Objectives:Embedded pragmatic clinical trials (ePCTs) are conducted as part of routine clinical care and therefore use data collected from real-world data sources, such as electronic health record systems and administrative claims. A common approach for using these types of data across all phases of trial conduct is to create a computable phenotype (an explicitly defined data query data, including specified data types, data codes, and logical parameters) to capture patients with a clinical condition, exposure, symptom, characteristic, treatment, or outcome of interest. Materials and Methods:The Electronic Health Record (EHR) Core Working Group of the NIH Pragmatic Trials Collaboratory captured the experiences of investigators in developing, adapting, and applying computable phenotype definitions in their studies. Results:Four case studies describe different approaches to developing and using computable phenotypes in pragmatic trials. Discussion:We recommend: (1) developing computable phenotypes as part of a team with multiple areas of expertise; (2) appropriate validation; (3) dissemination of salient details regarding phenotype creation; and (4) capturing and reporting any modifications made to phenotypes during the conduct of the trial. Conclusion:A range of issues and decisions influence how computable phenotypes are developed and used in pragmatic clinical trials. Multidisciplinary teams that understand the context of the data, including the reason the data are collected and potential sources of bias, are best suited for the development and validation of phenotypes for ePCTs.

OBJECTIVE:The PATHWAYS Study utilized data from the PCORnet® Common Data Model (CDM) at 4 sites participating in the STAR Clinical Research Network to assess the frequency of cardiology encounters for under-represented racial and ethnic minority group people living with Human Immunodeficiency Virus and to evaluate the determinants associated with specialty encounters from 2014 to 2020. This study dealt with several factors that other projects leveraging PCORnet might face. We describe benefits of working with the network, challenges, and recommendations for future study teams. METHODS:PATHWAYS used a mix of queries through the study, including study-specific data quality and analytic queries. A "sidecar" table was created for the PCORnet® Common Data Model to support the inclusion of referral data. Linkage to the National Death Index was incorporated into the study to allow for more comprehensive information on participant deaths. RESULTS:Data quality assessments identified several issues over the course of the study that needed to be addressed by the data teams at each site. The referral data proved not to be robust enough to support the proposed analyses, so an alternative strategy was required that leveraged encounter information. The National Data Index included information on participant deaths that were not part of each site's PCORnet® CDM. CONCLUSION:Incorporating study-specific data characterization into the overall analysis plan is important. When working with new data, or variables not commonly used within studies, teams should include time and effort for site resources to investigate their local clinical workflows and potential mappings to the PCORnet® CDM.

BACKGROUND:Institutions that participate in PCORnet® transform their local electronic health record (EHR) data into the PCORnet® Common Data Model (CDM), which is then used to generate data extracts for PCORnet® Studies. PCORnet® Studies can also include institutions that do not participate in PCORnet, and for these organizations, the cost of instantiating a PCORnet® CDM can be prohibitive. Fast Health care Interoperability Resources (FHIR) provides an alternative method of obtaining EHR data. OBJECTIVE:To determine whether data obtained through FHIR might be a viable study solution for those sites that do not participate in PCORnet.® This mixed-methods project had 2 objectives: (1) survey sites participating in PCORnet on the availability of FHIR (FHIR survey); (2) compare the coverage of a FHIR-based data extract using REDCap with one from the PCORnet® CDM across 3 sites (FHIR extract). METHODS:(1) FHIR survey: A series of questions were asked about the use of FHIR in a production capacity. (2) FHIR extract: REDCap FHIR and PCORnet® CDM extracts were created based on study variables from 2 prior PCORnet® Studies. Data were extracted for 40 patients and concordance measures were computed between the 2 sources. RESULTS:(1) FHIR survey: Of responding organizations, 73% (n=49) reported that FHIR was deployed in a production capacity. (2) FHIR extract: Results were highly variable. Cohen kappa ranged from 0.01 to 0.76 for certain diagnoses, 0.24 to 0.84 for laboratory results, and 0.1 to 0.87 for medications. CONCLUSIONS:Despite differences in data, certain studies may be well-suited for FHIR-based extracts.

Objectives/Goals: We aimed to evaluate the representativeness of the patient population that is accessible through PCORnet®, both at the network level and within individual studies that enroll patients using the network, and to develop concrete recommendations regarding the design and evaluation of future studies. Methods/Study Population: We evaluated potential study participants that could be recruited through PCORnet, defined as patients with a healthcare encounter and any diagnosis recorded at a participating network site in 2023, relative to US population data from the American Community Survey (ACS) with respect to demographic, geographic, and socioeconomic summary data. Individual studies, conducted using the network, rarely involve all PCORnet patients, but rather those who meet clinical eligibility criteria and pass through a series of operational filters, such as site selection. We evaluated all patients at sites that have participated in at least 10 studies, as well as individual studies published in the COVID-19 clinical domain, compared to corresponding, eligible patients across the entire network. Results/Anticipated Results: PCORnet included data from over 47 million people across the USA with everyday healthcare encounters in 2023. This population of potential study participants was generally representative of the US population, though less likely to be from rural communities (11.7% vs. 18.4%) and low socioeconomic status (SES) (18.9% vs 25%). Patients from sites that frequently participate in PCORnet studies (10+) were representative of PCORnet overall, with an opportunity to improve inclusion of Hispanic populations. Among individual studies in the COVID-19 clinical domain, retrospective studies were largely representative, but prospective studies demonstrated large gaps in representation. Discussion/Significance of Impact: PCORnet provides access to a representative population of patients receiving healthcare. Differences in SES and rurality may be explained by differences in healthcare access. Population-level data, available through PCORnet, can be used to improve study design and evaluation, particularly for prospective studies.

IntroductionA tremendous amount of clinical data is collected and stored in electronic health records (EHRs). Whether this data can be harnessed at scale to facilitate clinical trial conduct remains to be seen.MethodsThe Effect of Evolocumab in Patients at High Cardiovascular Risk Without Prior Myocardial Infarction or Stroke (VESALIUS-CV) trial included an embedded study evaluating the fitness-for-use of EHR data to ascertain baseline demographics (age, sex, race/ethnicity, history of coronary disease, cerebrovascular disease, peripheral artery disease, heart failure, diabetes, hypertension, and atrial fibrillation), laboratory (creatinine, lipid values), and outcomes (myocardial infarction, stroke, revascularization, and heart failure) data, compared to trial data collected through a study case report form (CRF). These are described using concordance statistics (overall agreement, sensitivity, specificity, negative predictive value, positive predictive value, kappa statistic and bias).ResultsThe VESALIUS-CV EHR substudy included 75 participants (9 sites). For categorical baseline variables, the overall agreement between EHR and CRF data ranged between 81.3 and 98.7%, sensitivity ranged between 42.9 and 100%, specificity ranged between 50.0 and 100%, and kappa statistic ranged between 0.2 and 0.8. Identification of a history of peripheral artery disease in EHR data was poor (positive predictive value 23.1%). Of 431 continuous variables, bias was generally low but was associated with moderate imprecision, with exact matches observed in 95.8% of cases. Of a total of 20 outcome events, the overall agreement ranged between 86.7 and 100%, sensitivity ranged between 50 and 100%, specificity ranged between 86.7 and 100%, and kappa statistic ranged between 0.7 and 1.0.ConclusionEHR data has the potential to facilitate and reduce the burden of data collection for clinical trials, but further work is required to optimize data extraction and improve accuracy.

Purpose:We describe the steps taken to assess and improve the research readiness of data within PCORnet®, specifically focusing on the results of the PCORnet data curation process between Cycle 7 (October 2019) and Cycle 16 (October 2024). Material and methods:We describe the process for extending the PCORnet® CDM and for creating data checks. Results:We highlight growth in the number of records available across PCORnet between data curation Cycles 7 and 16 (e.g., diagnoses increasing from ∼3.7B to ∼6.9B and laboratory results from ∼7.7B to ∼15.1B among legacy DataMarts), present the current list of data checks and describe performance of the network. We highlight examples of data checks with relatively stable performance (e.g., future dates), those where performance has improved (e.g., RxNorm mapping), and others performance is more variable (e.g., persistence of records). Conclusion:Studies are a crucial source of information on the design of new data checks. The attention of PCORnet partners is focused primarily on those metrics that are generally modifiable. A transparent data curation process is an essential component of PCORnet, allowing network partners to learn from one another, while also informing the decisions of study investigators on which sites to include in their projects. The quality issues that exist within PCORnet stem from the way that data are captured within healthcare generally. We have been able to make to make great strides on improving data quality and research readiness. Many of the techniques piloted within PCORnet will be broadly applicable to other efforts.

Background People with HIV (PWH) have elevated cardiovascular risk. Underrepresented racial and ethnic groups in the southern United States are disproportionately affected, yet whether cardiology care for this at‐risk group improves blood pressure and lipid control or prevents cardiovascular events is unknown. Methods and Results We evaluated a cohort of PWH from underrepresented racial and ethnic groups who received HIV‐related care at 4 centers in the southern United States during 2015 to 2018 with follow‐up through 2020. Primary outcomes were blood pressure control (<140/90 mm Hg) and lipid control (low‐density lipoprotein cholesterol ≤100 mg/dL) over 2 years and time to first major adverse cardiovascular event. Statistical analyses were adjusted for cohort/site and patient sociodemographic factors, HIV measures, and comorbidities. Among 3972 included PWH (median age, 47 years; 32.6% women) without diagnosed cardiovascular disease, 276 (6.9%) had a cardiology clinic visit. Cardiology visits were not associated with subsequent blood pressure control (adjusted odds ratio, 0.78 [95% CI, 0.49–1.24]; P=0.29) or lipid control (adjusted odds ratio, 2.25 [95% CI, 0.72–7.01]; P=0.16). Over 5‐year follow‐up, patients who had a cardiology visit had a higher risk of a major adverse cardiovascular event, death, and falsification end points, even after adjusting for measured risk factors. Conclusions Among PWH from underrepresented racial and ethnic groups at elevated risk for cardiovascular disease, a cardiology clinic visit was not associated with risk factor improvement or reduced risk of a major adverse cardiovascular event. Our study suggests that seeing a cardiologist is not sufficient to promote cardiovascular health or prevent cardiovascular events among PWH, but with low confidence given the higher risk among those who had a cardiology visit.

BACKGROUND:The PCORnet® infrastructure was funded by PCORI in 2014 to streamline clinical trials, increase patient-centered research, and generate knowledge that leads to improved health care and outcomes. In this paper, we summarize the significant achievements of the infrastructure over the last decade as well as recent accomplishments. We also provide an update on the expanded patient population who receive care at sites participating in PCORnet® Clinical Research Networks (CRNs) and share priorities for the future. METHODS:The electronic health records of 71 health systems participating in PCORnet® CRNs as of July 2024 were queried, and data were analyzed to describe 10 common health conditions, stratified by demographic characteristics of age, sex, race, ethnicity, and an index of social deprivation. RESULTS:Out of over 100M total patients with activity in the last 10 years, health systems participating in PCORnet® CRNs had over 47 million unique patients with at least one encounter in 2023. The most common chronic conditions among these patients were hypertension (18%), anxiety disorders (10%), type 2 diabetes (8%), and asthma (5%). Over 20% of patients receiving care at a site participating in PCORnet were in the top 50% of metrics for area deprivation. The PCORnet infrastructure supported 51 PCORnet® studies, all of which met established guidelines for use of the PCORnet® Common Data Model (CDM), patient-engagement, and commitment to return of results. DISCUSSION:PCORnet® CRNs represent a diverse and expanding patient population and often include data on the socioeconomic status of the communities. Through continued efforts to engage communities and patients and national-scale research, the PCORnet® infrastructure can help improve care and outcomes for patients affected by common and rare conditions.

Background: Tirzepatide is a dual glucose-dependent insulinotropic polypeptide and glucagon like peptide-1 receptor agonist that has been shown to have cardiometabolic benefits. However, the real-world eligibility, uptake, and population level impact remains unclear. Research Question/Aims: To describe patients in the U.S. who meet an indication for tirzepatide, to understand its real-world uptake, and to estimate the population level impact on cardiometabolic risk factors if uptake were to increase. Methods: A retrospective study using electronic medical record data from 5 US health systems in PCORnet® identified a cohort of adults between 3/31/2023-3/31/2024 who were obese (BMI of ≥30 kg/m2) or overweight (27 kg/m2 - < 30 kg/m2) with at least one weight-related co-morbidity). Effect estimates of cardiometabolic risk factor changes from the SURMOUNT-1 trial and SURPASS-2 trial were applied to estimate population-level impacts of tirzepatide on weight and other cardiometabolic risk factors. Results: A total of 2,092,393 individuals had obesity or were overweight with at least one weight related comorbidity. About 25% were overweight; and 39%, 20%, and 16% had class 1, 2 and 3 obesity, respectively. The mean age (SD) was 54 (17.1) years, 57.2% were female, 19.7% were Black or African American, 9.1% were of Hispanic ethnicity, and 83.6% had commercial or government insurance. Approximately 20% had diabetes, and 21% had 4 or more obesity-related comorbidities. Overall, 1.3% (n=26,577) were prescribed any dose of tirzepatide, including 3.9% of those with diabetes and 0.6% of those without diabetes. Among those with diabetes, 7.0% of those with commercial and 2.4% of those with government insurance were prescribed tirzepatide; among those without diabetes, the respective proportions were 0.9% and 0.4%. A substantial reduction in weight was estimated with tirzepatide. The figure shows estimated changes in population-level distribution of weight if tirzepatide (at varying doses) was used by all eligible patients, stratified by diabetes status. Changes in other cardiometabolic risk factors were also estimated to be improved. Conclusions: Across 5 large US health systems, only 1.3% of eligible patients were prescribed tirzepatide. Greater uptake is projected to be associated with population-level improvements in weight and other cardiometabolic risk factors. There is a critical need to overcome access and other barriers to the use of effective obesity medications.

Background: Evaluation of nonvalvular atrial fibrillation/atrial flutter (NVAF) in under-represented racial and ethnic minority groups (UREGs) living with HIV has not been adequately studied. Objectives: The purpose of this study was to describe the incidence of NVAF, identify associated factors, and describe oral anticoagulation (OAC) patterns among UREGs with HIV. Methods: This is a secondary analysis of data collected in PATHWAYS (Pathways to Cardiovascular Disease Prevention and Impact of Specialty Referral in Underrepresented Racial and Ethnic Minorities with HIV; NCT04025125), a retrospective population-based study of HIV care among UREGs with HIV. We investigated the independent associations of cardiovascular and HIV-specific risk factors with incident NVAF using Cox regression analysis and examined appropriate OAC use. Results: From 2015 to 2019, 11,066 UREGs with HIV met entry criteria; 10,945 were without NVAF at baseline. On average, patients were 44 years of age, 67.2% male, 94.4% Black, and 8.5% Hispanic. Average follow-up was 3.4 years, and 63.4% were on antiretroviral therapy. Incidence of NVAF was 4.54 incident cases per 1,000 person-years with a cumulative incidence at one and 5 years after HIV diagnosis of 0.48% and 2.16%, respectively. Age, diabetes, heart failure, severe renal disease, and antiretroviral therapy regimens including a protease inhibitor and/or integrase strand transfer inhibitor were independently associated with incident NVAF. Of those with NVAF meeting qualifying CHA2DS2-VASc scores, only 44.2% received any OAC prescription for stroke prophylaxis. Conclusions: In this cohort of UREGs living with HIV, both traditional and HIV-specific risk factors were associated with incident NVAF. Rates of appropriate OAC prescribing were low.

Placebo-controlled trials of sodium-glucose cotransporter-2 inhibitors (SGLT2i) demonstrate kidney and cardiovascular benefits for people with type 2 diabetes (T2D) and chronic kidney disease (CKD). We used real-world data to compare the kidney and cardiovascular effectiveness of empagliflozin to dipeptidyl peptidase-4 inhibitors (DPP4i), a commonly prescribed antiglycemic medication, in a diverse population with and without CKD. Using electronic health record data from 20 large US health systems, we leveraged propensity overlap weighting to compare outcomes for empagliflozin and DPP4i initiators with T2D between 2016 and 2020. The primary composite kidney outcome included 40% estimated glomerular filtration rate (eGFR) decline, incident end-stage kidney disease (ESKD), or all-cause mortality through 2 years or censoring. We also assessed cardiovascular and safety outcomes. Among 62,197 new users, 20,279 initiated empagliflozin, and 41,918 initiated DPP4i. Over a median follow-up of 1.1 years, empagliflozin prescription was associated with a lower risk of the primary outcome (HR 0.75, 95% CI 0.65-0.87) compared with DPP4i. Risks for mortality (HR 0.76, 95% CI 0.62-0.92) and a cardiovascular composite of stroke, myocardial infarction, or all-cause mortality (HR 0.81, 95% CI 0.70-0.95) were also lower for empagliflozin initiators. No difference in heart failure hospitalization risk between groups was observed. Genital mycotic infections were more common in patients prescribed empagliflozin (HR 1.72, 95% CI 1.58 - 1.88). Empagliflozin was associated with a lower risk of the primary outcome in patients with CKD (HR 0.68, 95% CI 0.53-0.88) and those without CKD (HR 0.79, 95% CI 0.67 - 0.94). In conclusion, initiation of empagliflozin was associated with a significantly lower risk of kidney and cardiovascular outcomes compared with DPP4i over a median of just over 1 year. The association with a lower risk for clinical outcomes was apparent even for people without known CKD at baseline.

Background Emerging data suggest that glucagon-like peptide-1 receptor agonists (GLP-1 RAs) improve kidney outcomes for people with type 2 diabetes (T2D). Direct comparisons of the kidney and cardiovascular effectiveness of GLP-1 RA with sodium-glucose cotransporter 2 inhibitors (SGLT2i), a first-line therapy for this population, are needed. Objectives The authors compared kidney and cardiovascular outcomes for new users of SGLT2i and GLP-1 RAs with T2D. Methods Using propensity score overlap weighting, we analyzed electronic health record data from 20 U.S. health systems contributing to PCORnet between 2015 and 2020. The primary kidney outcome was a composite of sustained 40% estimated glomerular filtration rate (eGFR) decline, incident end-stage kidney disease, or all-cause mortality over 2 years or until censoring. In addition, we examined cardiovascular and safety outcomes. Results The weighted study cohort included 35,004 SGLT2i and 47,268 GLP-1 RA initiators. Over a median of 1.2 years, the primary outcome did not differ between treatments (HR: 0.91; 95% CI: 0.81-1.02), although SGLT2i were associated with a lower risk of 40% eGFR decline (HR: 0.77; 95% CI: 0.65-0.91). Risks of mortality (HR: 1.08; 95% CI: 0.92-1.27), a composite of stroke, myocardial infarction, or death (HR: 1.03; 95% CI: 0.93-1.14), and heart failure hospitalization (HR: 0.95; 95% CI: 0.80-1.13) did not differ. Genital mycotic infections were more common for SGLT2i initiators, but other safety outcomes did not differ. The results were similar regardless of chronic kidney disease status. Conclusions SGLT2i and GLP-1 RAs led to similar kidney and cardiovascular outcomes in people with T2D, though SGLT2i initiation was associated with a lower risk of 40% eGFR decline. (Evaluating Comparative Effectiveness of Empagliflozin in Type 2 Diabetes Population With and Without Chronic Kidney Disease; NCT05465317)

The NIH Pragmatic Trials Collaboratory supports the design and conduct of 27 embedded pragmatic clinical trials, and many of the studies collect patient reported outcome measures as primary or secondary outcomes. Study teams have encountered challenges in the collection of these measures, including challenges related to competing health care system priorities, clinician's buy-in for adoption of patient-reported outcome measures, low adoption and reach of technology in low resource settings, and lack of consensus and standardization of patient-reported outcome measure selection and administration in the electronic health record. In this article, we share case examples and lessons learned, and suggest that, when using patient-reported outcome measures for embedded pragmatic clinical trials, investigators must make important decisions about whether to use data collected from the participating health system's electronic health record, integrate externally collected patient-reported outcome data into the electronic health record, or collect these data in separate systems for their studies.

Introduction & Objective: In placebo-controlled trials, SGLT2 inhibitors (SGLT2i) and GLP-1 receptor agonists (GLP1RA) improve kidney and cardiovascular outcomes. However, direct comparisons are lacking. Methods: We analyzed EHR data from 20 US health systems that contribute to PCORnet. Using propensity scores and overlap weighting, we compared kidney and cardiovascular outcomes for initiators of SGLT2i and GLP1RA with type 2 diabetes between 2015-2020. The primary kidney outcome was a composite of 40% eGFR decline, incident ESKD, or all-cause mortality over 2 years or until censoring. We also examined cardiovascular and safety outcomes. Results: Over a median of 1.2 years, risk for the primary outcome did not differ between the 35,004 SGLT2i and 47,268 GLP1RA initiators, though SGLT2i initiation was associated with lower risk of 40% eGFR decline (Table). Risks for mortality, cardiovascular outcomes, and most safety outcomes did not differ between groups, though genital mycotic infections were more common for SGLT2i initiators. Results were similar in patients with and without CKD. Conclusion: In a large, real-world population with type 2 diabetes, risk for a composite kidney outcome, all-cause mortality, or cardiovascular outcomes was similar between initiators of SGLT2i and GLP1RA, though SGLT2i initiation was associated with a lower risk of 40% eGFR decline. Disclosure D. Edmonston: None. H. Mulder: None. E. Lydon: None. K. Chiswell: None. Z. Lampron: None. C.M. Shay: Employee; Boehringer-Ingelheim. K. Marsolo: Research Support; Boehringer-Ingelheim, Amgen Inc., Bayer Inc., Bristol-Myers Squibb Company, Novartis Pharmaceuticals Corporation, Seqirus, Genentech, Inc., Pfizer Inc. H.B. Bosworth: Research Support; BeBetter Therapeutics, Boehringer-Ingelheim, Improved Patient Outcomes, ESPERION Therapeutics, Inc., Novo Nordisk, Otsuka America Pharmaceutical, Inc., Sanofi, National Institutes of Health, Veterans Administration, elton john foundation. N. Pagidipati: Research Support; Alnylam Pharmaceuticals, Inc., Amgen Inc., Eli Lilly and Company. Consultant; ESPERION Therapeutics, Inc., AstraZeneca. Research Support; Boehringer-Ingelheim, Novartis AG, Novo Nordisk. Consultant; CRISPR Therapeutics. Research Support; Bayer Inc., Merck & Co., Inc. Consultant; Merck & Co., Inc., Boehringer-Ingelheim, Novo Nordisk, Bayer Inc., Novartis Pharmaceuticals Corporation. Other Relationship; Janssen Pharmaceuticals, Inc., Novartis Pharmaceuticals Corporation. Funding Boehringer Ingelheim

Introduction:(1) Assess the harmonization of structured electronic health record data (laboratory results and medications) to reference terminologies and characterize the severity of issues. (2) Identify issues of data completeness by comparing complementary data domains, stratifying by time, care setting, and provenance. Methods:Queries were distributed to 3 Data Partners (DP). Using harmonization queries, we examined the top 200 laboratory results and medications by volume, identifying outliers and computing summary statistics. The completeness queries looked at 4 conditions of interest and related clinical concepts. Counts were generated for each condition, stratified by year, encounter type, and provenance. We analyzed trends over time within and across DPs. Results:We found that the median number of codes associated with a given laboratory/medication name (and vice versa) generally met expectations, though there were DP-specific issues that resulted in outliers. In addition, there were drastic differences in the percentage of patients with a given concept depending on provenance. Conclusions:The harmonization queries surfaced several mapping errors, as well as issues with overly specific codes and records with "null" codes. The completeness queries demonstrated having access to multiple types of data provenance provides more robust results compared with any single provenance type. Harmonization errors between source data and reference terminologies may not be widespread but do exist within CDMs, affecting tens of thousands or even millions of records. Provenance information can help identify potential completeness issues with EHR data, but only if it is represented in the CDM and then populated by DPs.

PURPOSE:The US Food and Drug Administration's Sentinel Innovation Center aimed to establish a query-ready, quality-checked distributed data network containing electronic health records (EHRs) linked with insurance claims data for at least 10 million individuals to expand the utility of real-world data for regulatory decision-making. METHODS:In this report, we describe the resulting network, the Real-World Evidence Data Enterprise (RWE-DE), including data from two commercial EHR-claims linked assets collectively termed the Commercial Network covering 21 million lives, and four academic partner institutions collectively termed the Development Network covering 4.5 million lives. RESULTS:We discuss provenance and completeness of the data converted in the Sentinel Common Data Model (SCDM), describe patient populations, and report on EHR-claims linkage characterization for all contributing data sources. Further, we introduce a standardized process to store free-text notes in the Development Network for efficient retrieval as needed. CONCLUSIONS:Finally, we outline typical use cases for the RWE-DE where it can broaden the reach of the types of questions that can be addressed by the Sentinel system.

Background:People with HIV (PWH) are at elevated risk for atherosclerotic cardiovascular disease (ASCVD). Underrepresented racial and ethnic groups (UREGs) with HIV in the southern U.S. are disproportionately affected, yet whether cardiology specialist care for this at-risk group improves blood pressure and lipid control or prevents cardiovascular events is unknown. Methods:We evaluated a cohort of PWH from UREGs at elevated ASCVD risk without known cardiovascular disease who received HIV-related care from 2015-2018 at four academic medical centers in the Southern United States with follow up through 2020. Primary outcomes were blood pressure control (<140/90 mmHg) and lipid control (LDL-C ≤ 100 mg/dl) over 2 years and time to first major adverse cardiovascular (MACE) event. Statistical analyses were adjusted for cohort/site and patient factors including HIV measures and comorbidities. Results:Among 3972 included PWH (median age 47 years old, 32.6% female) without diagnosed cardiovascular disease, 276 (6.9%) had a cardiology clinic visit. Cardiology clinic visits were not significantly associated with subsequent blood pressure control (adjusted OR 0.78, 95% CI 0.49-1.24, p=0.29) or lipid control (adjusted OR 2.25, 95% CI 0.72-7.01, p=0.16). Over a median follow up of 5 years, patients who had a cardiology clinic visit had higher risk of MACE, overall mortality, and falsification endpoints (hospitalization or death from accident/trauma and pneumonia/sepsis) indicating a higher risk group overall, even after adjusting for measured risk factors. Conclusions:Among UREG PWH at elevated cardiovascular risk, a cardiology clinic visit was not associated with improved cardiovascular risk factors or reduced risk of cardiovascular events. Our study suggests that seeing a cardiologist is not alone sufficient to promote cardiovascular health or prevent cardiovascular events among PWH, but with low confidence given the higher risk among those who had a cardiology visit.