Ahmad, Faraz

Hypertension presents the largest modifiable public health challenge due to its high prevalence, its intimate relationship to cardiovascular diseases, and its complex pathogenesis and pathophysiology. Low awareness of blood pressure elevation and suboptimal hypertension diagnosis serve as the major hurdles in effective hypertension management. Advances in artificial intelligence in hypertension have permitted the integrative analysis of large data sets including omics, clinical (with novel sensor and wearable technologies), health-related, social, behavioral, and environmental sources, and hold transformative potential in achieving large-scale, data-driven approaches toward personalized diagnosis, treatment, and long-term management. However, although the emerging artificial intelligence science may advance the concept of precision hypertension in discovery, drug targeting and development, patient care, and management, its clinical adoption at scale today is lacking. Recognizing that clinical implementation of artificial intelligence–based solutions need evidence generation, this opinion statement examines a clinician-centric perspective of the state-of-art in using artificial intelligence in the management of hypertension and puts forward recommendations toward equitable precision hypertension care.

Hypertension is among the most important risk factors for cardiovascular disease, chronic kidney disease, and dementia. The artificial intelligence (AI) field is advancing quickly, and there has been little discussion on how AI could be leveraged for improving the diagnosis and management of hypertension. AI technologies, including machine learning tools, could alter the way we diagnose and manage hypertension, with potential impacts for improving individual and population health. The development of successful AI tools in public health and health care systems requires diverse types of expertise with collaborative relationships between clinicians, engineers, and data scientists. Unbiased data sources, management, and analyses remain a foundational challenge. From a diagnostic standpoint, machine learning tools may improve the measurement of blood pressure and be useful in the prediction of incident hypertension. To advance the management of hypertension, machine learning tools may be useful to find personalized treatments for patients using analytics to predict response to antihypertension medications and the risk for hypertension-related complications. However, there are real-world implementation challenges to using AI tools in hypertension. Herein, we summarize key findings from a diverse group of stakeholders who participated in a workshop held by the National Heart, Lung, and Blood Institute in March 2023. Workshop participants presented information on communication gaps between clinical medicine, data science, and engineering in health care; novel approaches to estimating BP, hypertension risk, and BP control; and real-world implementation challenges and issues.

Abstract Aims Heart failure (HF) is a common and morbid condition impacting multiple health domains. We previously reported the development of the PROMIS®-Plus-HF (PROMIS+HF) profile measure, including universal and HF-specific items. To facilitate use, we developed shorter, PROMIS+HF profiles intended for research and clinical use. Methods and results Candidate items were selected based on psychometric properties and symptom range coverage. HF clinicians (n = 43) rated item importance and clinical actionability. Based on these results, we developed the PROMIS+HF-27 and PROMIS+HF-10 profiles with summary scores (0–100) for overall, physical, mental, and social health. In a cross-sectional sample (n = 600), we measured internal consistency reliability (Cronbach's alpha and Spearman–Brown), test–retest reliability (intraclass coefficient; n = 100), known-groups validity via New York Heart Association (NYHA) class, and convergent validity with Kansas City Cardiomyopathy Questionnaire (KCCQ) scores. In a longitudinal sample (n = 75), we evaluated responsiveness of baseline/follow-up scores by calculating mean differences and Cohen's d and comparing with paired t-tests. Internal consistency was good to excellent (α 0.82–0.94) for all PROMIS+HF-27 scores and acceptable to good (α/Spearman–Brown 0.60–0.85) for PROMIS+HF-10 scores. Test–retest intraclass coefficients were acceptable to excellent (0.75–0.97). Both profiles demonstrated known-groups validity for the overall and physical health summary scores based on NYHA class, and convergent validity for nearly all scores compared with KCCQ scores. In the longitudinal sample, we demonstrated responsiveness for PROMIS+HF-27 and PROMIS+HF-10 overall and physical summary scores. For the PROMIS+HF overall summary scores, a group-based increase of 7.6–8.3 points represented a small to medium change (Cohen's d = 0.40–0.42). For the PROMIS+HF physical summary scores, a group-based increase of 5.0–5.9 points represented a small to medium change (Cohen's d = 0.29–0.35). Conclusions The PROMIS+HF-27 and PROMIS+HF-10 profiles demonstrated good psychometric characteristics with evidence of responsiveness for overall and physical health. These new measures can facilitate patient-centred research and clinical care, such as improving care quality through symptom monitoring, facilitating shared decision-making, evaluating quality of care, assessing new interventions, and monitoring during the initiation and titration of guideline-directed medical therapy.

Background The National Patient‐Centered Clinical Research Network Blood Pressure Control Laboratory Surveillance System was established to identify opportunities for blood pressure (BP) control improvement and to provide a mechanism for tracking improvement longitudinally. Methods and Results We conducted a serial cross‐sectional study with queries against standardized electronic health record data in the National Patient‐Centered Clinical Research Network (PCORnet) common data model returned by 25 participating US health systems. Queries produced BP control metrics for adults with well‐documented hypertension and a recent encounter at the health system for a series of 1‐year measurement periods for each quarter of available data from January 2017 to March 2020. Aggregate weighted results are presented overall and by race and ethnicity. The most recent measurement period includes data from 1 737 995 patients, and 11 956 509 patient‐years were included in the trend analysis. Overall, 15% were Black, 52% women, and 28% had diabetes. BP control (<140/90 mm Hg) was observed in 62% (range, 44%–74%) but varied by race and ethnicity, with the lowest BP control among Black patients at 57% (odds ratio, 0.79; 95% CI, 0.66–0.94). A new class of antihypertensive medication (medication intensification) was prescribed in just 12% (range, 0.6%–25%) of patient visits where BP was uncontrolled. However, when medication intensification occurred, there was a large decrease in systolic BP (≈15 mm Hg; range, 5–18 mm Hg). Conclusions Major opportunities exist for improving BP control and reducing disparities, especially through consistent medication intensification when BP is uncontrolled. These data demonstrate substantial room for improvement and opportunities to close health equity gaps.