Racial, Ethnic, and Socioeconomic Disparities in Access to Transcatheter Aortic Valve Replacement Within Major Metropolitan Areas

Key Points

Question  Are there socioeconomic, racial, and ethnic disparities in access to transcatheter aortic valve replacement (TAVR) services among patients living within geographic proximity to a TAVR-capable hospital?

Findings  In this observational cohort analysis of 7590 individual zip codes, zip codes with higher proportions of socioeconomically disadvantaged, Black, and Hispanic populations had lower rates of TAVR compared with zip codes with more affluent and White populations.

Meaning  In addition to geographic proximity, accessing health care services requires surmounting other structural barriers to high-quality care.

Importance  Despite the benefits of high-technology therapeutics, inequitable access to these technologies may generate disparities in care.

Objective  To examine the association between zip code–level racial, ethnic, and socioeconomic composition and rates of transcatheter aortic valve replacement (TAVR) among Medicare patients living within large metropolitan areas with TAVR programs.

Design, Setting, and Participants  This multicenter, nationwide cross-sectional analysis of Medicare claims data between January 1, 2012, and December 31, 2018, included beneficiaries of fee-for-service Medicare who were 66 years or older living in the 25 largest metropolitan core-based statistical areas.

Exposure  Receipt of TAVR.

Main Outcomes and Measures  The association between zip code–level racial, ethnic, and socioeconomic composition and rates of TAVR per 100 000 Medicare beneficiaries.

Results  Within the studied metropolitan areas, there were 7590 individual zip codes. The mean (SD) age of Medicare beneficiaries within these areas was 71.4 (2.0) years, a mean (SD) of 47.6% (5.8%) of beneficiaries were men, and a mean (SD) of 4.0% (7.0%) were Asian, 11.1% (18.9%) were Black, 8.0% (12.9%) were Hispanic, and 73.8% (24.9%) were White. The mean number of TAVRs per 100 000 Medicare beneficiaries by zip code was 249 (IQR, 0-429). For each $1000 decrease in median household income, the number of TAVR procedures performed per 100 000 Medicare beneficiaries was 0.2% (95% CI, 0.1%-0.4%) lower (P = .002). For each 1% increase in the proportion of patients who were dually eligible for Medicaid services, the number of TAVR procedures performed per 100 000 Medicare beneficiaries was 2.1% (95% CI, 1.3%-2.9%) lower (P < .001). For each 1-unit increase in the Distressed Communities Index score, the number of TAVR procedures performed per 100 000 Medicare beneficiaries was 0.4% (95% CI, 0.2%-0.5%) lower (P < .001). Rates of TAVR were lower in zip codes with higher proportions of patients of Black race and Hispanic ethnicity, despite adjusting for socioeconomic markers, age, and clinical comorbidities.

Conclusions and Relevance  Within major metropolitan areas in the US with TAVR programs, zip codes with higher proportions of Black and Hispanic patients and those with greater socioeconomic disadvantages had lower rates of TAVR, adjusting for age and clinical comorbidities. Whether this reflects a different burden of symptomatic aortic stenosis by race and socioeconomic status or disparities in use of TAVR requires further study.

Transcatheter aortic valve replacement (TAVR) transformed the management of aortic stenosis (AS) when it was approved by the US Food and Drug Administration in 2011 by expanding the population of patients eligible for life-prolonging valve replacement to include patients who were previously ineligible for surgical aortic valve replacement (SAVR).^1-4 Subsequently, TAVR has been shown to offer a noninferior alternative to SAVR for patients at lower risk.^5-7 However, the complexity of TAVR, along with regulatory requirements for TAVR, has led to concerns regarding inequities in access to this procedure.^8-10

Indeed, the considerable numerical growth in TAVR programs in the US over the previous decade has not been equitable. Hospitals starting TAVR programs cared for patients with greater wealth compared with similar hospitals that did not start TAVR programs.¹¹ Additionally, communities of lower socioeconomic status had lower TAVR rates than communities of higher socioeconomic status, although questions remain on the prevalence of symptomatic aortic stenosis in different populations.¹¹ Some of this disparity is explained by geographic factors; most TAVR centers opened in metropolitan areas with existing TAVR centers, and few TAVR centers were initially located in rural areas.^11,12

However, physical proximity may not be sufficient to guarantee access to a procedure. Biases in initial diagnosis, care delivery, referral patterns, social determinants of health (such as the ability to miss work for medical appointments), availability of transportation to subspecialist appointments and testing, language barriers, lack of trust in the health care system, and other structural factors may also represent barriers to accessing high-technology health care for vulnerable populations.¹³ To better understand the association of barriers other than physical proximity and insurance status with accessing TAVR, we examined the association between zip code–level racial, ethnic, and socioeconomic composition and rates of TAVR among Medicare patients living within large metropolitan areas with TAVR programs.

This study was determined to be exempt from review by the institutional review board at the University of Pennsylvania because deidentified patient information was used in the analysis. Consent was not obtained from individual patients, given the use of an administrative dataset.

Patient and hospital zip code data were obtained from the Medicare Hospital Data Claims and Demographic Data files. Patients and hospitals were assigned to individual core-based statistical areas (CBSAs) using zip code information from US Department of Housing crosswalk files. The CBSAs, as defined by the US Office of Management and Budget, are distinct geographic areas consisting of an urban center and surrounding counties that are socioeconomically linked to the urban center by commuting, representing areas where people work and live. The Office of Management and Budget defines metropolitan areas as urban clusters of at least 50 000 people.

Among metropolitan CBSAs with at least 1 hospital with a TAVR program, we identified the 25 largest metropolitan CBSAs by population as of the 2010 US census.¹⁴ We defined a hospital as having a TAVR program if they performed at least 1 TAVR between January 1, 2012, and December 31, 2018.

Within each identified CBSA, the Medicare Provider Analysis and Review data files and the Master Beneficiary Summary data files were used to identify Medicare fee-for-service beneficiaries 66 years or older who underwent TAVR. An age cutoff of 66 years, rather than the eligibility cutoff age of 65 years, was chosen to allow for a 1-year lookback period to assess comorbidities. Patients undergoing TAVR were identified using International Classification of Diseases, Ninth Edition procedure codes (35.05 and 35.06) and International Classification of Diseases, Tenth Edition procedure codes (02RF37Z, 02RF38Z, 02RF3JZ, 02RF3KZ, 02RF37H, 02RF38H, 02RF3JH, and 02RF3KH).

For Medicare beneficiaries within metropolitan CBSAs, race and ethnicity were determined from Medicare Demographic Data files. Socioeconomic status of Medicare fee-for-service beneficiaries within each metropolitan CBSA was assessed using 3 measures. First, we identified median household income based on zip code using the Dartmouth Atlas.¹⁵ Second, we assessed dual-eligibility status for Medicaid using the Medicare Denominator files, which have been used previously as a measure of poverty and socioeconomic disadvantage.¹⁶ Third, we identified the Distressed Communities Index (DCI) score based on zip code using provided crosswalk files.¹⁷ The DCI combines 7 economic indicators (percentage of population with a high school diploma, housing vacancy rate, percentage of adults not working, poverty rate, median income ratio, change in employment, and change in business establishments) to generate a single index score, with a range from 0 (least distressed) to 100 (most distressed). The DCI is available at the zip code level for areas containing at least 50 000 people (missing in 33% of zip codes in metropolitan CBSAs).

Baseline characteristics of Medicare beneficiaries residing within the 25 largest metropolitan CBSAs with a TAVR center are presented. Continuous variables are presented as means (SDs) or medians (IQRs), and categorial variables are presented as counts with proportions.

The primary outcome of interest was the age-adjusted rate of TAVR per 100 000 Medicare beneficiaries for each zip code within the 25 largest metropolitan CBSA during the entire study period. Age adjustment was performed by dividing the study population in each zip code into 10-year age increments (66-75, 76-85, and ≥86 years) and taking the ratio of the observed rate of TAVR per 100 000 Medicare beneficiaries in an age bracket within the zip code to the rate of TAVR per 100 000 Medicare beneficiaries nationwide in that age bracket. To calculate the age-adjusted TAVR rate per zip code, the ratio for each age bracket was then multiplied by the overall nationwide TAVR rate per 100 000 Medicare beneficiaries, and these products were summed across age groups.¹⁸ Age-adjusted TAVR rates were compared across zip code–level tertiles of socioeconomic indicators using the Kruskal-Wallis test.

To visualize variation in rates of TAVR within metropolitan CBSAs, we plotted choropleths, or color-coded maps, demonstrating age-adjusted rates of TAVR by zip code. To visualize the association between TAVR rates and zip code–level racial, ethnic, and socioeconomic composition, we generated corresponding zip code–level choropleths of the proportion of patients who were dually eligible for Medicaid and the proportion of Black or Hispanic patients. Choropleths of median household income and DCI were similar to choropleths of dual eligibility for Medicaid services and are not included for clarity.

We then modeled the association between zip code–level racial, ethnic, and socioeconomic composition and rates of TAVR per 100 000 Medicare beneficiaries using generalized linear models with a Poisson distribution and a log-link function. The dependent variable was the number of TAVRs performed per 100 000 Medicare beneficiaries for each zip code in the studied metropolitan CBSAs. In the primary model, we adjusted for age, geographic region (Northeast, Midwest, South, and West), and clinical comorbidities (Table 1).¹⁹ Each of the 3 indicators for socioeconomic status (median household income, proportion of beneficiaries dually eligible for Medicaid, and mean DCI score) was introduced separately as covariates into the model. Clustering was performed at the level of individual CBSAs using generalized estimating equations with an exchangeable correlation structure. In the secondary model, we added covariates for the proportion of Black or Hispanic beneficiaries within each zip code, as well as an interaction parameter between Black race and median household income and between Hispanic ethnicity and median household income.

As sensitivity analyses, we assessed the association between race, ethnicity, and markers of socioeconomic status within a zip code and the total number of aortic valve replacements performed (both TAVR and SAVR), as well as SAVR alone, to understand if disparities existed for all major treatments for this condition. The primary and secondary models previously described were used; however, the dependent variable in the models were the number of aortic valve replacements (TAVR plus SAVR) and SAVRs performed per 100 000 Medicare beneficiaries, respectively.

Statistical analyses were performed using SAS version 9.4 (SAS Institute), and choropleths were generated using R Studio version 1.3.959 (R Foundation for Statistical Computing). All statistical testing was 2-tailed, with P values <.05 designated statistically significant. Data analysis was performed between July 1, 2020, and July 1, 2021.

Within the 25 largest CBSAs with TAVR programs between January 1, 2012, and December 31, 2018, there were 7590 individual zip codes (eTable 1 in the Supplement). The median (IQR) number of TAVR centers per CBSA was 7 (5-11). The mean (SD) age of Medicare beneficiaries within these areas was 71.4 (2.0) years (Table 1), a mean (SD) of 47.6% (5.8%) of beneficiaries were men, and a mean (SD) of 4.0% (7.0%) were Asian, 11.1% (18.9%) were Black, 8.0% (12.9%) were Hispanic, and 73.8% (24.9%) were White. The median (IQR) household income was $62 348 ($46 559-$83 206), the median (IQR) proportion of patients dually eligible for Medicaid was 7.1% (4.1%-13.4%), and the median (IQR) DCI score was 28.6 (11.9-56.1).

Within the studied major metropolitan CBSAs with TAVR programs, the median (IQR) rate of TAVR per 100 000 Medicare beneficiaries by zip code was 249 (0-429). Unadjusted rates of TAVR per 100 000 Medicare beneficiaries were lower among Black and Hispanic patients compared with White patients across each tertile of median household income (eg, in the highest income tertile: Asian populations, 113; Black populations, 154; Hispanic populations, 194; White populations, 451 per 100 000 Medicare beneficiaries), dual-eligibility status for Medicaid services (Asian populations, 93; Black populations, 98; Hispanic populations, 155; White populations, 224 per 100 000 Medicare beneficiaries), and each tertile of DCI (eg, least distressed tertile: Asian populations, 109; Black populations, 144; Hispanic populations, 181; White populations, 425 per 100 000 Medicare beneficiaries) (eTable 2 in the Supplement).

When we divided zip codes into tertiles by markers of socioeconomic status, age-adjusted rates of TAVR were higher in zip codes with higher median household incomes (median [IQR], 317.5 [135-499] TAVR per 100 000 beneficiaries) compared with lower median incomes (median [IQR], 170 [0-324] TAVR per 100 000 beneficiaries; P < .001) (Table 2). Age-adjusted rates of TAVR were higher in the zip codes with the lowest proportion of patients dually eligible for Medicaid (median [IQR], 310 [0-499] TAVR per 100 000 beneficiaries) compared with those with the highest proportion (median [IQR], 178.5 [0-324] TAVR per 100 000 beneficiaries; P < .001). These rates were also higher in zip codes with low DCI (median [IQR], 343 [234-489] TAVR per 100 000 beneficiaries) compared with high DCI (median [IQR], 215 [107-345] TAVR per 100 000 beneficiaries; P < .001).

Choropleths of the studied metropolitan CBSAs with zip code levels of age-adjusted rates of TAVR per 100 000 Medicare beneficiaries, as well as median household incomes and proportions of Black and Hispanic patients were generated. As an example, the Philadelphia, Pennsylvania–Camden, New Jersey–Wilmington, Delaware CBSA is presented in the Figure. Suburban areas immediately to the west of the city are highly affluent, have lower rates of Black and/or Hispanic patients, and had high relative local rates of TAVR per 100 000 Medicare beneficiaries. Among all studied CBSAs, zip codes with higher age-adjusted rates of TAVR colocalized with those having a lower proportion of Black and/or Hispanic patients and those having a smaller proportion of patients dually eligible for Medicaid services (eFigure in the Supplement).

Results of the primary model to understand the association between markers of socioeconomic status within each zip code with rates of TAVR per 100 000 Medicare beneficiaries, adjusting for age and clinical comorbidities, are presented in Table 3. For each $1000 decrease in median household income, the number of TAVR procedures performed per 100 000 Medicare beneficiaries was 0.2% (95% CI, 0.1%-0.4%) lower (P = .002). For each 1% increase in the proportion of patients dually eligible for Medicaid services, the number of TAVR procedures performed per 100 000 Medicare beneficiaries was 2.1% (95% CI, 1.3%-2.9%) lower (P < .001). For each 1-unit increase in the DCI score, the number of TAVR procedures performed per 100 000 Medicare beneficiaries was 0.4% (95% CI, 0.2%-0.5%) lower (P < .001). In sensitivity analyses examining the association between markers of socioeconomic status and rates of all aortic valve replacement (TAVR plus SAVR) or SAVR alone, results were similar (eTables 3 and 4 in the Supplement).

The associations of race and ethnicity with rates of TAVR per 100 000 Medicare beneficiaries from the secondary model are presented in Table 4. After adjustment for median household income, for each 1% increase in the proportion of Black patients within a zip code, the number of TAVR procedures performed per 100 000 Medicare beneficiaries decreased by 1.1% (95% CI, 0.6%-1.7%; P < .001), and for each 1% increase in the proportion of Hispanic patients, the number of TAVR procedures decreased by 1.2% (95% CI, 0.2%-2.2%; P = .03). There was no significant interaction between median household income and Black race or Hispanic ethnicity. Results were similar when proportion of patients dually eligible for Medicaid services and DCI were entered into the model as markers of socioeconomic status. In a sensitivity analysis examining the association between markers of race and ethnicity and rates of all aortic valve replacement (TAVR plus SAVR) or SAVR alone, results were similar (eTables 5 and 6 in the Supplement).

In this analysis of major metropolitan areas within the US, we evaluated racial, ethnic, and socioeconomic disparities in TAVR despite geographic proximity to hospitals that offer this procedure. We found that zip codes with markers of low socioeconomic status had lower rates of TAVR after adjusting for age and other clinical comorbidities. Further, areas with higher proportions of patients of Black race and Hispanic ethnicity had lower rates of TAVR, even after adjusting for indicators of socioeconomic status, age, and clinical comorbidities. While it is unclear whether this reflects different burdens of symptomatic aortic stenosis by race and socioeconomic status or disparities in use of TAVR, these findings may suggest that access to high-technology therapeutics require more than geographic proximity and adequate health insurance and systemic barriers can limit the receipt of high-technology health care by marginalized populations.

While all forms of health care services are subject to potential barriers to accessing care, high-technology procedures may be particularly vulnerable to inequitable uptake, especially among marginalized groups. The disparate availability of a technology may contribute to the presence of structural racism within medicine, which is defined as macrolevel systems, societal forces, institutions, ideologies, and processes that generate and reinforce inequities among racial and ethnic groups.^20,21 Preferential access to health care among only privileged racial and ethnic groups will result in differences in health and quality of life. Racial and ethnic segregation of people living in metropolitan areas may result in the concentration of vulnerable groups in areas of poverty, where less public investment may make it more difficult to access care despite seeming geographic proximity to major advanced medical centers.^22,23

The procedures TAVR (and SAVR) may be uniquely vulnerable to disparate access among vulnerable racial and ethnic groups and patients with socioeconomic disadvantages, given a complex treatment pathway requiring numerous office visits with multiple physicians and extensive preprocedural testing.²⁴ During this process, social determinants of health, including an inability to afford copayments, inadequate social supports, difficulty with transportation, and a lack of investment in infrastructure among disadvantaged communities, may prevent patients who could benefit from TAVR from ultimately receiving the procedure. Importantly, although CBSAs represent communities linked by commuting patterns and travel within the CBSA should not be prohibitive, some patients within a CBSA may not have the resources to travel to a TAVR center. Furthermore, language barriers and a lack of trust in the US health care system, given a long history of discrimination, may also contribute to the findings among certain patient groups.²⁵

Prior studies^11,12 have demonstrated an urban-rural divide in the availability of TAVR services, with few TAVR centers present in rural areas. However, within major metropolitan areas with existing TAVR programs, in which the local population should have ready geographic access to a hospital capable of TAVR, there was tremendous racial, ethnic, and socioeconomic variation in TAVR rates, which we used as a surrogate for procedure availability. Areas with higher concentrations of vulnerable racial, ethnic, and socioeconomic groups had significantly lower rates of TAVR, despite adjustment for age and clinical comorbidities. Race and ethnicity were independently associated with lower rates of TAVR, even after adjustment for socioeconomic markers. Importantly, our sensitivity analyses assessing disparities in the rate of aortic valve replacement, both TAVR and SAVR, and SAVR alone paralleled our primary findings. The SAVR procedure is the alternative treatment modality for aortic stenosis, and we continued to find lower rates of SAVR in marginalized populations. The associations of inequities with TAVR and SAVR were similar. Although SAVR has fewer mandatory specialist visits, these are both high-technology procedures requiring extensive preprocedural testing, including echocardiography, pulmonary function testing, preprocedure cardiac catheterization, and laboratory work. The extent of preprocedural testing may make both procedures equally inaccessible to vulnerable groups. These data highlight the fact that observed zip code–level differences in TAVR rates are reflective of disparities in the entire process of aortic stenosis care, from diagnosis to referral to treatment.

These findings are highly suggestive of the presence of racial, ethnic, and socioeconomic barriers in access to TAVR and SAVR within major metropolitan areas. While TAVR and SAVR are the result of a complex referral pathway, patients in marginalized groups likely face barriers at multiple steps, from initial diagnosis at the primary care level to diagnostic testing and subsequent referral to a heart valve team. Achieving equitable delivery of appropriate aortic stenosis care will require specific efforts to identify and surmount those barriers, targeted toward communities in metropolitan areas with lower aortic valve replacement rates. One potential solution may be the use of patient navigators, who have been successfully involved in helping patients with cancer and sociocultural vulnerabilities through the health care system by addressing language and cultural barriers and fostering trust and empowerment among previously marginalized groups.²⁶ It may be possible to adapt these processes for patients with valvular heart disease; however, the care pathways for patients with aortic stenosis and cancer are different, and research focusing on the specific barriers to aortic stenosis care among patients from marginalized groups in major metropolitan areas is needed.

It is important to note that questions remain on the biology of aortic stenosis as a function of ancestry, since few large-scale, population-based screening and natural history studies have been performed. Although our study design does not allow us to assess the burden of undiagnosed aortic stenosis among racial and ethnic groups, inequities within medicine may not only limit the ability of patients in marginalized groups to progress through the referral pathway but also from being diagnosed with aortic stenosis at all, as these patients may not have ready access to cardiology consultation or cardiac testing.²⁷ While prior studies have postulated lower rates of aortic stenosis among Black patients, this may reflect undertesting in this population, who have had traditionally been underdiagnosed with cardiac diseases.^28-30 In the Multi-Ethnic Study of Atherosclerosis, Black race and Hispanic ethnicity were not associated with differential progression of aortic valve calcium compared with White patients, although Black patients free of aortic stenosis at baseline had a lower rate of developing aortic stenosis over long-term follow-up than White patients.^31,32 Moreover, there do not appear to be any differences in genetic polymorphisms associated with the progression of aortic stenosis with varying ancestry groups.³³ In the absence of clear evidence of an association between race and ethnicity and development of severe aortic stenosis, our findings of lower rates of TAVR in marginalized populations, despite geographic access to the procedure, suggest that racial, ethnic, and socioeconomic barriers exist in the treatment of these patients. More work needs to be done to understand the true extent of barriers faced by patients with aortic stenosis in marginalized groups, as well as solutions to these disparities.

Our study has several limitations. First, the use of administrative data precludes a granular understanding of the specific reasons why individual patients may not have undergone TAVR, which may include reasons such as local specialist referral patterns or practices, patient preference, or patient values. Despite the reasons underlying varying rates of TAVR, inequities in the age-adjusted rates of TAVR were nevertheless observed in geographic areas with TAVR centers. Second, our analysis was limited to beneficiaries of fee-for-service care and may not be generalizable to patients receiving Medicare Advantage. We were also unable to characterize inequities among those patients without documentation for immigration, who are not eligible for Medicare, although these patients likely face even larger barriers in access to care than the findings highlighted in this study. Third, given that age is strongly correlated with the prevalence of aortic stenosis within a population, our study is vulnerable to some degree of survivor bias in raw estimates, based on differences in life expectancy in different racial and ethnic groups. To address this limitation, all analyses are adjusted for age. We observed significant variability in the rates of TAVR within metropolitan areas, with areas with higher proportions of vulnerable populations having lower rates of TAVR despite adjustments for age and clinical comorbidities.

Within major metropolitan areas in the US with TAVR programs, zip codes with higher proportions of Black and Hispanic patients and patients with greater socioeconomic disadvantages had lower rates of TAVR, adjusting for age and clinical comorbidities. Similar findings were seen in SAVR, suggesting an overall disparity in the management of patients with aortic stenosis. These findings highlight that access to health care services may require more than geographic proximity, but also surmounting structural racial, ethnic, and socioeconomic barriers to high-quality care.

Accepted for Publication: August 19, 2021.

Published Online: November 17, 2021. doi:10.1001/jamacardio.2021.4641

Corresponding Author: Ashwin S. Nathan, MD, MS, Division of Cardiology, Hospital of the University of Pennsylvania, 3400 Civic Center Blvd, South Tower, 11th Floor, Philadelphia, PA 19104 (ashwin.nathan@pennmedicine.upenn.edu).

Author Contributions: Drs Nathan and Yang had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Nathan, Eberly, Khatana, Desai, Szeto, Giri, Fanaroff.

Acquisition, analysis, or interpretation of data: Nathan, L. Yang, N. Yang, Eberly, Dayoub, Vemulapalli, Julien, Cohen, Nallamothu, Baron, Herrmann, Groeneveld, Giri, Fanaroff.

Drafting of the manuscript: Nathan.

Critical revision of the manuscript for important intellectual content: L. Yang, N. Yang, Eberly, Khatana, Dayoub, Vemulapalli, Julien, Cohen, Nallamothu, Baron, Desai, Szeto, Herrmann, Groeneveld, Giri, Fanaroff.

Statistical analysis: Nathan, L. Yang, N. Yang, Desai, Groeneveld, Fanaroff.

Administrative, technical, or material support: Eberly, Dayoub, Groeneveld, Giri, Fanaroff.

Supervision: Julien, Szeto, Groeneveld, Giri, Fanaroff.

Conflict of Interest Disclosures: Dr Cohen has received research grant support and consulting fees from Edwards Lifesciences, Medtronic, Abbott, and Boston Scientific outside the submitted work. Dr Baron has served on an advisory board for Boston Scientific Corporation and Abiomed, has received grant support from Boston Scientific Corporation and Abiomed, and has been a consultant for Abbott, Abiomed, Edwards Lifesciences, and MitraLabs outside the submitted work. Dr Desai reports speaker and consultant fees from Gore, Medtronic, and Terumo and grants from Gore and Medtronic outside the submitted work. Dr Szeto reports investigator and advisory board fees from Edwards Lifesciences and Medtronic. Dr Herrmann reports institutional research funding from Abbott Vascular, Boston Scientific, Edwards Lifesciences, and Medtronic and consulting fees and honoraria from Edwards Lifesciences and Medtronic during the conduct of the study, as well as grants from Ancora, Shockwave, and Gore and equity from Holistick Medical and Microinterventional Devices outside the submitted work. Dr Giri has served on an advisory board for AstraZeneca and received research support to the institution from Recor Medical and St. Jude Medical; Dr Giri also reported personal fees from Boston Scientific, Inari Medical, AstraZeneca, and Philips Medical and grants from Boston Scientific and Inari Medical outside the submitted work. Dr Fanaroff receives research support from the American Heart Association and Boston Scientific and honoraria from the American Heart Association outside the submitted work. Dr Khatana reported grants from National Heart, Lung, and Blood Institute (grant 1K23HL153772-01) and American Heart Association (grant 20CDA35320251) during the conduct of the study. Dr Vemulapalli reported grants from American College of Cardiology, Society of Thoracic Surgeons, Abbott Vascular, and Boston Scientific outside the submitted work; and grants or contracts from the US Food and Drug Administration (Nest CC); National Institutes of Health (R01 and Small Business Innovation Research) and Cytokinetics; consulting and advisory board involvement with Boston Scientific, Janssen, HeartFlow, and the American College of Physicians. Dr Nallamothu reported being a principal investigator or coinvestigator on research grants from the National Institutes of Health, Veterans Administration Health Science Research and Development, the American Heart Association, Apple, Toyota, and Janssen. Dr Nallamothu also receives compensation as editor-in-chief of Circulation: Cardiovascular Quality & Outcomes, a journal of the American Heart Association, and is a coinventor on US utility patent US 9,962,124 as well as a provisional patent application (54423), which both use software technology with signal processing and machine learning to automate the reading of coronary angiograms and are held by the University of Michigan and licensed to AngioInsight Inc, in which he holds ownership shares and receives consultancy fees. The University of Michigan also has filed patents on Dr Nallamothu’s behalf on the use of computer vision for imaging applications in gastroenterology, with technology elements licensed to Applied Morphomics Inc, in which he has no relationship or stake. No other disclosures were reported.