Clinical Decision Support for the Diagnosis and Management of Adult and Pediatric Hypertension

High Blood Pressure in Children Aged 3 to 12 Years Old With Overweight or Obesity

Objective: (1) To describe the prevalence of high blood pressure (BP) and the association with BMI in young children with overweight/obesity; (2) to evaluate the accuracy of a single high BP to diagnose sustained hypertension over three visits. Methods: We used pre-intervention data from the Improving Pediatric Obesity Practice Using Prompts (iPOP-UP) trial. We included children aged 3-12 years with BMI ≥85th percentile at well-visits in 2019-2021 at 84 primary care practices in 3 US health systems in the Northeast, Midwest, and South. BP percentiles were calculated from the first visit with BP recorded during the study period. Hypertensive-range BP was defined by the 2017 American Academy of Pediatrics guideline. We tested the association between BMI classification and hypertensive BP using multivariable logistic regression. Results: Of 78,280 children with BMI ≥85th percentile, 76,214 (97%) had BP recorded during the study period (mean 7.4 years, 48% female, 53% with overweight, and 13% with severe obesity). The prevalence of elevated or hypertensive BP was 31%, including 27% in children with overweight and 33%, 39%, and 49% with class I, II, and III obesity, respectively. Higher obesity severity was associated with higher odds of hypertensive BP in the multivariable model. Stage 2 hypertensive BP at the initial visit had specificity of 99.1% (95% confidence interval 98.9-99.3) for detecting sustained hypertension over ≥3 visits. Conclusions: High BP is common in 3- to 12-year-olds with overweight/obesity, with higher obesity severity associated with greater hypertension. Children with overweight/obesity and stage 2 BP are likely to have sustained hypertension and should be prioritized for evaluation. Trial Registration: ClinicalTrials.gov Identifier: NCT05627011.

What characteristics of clinical decision support system implementations lead to adoption for regular use? A scoping review

INTRODUCTION

Digital healthcare innovation has yielded many prototype clinical decision support (CDS) systems, however, few are fully adopted into practice, despite successful research outcomes. We aimed to explore the characteristics of implementations in clinical practice to inform future innovation.

METHODS

Web of Science, Trip Database, PubMed, NHS Digital and the BMA website were searched for examples of CDS systems in May 2022 and updated in June 2023. Papers were included if they reported on a CDS giving pathway advice to a clinician, adopted into regular clinical practice and had sufficient published information for analysis. Examples were excluded if they were only used in a research setting or intended for patients. Articles found in citation searches were assessed alongside a detailed hand search of the grey literature to gather all available information, including commercial information. Examples were excluded if there was insufficient information for analysis. The normalisation process theory (NPT) framework informed analysis.

RESULTS

22 implemented CDS projects were included, with 53 related publications or sources of information (40 peer-reviewed publications and 13 alternative sources). NPT framework analysis indicated organisational support was paramount to successful adoption of CDS. Ensuring that workflows were optimised for patient care alongside iterative, mixed-methods implementation was key to engaging clinicians.

CONCLUSION

Extensive searches revealed few examples of CDS available for analysis, highlighting the implementation gap between research and healthcare innovation. Lessons from included projects include the need for organisational support, an underpinning mixed-methods implementation strategy and an iterative approach to address clinician feedback.

Artificial Intelligence-Based Clinical Decision Support Systems in Cardiovascular Diseases

Despite all the advancements in science, medical knowledge, healthcare, and the healthcare industry, cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide. The main reasons are the inadequacy of preventive health services and delays in diagnosis due to the increasing population, the failure of physicians to apply guide-based treatments, the lack of continuous patient follow-up, and the low compliance of patients with doctors' recommendations. Artificial intelligence (AI)-based clinical decision support systems (CDSSs) are systems that support complex decision-making processes by using AI techniques such as data analysis, foresight, and optimization. Artificial intelligence-based CDSSs play an important role in patient care by providing more accurate and personalized information to healthcare professionals in risk assessment, diagnosis, treatment optimization, and monitoring and early warning of CVD. These are just some examples, and the use of AI for CVD decision support systems is rapidly evolving. However, for these systems to be fully reliable and effective, they need to be trained with accurate data and carefully evaluated by medical professionals.

Adoption of shared decision-making and clinical decision support for reducing cardiovascular disease risk in community health centers

Objective

Electronic health record (EHR)-based shared decision-making (SDM) and clinical decision support (CDS) systems can improve cardiovascular disease (CVD) care quality and risk factor management. Use of the CV Wizard system showed a beneficial effect on high-risk community health center (CHC) patients' CVD risk within an effectiveness trial, but system adoption was low overall. We assessed which multi-level characteristics were associated with system use.

Materials and Methods

Analyses included 80 195 encounters with 17 931 patients with high CVD risk and/or uncontrolled risk factors at 42 clinics in September 2018-March 2020. Data came from the CV Wizard repository and EHR data, and a survey of 44 clinic providers. Adjusted, mixed-effects multivariate Poisson regression analyses assessed factors associated with system use. We included clinic- and provider-level clustering as random effects to account for nested data.

Results

Likelihood of system use was significantly higher in encounters with patients with higher CVD risk and at longer encounters, and lower when providers were >10 minutes behind schedule, among other factors. Survey participants reported generally high satisfaction with the system but were less likely to use it when there were time constraints or when rooming staff did not print the system output for the provider.

Discussion

CHC providers prioritize using this system for patients with the greatest CVD risk, when time permits, and when rooming staff make the information readily available. CHCs' financial constraints create substantial challenges to addressing barriers to improved system use, with health equity implications.

Conclusion

Research is needed on improving SDM and CDS adoption in CHCs.

Trial Registration

ClinicalTrials.gov, NCT03001713, https://clinicaltrials.gov/.

Perspectives of Primary Care Clinicians on the Diagnosis and Treatment of Pediatric Hypertension

The purpose of this study was to contextualize the challenges of diagnosing and managing pediatric hypertension (pHTN) in federally qualified health centers. We conducted a survey among primary care clinicians (N = 72) who treat children (3-17 years old) in a national network of health centers. Clinicians reported practices of blood pressure (BP) measurement, barriers to diagnosis and management of pHTN, and use of population health tools. Most clinicians (83%) used electronic devices to measure BP, only 49% used manual BP readings for follow-up measurements, and more than half measured BP at each encounter. The highest-rated barrier to pHTN management was lack of comfort with antihypertensive medications (71% of respondents). Few clinicians (10%) had used population health tools, but most (78%) indicated they would like to use them for pHTN. These results offer clinician-level insights regarding implementation of the pHTN guideline in pediatric primary care settings.

The enigma of primary hypertension in childhood

Beginning in the 1970s, hypertension in children and adolescents has been defined as systolic and/or diastolic blood pressure (BP) that is equal to or greater than the 95th percentile of the normal BP distribution in healthy children. The definition of hypertension in adults is based on longitudinal data that links a BP level with an increased risk for subsequent adverse outcomes related to hypertension including heart failure, kidney failure, stroke, or death. The statistical definition of hypertension continues to be used in childhood because there have been no data that link a BP level in childhood with a heightened risk for adverse outcomes in adulthood. Findings from clinical and epidemiologic research have advanced understanding of high BP in childhood. While hypertension in some children can be secondary to underlying kidney, cardiovascular, or endocrine disorder, it is now known that primary (essential) hypertension can be present in childhood. The prevalence of hypertension in childhood is approximately 2–5% and another 13–18% of children and adolescents have elevated BP and are at heightened risk for developing hypertension. The leading cause of childhood hypertension is primary hypertension, especially in adolescents. For children and adolescents with secondary hypertension, the treatment can focus on managing the underlying cause of hypertension. Less is known about managing primary hypertension in childhood, including diagnosis, evaluation, treatment, and possibilities for prevention. The phenotype of primary hypertension in childhood and recent findings will be discussed.

A stakeholder-driven method for selecting implementation strategies: a case example of pediatric hypertension clinical practice guideline implementation

BACKGROUND

This article provides a generalizable method, rooted in co-design and stakeholder engagement, to identify, specify, and prioritize implementation strategies. To illustrate this method, we present a case example focused on identifying strategies to promote pediatric hypertension (pHTN) Clinical Practice Guideline (CPG) implementation in community health center-based primary care practices that involved meaningful engagement of pediatric clinicians, clinic staff, and patients/caregivers. This example was chosen based on the difficulty clinicians and organizations experience in implementing the pHTN CPG, as evidenced by low rates of guideline-adherent pHTN diagnosis and treatment.

METHODS

We convened a Stakeholder Advisory Panel (SAP), comprising 6 pediatricians and 5 academic partners, for 8 meetings (~12 h total) to rigorously identify determinants of pHTN CPG adherence and to ultimately develop a testable multilevel, multicomponent implementation strategy. Our approach expanded upon the Expert Recommendations for Implementation Change (ERIC) protocol by incorporating a modified Delphi approach, user-centered design methods, and the Implementation Research Logic Model (IRLM). At the recommendation of our SAP, we gathered further input from youth with or at-risk for pHTN and their caregivers, as well as clinic staff who would be responsible for carrying out facets of the implementation strategy.

RESULTS

First, the SAP identified 17 determinants, and 18 discrete strategies were prioritized for inclusion. The strategies primarily targeted determinants in the domains of intervention characteristics, inner setting, and characteristics of the implementers. Based on SAP ratings of strategy effectiveness, feasibility, and priority, three tiers of strategies emerged, with 7 strategies comprising the top tier implementation strategy package. Next, input from caregivers and clinic staff confirmed the feasibility and acceptability of the implementation strategies and provided further detail in the definition and specification of those strategies.

CONCLUSIONS

This method-an adaptation of the ERIC protocol-provided a pragmatic structure to work with stakeholders to efficiently identify implementation strategies, particularly when supplemented with user-centered design activities and the intuitive organizing framework of the IRLM. This generalizable method can help researchers identify and prioritize strategies that align with the implementation context with an increased likelihood of adoption and sustained use.

Association of Differences in Treatment Intensification, Missed Visits, and Scheduled Follow-up Interval With Racial or Ethnic Disparities in Blood Pressure Control

Importance

Black patients with hypertension often have the lowest rates of blood pressure (BP) control in clinical settings. It is unknown to what extent variation in health care processes explains this disparity.

Objective

To assess whether and to what extent treatment intensification, scheduled follow-up interval, and missed visits are associated with racial and ethnic disparities in BP control.

Design, Setting, and Participants

In this cohort study, nested logistic regression models were used to estimate the likelihood of BP control (defined as a systolic BP [SBP] level <140 mm Hg) by race and ethnicity, and a structural equation model was used to assess the association of treatment intensification, scheduled follow-up interval, and missed visits with racial and ethnic disparities in BP control. The study included 16 114 adults aged 20 years or older with hypertension and elevated BP (defined as an SBP level ≥140 mm Hg) during at least 1 clinic visit between January 1, 2015, and November 15, 2017. A total of 11 safety-net clinics within the San Francisco Health Network participated in the study. Data were analyzed from November 2019 to October 2020.

Main Outcomes and Measures

Blood pressure control was assessed using the patient's most recent BP measurement as of November 15, 2017. Treatment intensification was calculated using the standard-based method, scored on a scale from -1.0 to 1.0, with -1.0 being the least amount of intensification and 1.0 being the most. Scheduled follow-up interval was defined as the mean number of days to the next scheduled visit after an elevated BP measurement. Missed visits measured the number of patients who did not show up for visits during the 4 weeks after an elevated BP measurement.

Results

Among 16 114 adults with hypertension, the mean (SD) age was 58.6 (12.1) years, and 8098 patients (50.3%) were female. A total of 4658 patients (28.9%) were Asian, 3743 (23.2%) were Black, 3694 (22.9%) were Latinx, 2906 (18.0%) were White, and 1113 (6.9%) were of other races or ethnicities (including American Indian or Alaska Native [77 patients (0.4%)], Native Hawaiian or Pacific Islander [217 patients (1.3%)], and unknown [819 patients (5.1%)]). Compared with patients from all racial and ethnic groups, Black patients had lower treatment intensification scores (mean [SD], -0.33 [0.26] vs -0.29 [0.25]; β = -0.03, P < .001) and missed more visits (mean [SD], 0.8 [1.5] visits vs 0.4 [1.1] visits; β = 0.35; P < .001). In contrast, Asian patients had higher treatment intensification scores (mean [SD], -0.26 [0.23]; β = 0.02; P < .001) and fewer missed visits (mean [SD], 0.2 [0.7] visits; β = -0.20; P < .001). Black patients were less likely (odds ratio [OR], 0.82; 95% CI, 0.75-0.89; P < .001) and Asian patients were more likely (OR, 1.13; 95% CI, 1.02-1.25; P < .001) to achieve BP control than patients from all racial or ethnic groups. Treatment intensification and missed visits accounted for 21% and 14%, respectively, of the total difference in BP control among Black patients and 26% and 13% of the difference among Asian patients.

Conclusions and Relevance

This study's findings suggest that racial and ethnic inequities in treatment intensification may be associated with more than 20% of observed racial or ethnic disparities in BP control, and racial and ethnic differences in visit attendance may also play a role. Ensuring more equitable provision of treatment intensification could be a beneficial health care strategy to reduce racial and ethnic disparities in BP control.