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Improving hypertension using virtual modalities

A va project uncovers barriers to success..

• Remote blood pressure monitoring combined with patient education improves blood pressure control.
• Telehealth offers a flexible and effective approach for improving the management of uncontrolled hypertension.
• Innovative strategies are required to help improve Veterans Health Administration primary care workload and panel.

According to the U.S. Department of Veterans Affairs/Department of Defense (VA/DoD) Clinical Practice Guidelines (CPG), military com­bat exposure can increase the risk for hypertension (HTN) in veterans. The latest data ob- tained in 2011 from VA Health Services Research & Development states that HTN is the most prevalent medical condition among veterans. (See HTN facts .)

At the Fayetteville VA Medical Center (FVAMC), managing uncontrolled HTN presents challenges due to time constraints, inadequate staffing, and overloaded patient align care teams (PACTs). Accessing most health services requires a Veterans Health Administration (VHA) primary care team, but excessive workloads result in a high turnover rate. In addition, the hiring process for all FVAMC staff takes about 4 to 6 months, which further complicates staffing issues. Current primary care panel sizes range from 700 to 1,400 veterans; some led by advance practice RNs (APRNs). The ideal panel size for an APRN is between 700 and 900 patients.

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According to the World Health Organization and the Centers for Disease Control and Prevention, approximately 1.28 billion individuals worldwide have been diagnosed with hypertension (HTN), which increases their risk for microvascular and macrovascular complications such as cardiovascular disease (CVD).

• HTN is associated with a strong risk for CVD compared to other risk factors such as obesity, hyperlipidemia, and tobacco use.
• According to Arnett and colleagues, a 20 mmHg higher systolic blood pressure and a 10 mmHg higher diastolic blood pressure correspond with a doubled risk for death from stroke and CVD.
• Despite interventions with proven effectiveness in HTN management, disease prevalence continues to increase.
• This preventable illness demands innovative strategies to improve health outcomes.

Barriers to care

Nursing intake (about 30 minutes for new patients and 15 minutes for follow-up visits) requires sufficient time to collect vital signs, discuss patient concerns, and complete required VHA healthcare clinical reminders. Clinicians use clinical reminders to help manage preventive care and chronic health conditions; however, the documentation process can prove arduous depending on the type of reminder. Reminders related to post-traumatic stress disorder, toxic exposure, traumatic brain injury, deployments, and military sexual trauma typically aren’t seen in civilian primary care practices. In addition, VHA policy requires that a nurse take a patient’s blood pressure (BP) three times at 5-minute intervals for any BP above 140/90 mmHg. Intake nurses also frequently re­in­force patient education regarding disability claims, medications, specialty services, consults, and prosthetic items.

All of these factors can result in less time for preventive health education. In VHA primary care, most of the time available after nurse intake focuses on addressing chronic pain, mental health, and disability ratings. These conditions take at least 30 minutes to assess, which further limits diagnosing and managing common primary care conditions such as HTN, diabetes, hyperlipidemia, and obesity. In addition, administrative tasks and frequent interruptions throughout the day complicate the ability of PACTs to provide effective primary care. This overwhelming workload leads to increased burnout, call-outs, and appointment cancellations.

An innovative and multifactorial approach, such as frequent use of telehealth services by primary care clinicians, can help decrease the impact of these challenges and improve healthcare outcomes. Early diagnosis and management of HTN is instrumental in preventing long-term effects. Despite known interventions proven to treat HTN effectively, the data support a need for innovative strategies to decrease the incidence and prevalence of the condition. One PACT at FVAMC implemented a virtual approach focused on HTN management to help address barriers to care.

Evidence-based nursing

In 2017, the American Heart Association (AHA) modified their BP parameters, which lowered the threshold for diagnosing HTN. After reviewing the most updated evidence, VA/DoD adopted the new parameters in 2020. Despite this change, a gap persisted in HTN education among healthcare staff at FVAMC, which resulted in inconsistent information provided to veterans.

When performed correctly, patient education can help improve patient health literacy and save costs for the patient and the health system. A systematic review of 18 randomized control trials by Tan and colleagues found that virtual education sessions conducted at home rather than education sessions conducted in a clinic resulted in better medication adherence for patients with HTN, diabetes, and hyperlipidemia. Hoppe and colleagues and Tan and colleagues noted that digital tools such as virtual educational programs, mobile applications, and remote monitoring improved BP control, quality of life, cost effectiveness, and patient adherence. Despite these positive effects, insufficient evidence exists for telehealth use with veterans overall and veterans with uncontrolled HTN specifically.

Various studies examining the effects of telemonitoring in primary care settings show favorable results in patient outcomes. Remote BP monitoring, for example, allows patients to take their BP in a familiar and comfortable environment, which reduces the white coat HTN frequently seen in clinic settings. Virtual modalities also provide flexibility and can be used by any healthcare professional. Hoppe and colleagues showed the association between the use of remote monitoring in combination with standardized HTN management and reduced hospital readmissions and improved BP control. In a pilot video of BP innovation at the VA Boston Health Care System and the Gulf Coast Veterans Health Care System, 96% of patients recommended virtual BP visits and 87% preferred virtual BP visits over traditional in-person visits. In addition, 100% of clinicians reported satisfaction with virtual video BP visits to manage HTN. Remote monitoring allows timelier treatment decisions, which can optimize HTN management.

Virtual HTN clinic

We conducted this quality improvement (QI) project to address the management of uncontrolled HTN in veterans within primary care. The goal was to pilot a virtual hypertension clinic and evaluate its effects on disease management in veterans. We had three aims: decrease systolic BP and/or diastolic BP by 5 mmHg for 20% of enrolled participants with uncontrolled HTN by the end of the pilot, increase use of virtual video connect visits by 5% among the PACT, and achieve at least 50% remote monitoring adherence by the end of the pilot.

What we did

Members of the PACT reviewed the schedule daily to identify potential participants who met inclusion criteria (last documented systolic BP above 130 mmHg and/or diastolic BP above 80 mmHg). Exclusion criteria included secondary causes of HTN, cancer, cirrhosis, estimated glomerular filtration rate <30 mL/min, dialysis, and prescriptions for more than three antihypertensives. A sample size of 27 veterans consented verbally to the pilot. Patient responsibilities included taking their BP daily via a VA-issued validated sphygmomanometer, at least one telephone encounter with the PACT RN, and at least one virtual visit with the PACT provider. The single-arm pro­spective cohort study design compared baseline BPs (last documented BP greater than 130/80 mmHg in the electronic health record [EHR]) to those after the intervention.

The pilot ran for 12 weeks. Participants received a packet of HTN educational resources, which included an updated BP chart from the AHA as well as VHA patient education printouts.

The PACT team entered home telehealth consults for the participants, which required additional processing by the receiving telehealth nurse. The nurse provided additional education and mailed a kit to participants for uploading their BP. Before the virtual clinic, enrolled participants received virtual video connect education from the PACT licensed pract­ical nurse (LPN) or RN. During the RN telephone visit, the RN reviewed the educational packets and current BPs with the participants. Veterans received education on how to take BP measurements, such as keeping the arm above heart level and not crossing the legs. The RN ordered a complete blood count, lipid panel, and comprehensive metabolic pan­el if one wasn’t in the EHR within 6 months. Some veterans participated in several virtual encounters with PACT depending on factors such as medication changes, abnormal labs, and abnormal BPs.

One month after the RN visit, the PACT scheduled participants for a 60-minute virtual visit with their primary care provider. During this visit, the LPN completed patient care clinical reminders and entered updated BP and heart rate data into the EHR. The provider also offered additional HTN education, reviewed labs, and updated the plan of care as needed. (See Statistical analysis and results .)

Statistical analysis and results

The project team performed its statistical analysis using IBM SPSS version 28 with a level of significance set to P < .05. The patient aligned care team (PACT) licensed practical nurse entered data, and the primary investigator validated all values to ensure accuracy.

• To examine Aim 1, difference scores from pre- to postintervention for systolic blood pressure (BP) and diastolic BP were computed and coded into a dichotomous variable using 1 = decrease of 5 or more and 0 = decrease of less than 5. The percentages with a decrease of 5 or more for systolic BP, diastolic BP, and both were computed and compared against the 20% goal rate. In addition, paired t-tests were conducted to determine whether BP readings showed a statistically significant change after intervention.
• For analysis of Aim 2, the virtual video connect rate pre- and postintervention from VHA Support Service Center Capital Assets (VSSC) were used. Data were extracted from VSSC databases.
• For Aim 3, BP monitoring adherence rates were compared using a percentage difference calculation to determine whether the 50% increase was met.

Results Of the 27 veterans who initially enrolled in the virtual clinic, 18 completed the project. Of the initial sample, all were men, 18 were Black, eight were White, and one was Native American. Remote BP monitoring adherence was the main reason many didn’t complete the project. Contributing factors for nonadherence included knowledge, age, and culture. Some simply no longer wanted to participate. Of the 18 veterans who completed the project, 61.1% were Black and 38.9% were White. Mean age was 54.89 (SD = 10.93) years with a range from 39 to 72.

According to Oparil and colleagues, a BP reduction by a few millimeters of mercury can meaningfully decrease mortality risk. A systolic BP reduction of 5 mmHg can decrease stroke mortality by 14% and CVD mortality by 9%.

In this pilot, the systolic BP baseline mean of 148.61 mmHg (SD = 11.21) decreased to 128.83 (SD = 11.55), t (17) = 7.07, P <.001. The diastolic BP baseline mean of 86.28 mmHg (SD = 11.45) decreased to 78.0 (SD = 11.71), t (17) = 2.30, P = .034. The average decline for systolic BP was 19.78 mmHg, and the average decline for diastolic BP was 8.28 mmHg. Analysis also showed that 94.4% (17 out of 18) of participants had a decline of 5 mmHg or more for their systolic BP, and 66.7% (12 out of 18) had a decline of 5 mmHg or more for their diastolic BP.

PACT use of the virtual video clinic increased from 0% to 27.7% from June 2022 to August 2022. Use also increased among veterans, which improved their comfort level for future use. Thirteen veterans took their BP as instructed (72.2%), and five did not (27.8%). Despite the statistical insignificance of the data, two veterans participated in and completed the project using a VA-issued tablet, which improved their BP control.

What we learned

During the course of the pilot, we discovered barriers to successful implementation of a virtual HTN clinic, including staff shortages, knowledge gaps among clinic staff, and inconsistent scheduling grids.

Staff shortages. The staffing turnover rate continues to increase among nurses and clinical providers at FVAMC. Cancellation of traditional in-person appointments prevents the collection of data critical to effective HTN treatment, which interferes with timely interventions and follow up. Despite cancelled appointments due to call-outs from burnout or staff turnover, the virtual clinic allowed veterans to continue remote BP monitoring.

Staffing shortages resulted in some veterans enrolling in telehealth after we initiated the virtual clinic. For example, from March to July 2022, the pilot PACT lost two key team members, the LPN and RN. Both had transitioned into other VHA positions due to the strenuous primary care workload.

Before the pilot, we found that the home telehealth (HTH) department also experienced a staffing shortage and that program eligibility requirements were based on the VHA national guidelines of a BP of <140/90 mmHG, rather than the VA/DoD CPG of <130/80 mmHg. Some veterans enrolled in the project with a BP of <140/90 mmHg experienced delays in telehealth consult processing due to the eligibility guidelines. These unfortunate events resulted in some veterans receiving delayed HTH consults.

Knowledge gap. During virtual clinic follow-up visits with the primary care providers, some veterans received outdated BP information from the nursing staff in HTH and primary care. Several clinic staff weren’t aware of the updated AHA BP categories, which likely resulted from inconsistent BP standards within the VA enterprise. Although the VA/DoD guideline committee supports the 2017 AHA BP guidelines, the VHA national guidelines are 10 mmHg higher.

During chart review, we found inconsistent information among nursing staff throughout the enterprise regarding when to take blood pressures. This caused some confusion among veterans, which required additional education and reinforcement during their primary care provider appointment. Consistent patient education among clinic staff in conjunction with telehealth may help improve HTN management among veterans.

Scheduling grid. Telehealth continues to transform how individuals receive healthcare across the world, and increased use of primary care settings can benefit patients with chronic illness who require frequent followup. Sustainability of a virtual HTN clinic in primary care is questionable, as it requires support of and collaboration with administrative and clinic staff. Primary care administrative staff typically coordinate and assist with special requests to allow modifications of providers’ scheduling grids; how­ever, the pilot shows the need for more support. Currently, no support exists for developing appropriate scheduling grids for regular patient encounters, which leads providers to shy away from using virtual VHA applications such as virtual video connect.

Address barriers to telehealth success

Telehealth can significantly improve access to care. Its use for managing chronic diseases such as HTN improves patient outcomes and can help decrease hospitalizations. It can serve as a cost-effective and flexible innovation for improving health outcomes in veterans with uncontrolled HTN who also may experience chronic pain that prevents them from attending in-person appointments. To ensure consistent and effective application of telehealth, we must address implementation barriers. Work with nursing and organization leadership to develop solutions to the challenges of staff shortages, knowledge gaps among healthcare professionals, and administrative roadblocks.

Ron’Nisha D. Baldwin is a board-certified adult gerontological primary care nurse practitioner in the Department of Veteran Affairs and a consulting associate in the Duke University School of Nursing. Bradi Granger is a research professor at Duke University Health System and Duke University School of Nursing. Julie Thompson is a consulting associate in the Duke University School of Nursing. Margaret T. Bowers is a cardiology nurse practitioner in the Duke University Health System and a clinical professor in the Duke University School of Nursing. Audrey Kizzie is a health promotion, disease prevention program manager in the Department of Veteran Affairs.

American Nurse Journal. 2023; 18(11). Doi: 10.51256/ANJ11232326

Arnett DK, Blumenthal RS, Albert MA, et al. 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: A report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol . 2019;74(10):e177-232. doi:10.1016/j.jacc.2019.03.010

Centers for Disease Control and Prevention. Facts about hypertension. July 6, 2023. cdc.gov/bloodpressure/facts.htm

Centers for Disease Control and Prevention. Hypertension prevalence and control among adults: United States, 2015–2016. October 2017. cdc.gov/nchs/products/databriefs/db289.htm

Department of Veteran Affairs and Department of Defense. VA/DoD clinical practice guideline for the diagnosis and management of hypertension in the primary care setting. 2020. healthquality.va.gov/guidelines/CD/htn/VADoDHypertensionCPG508Corrected792020.pdf

Fayetteville VA Health Care Center. Fact sheet. August 2016. va.gov/files/2021-02/Fayetteville_VA_Health_Care_Center_Fact_Sheet_Aug_2016.pdf

Hoppe KK, Thomas N, Zernick M, et al. Telehealth with remote blood pressure monitoring compared with standard care for postpartum hypertension. Am J Obstet Gynecol . 2020;223(4):585-8. doi:10.1016/j.ajog.2020.05.027

Lu JF, Chen CM, Hsu CY. Effect of home telehealth care on blood pressure control: A public healthcare centre model. J Telemed Telecare . 2019;25(1):35-45. doi:10.1177/1357633X17734258

Mirzaei M, Mirzaei M, Bagheri B, Dehghani A. Awareness, treatment, and control of hypertension and related factors in adult Iranian population. BMC Public Health . 2020;20(1):667. doi:10.1186/s12889-020-08831-1

Office of Disease Prevention and Health Promotion. Health literacy. health.gov/healthypeople/priority-areas/social-determinants-health/literature-summaries/health-literacy

Oparil S, Acelajado MC, Bakris GL, et al. Hypertension. Nat Rev Dis Primers . 2018;4:18014. doi:10.1038/nrdp.2018.14

Ozoemena EL, Iweama CN, Agbaje OS, et al. Effects of a health education intervention on hypertension-related knowledge, prevention and self-care practices in Nigerian retirees: A quasi-experimental study. Arch Public Health . 2019;77:23. doi:10.1186/s13690-019-0349-x

Tan JP, Cheng KKF, Siah RC. A systematic review and meta-analysis on the effectiveness of education on medication adherence for patients with hypertension, hyperlipidemia and diabetes. J Adv Nurs . 2019;75(11):2478-94. doi:10.1111/jan.14025

Thangada ND, Garg N, Pandey A, Kumar N. The emerging role of mobile-health applications in the management of hypertension. Curr Cardiol Rep . 2018;20(9):78. doi:10.1007/s11886-018-1022-7

U.S. Department of Veterans Affairs. Health services research & development: Spotlight: Hypertension and stroke. June 2011. hsrd.research.va.gov/news/feature/hypertension_stroke.cfm

U.S. Department of Veterans Affairs. VA/DoD Clinical practice guideline: Diagnosis and management of hypertension patient summary. 2020. healthquality.va.gov/guidelines/CD/htn/VADODHypertensionPatientSummary20Apr2020.pdf

VA Diffusion of Excellence. Video blood pressure visits (VBPV). 2022. marketplace.va.gov/innovations/video-blood-pressure-visits

World Health Organization Hypertension. March 16, 2023. who.int/news-room/fact-sheets/detail/hypertension

Key words: hypertension, remote blood pressure monitoring, patient education, veterans’ health

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Baldwin R, Granger B, Thompson JA, Bowers MT, Kizzie A. Improving hypertension using virtual modalities. American Nurse Journal. 2023;18(11):26-30. doi:10.51256/anj11232326 https://www.myamericannurse.com/improving-hypertension-using-virtual-modalities/

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Best practices for measuring and managing blood pressure.

Mayo Clinic physicians  Sandra J. Taler, M.D. , Nephrology and Hypertension, and  Randal J. Thomas, M.D.,  Preventive Cardiology, discuss recent American College of Cardiology/American Heart Association blood pressure guidelines in this video first shown on Medscape Cardiology.

November 27, 2019

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Blood Pressure Visit Intensification in Treatment (BP-Visit) Findings: a Pragmatic Stepped Wedge Cluster Randomized Trial

Kevin fiscella.

1 University of Rochester, Rochester, NY USA

2 Family Medicine Research, 1381 South Ave, Rochester, NY 14620 USA

3 Tulane University, New Orleans, LA USA

Mechelle Sanders

Andrea cassells.

4 Clinical Directors Network (CDN), New York, NY USA

Jennifer K. Carroll

5 University of Colorado, Anschutz Medical Campus, Aurora, CO USA

Stephen K. Williams

6 NYU Langone Health, New York, NY USA

Jerry Cornell

Tameir holder, chamanara khalida, jonathan n. tobin.

7 The Rockefeller University Center for Clinical and Translational Science, New York, NY USA

Associated Data

Shortening time between office visits for patients with uncontrolled hypertension represents a potential strategy for improving blood pressure (BP).

We evaluated the impact of multimodal strategies on time between visits and on improvement in systolic BP (SBP) among patients with uncontrolled hypertension.

We used a stepped-wedge cluster randomized controlled trial with three wedges involving 12 federally qualified health centers with three study periods: pre-intervention, intervention, and post-intervention.

Participants

Adult patients with diagnosed hypertension and two BPs ≥ 140/90 pre-randomization and at least one visit during post-randomization control period ( N = 4277).

Intervention

The core intervention included three, clinician hypertension group-based trainings, monthly clinician feedback reports, and monthly meetings with practice champions to facilitate implementation.

Main Measures

The main measures were change in time between visits when BP was not controlled and change in SBP. A secondary planned outcome was changed in BP control among all hypertension patients in the practices.

Key Results

Median follow-up times were 34, 32, and 32 days and the mean SBPs were 142.0, 139.5, and 139.8 mmHg, respectively. In adjusted analyses, the intervention did not improve time to the next visit compared with control periods, HR = 1.01 (95% CI: 0.98, 1.04). SBP was reduced by 1.13 mmHg (95% CI: −2.10, −0.16), but was not maintained during follow-up. Hypertension control (< 140/90) in the practices improved by 5% during intervention (95% CI: 2.6%, 7.3%) and was sustained post-intervention 5.4% (95% CI: 2.6%, 8.2%).

Conclusions

The intervention failed to shorten follow-up time for patients with uncontrolled BP and showed very small, statistically significant improvements in SBP that were not sustained. However, the intervention showed statistically and clinically relevant improvement in hypertension control suggesting that the intervention affected clinician decision-making regarding BP control apart from visit frequency. Future practice initiatives should consider hypertension control as a primary outcome.

Clinical Trial

www.ClinicalTrials.gov Identifier: {"type":"clinical-trial","attrs":{"text":"NCT02164331","term_id":"NCT02164331"}} NCT02164331

Supplementary Information

The online version contains supplementary material available at 10.1007/s11606-021-07016-9.

Hypertension is a common primary care medical diagnosis[ 1 , 2 ] and a leading, modifiable risk factor for cardiovascular disease (CVD).[ 1 , 3 ] Despite hypertension national guidelines, blood pressure (BP) control remains suboptimal, particularly in low-income and minority populations. Sub-optimally controlled BP is a major contributor to Black-White disparities in cardiovascular morbidity and mortality.[ 4 ]

The Seventh and Eighth Joint National Committees (JNC-7 and JNC-8) on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure recommend 4-week follow-up visits for patients not at BP goal.[ 3 , 5 ] However, most hypertension patients with uncontrolled BP are not re-assessed within 4 weeks.[ 6 – 9 ] We hypothesized that shortening follow-up time when BP was uncontrolled would improve BP and hypertension control.

We designed a multimodal intervention to promote guideline adoption and assessed follow-up time for patients with uncontrolled BP and systolic blood pressure (SBP) using a pragmatic, stepped wedge, cluster randomized controlled trial (SWCRT). Our premise was that an intervention that targeted primary care clinician (PCC) awareness, confidence, norms , perceived control, and routines relevant to this recommendation would (1) reduce visit follow-up time for hypertensive patients with uncontrolled BP and (2) reduce SBP.[ 10 ]

We conducted a SWCRT between 4/1/2014 and 12/13/2017 involving 12 federally qualified health centers (FQHCs). The stepped wedged design is a unidirectional crossover design on clusters from the control to intervention conditions on a staggered schedule, so each cluster serves as its own control. We used a SWCRT, given sites’ wishes to receive the intervention and the need for sequential rollout (Fig. ​ (Fig.1). 1 ). The 12 FQHCs were randomized to one of three phases of intervention with each phase involving 4 FQHCs. The intervention period lasted 1 year and the number of control periods ranged from 1 to 3 depending on which phase the FQHC was randomly assigned. The post-intervention was defined just after the intervention period ends (Fig. ​ (Fig.1 1 ).

Stepped wedge time periods. The stepped wedge cluster randomized trial involved rollout in three steps. Each wedge involved four FQHCs, with three time periods—pre-intervention (control), intervention, and post-intervention periods. The pre-randomization eligibility period is not included

Randomization and Blinding

An offsite study statistician (HH) conducted randomization using computer-generated random numbers using the FQHC study identification number. The study was unblinded given the use of a SWCRT.

PARTICIPANTS AND SETTING

The 12 FQHCs are located within urban, low-income neighborhoods in New York City/New Jersey. Practices were recruited through CDN ( www.CDNetwork.org ), recognized by NIH as a best-practice, practice-based research network and by the Agency for Healthcare Research and Quality as a Center of Excellence (P30). Practice, PCC. Patient eligibility are shown (Table ​ (Table1). 1 ). Patients were eligible for the inception cohort for primary analysis if their PCC consented, they had two consecutive visits with BP > 140/90 during pre-randomization, at least one visit and BP reading post-randomization. Patients without subsequent visits/readings were censored at the end of the period in which their last visit/BP reading was recorded.

Eligibility Criteria

*Primary care clinicians (PCCs) include family physicians, internal medicine physicians, geriatricians, nurse practitioners, and physician assistants

Ethical approval was obtained from the University of Rochester, CDN, and New York University Institutional Review Boards. We obtained waivers of informed consent for accessing de-identified data for all patient participants, with encrypted numbers for patients and PCCs.[ 11 , 12 ] The study is registered at www.ClinicalTrials.gov ( {"type":"clinical-trial","attrs":{"text":"NCT 02164331","term_id":"NCT02164331"}} NCT 02164331 ).

INTERVENTION

The intervention targeted both the PCCs and the practices and involved three core strategies: three PCC trainings on hypertension guidelines (JNC-8) and application, monthly PCC audit and feedback reports regarding their performance, and monthly practice champion calls. Strategies targeted PCC attitudes, norms, and perceived control and included training in hypertension follow-up visits, BP targets, antihypertensive agents, and patient-centered counseling, in addition to practice-level processes, e.g., treatment algorithms and EHR templates.[ 13 ] Supplementary strategies included outreach to patients with uncontrolled BP not seen within 90 days, informational newsletters, and PCC access to a hypertension specialist (SW). We trained practice staff in BP measurement using automated office blood pressure (AOBP) devices, widely available in the practices.[ 14 ]

Usual Care (Control)

During the control (pre-intervention) period, FQHCs continued with their usual management of patients with uncontrolled hypertension.

Data Collection

The unit of randomization was the practice, and the unit of analysis was the patient, nested within the PCC and practice. Following PRECIS-2 domains for pragmatic trials,[ 15 ] we used EHR data for patient selection, assessment of intervention implementation, and outcomes. Patient and site measures were assessed through export, transfer, loading, cleaning, and standardization of data from the EHR. We obtained continuous EHR data to cover relevant time periods using unique encrypted identifiers for practices, PCCs, and patients. Using these data, we assessed patient age, sex, race/ethnicity, insurance, smoking, BMI, comorbidity conditions (based on ICD9/10 codes), and codes for hypertension (ICD9 codes 401x - 405x and ICD-10 I10.x), as well as dates of primary care visits and corresponding recorded BP readings.

MAIN MEASURES

Our two primary outcomes were (1) time (# days) to subsequent visit following a visit with uncontrolled BP defined as ≥ 140/90 mmHg and (2) systolic blood pressure at the last visit of the period. A secondary outcome was BP control (< 140/90 mmHg), based on the last recorded BP during each period. We compared the intervention and post-intervention periods to the pre-intervention period by examining the changes in the measures over time. When more than one BP reading was available for a visit, BPs were averaged. As previously reported,[ 13 ] we estimated 80% power with an alpha of 0.05 to detect a 0.8 mmHg improvement in SBP with a sample size of 200 patients per site.

Statistical Analysis

Our primary analysis used an intention-to-treat (ITT) analysis based on an inception cohort of eligible patients who had two consecutive visits with BP ≥ 140/90 during pre-randomization and ≥ one visit with consented PCCs during post-randomization control periods. For patients with loss-of-follow-up, they were censored at the end of treatment condition period in which their last visit occurred.

Cox proportional hazard regression models were conducted to examine the effects of the intervention on the time to the follow-up visits with the 3-level treatment condition included as the primary predictor. The relative risk for follow-up visit is assessed by the hazard ratio defined as the exponential of the regression coefficient. The hazard ratios defined as the exponential of the regression coefficient were provided to assess the relative risk of follow-up visit for comparison of groups. In the Cox regression models, the control is treated as the reference level, so the estimated hazard ratio is the relative risk of follow-up visit comparing the intervention/post-intervention period to the control period. The primary predictor is the three-level indicator of the treatment period, 0=pre-intervention (control), 1= intervention period, and 2=post-intervention periods. A hazard ratio > 1.0 means that the follow-up visit time is shorter than the control period. We included site as a covariate to account for clustering. We applied a shared frailty model to take into account the correlations for visits among the same PCC using PCC as random effect.[ 16 ] We also used empirical variance-covariance estimate to address correlations arising from repeated measures due to multiple visits for individual patients. By employing the fixed effect of the site, PCC random effect, and empirical variance-covariance estimate, the multi-level, clustering effects are well accounted for in the analysis. We controlled for patient demographics, i.e., age, race and ethnicity, smoking status, health insurance, comorbidity, and BMI in the models.

To assess the intervention effect on the change of SBP at the last visit during the intervention period and during the follow-up period SBP, we conducted linear, mixed-effects models with the same set of predictor/covariates in the aforementioned Cox model as fixed effects. To account for the multilevel clustering effects arising from the site, PCC, and repeated measures, similar to the Cox model, in addition to including the site as fixed effect, we included a random PCC intercept in the model and applied empirical variance-covariance estimate to address correlations from repeated measures and any residual effects due to clustering. We examined the heterogeneity of treatment effects (HTEs) for several pre-specified subgroups using the same methods for patients with higher SBP ≥ 160 mmHg at baseline. We also applied the JNC-8, age-based definitions of SBP control, i.e., patients age ≥ 60 years with SBP ≥ 150 or age < 60 years with SBP ≥ 140. Last, we assessed the relationship between changes in follow-up time and SBP.

In planned secondary analyses,[ 13 ] we replaced SBP with BP control. We used a generalized linear mixed-effect model (GLMM) with an identity link in the model, so the coefficient can be interpreted as a change of probability of having the BP controlled with positive (negative) coefficient indicating a higher (lower) probability of having the BP controlled. Similar strategies as those for the primary outcomes are used to address the multi-level clustering effect. Both unadjusted and adjusted analyses were conducted to examine the intervention effect with and without potential confounders controlled. The control variables are the same set of variables used in the primary analysis. We examined the potential confounding by wedge by including wedge in the model and observed the same estimated treatment effects. The results reported here do not include wedge controls. Last, we surveyed PCCs at baseline and post-intervention regarding their follow-up time for patients with uncontrolled BP and blood pressure goals.

We successfully extracted EHR data from seven different EHR vendor systems across all 12 practices. Pre-randomization hypertension control ranged from 47 to 69%. PCCs per practice ranged from 5 to 10. Seventy-seven PCCs consented: two-thirds were physicians and one-third were nurse practitioners or physician assistants. Two-thirds of the sample were female; two-thirds were Black or Hispanic; and the majority had worked at the FQHC for 5 or more years. PCC turnover by site ranged from none to 80%. Between 40 and 100% of consented PCCs attended training sessions. All received audit and feedback reports (Figures S 1 , S 2 ). All practice champions regularly attended monthly calls and half of practices conducted patient outreach.

Patient characteristics by step and overall are shown (Table ​ (Table2). 2 ). In unadjusted analyses, the overall median (interquartile range) times in days to follow-up were 34 (69), 32 (59), and 32 (54) for pre-intervention, intervention, and post-intervention periods, respectively. The overall unadjusted mean (median) SBPs in mmHg at the last visit of each period were 140.96 (140), 139.69 (138), and 140.47 (138) in the pre-intervention, intervention, and post-intervention periods, respectively. Within patient SBP, visit-to-visit variation was high (standard deviation = 14.4 mmHg).

Practice and Patient Factors at Baseline by Step with the SWCRT for Consented Patients

*Missing data refers to data that were not present in the EHR at the time of extraction

†Visit follow-up time when the BP > 140/90 at the preceding visit for each period, i.e., baseline, intervention, post-intervention, averaged across each period; IQR is for inter-quartile range

‡Systolic blood pressures (SBP) averaged across each period, i.e., baseline, intervention, and post-intervention,N=Number, SD= Standard Deviation

The full models based on the ITT analysis for the inception cohort with each site as a fixed effect are shown in Table ​ Table3 3 for the time to the next visit and Table ​ Table4 4 for SBP measures. Compared to the control period, overall the follow-up visit time was not improved during the intervention period with hazard ratios (HR) = 1.01 (95% CI: [0.98, 1.04]), but was slightly lengthened for the post-intervention period with HR = 0.97 (95% CI: [0.93, 1.00]) (Table ​ (Table3). 3 ). There was variation in follow-up visit time across the study sites. Treating one of the sites (site 12), based on alphabetical order, as the reference site, HRs ranged from 1.44 (95% CI: [1.27, 1.63]) for shorter follow-up visit time to HR = 0.48 (95% CI: [0.42, 0.55]) for a longer follow-up visit time. Older patients, females, and those with more comorbidity and insurance coverage showed shorter follow-up times. Results were not significant when follow-up visits were dichotomized at 4 weeks.

Intervention Effects on Time to The Next Primary Care Visit from Initial Uncontrolled BP Visit for Patients with Consented PCC Based on Proportional Hazards Model

*BMI Missing refers to data that were not present in the EHR when data were extracted

Intervention Effects on SBP for Patients with Consented PCC by Linear Mixed-Effect Model

*Missing refers to data that were not present in the EHR when data were extracted

Compared to the control period, the intervention was associated with a very small but statistically significant reduction in SBP −1.13 mmHg (95% CI: [−0.16, −2.10]) (Table ​ (Table4). 4 ). However, this reduction in SBP was not sustained during follow-up with reduction in SBP of 0.29 mmHg (95% CI: [−1.37, 0.78]). The changes in SBP among practices ranged from a reduction of 6.32 mmHg (95% CI: [−7.66, −4.98]) to an increase of 8.58 mmHg (95% CI: [4.92, 12.23]) compared to the reference site (Table ​ (Table4 4 ).

Sensitivity analyses, including use of age-based thresholds (from the panel appointed to JNC-8) of < 60 vs. ≥ 60 years, showed no significant reductions in time between visits and the reductions in SBP observed during the intervention, 0.80 mmHg (95% CI: [−1.91, 0.30]), increased to 1.52 mmHg (95% CI: [−2.75, −0.29]), and became statistically significant during the follow-up period. Sensitivity analyses based on SBP ≥ 160 mmHg at baseline showed no significant reduction in time between visits for these patients with higher baseline SBP. Among this subset of patients, SBP reductions were only 0.82 mmHg (95% CI: [−1.73, 0.10]) and increased to 2.48 mmHg (95% CI: [−4.38, −0.58]) at follow-up.

We also examined the association between the follow-up time with uncontrolled SBP and changes in SBP. The unadjusted association between visit follow-up and SBP was 0.01 mmHg/day (95% CI: [0.006, 0.01], p < 0.001), i.e., every 1-day delay in coming back to the office for a follow-up visit was associated with a negligible 0.01 mmHg increase in SBP.

Unadjusted hypertension control improved from 45 to 50.7% during intervention and 51.5% post intervention. Adjusted analyses (Table S 1 ) confirmed a 5% statistically significant improvement during intervention (95% [CI: 2.6%, 7.3%]) which was sustained post-intervention 5.4% (95% [CI: 2.6%, 8.2%]).

Last, we examined survey responses (96% response rate) from consented PCCs (Table S 2 ). At baseline, 94% of PCCs reported scheduling patients with uncontrolled BP within a month. This increased to 98% at follow-up suggesting potential ceiling effects on PCC follow-up plans. At baseline and follow-up, most PCCs reported using the age-based, SBP thresholds for initiating treatment.

We designed and implemented a multimodal approach to promote monthly office visits among FQHC patients with uncontrolled hypertension and to optimize their BP management. The intervention failed to achieve a statistically significant reduction in time between visits when BP was not controlled. The intervention achieved a statistically significant but clinically insignificant improvement in SBP, which was not maintained. However, with regard to a secondary clinical outcome, BP control, we observed statistically and clinically significant improvements in BP control during the intervention that were sustained post-intervention.

Our results for our primary outcomes are consistent with a number of prior studies that have shown either small or null effects of randomized trials to improve BP within FQHCs and safety net practices in the USA.[ 17 – 19 ] Minimal findings for our primary outcomes likely reflect higher than anticipated commitment by PCCs to scheduling patients with uncontrolled hypertension to return in 4 weeks. More than 90% of PCCs reported that they already do so and the median follow-up time was 34 days, creating a ceiling effect and thus reducing the opportunity for further improvement. Minimal improvements in SBP seen within the inception cohort likely represent a combination of no change in visit follow-up, pre-intervention SBPs that were already close to 140 mmHg, and high patient visit-to-visit SBP variability. Potentially, these limitations resulted in patients often having SBP controlled at one visit and not at the next, potentially contributing to PCC uncertainty regarding the need for shorter follow-up intervals and pharmacological intensification. Notably, prior studies show that high visit-to-visit BP variability partly reflects suboptimal medication adherence and is an independent cardiovascular risk factor.[ 20 , 21 ]

The minimal effects when SBP was used as the primary outcome as compared to statistically and clinically significant effects when BP control was used as the outcome seem paradoxical. It likely represents most patients being very close to a SBP of 140 and PCC’s addressing reduction in SBP and DBP . We chose SBP as our primary outcome because it is a continuous measure and as is strongly associated with CVD outcomes. In retrospect, BP control is arguably the more pragmatic outcome because PCCs make clinical decisions based on it and because control rate is the measure used by practices to monitor and report their hypertension control quality performance.

Notably, the 5% improvement in hypertension control occurred despite challenges faced by FQHCs. PCC turnover varied widely, with one site experiencing 80% turnover. PCC turnover reduces exposure to the intervention and impacts patients by interrupting continuity of care and disrupting patient-PCC relationships, potentially undermining patient adherence and continuity with the practice.[ 22 – 25 ]

Patient no-show rates within practices ranged from 30 to 50%, which likely blunted effects on actual visit follow-up time and SBP reduction. A systematic review of no-show appointments that included studies conducted internationally showed cross-sectional associations between uncontrolled BP and missed appointments.[ 26 ] Rowan et al. reported that patients with stage 2 hypertension who had no baseline office visits were twice as likely not to achieve BP control compared with patients who had visits every other month.[ 27 ]

JNC-8 guidelines released just before the study began may have blunted the effects. Most PCCs reported following these age-based treatment guidelines. Sensitivity analyses showed larger effects in the post-intervention period when age-based thresholds were used, likely reflecting PCCs’ practice patterns. Moreover, effects were larger in the post-intervention period among patients with higher SBP, suggesting possible lagged effects. Last, our findings suggest that practices may respond differently to the same intervention and raise questions regarding how to better adapt the intervention to the specific needs of each practice, although we could not discern a relationship between the practice-level factors and improved SBP.

Strengths and Limitations of the Study

Strengths include successful design, implementation, and completion of a SWCRT among FQHCs with primary outcomes and covariates from extracted EHR data. These strengths improve generalizability to other FQHCs and safety-net primary care practices. Limitations include floor effects on improvement in visit follow-up and challenges of conducting pragmatic studies. We did not use standardized research-grade procedures for measuring BP, but rather, we trained staff in appropriate BP measurement using their existing automated devices, which generally align with the gold standard of ambulatory readings.[ 14 ] We lacked reliable data on hypertensive pharmacological treatment inertia, thus precluding evaluation of this potential mechanism.

A multimodal, evidence-based intervention with 12 FQHCs did not reduce time between visits or sustain improvements in SBP in the inception cohort, potentially due to baseline ceiling effects for improvement in follow-up times and SBP and to patient visit-to-visit variability in SBP. The notable improvement in our secondary outcome of hypertension control suggests that while the intervention did not improve follow-up time or SBP levels, the multimodal intervention did improve overall hypertension control. This finding suggests that PCC training, audit and feedback reports, and practice champion meetings likely affected clinician-decision surrounding BP control within FQHC practices more broadly. Future practice-wide interventions might consider how to tailor the intervention to the practice and examine hypertension control rates as a primary outcome.

S upplementary I nformation

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Contributors

Study data were collected and managed using REDCap electronic data capture tools from NCATS/NIH. REDCap (Research Electronic Data Capture) is a secure, web-based application designed to support data capture for research studies, providing (1) an intuitive interface for validated data entry; (2) audit trails for tracking data manipulation and export procedures; (3) automated export procedures for seamless data downloads to common statistical packages; and (4) procedures for importing data from external sources. Participating FQHCs: New York sites (1) Brownsville Multi-Service Family Health Center, Brooklyn; (2) Caribbean-American: NYU Lutheran Family Health Center, Brooklyn; (3) Community Healthcare Network – Lower East Side and CABS, New York City and Brooklyn; (4) Community Healthcare Network – Jamaica, Queens; (5) Joseph P. Addabbo Family Health Center, Queens; (6) Morris Heights Health Center, Bronx; (7) ODA Primary Health Care, Brooklyn; and (8) Urban Health Plan Inc., Bronx; New Jersey sites (1) CamCare Health Corporation, Camden; (2) Henry J. Austin Health Center, Trenton; (3) Metropolitan Family Health Network, Jersey City; and (4) Newark Community Health Centers, Newark.

Technical Assistance

Kathleen Silver provided vital assistance in editing/proofing, formatting, generation of figures, and submission.

This study was funded by a research dissemination and implementation grant (1R18HL117801) from the National Heart, Lung, and Blood Institute of the National Institutes of Health (NIH). Resources included data collection, which occurred at the University of Rochester with Clinical and Translational Science Institute grant support (UL1 TR002001) from the National Center for Advancing Translational Sciences (NCATS)/NIH) and the Network of Practice-based Research Networks (N 2 -PBRN) at Clinical Directors Network with funding from the AHRQ-Designated Center of Excellence (P30) for Practice-based Research and Learning: “N 2 : Building a Network of Safety Net PBRNs” (Grant No. 1 P30-HS-021667). We would like to acknowledge the guidance of our Program Officers, Paula Einhorn, MD (NHLBI R18) and Rebecca Roper, MS MPH (AHRQ P30) and assistance from Subrina Farah and the DARTNet Instutite for EHR data extraction and standardization.

Declarations

The study is registered at www.ClinicalTrials.gov ( {"type":"clinical-trial","attrs":{"text":"NCT 02164331","term_id":"NCT02164331"}} NCT 02164331 ). Ethical approval was obtained from the University of Rochester, CDN, and New York University Institutional Review Boards (IRBs). We obtained waivers of informed consent for accessing de-identified data for patient participants.

The authors declare that they do not have a conflict of interest.

Prior Presentations: None.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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