enoxaparin Anticoagulant Efficacy in Whole Blood

There is the need for a clinical assay to determine the extent to which a patient's blood is effectively anticoagulated by the low-molecular-weight-heparin (LMWH), enoxaparin. There are also urgent clinical situations where it would be important if this could be determined rapidly. The present assay is designed to accomplish this. We only assayed human blood samples that were spiked with known concentrations of enoxaparin. The essential feature of the present assay is the quantification of the efficacy of enoxaparin in a patient's blood sample by degrading it to complete inactivity with heparinase. Two blood samples were drawn into Vacutainer tubes (Becton-Dickenson; Franklin Lakes, NJ) that were spiked with enoxaparin; one sample was digested with heparinase for 5 min at 37 &deg;C, the other sample represented the patient's baseline anticoagulated status. The percent shortening of clotting time in the heparinase-treated sample, as compared to the baseline state, yielded the anticoagulant contribution of enoxaparin. We used the portable, battery operated Hemochron 801 apparatus for measurements of clotting times (International Technidyne Corp., Edison, NJ). The apparatus has 2 thermostatically controlled (37 &deg;C) assay tube wells. We conducted the assays in two types of assay cartridges that are available from the manufacturer of the instrument. One cartridge was modified to increase its sensitivity. We removed the kaolin from the FTK-ACT cartridge by extensive rinsing with distilled water, leaving only the glass surface of the tube, and perhaps the detection magnet, as activators. We called this our minimally activated assay (MAA). The use of a minimally activated assay has been studied by us and others. 2-4 The second cartridge that was studied was an activated partial thromboplastin time (aPTT) assay (A104). This was used as supplied from the manufacturer. The thermostated wells of the instrument were used for both the heparinase digestion and coagulation assays. The assay can be completed within 10 min. The MAA assay showed robust changes in clotting time after heparinase digestion of enoxaparin over a typical clinical concentration range. At 0.2 anti-Xa I.U. of enoxaparin per ml of blood sample, heparinase digestion caused an average decrease of 9.8% (20.4 sec) in clotting time; at 1.0 I.U. per ml of enoxaparin there was a 41.4% decrease (148.8 sec). This report only presents the experimental application of the assay; its value in a clinical setting must still be established.

Demonstration of a rapid quantitative assay of the inhibition of blood coagulation by the low-molecular-weight-heparin, enoxaparin. The contribution of enoxaparin is assessed by removing its influence through digestion with heparinase. A fuller description of the assay is detailed in our published paper.1 The assay still requires clinical confirmation.

There is the need for a clinical assay to determine the extent to which a patient's blood is effectively anticoagulated by the ...

Non-invasive Assessments of Subjective and Objective Recovery Characteristics Following an Exhaustive Jump Protocol

Fast recovery after strenuous exercise is important in sports and is often studied via cryotherapy applications. Cryotherapy has a significant vasoconstrictive effect, which seems to be the leading factor in its effectiveness. The resulting enhanced recovery can be measured by using both objective and subjective parameters. Two commonly measured subjective characteristics of recovery are delayed-onset muscle soreness (DOMS) and ratings of perceived exertion (RPE). Two important objective recovery characteristics are countermovement jump (CMJ) performance and peak power output (PPO). Here, we provide a detailed protocol to induce muscular exhaustion of the frontal thighs with a self-paced, 3 x 30 countermovement jump protocol (30-s rest between each set). This randomized controlled trial protocol explains how to perform local cryotherapy cuff application (+ 8 &#176;C for 20 min) and thermoneutral cuff application (+ 32 &#176;C for 20 min) on both thighs as two possible post-exercise recovery modalities. Finally, we provide a non-invasive protocol to measure the effects of these two recovery modalities on subjective (i.e., DOMS of both frontal thighs and RPE) and objective recovery (i.e., CMJ and PPO) characteristics 24, 48, and 72 h post-application. The advantage of this method is that it provides a tool for researchers or coaches to induce muscular exhaustion, without using any expensive devices; to implement local cooling strategies; and to measure both subjective and objective recovery, without using invasive methods. Limitations of this protocol are that the 30 s rest period between sets is very short, and the cardiovascular demand is very high. Future studies may find the assessment of maximum voluntary contractions to be a more sensitive assessment of muscular exhaustion compared to CMJs.

This protocol describes the procedure for non-invasive recovery assessments during a 72 h recovery period. This protocol induces muscular exhaustion of the frontal thighs using countermovement jumps and uses either local cold-cuff or thermoneutral-cuff application as a recovery modality.

Fast recovery after strenuous exercise is important in sports and is often studied via cryotherapy applications. Cryotherapy has a significant ...

Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans

Coronary artery calcification (CAC) provides an objective measure of coronary artery disease and can readily be identified on non-gated computed tomography (CT) scans with a high correlation with gated cardiac CT scans. This standardized protocol takes a step-wise approach to not only optimizing an image for the identification of calcification but also to distinguishing CAC from other common causes of calcification in the cardiac silhouette. Recognition of CAC on non-gated CT scans helps to identify a very powerful prognostic factor that can influence therapeutic interventions or downstream diagnostic testing without requiring a gated cardiac scan. These non-gated CT scans are often acquired as part of the routine care of the patient, and this data is readily available without another dose of&#160;ionizing radiation. This protocol allows for the precise and accurate extraction of this data for the purposes of retrospective data analysis in clinical research studies, but also in the clinical evaluation and management of patients.

Here, we present a protocol to reliably and systematically identify coronary artery calcification (CAC) on non-gated computed tomography (CT) scans of the chest or abdomen. CAC provides an objective measure of coronary artery disease for both research and clinical purposes.

Coronary artery calcification (CAC) provides an objective measure of coronary artery disease and can readily be identified on non-gated computed ...

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes

White blood cell (WBC) is an important indicator of inflammation in the body, and it can help distinguish between bacterial and viral infections. At present, most primary medical institutions in China have a poor percentage of adoption of blood-testing technology, and a hematology detection system with a high price to performance ratio and easy operation is urgently needed in primary healthcare centers. This paper introduces the principle and operation procedures of a point-of-care testing (POCT) card-based leukocyte analyzer (evaluated system), which was used to detect WBC indexes such as neutrophils, lymphocytes, and intermediate group cells (including eosinophils, basophils, and monocytes) in whole blood. The results from the evaluated system were compared to those from two commercial automatic hematology analyzers (reference system). The correlation and consistency between the evaluated system and the commercial reference systems were analyzed. The results showed that WBC count and number of granulocytes detected by the evaluated and reference systems showed a strong positive correlation (rs = 0.972 and 0.973, respectively), while the number of lymphocytes showed a relatively low correlation (rs = 0.851). A Bland-Altman plot showed that the major difference between the values detected by the evaluated system and the reference systems is within 95% limits of agreement (LoA), indicating that the two systems are in good agreement. In conclusion, the evaluated system has an excellent correlation, robust consistency, and a reliable comparison with the results of the widely used automatic hematology analyzers. It is ideal for WBC detection in primary medical institutions where a full-automatic five-category hematology analyzer is unavailable, especially during the COVID-19 normalized prevention and control period.

A protocol to measure peripheral blood leukocytes using a POCT card-based leukocyte analyzer is presented here. Same blood samples were tested by two automated hematology analyzers to evaluate the consistency and accuracy of the results. The results showed that the evaluated analyzer had a good correlation with the reference system.

White blood cell (WBC) is an important indicator of inflammation in the body, and it can help distinguish between bacterial and viral infections. At ...

Oxygenation-sensitive Cardiac MRI with Vasoactive Breathing Maneuvers for the Non-invasive Assessment of Coronary Microvascular Dysfunction

Oxygenation-sensitive cardiac magnetic resonance imaging (OS-CMR) is a diagnostic technique that uses the inherent paramagnetic properties of deoxyhemoglobin as an endogenous source of tissue contrast. Used in combination with standardized vasoactive breathing maneuvers (hyperventilation and apnea) as a potent non-pharmacologic vasomotor stimulus, OS-CMR can monitor changes in myocardial oxygenation. Quantifying such changes during the cardiac cycle and throughout vasoactive maneuvers can provide markers for coronary macro- and microvascular function and thereby circumvent the need for any extrinsic, intravenous contrast or pharmacologic stress agents.
OS-CMR uses the well-known sensitivity of T2*-weighted images to blood oxygenation. Oxygenation-sensitive images can be acquired on any cardiac MRI scanner using a modified standard clinical steady-state free precession (SSFP) cine sequence, making this technique vendor-agnostic and easily implemented. As a vasoactive breathing maneuver, we apply a 4-min breathing protocol of 120 s of free breathing, 60 s of paced hyperventilation, followed by an expiratory breath-hold of at least 30 s. The regional and global response of myocardial tissue oxygenation to this maneuver can be assessed by tracking the signal intensity change. The change over the initial 30 s of the post-hyperventilation breath-hold, referred to as the breathing-induced myocardial oxygenation reserve (B-MORE) has been studied in healthy people and various pathologies. A detailed protocol for performing oxygen-sensitive CMR scans with vasoactive maneuvers is provided.
As demonstrated in patients with microvascular dysfunction in yet incompletely understood conditions, such as inducible ischemia with no obstructive coronary artery stenosis (INOCA), heart failure with preserved ejection fraction (HFpEF), or microvascular dysfunction after heart transplantation, this approach provides unique, clinically important, and complementary information on coronary vascular function.

The assessment of microvascular function by oxygenation-sensitive cardiac magnetic resonance imaging in combination with vasoactive breathing maneuvers is unique in its ability to assess rapid dynamic changes in myocardial oxygenation in vivo and, thus, may serve as a critically important diagnostic technique for coronary vascular function.

Oxygenation-sensitive cardiac magnetic resonance imaging (OS-CMR) is a diagnostic technique that uses the inherent paramagnetic properties of ...

Point-Of-Care Ultrasound Screening for Proximal Lower Extremity Deep Venous Thrombosis

Acute lower extremity deep venous thrombosis (DVT) is a serious vascular disorder that requires accurate and early diagnosis to prevent life-threatening sequelae. While whole leg compression ultrasound with color and spectral Doppler is commonly performed in radiology and vascular labs, point-of-care ultrasound (POCUS) is becoming more common in the acute care setting. Providers appropriately trained in focused POCUS can perform a rapid bedside examination with high sensitivity and specificity in critically ill patients. This paper describes a simplified yet validated approach to POCUS by describing a three-zone protocol for lower extremity DVT POCUS image acquisition. The protocol explains the steps in obtaining vascular images at six compression points in the lower extremity. Beginning at the level of the proximal thigh and moving distally to the popliteal space, the protocol guides the user through each of the compression points in a stepwise manner: from the common femoral vein to the femoral and deep femoral vein bifurcation, and, finally, to the popliteal vein. Further, a visual aid is provided that may assist providers during real-time image acquisition. The goal in presenting this protocol is to help make proximal lower extremity DVT exams more accessible and efficient for POCUS users at the patient&#39;s bedside.

Traditionally, lower extremity deep venous thrombosis (DVT) is diagnosed by radiology-performed venous duplex ultrasound. Providers appropriately trained in focused point-of-care ultrasound (POCUS) can perform a rapid bedside examination with high sensitivity and specificity in critically ill patients. We describe the scanning technique for focused POCUS DVT lower extremity examination.

Acute lower extremity deep venous thrombosis (DVT) is a serious vascular disorder that requires accurate and early diagnosis to prevent life-threatening ...

Point-of-Care Lung Ultrasound in Adults: Image Acquisition

Consultative ultrasound performed by radiologists has traditionally not been used for imaging the lungs, as the lungs&#39; air-filled nature normally prevents direct visualization of the lung parenchyma. When showing the lung parenchyma, ultrasound typically generates a number of non-anatomic artifacts. However, over the past several decades, these artifacts have been studied by diagnostic point-of-care ultrasound (POCUS) practitioners, who have identified findings that have value in narrowing the differential diagnoses of cardiopulmonary dysfunction. For instance, in patients presenting with dyspnea, lung POCUS is superior to chest radiography (CXR) for the diagnosis of pneumothorax, pulmonary edema, lung consolidations, and pleural effusions. Despite its known diagnostic value, the utilization of lung POCUS in clinical medicine remains variable, in part because training in this modality across hospitals remains inconsistent. To address this educational gap, this narrative review describes lung POCUS image acquisition in adults, including patient positioning, transducer selection, probe placement, acquisition sequence, and image optimization.

Point-of-care ultrasound (POCUS) of the lungs provides quick answers in rapidly changing clinical scenarios. We present an efficient and informative protocol for image acquisition for use in acute care settings.

Consultative ultrasound performed by radiologists has traditionally not been used for imaging the lungs, as the lungs&#39; air-filled nature normally ...

Point-of-Care Ultrasound: A Review of Ultrasound Parameters for Predicting Difficult Airways

Airway management remains a crucial part of perioperative care. The conventional approach to assessing potentially difficult airways emphasizes the LEMON method, which looks for and evaluates the Mallampati classification, signs of obstruction, and neck mobility. Clinical findings help predict a higher likelihood of difficult tracheal intubation, but no clinical result reliably excludes difficult intubation. Ultrasound as an adjunct to clinical examination can provide the clinician with a dynamic anatomical airway assessment, which is impossible with clinical examination alone. In the hands of anesthesiologists, ultrasound is becoming more popular in the perioperative period. This method is particularly applicable for identifying proper endotracheal tube positioning in specific patient populations, such as those who are morbidly obese and patients with head and neck cancer or trauma. The focus is on identifying the normal anatomy, correctly positioning the endotracheal tube, and refining the parameters that predict difficult intubation. Several ultrasound measurements are clinical indicators of difficult direct laryngoscopy in the literature. A meta-analysis revealed that the distance from the skin to the epiglottis (DSE) is most associated with a difficult laryngoscopy. An ultrasound of the airway could be applied in routine practice as an adjunct to the clinical examination. A full stomach, rapid sequence intubation, gross visual anatomical abnormalities, and restricted neck flexibility prevent using ultrasound to assess the airway. The airway evaluation is performed with a linear array transducer of 12-4 MHz, with the patient in the supine position, with no pillow, and with the head and neck in a neutral position. The central axis of the neck is where the ultrasound parameters are measured. These image acquisitions guide the standard ultrasound examination of the airway.

A point-of-care ultrasound (POCUS) is a simple, non-invasive, and portable tool that enables dynamic airway assessment. Several studies have attempted to determine the role of ultrasound parameters as an adjunct to clinical examination in predicting difficult laryngoscopies.

Airway management remains a crucial part of perioperative care. The conventional approach to assessing potentially difficult airways emphasizes the LEMON ...

Optic Nerve Sheath Point of Care Ultrasound: Image Acquisition

The goal of this protocol is to develop a standardized method for acquiring images of the optic nerve sheath and measuring the optic nerve sheath diameter (ONSD). Diagnostic ultrasound of the ONSD to detect intracranial hypertension has traditionally faced many problems because of methodologic discrepancies. Due to inconsistencies in the measuring techniques, the potential for ONSD to become a non-invasive bedside monitoring tool for ICP has been hampered. However, establishing a transparent, consistent methodology for measuring the ONSD would support its use as a valid and reliable method of identifying intracranial hypertension. This is important as it has both high sensitivity and specificity in acute care settings. This narrative review describes ONSD POCUS image acquisition, including patient positioning, transducer selection, probe placement, the acquisition sequence, and image optimization. Further, visual aids are provided to assist in real-time during image acquisition. This method should be considered for patients for whom there are concerns regarding intracranial hypertension but who do not have an intracranial monitor in place.

Point-of-care ultrasound (POCUS) of the optic nerve sheath diameter (ONSD) has been shown to be useful in identifying patients with increased intracranial pressure (ICP). However, the non-standardized technique for this POCUS has hampered its use. We present a standardized image acquisition protocol for use in the acute care setting.

The goal of this protocol is to develop a standardized method for acquiring images of the optic nerve sheath and measuring the optic nerve sheath diameter ...

Gastric Ultrasound Technique for Mitigating Peri-Procedural Aspiration Risk

Gastric Point of Care Ultrasound in Adults: Image Acquisition and Interpretation

Over the past two decades, diagnostic point-of-care ultrasound (POCUS) has emerged as a rapid and non-invasive bedside tool for addressing clinical inquiries related to gastric content. One emerging concern pertains to patients about to undergo sedation and/or endotracheal intubation: the elevated risk of aspiration from the patient&#39;s stomach contents. Aspiration of gastric contents into the lungs poses a serious and potentially life-threatening complication. This occurs more frequently when the stomach is considered &#34;full&#34; and can be affected by the techniques employed for airway management, making it potentially preventable. To mitigate the risk of peri-procedural aspiration, two distinct medical specialties (anesthesiology and critical care medicine) have independently developed techniques to utilize ultrasonography for identifying patients requiring &#34;full stomach&#34; precautions. Due to these separate specialties, the work of each group remains relatively unfamiliar outside its respective field. This article presents descriptions of both techniques for gastric ultrasound. Furthermore, it explains how these approaches can complement each other when one of them falls short. Regarding image acquisition, the article covers the following topics: indications and contraindications, selection of the appropriate probe, patient positioning, and troubleshooting. The article also delves into image interpretation, complete with example images. Additionally, it demonstrates how one of the two techniques can be employed to estimate gastric fluid volume. Lastly, the article briefly discusses medical decision-making based on the findings of this examination.

Author Spotlight: Point-of-Care Ultrasound for Gastric Content Assessment and Risk Stratification in Perioperative Care 

This protocol introduces two methods for image acquisition in gastric ultrasonography. Additionally, tips are provided for interpreting this information to assist in medical decision-making.

Over the past two decades, diagnostic point-of-care ultrasound (POCUS) has emerged as a rapid and non-invasive bedside tool for addressing clinical ...