BACKGROUND:The impact of telemedicine use on heart failure (HF) care is unknown. We assessed the association between telemedicine use and rates of diagnostic testing and prescribing for patients with HF. METHODS:We extracted electronic health record data for patients with HF at an academic cardiovascular center in Northern California. We assigned patients to the cardiologist providing >50% of their visits from March 2019 to May 2023. This interval was divided into 8 nonoverlapping 6-month periods, excluding March through May of 2020. All patients seen and orders placed in each period were included. Orders of interest included diagnostic tests (Holter monitors, electrocardiograms, echocardiograms, natriuretic peptide tests, chemistry panels) and new prescriptions (beta blockers, aldosterone antagonists, renin angiotensin inhibitors, hydralazine, nitrates, total guideline-directed medical therapy, diuretics). We assessed the association between clinician-period ordering rates and telemedicine use (proportion of visits via video/phone) using negative binomial regression with adjustment for the period, number of patients, and clinician random intercepts. We report incident rate ratios of per-patient ordering associated with a 50-percentage-point increase in telemedicine use. Subgroup analyses were conducted for patients with reduced/preserved ejection fraction. RESULTS:There were 7741 patients seen by 44 clinicians (1941 patients with HF with reduced ejection fraction seen by 28 clinicians). Mean age was 65 years (SD, 16.8) with 41% female. A 50-percentage-point increase in telemedicine use was associated with significantly reduced ordering of electrocardiograms (incident rate ratio, 0.30, [95% CI 0.25-0.35]), echocardiograms (0.70 [0.59-0.82]), natriuretic peptide tests (0.66 [0.49-0.88]), and chemistry panels (0.66 [0.56-0.76]). For the HF with reduced ejection fraction subgroup, this increase in telemedicine use was associated with reduced ordering of aldosterone antagonists (0.72 [0.52-0.99]) and total guideline-directed medical therapy (0.80 [0.69-0.94]). CONCLUSIONS:Greater clinician telemedicine use was associated with decreased diagnostic testing for patients with HF and reduced guideline-directed medical therapy initiation for patients with HF with reduced ejection fraction. Novel interventions are needed to ensure guideline-concordant care regardless of care modality.

Background Telemedicine use has increased, but its impact on access to initial cardiac care remains understudied. We assessed the association of new patient visit modality with geographic reach and wait times at an academic cardiovascular center. Methods We extracted electronic health record data for NPVs from January 2017 to November 2023. We defined the center's traditional catchment area using the 80th percentile of patient–clinic distance for in‐person NPVs before March 2020. For NPVs from July 2021 to November 2023 (study period), we used multivariable regression to assess the association of visit modality with the likelihood of living outside the catchment area and the time from scheduling to initial visit. Results There were 16 407 NPVs (45.4% telemedicine). Average age was 58.6, with 50.4% women, 2.8% Black individuals, 12.7% Hispanic individuals, and 10.3% patients on Medicaid. Patients receiving telemedicine NPVs were more likely to live outside the catchment area (adjusted odds ratio, 1.83 [95% CI, 1.55–2.16]). Subgroup analyses revealed a larger effect for patients aged 65 to 79 years versus 45 to 64 years (adjusted odds ratio, 2.05 versus 1.53; P=0.03). Telemedicine NPVs had shorter times to visit than in‐person NPVs (−8 days [95% CI, −3 to −13]). This effect was larger for patients on Medicaid versus private insurance (−13 versus −6 days; P=0.03). Conclusions New patients seen via telemedicine were more likely to reside outside the center's catchment area and had shorter wait times. Effects were more pronounced for older patients and those on Medicaid. Future studies should evaluate whether strategic implementation of telemedicine can broaden the geographic reach of cardiovascular centers and improve access for new patients.

Funding This work was funded by Novartis. Background/Synopsis Prediction of those at high risk of atherosclerotic cardiovascular disease (ASCVD) is crucial for guiding primary prevention. The Predicting Risk of cardiovascular disease Events (PREVENT) equation is the latest risk prediction model for ASCVD, but has not yet been compared to the prior Pooled Cohort Equations (PCE) in the Veteran's Health Administration (VA), the nation's largest integrated health system. Objective/Purpose To compare the discrimination and calibration of the PREVENT equation and the PCE to predict ASCVD risk in the VA. Methods This was a retrospective analysis of Veterans ages 30 to 79 years old. Veterans were included if they had no diagnosis of ASCVD event (prior MI, stroke, revascularization) before January 1, 2012, had all required PREVENT and PCE variables in the VA electronic health record, and had at least one primary care visit between 2010-2011. The outcome was ASCVD event, a composite of coronary heart disease death, non-fatal MI, or fatal or non-fatal stroke from 2012-2021. Cause of death was ascertained by death certificate data. Non-fatal stroke and MI were identified from the principal diagnosis of MI or stroke during any hospitalization. Discrimination of the PCE and PREVENT equations to predict ASCVD risk was evaluated with Harrell's C-statistic for a time-to-event model. Calibration of the PCE and PREVENT equations was evaluated using calibration plots of observed versus predicted 10-year ASCVD risk, with calibration slope calculated by dividing the observed by predicted risk (ideal calibration = 1.0). Results 2,552,340 Veterans met inclusion criteria. The median age was 61, with 7.5% female, 18.3% Black, and 5.0% Hispanic participants. The mean 10-year ASCVD predicted risk was 13.1% with the PCE and 6.6% with the PREVENT equation. We observed 212,942 (0.9% per year) ASCVD events over the 10-year follow-up period. The discrimination of the PREVENT equation was greater than the PCE (C = 0.714 vs. C = 0.686, p < 0.05). The calibration of the PREVENT equation was superior to the PCE (calibration slope 1.04 vs. 0.36, Figure 1). Conclusions Among a primary prevention cohort of VA patients, the discrimination of the PREVENT equation was fair, and the calibration was excellent. Both the discrimination and calibration of PREVENT were superior to the PCE at the VA. The PREVENT equation markedly improves the ability to accurately predict 10-year ASCVD risk in a Veteran cohort.

Background:Telemedicine use has increased significantly in cardiology clinics, but the impact of initial telemedicine evaluation on total visit usage is unknown. Objective:This study aimed to determine the effect of initial telemedicine evaluation on the number of follow-up visits within 6 months for common cardiovascular conditions at an academic health system. Methods:Electronic health records data were extracted for general cardiology visits. New patient visits (NPVs) were included occurring from June 1, 2020, to May 31, 2023, for 10 common cardiovascular conditions-atrial fibrillation or flutter, chest pain, coronary artery disease, dyslipidemia, dyspnea, heart failure, hypertension, palpitations, preoperative evaluation, and syncope or dizziness. The effect of initial telemedicine versus in-person evaluation on follow-up visits within 6 months was assessed using a 2-stage least squares instrumental variable model with the proportion of clinician telemedicine use as the instrument and adjustment for patient and visit characteristics. Results:There were 5528 NPVs conducted by 40 general cardiology clinicians during the study period. The average patient age was 56 (SD 17.5) years, 54.2% (2998/5528) were female, 43.2% (2389/5528) were non-Hispanic White, 24.7% (1368/5528) were Asian, 13.8% (761/5528) were Hispanic, 34.4% (1904/5528) were on Medicare, and 13.2% (729/5528) were on Medicaid. Of the NPVs, 53.5% (2959/5528) were conducted via telemedicine (2814/5528, 50.9% via video and 145/5528, 2.6% via phone). Telemedicine use for NPVs ranged from 0% to 100% (N=40) across individual clinicians. The average number of follow-up visits was 57 visits per 100 patients within 6 months across all diagnosis groups. Patients receiving telemedicine NPVs were more likely to have telemedicine follow-up visits than those receiving in-person NPVs (1354/1619, 83.6% vs 680/1533, 44.4%). In the instrumental variable analysis, the impact of initial telemedicine evaluation differed by presenting condition. There was an increase in follow-up visits for patients with syncope or dizziness (29.8 visits/100 patients, 95% CI 6.4-53.1), palpitations (34.9 visits/100 patients, 95% CI 18.6-51.1), chest pain (36.9 visits/100 patients, 95% CI 18.5-55.2), and dyspnea (37.0 visits/100 patients, 95% CI 11.8-62.0). There was a decrease in follow-up visits for patients with coronary artery disease (-29.5 visits/100 patients, 95% CI -50.3 to -8.6) and dyslipidemia (-24.5 visits/100 patients, 95% CI -40.2 to -8.8). There was no significant effect for patients presenting for atrial fibrillation or flutter, heart failure, hypertension, and preoperative evaluation. Conclusions:The effect of initial telemedicine evaluation on follow-up visits varied significantly by presenting condition in this cardiology practice. Telemedicine use resulted in increased follow-up visits for patients presenting with symptomatic complaints, while for those presenting with chronic conditions, there was no significant effect or a decrease in visits. Future studies should assess strategies to target initial care modalities to appropriate patients in cardiology clinics with early in-person evaluation for symptomatic patients.

BACKGROUND:Guidelines recommend timely follow-up with a cardiology specialist for patients hospitalized with heart failure (HF), but it is unknown whether the timeliness of specialty cardiovascular care after discharge correlates with clinical risk. We south to assess the association between estimated mortality risk and post-HF hospitalization cardiology follow-up. METHODS AND RESULTS:In a cohort of veterans hospitalized with HF in acute care Veterans Health Administration (VA) hospitals between January 1, 2018, and September 15, 2022, we estimated the association of mortality risk at discharge with postdischarge cardiology encounters via logistic regression. We also evaluated the association between cardiology visits and sociodemographic and clinical characteristics, and described variability in postdischarge follow-up rates across VA facilities. We identified a cohort of 84,348 veterans hospitalized with HF with 120,619 hospital admissions. Of a subcohort of 57,554 veterans with 79,866 hospitalizations surviving at least 1 year after discharge, 32.1% of hospitalizations were followed by a cardiology visit within 2 weeks, and 49.3% within 1 month. Marginal probabilities of 2-week and 1-month follow-up were higher for hospitalizations in the highest-risk quintile than those in the lowest-risk quintile (34% vs. 30% and 51% vs. 47%, respectively; P < 0.001 for both intervals). In a time-to-event model in the full cohort, there was a slightly negative association between risk and likelihood of 1-month follow-up (coefficient for MAGGIC score = -0.004, 95% confidence interval [CI] -0.005 to -0.003). Black veterans were less likely to have either 2-week or 1-month follow-up (adjusted odds ratios, 0.93 [95% CI 0.90-0.97] for 2 weeks and 0.93 [95% CI 0.89-0.96] for 1 month). Female veterans were also less likely to have follow-up within 1 month of hospital discharge (adjusted odds ratio 0.90 [95% CI 0.90-0.98]). Conversely, patients with a primary vs secondary hospital diagnosis of HF and those with reduced vs preserved ejection fraction were more likely to have 2-week follow-up (adjusted odds ratios 1.67 [95% CI 1.62-1.73] and 1.72 [95% CI 1.67-1.78], respectively) and 1-month follow-up (adjusted odds ratios 1.83 [95% CI 1.78-1.88] and 1.85 [95% CI 1.80-1.90], respectively). The 1-month follow-up rates varied from 5% to 69% across VA facilities. CONCLUSIONS:The rate of visits with a cardiologist within 2 weeks or 1 month after HF hospitalization was low overall, was at most modestly associated with estimated mortality risk at discharge, and varied by sex, race/ethnicity, and across VA facilities. Increasing the visit rate after HF hospitalization should be evaluated as a mechanism to improve outcomes after HF hospitalizations, particularly for higher-risk individuals.

BackgroundTelehealth is a potential tool to alleviate geographic clinician shortages, but there are limited data regarding current telehealth use for common cardiology conditions, including atrial fibrillation (AF). ObjectiveWe aimed to evaluate variation in telehealth use in primary care and cardiology clinics for patients with incident AF in the Veterans Health Administration. MethodsWe included patients diagnosed with AF in the outpatient setting between January 2022 and September 2023. We assessed the association between any video visit and any telehealth use (including phone) for primary care or cardiology visits within 90 days of an AF diagnosis, adjusting for selected patient- and facility-level characteristics using Bayesian logistic regression with facility-level random intercepts. We evaluated facility variation in video visit and telehealth use with the median odds ratio (MOR). ResultsOur cohort included 36,929 patients with 80,596 visits across 125 facilities. Of the 63,835 primary care visits, 2088 (3.27%) were delivered via video and 13,403 (21%) via telehealth; of the 16,761 cardiology visits, 323 (1.93%) were delivered via video and 3288 (19.62%) via telehealth. On average, the mean age of the patients was 73.6 (SD 10.9) years; 2.91% (1075/36,929) were female; 77.71% (28,698/36,929) were White. In adjusted analyses, older age was associated with lower use of video visits for both primary and cardiology care and lower use of any telehealth for cardiology care (eg, adjusted odds ratio [AOR] 0.61, 95% credible interval [CrI] 0.42-0.85 for the use of video cardiology care for patients aged above 77 years). Living more than 65 km from the care site was associated with increased use of both video and any telehealth for primary and cardiology care (eg, AOR 1.91, 95% CrI 1.21-3.00 for video cardiology care); however, living in a rural location was associated with lower odds of using video or any telehealth for primary care (video: AOR 0.73, 95% CrI 0.64-0.84; telehealth: AOR 0.89, 95% CrI 0.83-0.96). There was marked variability across facilities in the use of video care (range 0%-17.4% of visits for cardiology care; 0%-12.5% for primary care) and telehealth (range 0%-82.6% for cardiology care; 3.8%-61.6% for primary care). The facility-level adjusted MOR for video care use was 1.97 (95% CrI 1.77-2.24) for primary care and 4.95 (95% CrI 3.39-7.98) for cardiology care. Similarly, the adjusted MOR for any telehealth use was 1.79 for primary care (95% CrI 1.65-1.96) and 2.61 for cardiology care (95% CrI 2.25-3.13). ConclusionsFollowing an incident AF diagnosis, telehealth may increase access to primary and cardiology care for veterans living at a distance, but its use remains lower for older patients and those in rural areas. There was substantial variation in telehealth use across facilities, which was not explained by differences in patient and facility characteristics. Standardizing telehealth use across Veterans Health Administration facilities may improve access to AF care.

BACKGROUND:The prevalence of heart failure with improved ejection fraction (HFimpEF) is growing. The association of ejection fraction (EF) recovery and changes in health status has not been previously reported. We aimed to characterize patient-reported health status among patients with HFimpEF, heart failure with reduced ejection fraction, and heart failure with midrange (HFmrEF) or preserved ejection fraction (HFpEF). METHODS:We identified patients with heart failure with 2 clinic visits, who completed a Kansas City Cardiomyopathy Questionnaire-12 from August 2020 to October 2023. HFimpEF was defined as most recent EF >40% from a preceding echocardiogram ≥30 days prior with EF ≤40%. We analyzed Kansas City Cardiomyopathy Questionnaire-12 Overall Summary Score across EF classifications with and without adjustment for patient characteristics via multivariable linear regression. We calculated the R2 to determine the impact of clinical characteristics on variation in health status among patients with HFimpEF. RESULTS:A total of 2519 patients were included, of which 18.7% had HFimpEF, 55.7% had HFmrEF/HFpEF, and 25.6% had HFrEF. Patients with HFimpEF were less likely to be women compared with patients with HFmrEF/HFpEF (41% versus 58%, P<0.01). Overall Summary Score was 5.2 points lower among patients with HFrEF versus HFimpEF (95% CI, -8.2 to -2.3); P=0.010) but patients with HFimpEF had similar scores as HFmrEF/HFpEF (-1.1 [95% CI, -3.7 to 1.6]; P=0.43). A multivariable regression model explained 16% of the variation in the Overall Summary Score in the HFimpEF subgroup. CONCLUSIONS:Despite improvement in EF, patients with HFimpEF continue to have impaired health status similar to HFpEF. Further research is needed to understand and improve health status among patients with HFimpEF.

This cross-sectional study evaluates contemporary patterns of lipoprotein a testing among US veterans receiving care through the Veterans Health Administration.

BACKGROUND: A limited transthoracic echocardiogram (TTE) can be an appropriate, lower-cost substitute for a full TTE. We assessed the impact of an electronic health record alternative alert promoting the adoption of limited TTEs on the ordering practices of cardiology clinicians and primary care providers and captured their perspectives on the initiative. METHODS: The alert was deployed in a cardiology clinic and 4 primary care clinics at an academic medical center. The alert provided clinical guidance on the appropriate use of limited TTEs when a clinician selected a full TTE order. We used logistic regression to estimate the change in the proportion of limited versus full TTEs ordered between the baseline and intervention periods in clinics with and without the alert. We also conducted interviews with 24 clinicians (5 cardiologists and 19 primary care providers) to identify implementation barriers and facilitators. RESULTS: Cardiology clinicians ordered 10 654 and 3761 TTEs during the baseline and intervention periods, respectively, for 9100 patients. Primary care providers ordered 723 and 617 TTEs during the baseline and intervention periods for 1273 patients. The model estimated that the percentage of limited TTEs ordered increased by 16.1 +/- 2.3 percentage points (P<0.0001) in the cardiology clinic with the alert and by 13.2 +/- 1.5 percentage points (P<0.0001) in the primary care clinics with the alert from baseline to post-intervention. Ordering practices did not change in the cardiology (0.7 +/- 0.6 percentage points; P=0.24) or primary care (0.7 +/- 1.0 percentage points; P=0.52) clinics without the alert. Clinicians viewed the alert as acceptable. Cardiologists appreciated that the alert was concise, whereas primary care providers wanted more information from the alert. CONCLUSIONS: An alternative alert providing clinical guidance on the use of limited TTEs at the point of care increased the selection of this lower-cost test in cardiology and primary care clinics. Perspectives on the alert differed between specialists and nonspecialists, highlighting the importance of tailoring intervention design to clinical expertise.

In this cross-sectional study, we evaluated the completeness, readability, and syntactic complexity of cardiovascular disease prevention information produced by GPT-4 in response to 4 kinds of prompts.

Introduction: Telemedicine allows patients to attend visits from a distance and has been linked to reduced clinic wait times. However, little is known about its impact on catchment area and scheduling efficiency for new patients. We assessed the association of new patient visit (NPV) modality with patient–clinic distance and time to visit (wait time from day of scheduling to day of visit) across patient demographics and pandemic phases. Methods: We extracted visit data at an academic cardiovascular center in Northern California from Jan 2017 to Nov 2023. We used linear models with fixed effects for clinician and patient characteristics to assess the association of NPV modality with distance and time to visit. Results: There were 8,104 and 3,566 NPVs in the mid-pandemic (Jan 2021–Mar 2022) and post-pandemic (Apr 2023 onwards) periods respectively, conducted by 141 clinicians. The proportion of visits conducted by telemedicine decreased from 58% to 40% from the mid- to post-pandemic periods. During the mid-pandemic period, NPVs conducted by telemedicine were more likely to fall outside the center’s pre-pandemic catchment area (100-mile radius) with an adjusted odds ratio of 1.06 (95% CI 1.03, 1.09). This effect did not change significantly post-pandemic, nor did it vary by sex, race, ethnicity, or public insurance status in either period. Time to visit for telemedicine NPVs was 8 days shorter (95% CI 2, 14) during the mid-pandemic period, and 20 days shorter post-pandemic (95% CI 10, 29). During the post-pandemic period, those with Medicaid waited 28 fewer days (95% CI 16, 41) and those with Medicare waited 16 fewer days (95% CI 6, 25) when the modality was telemedicine, whereas those with private insurance saw a smaller reduction of 12 days (95% CI 3, 21). Conclusions: At this academic cardiovascular center, new patients seen via telemedicine were more likely to reside outside the center’s traditional catchment area and had shorter times to new cardiology visits. These effects persisted throughout and beyond the pandemic. Strategic implementation of telemedicine may allow cardiovascular practices to broaden their reach to new patients.

BACKGROUND:The impact of routine clinic use of patient-reported outcome (PRO) measures on clinical outcomes in patients with heart failure (HF) has not been well-characterized. We tested if clinic-based use of a disease-specific PRO improves patient-reported quality of life at 1 year.METHODS:The PRO-HF trial (Patient-Reported Outcome Measurement in Heart Failure Clinic) was an open-label, parallel, patient-level randomized clinical trial of routine PRO assessment or usual care at an academic HF clinic between August 30, 2021, and June 30, 2022, with 1 year of follow-up. In the PRO assessment arm, participants completed the Kansas City Cardiomyopathy Questionnaire-12 (KCCQ-12) at each HF clinic visit, and results were shared with their treating clinician. The usual care arm completed the KCCQ-12 at randomization and 1 year later, which was not shared with the treating clinician. The primary outcome was the KCCQ-12 overall summary score (OSS) between 12 and 15 months after randomization. Secondary outcomes included domains of the KCCQ-12, hospitalization and emergency department visit rates, HF medication therapy, clinic visit frequency, and testing rates.RESULTS:Across 17 clinicians, 1248 participants were enrolled and randomized to PRO assessment (n=624) or usual care (n=624). The median age was 63.9 years (interquartile range [IQR], 51.8-72.8), 38.9% were women, and the median baseline KCCQ-12 OSS was 82.3 (IQR, 58.3-94.8). Final KCCQ-12 (available in 87.9% of the PRO arm and 85.1% in usual care; P=0.16) median OSS were 87.5 (IQR, 68.8-96.9) in the PRO arm and 87.6 (IQR, 69.7-96.9) in the usual care arm with a baseline-adjusted mean difference of 0.2 ([95% CI, -1.7 to 2.0]; P=0.85). The results were consistent across prespecified subgroups. A post hoc analysis demonstrated a significant interaction with greater benefit among participants with a baseline KCCQ-12 OSS of 60 to 80 but not in less or more symptomatic participants. No significant differences were found in 1-year mortality, hospitalizations, emergency department visits, medication therapy, clinic follow-up, or testing rates between arms.CONCLUSIONS:Routine PRO assessment in HF clinic visits did not impact patient-reported quality of life or other clinical outcomes. Alternate strategies and settings for embedding PROs into routine clinical care should be tested.REGISTRATION:URL: https://www.clinicaltrials.gov; Unique identifier: NCT04164004.

Early in the COVID-19 pandemic, cardiology clinics rapidly implemented telemedicine to maintain access to care. Little is known about subsequent trends in telemedicine use and visit volumes across cardiology subspecialties. We conducted a retrospective cohort study including all patients with ambulatory visits at a multispecialty cardiovascular center in Northern California from March 2019 to February 2020 (pre-COVID) and March 2020 to February 2021 (COVID). Telemedicine use increased from 3.5% of visits (1200/33,976) during the pre-COVID period to 63.0% (21,251/33,706) during the COVID period. Visit volumes were below pre-COVID levels from March to May 2020 but exceeded pre-COVID levels after June 2020, including when local COVID-19 cases peaked. Telemedicine use was above 75% of visits in all cardiology subspecialties in April 2020 and stabilized at rates ranging from over 95% in electrophysiology to under 25% in heart transplant and vascular medicine. From June 2020 to February 2021, subspecialties delivering a greater percentage of visits through telemedicine experienced larger increases in new patient visits (r = 0.81, p = 0.029). Telemedicine can be used to deliver a significant proportion of outpatient cardiovascular care though utilization varies across subspecialties. Higher rates of telemedicine adoption may increase access to care in cardiology clinics.

Background To address geographic barriers to specialty care access for services such as cardiology, the Veterans Health Administration (VA) has implemented a novel, regionalized telehealth care hub. The Clinical Resource Hub (CRH) model extends care, including cardiology services, to individuals in low-access communities across the region. Little is known, however, about the reach of such programs. Objective This study aimed to describe the initial CRH program implementation in terms of growth in users and clinical encounters, as well as the association between user characteristics and the use of CRH cardiology care, in VA’s Sierra Pacific region (Northern California, Nevada, and the Pacific Islands). Methods We compared patients who used CRH cardiology services (CRH users) to those using non-CRH cardiology services (CRH nonusers) in the Sierra Pacific region between July 15, 2021, and March 31, 2023. After characterizing changes in the numbers of CRH users and nonusers and clinical encounters over the study period, we used multivariable logistic regression to estimate the association between patient-level factors and the odds of being a CRH user. Results There were 804 CRH users over the study period, with 1961 CRH encounters concentrated at 3 main CRH sites. The CRH program comprised a minority of cardiology users and encounters in the region, with 19,583 CRH nonusers with 83,489 encounters. The numbers of CRH patients and encounters both increased at a steady-to-increasing rate over the study period, with increases of 37% (n=292 vs n=213) in users and 64% (n=584 vs n=356) in encounters in the first quarter of 2023 compared with the last quarter of 2022. Among CRH users, 8.3% (67/804) were female and 41.4% (333/804) were aged ≥75 years, compared with 4.3% (840/19,583) and 49% (9600/19,583), respectively, among CRH nonusers. The proportions of rural (users: 205/804, 25.5%; nonusers: 4936/19,583, 25.2%), highly disabled (users: 387/804, 48.1%; nonusers: 9246/19,583, 47.2%), and low-income (users: 165/804, 20.5%; nonusers: 3941/19,583, 20.1%) veterans in both groups were similar. In multivariable logistic models, adjusted odds ratios of using CRH were higher for female veterans (1.70, 95% CI 1.29-2.24) and lower for older veterans (aged ≥75 years; 0.33, 95% CI 0.23-0.47). Rural veterans also had a higher adjusted odds ratio of using CRH (1.19, 95% CI 1.00-1.42; P=.046). Conclusions The VA’s Sierra Pacific CRH cardiology program grew substantially in its first 2 years of operation, serving disproportionately more female and rural veterans and similar proportions of highly disabled and low-income veterans compared to conventional VA care. This model appears to be effective for overcoming specialty care access barriers for certain individuals, although targeted efforts may be required to reach older veterans. While this study focuses on a single region, specialty, and health care system, lessons from implementing regionalized telehealth hub models may be applicable to other settings.

Objective Lipoprotein (a) [Lp(a)] is a causal, genetically-inherited risk amplifier for atherosclerotic cardiovascular disease (ASCVD). Practice guidelines increasingly recommend broad Lp(a) screening among various populations to optimize preventive care. Corresponding changes in testing rates and population-level detection of elevated Lp(a) in recent years has not been well described. Methods Using Veterans Affairs electronic health record data, we performed a retrospective cohort study evaluating temporal trends in Lp(a) testing and detection of elevated Lp(a) levels (defined as greater than 50 mg/dL) from January 1, 2014 to December 31, 2023 among United States Veterans without prior Lp(a) testing. Testing rates were stratified based on demographic and clinical factors to investigate possible drivers for and disparities in testing: age, sex, race and ethnicity, history of ASCVD, and neighborhood social vulnerability. Results Lp(a) testing increased nationally from 1 test per 10,000 eligible Veterans (558 tests) in 2014 to 9 tests per 10,000 (4,440 tests) in 2023, while the proportion of elevated Lp(a) levels remained stable. Factors associated with higher likelihood of Lp(a) testing over time were a history of ASCVD, Asian race, and residing in neighborhoods with less social vulnerability. Conclusion Despite a 9-fold increase in Lp(a) testing among US Veterans over the last decade, the overall testing rate remains extremely low. The steady proportion of Veterans with elevated Lp(a) over time supports the clinical utility of testing expansion. Efforts to increase testing, especially among Veterans living in neighborhoods with high social vulnerability, will be important to reduce emerging disparities as novel therapeutics to target Lp(a) become available.

HomeJournal of the American Heart AssociationVol. 13, No. 2Patient Representativeness With Virtual Enrollment in the PRO‐HF Trial Open AccessRapid CommunicationPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toOpen AccessRapid CommunicationPDF/EPUBPatient Representativeness With Virtual Enrollment in the PRO‐HF Trial Anshal Gupta, Megan Skye, Jamie Calma, Natasha Din, Zahra Azizi, Mario Funes Hernandez, Jimmy Zheng, Neil M. Kalwani, Sanjay Malunjkar, Jessica Schirmer, Paul Wang, Fatima Rodriguez, Paul Heidenreich and Alexander T. Sandhu Anshal GuptaAnshal Gupta , Stanford University School of Medicine, , Stanford, , CA, , Megan SkyeMegan Skye , Division of Cardiovascular Medicine and the Cardiovascular Institute, Department of Medicine, , Stanford University, , Stanford, , CA, , Veterans Affairs Palo Alto Health Care System, , Palo Alto, , CA, , Jamie CalmaJamie Calma https://orcid.org/0000-0002-6426-1336 , Division of Cardiovascular Medicine and the Cardiovascular Institute, Department of Medicine, , Stanford University, , Stanford, , CA, , Natasha DinNatasha Din https://orcid.org/0000-0001-5312-4451 , Veterans Affairs Palo Alto Health Care System, , Palo Alto, , CA, , Center for Digital Health, Department of Medicine, , Stanford University, , Stanford, , CA, , Zahra AziziZahra Azizi https://orcid.org/0000-0002-7897-0934 , Center for Digital Health, Department of Medicine, , Stanford University, , Stanford, , CA, , Mario Funes HernandezMario Funes Hernandez https://orcid.org/0000-0002-6545-3110 , Center for Digital Health, Department of Medicine, , Stanford University, , Stanford, , CA, , Jimmy ZhengJimmy Zheng https://orcid.org/0000-0002-2009-2059 , Stanford University School of Medicine, , Stanford, , CA, , Neil M. KalwaniNeil M. Kalwani https://orcid.org/0000-0003-0075-6206 , Division of Cardiovascular Medicine and the Cardiovascular Institute, Department of Medicine, , Stanford University, , Stanford, , CA, , Veterans Affairs Palo Alto Health Care System, , Palo Alto, , CA, , Sanjay MalunjkarSanjay Malunjkar , Research Technology, Stanford Medicine, , Stanford, , CA, , Jessica SchirmerJessica Schirmer https://orcid.org/0009-0007-2623-7515 , Division of Cardiovascular Medicine and the Cardiovascular Institute, Department of Medicine, , Stanford University, , Stanford, , CA, , Paul WangPaul Wang https://orcid.org/0000-0002-5467-5877 , Division of Cardiovascular Medicine and the Cardiovascular Institute, Department of Medicine, , Stanford University, , Stanford, , CA, , Center for Digital Health, Department of Medicine, , Stanford University, , Stanford, , CA, , Fatima RodriguezFatima Rodriguez https://orcid.org/0000-0002-5226-0723 , Division of Cardiovascular Medicine and the Cardiovascular Institute, Department of Medicine, , Stanford University, , Stanford, , CA, , Center for Digital Health, Department of Medicine, , Stanford University, , Stanford, , CA, , Paul HeidenreichPaul Heidenreich https://orcid.org/0000-0001-7730-8490 , Division of Cardiovascular Medicine and the Cardiovascular Institute, Department of Medicine, , Stanford University, , Stanford, , CA, , Veterans Affairs Palo Alto Health Care System, , Palo Alto, , CA, and Alexander T. SandhuAlexander T. Sandhu * Correspondence to: Alexander T. Sandhu, MD, MS, Stanford University, 870 Quarry Rd, Stanford, CA 94305. Email: E-mail Address: [email protected] https://orcid.org/0000-0003-3208-1143 , Division of Cardiovascular Medicine and the Cardiovascular Institute, Department of Medicine, , Stanford University, , Stanford, , CA, , Veterans Affairs Palo Alto Health Care System, , Palo Alto, , CA, , Center for Digital Health, Department of Medicine, , Stanford University, , Stanford, , CA, Originally published16 Jan 2024https://doi.org/10.1161/JAHA.123.030903Journal of the American Heart Association. 2024;13:e030903Patients with heart failure (HF) who are elderly, women, Black race, or Hispanic ethnicity are historically underrepresented in clinical trials.1 In‐person recruitment is a barrier to participation that virtual enrollment may overcome.2 Designed as a pragmatic trial with virtual enrollment (via email, text message, and telephone call) of patients seen in a HF clinic, the PRO‐HF (Patient‐Reported Outcome Measurement in Heart Failure Clinic) trial evaluates the impact of routine assessment of patient‐reported health status before HF clinic visits.3 The effect of pragmatic, virtual trial design on representation is unknown. We sought to identify shared characteristics of enrolled PRO‐HF trial patients.In the PRO‐HF trial, we recruited adults with Stanford HF clinic appointments between August 30, 2021 and June 30, 2022, via email 7 to 10 days previsit. Patients (n=372) were excluded because of competing trials with routine health status assessment. Consented patients completed baseline assessments via the Kansas City Cardiomyopathy Questionnaire‐12 online before their clinic visits. Patients who did not respond to 2 enrollment emails were contacted by telephone (telephone call followed by text message), repeated 3 to 5 days before their clinic visit. Enrolled patients were randomized to routine Kansas City Cardiomyopathy Questionnaire‐12 assessment or usual care. Detailed trial methods have been published previously.4 Data for this study are available from the corresponding author only on reasonable request.We obtained electronic health records of eligible patients from the Stanford Research Repository. Patient characteristics were defined by the date of first clinic visit during the recruitment period. Baseline demographics included age, sex, self‐reported race and ethnicity, comorbidities, and insurance. On the basis of home address 9‐digit zip code, we included the area deprivation index as a measure of neighborhood social risk. Clinical characteristics included a history of HF diagnosis, baseline comorbidities and medications, outpatient clinical encounters, hospitalizations in the prior year, and select laboratory measurements.We compared characteristics between those who did and did not enroll and by method of enrollment: email only versus follow‐up telephone (call, text, or both as secondary analysis). We used standardized mean differences (SMDs) via Cohen d to evaluate the magnitude of differences between groups.5 An SMD of 0.1 to 0.5 was considered small, and an SMD of >0.5 was considered medium to large.5 We compared statistical significance with t‐tests for continuous variables and χ2 tests for categorical variables. We performed a multivariable logistic regression model to determine associations between enrollment and select characteristics: age, sex, race, ethnicity, and prior ouptatient clinic visits. The Stanford Institutional Review Board approved this study.Of 5112 eligible patients, 1248 (24.4%) enrolled in the PRO‐HF trial (Table 1). This included 520 (41.7%) patients enrolled by email only and 728 (58.3%) enrolled by telephone. Enrolled patients were a median age of 63.8 years (interquartile range, 51.7–72.7 years), and 38.9% were women.Table 1. Baseline Characteristics of Patients Invited to Enroll in the PRO‐HF TrialVariableEnrolled (N=1248)Did not enroll (N=3864)SMD (enrolled vs did not enroll)Adjusted odds ratio (95% CI) of enrollment*Email enrollment (N=520)Telephone enrollment (N=728)SMD (email vs telephone)DemographicsAge, y63.8 (51.7–72.7)66.1 (52.1–75.8)0.08†0.99 (0.99–0.99)66.2 (55.2–74.3)61.9 (49.3–72.1)0.27†Female sex38.9 (485)38.7 (1496)0.011.03 (0.90–1.18)38.3 (199)39.3 (286)0.02Interpreter preferred0.1 (1)10.6 (409)0.48†0.0 (0)0.1 (1)0.05Race0.34†0.26†Asian12.1 (151)14.8 (570)0.56 (0.46–0.69)10.8 (56)13.0 (95)Black4.9 (61)6.9 (268)0.54 (0.40–0.73)3.5 (18)5.9 (43)Native American0.7 (9)0.3 (13)1.86 (0.77–4.50)0.2 (1)1.1 (8)Unknown14.7 (183)24.9 (864)0.49 (0.39–0.61)12.7 (66)15.1 (117)Pacific Islander1.5 (19)2.0 (78)0.47 (0.28–0.79)0.8 (4)2.1 (15)White66.1 (825)51.0 (1971)Reference72.1 (375)61.8 (450)Ethnicity0.19†0.23†Hispanic or Latinx8.0 (100)13.2 (509)0.76 (0.58–0.99)4.6 (24)10.4 (76)Non‐Hispanic87.7 (1095)81.2 (3139)Reference91.3 (475)85.2 (620)Unknown4.3 (53)5.6 (216)1.05 (0.73–1.51)4.1 (21)4.4 (32)ADI national ranking4.0 (1.0–13.0)5.0 (2.0–18.0)0.053.0 (1.0–11.0)5.0 (2.0–15.0)0.16†DiagnosesAtrial fibrillation35.0 (437)28.8 (1111)0.13†33.5 (174)36.1 (263)0.06CAD40.1 (500)32.6 (1261)0.15†41.5 (216)39.0 (284)0.05COPD14.1 (176)12.2 (473)0.0613.8 (72)14.3 (104)0.01Depression12.7 (159)9.2 (355)0.11†9.6 (50)15.0 (109)0.16†Diabetes18.8 (235)17.4 (671)0.0416.3 (85)20.6 (150)0.11HF or cardiomyopathy87.3 (1089)68.4 (2644)0.47†82.5 (429)90.7 (660)0.24†Hypertension52.7 (658)44.8 (1732)0.16†51.9 (270)53.3 (388)0.03PAD38.5 (481)24.2 (937)0.31†40.4 (210)37.2 (271)0.06Elixhauser comorbidity score4.0 (2.0–7.0)3.0 (2.0–6.0)0.16†4.0 (2.0–6.0)4.0 (2.0–7.0)0.16†Acute care servicesED visits in prior year13.5 (168)11.2 (431)0.07†12.3 (64)14.3 (104)0.06Hospitalizations, prior 90 d9.9 (123)9.0 (347)0.037.3 (38)11.7 (85)0.15†HF hospitalizations, prior 90 d4.7 (59)4.7 (180)0.003.3 (17)5.8 (42)0.12†Any hospitalization in prior year20.2 (252)16.8 (649)0.09†16.9 (88)22.5 (164)0.14†HF hospitalizations in prior year9.2 (115)8.1 (312)0.047.5 (39)10.4 (76)0.10Insurance0.11†0.26†Medicare47.2 (589)50.4 (1946)51.7 (269)44.0 (320)Medicaid7.7 (96)9.5 (369)4.2 (22)10.2 (74)Private37.7 (471)32.9 (1273)35.6 (185)39.3 (286)Other7.4 (92)7.2 (276)8.4 (44)6.6 (48)Outpatient encountersNew patients with HF22.9 (285)27.2 (1050)0.10†0.82 (0.76–0.86)21.3 (110)24.1 (175)0.07No. of prior HF clinic visits, prior year5.0 (1.0–11.0)2.0 (0.0–8.0)0.25†5.0 (1.0–12.0)5.0 (1.0–11.0)0.05Stanford primary care65.1 (809)46.1 (1781)0.39†2.11 (1.84–2.42)68.0 (351)63.1 (458)0.10Echocardiogram, prior year58.6 (728)53.6 (2072)0.10†56.6 (292)60.1 (436)0.07Left ventricular EF0.12†0.34†EF≤40%27.9 (348)17.3 (667)20.8 (108)33.0 (240)EF >40% and <50%17.1 (213)10.7 (413)14.6 (76)18.8 (137)EF≥50%54.8 (684)43.2 (1671)64.4 (335)47.9 (349)Missing0.2 (3)28.8 (1113)0.2 (1)0.3 (2)Vital signs and laboratory valuesBMI, kg/m227.0 (24.0–31.4)26.4 (23.3–30.6)0.15†26.7 (23.5–30.0)27.5 (24.2–32.4)0.26†eGFR <30 mL/min per 1.73 m23.4 (42)5.8 (226)0.19†2.5 (13)4.0 (29)0.10eGFR 30–44 mL/min per 1.73 m25.9 (74)6.6 (254)5.6 (29)6.2 (45)eGFR 45–59 mL/min per 1.73 m213.3 (166)11.0 (424)13.8 (72)12.9 (94)eGFR ≥60 mL/min per 1.73 m259.4 (741)48.9 (1889)60.0 (312)58.9 (429)eGFR missing18.0 (225)27.7 (1071)18.1 (94)18.0 (131)Medication therapiesACEI/ARB/ARNI45.7 (567)34.8 (1346)0.22†39.5 (204)50.0 (363)0.21†β‐Blocker54.1 (672)39.7 (1535)0.29†48.6 (251)58.0 (421)0.19†Loop diuretics27.0 (335)28.0 (1083)0.0222.5 (116)30.2 (219)0.18†MRA30.0 (372)22.7 (879)0.16†24.4 (126)33.9 (246)0.21†SGLT2i15.5 (192)10.0 (385)0.17†9.3 (48)19.8 (144)0.30†Continuous variables are expressed as median with 95% CI. Other variables are given as percentage (number). Email recruitment preceded telephone recruitment. ACEI indicates angiotensin‐converting enzyme inhibitor; ADI, area deprivation index; ARB, angiotensin receptor blocker; ARNI, angiotensin receptor/neprilysin inhibitor; BMI, body mass index; CAD, coronary artery disease; COPD, chronic obstructive pulmonary disease; ED, emergency department; EF, ejection fraction; eGFR, estimated glomerular filtration rate; HF, heart failure; MRA, mineralocorticoid receptor antagonist; PAD, peripheral artery disease; PRO‐HF, Patient‐Reported Outcome Measurement in Heart Failure Clinic; and SGLT2i, sodium‐glucose cotransporter‐2 inhibitor.*On the basis of multivariable logistic regression model with select patient characteristics: age, sex, race, ethnicity, new Stanford HF clinic patient, and prior Stanford primary care visit.†The SMDs when the differences between groups have a P<0.05 (based on t tests and χ2 analyses for continuous and categorical variables, respectively).Enrolled and non‐enrolled patients were similar by age and sex but had differences across race (SMD=0.34) and ethnicity (SMD=0.19). Greater proportions of enrolled patients identified as White race (66.1% enrolled versus 51.0% nonenrolled) or non‐Hispanic ethnicity (87.7% versus 81.2%) compared with patients who identified as Asian race (12.1% versus 14.8%), Black race (4.9% versus 6.9%), or Hispanic or Latinx ethnicity (8.0% versus 13.2%). All non‐White (those who identified themselves from a race other than the White race [Asian, Black, Native American, and Pacific Islander]) patients enrolled in greater proportions by telephone than by email, with the largest difference among Hispanic or Latinx patients (10.4% versus 4.6%). Patients requiring an interpreter enrolled at lower rates (0.1% versus 10.6%; SMD=0.48). Enrolled and nonenrolled patients had similar area deprivation index (SMD=0.05).Enrolled patients were more likely to have an existing HF/cardiomyopathy diagnosis and be prescribed HF medications. Enrolled patients had more HF clinic encounters in the prior year than nonenrollees (SMD=0.25) and were more likely to be seen by Stanford primary care (SMD=0.39). Enrolled patients had a slightly higher comorbidity burden, as defined by the Elixhauser score (SMD=0.16).Patients enrolled via email were more likely to be older (SMD=0.27), White race (SMD=0.26), and non‐Hispanic ethnicity (SMD=0.23). Patients enrolled via email/telephone were more likely to have Medicaid (SMD=0.26) and higher area deprivation index scores (SMD=0.16).In an adjusted analysis, Asian, Black, Pacific Islander, and Hispanic patients had lower odds of enrollment, whereas greater odds of enrollment were associated with prior Stanford primary care or HF clinic visits (Table 1). No significant association was found between sex and enrollment.Virtual enrollment in the PRO‐HF trial varied by patient characteristics and enrollment method. Among the strongest predictors of enrollment were prior primary care and HF clinic visits. This may reflect how trusting patient‐physician relationships enhance patient perceptions of trial participation.6 We identified racial and ethnic disparities similar to traditional trials. Compared with email only, follow‐up telephone recruitment enrolled greater proportions of younger patients, historically marginalized racial and ethnic groups, and Medicaid recipients.This study has several limitations. First, multicollinearity across patient characteristics is likely. Second, race was unknown for 25% of patients who did not enroll. Third, independent effects of telephone versus email cannot be assessed. Finally, virtual enrollment may vary across trials with greater participant burden.Virtual clinical trials enable efficient enrollment of large populations. Leveraging multiple recruitment modalities is important for achieving diverse representation in virtual trials that may lead to improved generalizability.Sources of FundingThe PRO‐HF (Patient‐Reported Outcomes in Heart Failure Clinic) trial is supported by the National Heart, Lung, and Blood Institute (1K23HL151672‐01) and Stanford institutional funding. The data collection is supported by the National Institutes of Health (UL1 TR001085).DisclosuresDr Sandhu is supported by the National Heart, Lung, and Blood Institute (1K23HL151672‐03) and has consulting relationships with Lexicon Pharmaceuticals and Reprieve Cardiovascular. Dr Rodriguez reports consulting relationships with Healthpals, Novartis, NovoNordisk, and AstraZeneca outside the submitted work. Dr Kalwani reports stock options from Gordy Health and funding from the US Agency for Healthcare Research and Quality (T32 HS026128). Drs Azizi, Hernandez, and Wang were funded by American Heart Association Strategically Focused Research Network. The remaining authors have no disclosures to report.Footnotes* Correspondence to: Alexander T. Sandhu, MD, MS, Stanford University, 870 Quarry Rd, Stanford, CA 94305. Email: ats114@stanford.eduThis article was sent to Francoise A. Marvel, MD, Guest Editor, for review by expert referees, editorial decision, and final disposition.For Sources of Funding and Disclosures, see page 4.References1 Tahhan AS, Vaduganathan M, Greene SJ, Fonarow G, Fiuzat M, Jessup M, Lindenfeld J, O'Connor CM, Butler J. Enrollment of older patients, women, and racial and ethnic minorities in contemporary heart failure clinical trials: a systematic review. JAMA Cardiol. 2018; 3:1011–1019. doi: 10.1001/jamacardio.2018.2559CrossrefMedlineGoogle Scholar2 Martin SS, Ou FS, Newby LK, Sutton V, Adams P, Felker GM, Wang TY. Patient‐ and trial‐specific barriers to participation in cardiovascular randomized clinical trials. J Am Coll Cardiol. 2013; 61:762–769. doi: 10.1016/j.jacc.2012.10.046CrossrefMedlineGoogle Scholar3 Sandhu AT, Zheng J, Kalwani N, Gupta A, Calma J, Skye M, Lan R, Yu B, Spertus J, Heidenreich P. Impact of patient‐reported outcome measurement in heart failure clinic on clinician health status assessment and patient experience: a sub‐study of the PRO‐HF trial. Circ Heart Fail. 2023; 16:e010280. doi: 10.1161/CIRCHEARTFAILURE.122.010280LinkGoogle Scholar4 Kalwani NM, Calma J, Varghese GM, Gupta A, Zheng J, Brown‐Johnson C, Amano A, Vilendrer S, Winget M, Asch S, et al. The patient‐reported outcome measurement in heart failure clinic trial: rationale and methods of the PRO‐HF trial. Am Heart J. 2023; 255:137–146. doi: 10.1016/j.ahj.2022.10.081CrossrefMedlineGoogle Scholar5 Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed.Routledge; 1988.Google Scholar6 Houghton C, Dowling M, Meskell P, Hunter A, Gardner H, Conway A, Treweek S, Sutcliffe K, Noyes J, Devane D, et al. Factors that impact on recruitment to randomised trials in health care: a qualitative evidence synthesis. Cochrane Database Syst Rev. 2020; 10:MR000045. doi: 10.1002/14651858.MR000045.pub2CrossrefMedlineGoogle Scholar eLetters(0) eLetters should relate to an article recently published in the journal and are not a forum for providing unpublished data. Comments are reviewed for appropriate use of tone and language. Comments are not peer-reviewed. Acceptable comments are posted to the journal website only. Comments are not published in an issue and are not indexed in PubMed. Comments should be no longer than 500 words and will only be posted online. References are limited to 10. Authors of the article cited in the comment will be invited to reply, as appropriate. 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Published on behalf of the American Heart Association, Inc., by Wiley BlackwellThis is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.https://doi.org/10.1161/JAHA.123.030903PMID: 38226522 Manuscript receivedMay 7, 2023Manuscript acceptedOctober 3, 2023Originally publishedJanuary 16, 2024 Keywordsheart failureKansas City cardiomyopathy Questionnaire‐12virtual enrollmentPDF download Subjects Health Services Heart Failure Quality and Outcomes