Beyond Thresholds: Why Continuous Risk Assessment Improves Hypertension Treatment

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Introduction

Hypertension treatment based solely on blood pressure thresholds may miss critical opportunities to prevent cardiovascular disease. For decades, physicians have relied on the 140/90 mm Hg cutoff to determine which patients require pharmacological intervention. However, recent evidence suggests that this binary approach fails to account for the continuous relationship between blood pressure and cardiovascular risk, potentially leading to both undertreatment and overtreatment.

The evolution of hypertension guidelines reflects a gradual shift toward risk-based assessment. While earlier versions of AHA hypertension guidelines emphasized rigid cutpoints, new hypertension guidelines increasingly incorporate overall cardiovascular risk profiles to guide treatment decisions. This shift recognizes that patients with identical blood pressure readings may have markedly different absolute risks of cardiovascular events based on age, comorbidities, and other factors. Furthermore, hypertension treatment guidelines have begun to recommend different blood pressure targets for various patient populations, acknowledging the heterogeneity of risk. Consequently, modern hypertension treatment algorithms now often begin with risk stratification rather than blood pressure measurement alone.

This article examines how continuous risk assessment improves hypertension management across the spectrum of cardiovascular risk. Based on recent clinical trials and large-scale meta-analyzes, we explore the practical implications of moving beyond thresholds toward a more nuanced approach that considers the patient’s overall risk profile and potential benefit from treatment.

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From Hypertension Thresholds to Risk-Based Thinking

The traditional binary approach to hypertension management is giving way to a more sophisticated risk-based treatment model. Treatment guidelines have evolved substantially, reflecting mounting evidence that cardiovascular risk exists along a continuum rather than at arbitrary blood pressure thresholds.

Evolution of hypertension treatment guidelines

Hypertension guidelines have undergone profound changes since the early standardized recommendations. In 2003, the Seventh Joint National Committee (JNC-7) established the widely accepted threshold of ≥140/90 mmHg for hypertension diagnosis and treatment [1]. This standard remained largely unchallenged until 2017, when the American College of Cardiology (ACC) and American Heart Association (AHA) released updated guidelines that dramatically lowered the diagnostic threshold to ≥130/80 mmHg [2]. This redefinition expanded the hypertension diagnosis to approximately 46% of U.S. adults, compared to 32% under previous guidelines [3].

The 2017 ACC/AHA guidelines eliminated the classification of prehypertension and instead created three distinct categories: elevated blood pressure (systolic 120-129 mmHg and diastolic <80 mmHg), stage 1 hypertension (systolic 130-139 mmHg or diastolic 80-89 mmHg), and stage 2 hypertension (systolic ≥140 mmHg or diastolic ≥90 mmHg) [4]. This reclassification was influenced by results from the Systolic Blood Pressure Intervention Trial (SPRINT), which demonstrated reduced cardiovascular events (5.2% vs. 6.8%) and all-cause mortality (3.3% vs. 4.5%) with lower blood pressure targets [4].

Most recently, the 2025 AHA/ACC hypertension guidelines have maintained the diagnostic threshold while introducing risk-based treatment initiation criteria, signaling a continued evolution toward personalized care approaches [5].

Limitations of the 140/90 mm Hg threshold

The long-standing 140/90 mmHg cutoff, although clinically intuitive, has several critical limitations. First, epidemiological studies have consistently shown that cardiovascular risk begins increasing at blood pressure levels far below this threshold—some studies suggest risk elevation begins at systolic pressures as low as 90 mmHg [5]. This relationship between blood pressure and cardiovascular outcomes exists on a continuum without a clear inflection point.

Additionally, the binary threshold approach fails to account for substantial variability in outcomes among patients with similar blood pressure readings. Two individuals with identical blood pressure values may have markedly different absolute cardiovascular risks based on other factors. This observation has been confirmed by numerous clinical trials showing that relative risk reduction from blood pressure-lowering medications remains consistent across various baseline blood pressure levels [5].

The traditional threshold-based approach also creates a paradoxical situation where high-risk individuals with blood pressure just below the cutoff receive no pharmacological intervention despite potentially substantial benefit, whereas lower-risk individuals just above the threshold receive medication with possibly minimal absolute benefit.

Incorporation of cardiovascular risk in recent guidelines

Perhaps the most significant paradigm shift in modern hypertension management is the integration of cardiovascular risk assessment into treatment decisions. The 2017 ACC/AHA guidelines were the first to formally incorporate this approach, recommending antihypertensive medication for stage 1 hypertension patients with a 10-year atherosclerotic cardiovascular disease risk ≥10% [3].

The 2025 guidelines have advanced this concept further by replacing the Pooled Cohort Equations with the PREVENT (Predicting Risk of Cardiovascular Disease Events) risk calculator [5]. Unlike its predecessor, PREVENT estimates risk for total cardiovascular disease (including heart failure) rather than just atherosclerotic events, better aligning with contemporary trial endpoints [6]. The new guidelines recommend pharmacological intervention for stage 1 hypertension when estimated 10-year cardiovascular risk exceeds 7.5% [3].

This risk-based approach offers several advantages. Treatment is directed toward those most likely to benefit, with lower numbers needed to treat among high-risk individuals. A meta-analysis from the Blood Pressure Lowering Treatment Trialists’ Collaboration demonstrated that absolute risk reduction increases proportionally with baseline cardiovascular risk, despite similar relative risk reductions across risk strata [5]. Moreover, a direct comparison of treatment strategies showed that a risk-based approach prevents more cardiovascular events for the same number of patients treated compared to a purely blood pressure-based approach [5].

The move toward risk-based hypertension treatment represents a significant conceptual shift—from managing a single risk factor value to preventing cardiovascular events through comprehensive risk assessment.

 

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Why Blood Pressure Alone Is Not Enough

Blood pressure measurements, even when accurate and consistent, tell only part of the story in cardiovascular risk assessment. Mounting evidence indicates that relying exclusively on isolated blood pressure readings can lead to suboptimal treatment decisions that fail to address the complex interplay of factors affecting cardiovascular outcomes.

Variability in outcomes at similar BP levels

Patients with identical blood pressure readings often experience vastly different cardiovascular outcomes. This heterogeneity stems partially from blood pressure variability (BPV) itself—an independent risk factor for dementia, stroke, end-stage renal disease, cardiovascular events, and mortality [7]. Short-term systolic BPV has been noted to be an effective predictor of clinical outcomes, but short-term diastolic BPV appears to be even better in many populations [7].

The mechanism connecting BPV to target-organ damage involves multiple pathways, primarily:

• Increased large artery stiffness
• Vascular remodeling in the microcirculation
• Activation of inflammatory cascades
• Activation of the sympathetic nervous system and renin-angiotensin-aldosterone system [7]

Visit-to-visit BP variability measured over years to decades in clinical trials and observational cohort studies has consistently shown that higher variability is associated with greater cardiovascular risk [7]. A meta-analysis found that increased long-term variability in systolic BP was associated with risk of all-cause mortality (hazard ratio 1.15), cardiovascular disease mortality (1.18), cardiovascular disease events (1.18), coronary heart disease (1.10), and stroke (1.15) [8]. In fact, the standardized hazard ratio for long-term variability in blood pressure on cardiovascular disease mortality (1.18) is comparable to those of cholesterol measures with cardiovascular disease [8].

J-curve phenomenon in low diastolic BP

The relationship between diastolic BP and cardiovascular outcomes displays a curious non-linear shape often called a “J curve” or “U curve.” This pattern is particularly evident when associating diastolic BP with ischemic heart disease events [9]. While high diastolic BP clearly increases cardiovascular risk, low diastolic BP (typically below 70 mm Hg) is likewise associated with increased risk, albeit to a lesser extent [9]. Diastolic BP levels in the 70-90 mm Hg range generally correlate with the lowest cardiovascular risk [9].

This J-curve relationship was first observed by Cruickshank et al. in 1987, who found that diastolic BP between 85 and 90 mm Hg was associated with the lowest risk of death from myocardial infarction, whereas values below or above this range were associated with a higher risk [9]. The key question remains whether this represents a causal relationship or merely an observational association confounded by other conditions.

Recent Mendelian randomization analyses provide compelling insights. Arvanitis et al. used polygenic risk scores to estimate genetically determined diastolic BP levels and found a linear relationship between diastolic BP and cardiovascular outcomes, even at levels as low as 55 mm Hg [9]. This suggests that the observed J-curve likely reflects confounding factors rather than low diastolic BP directly increasing risk [9].

Case for multivariable risk assessment

Cardiovascular disease is fundamentally multifactorial, with modest increases across several risk factors often leading to greater overall risk than severe elevation of a single factor [6]. The landmark MRFIT study demonstrated how heart disease death rates varied nearly sixfold among men with the same highest quintile of systolic BP (≥142 mm Hg) based solely on smoking status and cholesterol levels [6].

This risk factor clustering appears particularly pronounced in hypertensive patients. In the Framingham Heart Study, over 80% of hypertensive individuals had at least one coexisting risk factor, and 55% had two or more risk factors [6]. Similar analyzes from Kaiser Permanente showed that 56% of hypertensive patients had at least one additional risk factor such as diabetes mellitus, hyperlipidemia, or obesity [6].

The 2025 ACC/AHA hypertension guidelines acknowledge this reality by incorporating the PREVENT risk calculator, which estimates the 10-year risk of total cardiovascular disease based on multiple variables [6]. This approach targets treatment toward those most likely to benefit, with lower numbers needed to treat among high-risk individuals [6]. Accordingly, modern hypertension management increasingly recognizes that blood pressure represents just one element—albeit an important one—in a constellation of factors determining overall cardiovascular risk.

 

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How Continuous Risk Assessment Works in Practice

Risk assessment tools have transformed the practice of hypertension management from simple threshold-based decisions to nuanced calculations of cardiovascular probabilities. Modern risk calculators integrate multiple patient factors to generate individualized treatment plans that align with the latest hypertension treatment guidelines.

Use of QRISK2 and PREVENT models

Risk prediction tools have evolved substantially over the past few decades. The 2025 ACC/AHA High Blood Pressure Guideline Writing Committee has designated the PREVENT (Predicting Risk of Cardiovascular Disease Events) equations as the preferred approach for calculating cardiovascular disease (CVD) risk [4]. This recommendation stems from PREVENT’s superior discrimination and calibration compared to the previously used Pooled Cohort Equations (PCE) and its development using a contemporary, representative US population sample [4].

PREVENT represents an advancement over earlier tools such as QRISK2, which was widely used in countries like England to predict the 10-year risk of cardiovascular events [10]. QRISK2, later updated to QRISK3, incorporated factors such as ethnicity, social deprivation, and conditions including rheumatoid arthritis, atrial fibrillation, and chronic kidney disease [1]. Subsequently, QRISK3 added eight additional variables, including migraine, corticosteroid use, systemic lupus erythematosus, and mental illness [10].

The PREVENT model’s derivation sample included 25 datasets with individual-level data on 3,281,919 adults without CVD at baseline [4]. Notably, it enables 10-year risk estimation for adults aged 30-79 years and 30-year risk estimation for those aged 30-59 years [4]. Unlike PCE, which focused solely on atherosclerotic CVD, PREVENT includes three distinct prediction models: total CVD (atherosclerotic CVD plus heart failure), ASCVD alone, and heart failure alone [2].

Inputs: age, sex, cholesterol, smoking, comorbidities

PREVENT requires several core variables for its base model:

• Age and sex (the models are sex-specific but race-free)
• Smoking status
• Systolic blood pressure
• Cholesterol measurements
• Antihypertensive medication use
• Statin/lipid-lowering medication use
• Diabetes status
• Body Mass Index (BMI)
• Estimated glomerular filtration rate (eGFR) [2][11][3]

Beyond these core factors, PREVENT offers optional variables that improve risk prediction for specific populations:

• Urinary albumin-to-creatinine ratio (especially valuable for those with kidney disease, diabetes, or hypertension) [4][3]
• Hemoglobin A1c (for individuals with diabetes, prediabetes, or obesity) [4][3]
• Social Deprivation Index (as a measure of social determinants of health) [4][2]

These optional inputs provide modest yet statistically significant improvements in discrimination, with the C-statistic increasing by 0.004-0.005 when all three are included [11]. Moreover, calibration improves substantially for certain subgroups, especially those with marked albuminuria (>300 mg/g) [11].

Output: 10-year predicted cardiovascular risk

The primary output from PREVENT is a percentage representing the 10-year risk of total CVD (a composite of atherosclerotic events and heart failure) [4]. The 2025 hypertension guidelines establish a 7.5% 10-year risk threshold as the point at which antihypertensive drug therapy is recommended for patients with Stage 1 hypertension (130-139/80-89 mmHg) [3][2].

This 7.5% threshold with PREVENT replaces the previous 10% threshold used with PCE [2]. The change reflects both improved calibration of PREVENT and its expanded outcome definition, which includes heart failure alongside atherosclerotic events [2].

The risk score translates a patient’s constellation of risk factors into a single number representing their absolute probability of experiencing a cardiovascular event within the next decade. For perspective, the mean predicted 10-year risk in validation studies was 6.4% for men (with an observed risk of 7.5%) and 4.7% for women (with an observed risk of 5.8%) [12].

In practice, physicians enter patient data into electronic tools, with many health systems now integrating these calculators directly into electronic health records for seamless implementation. The output guides not just whether to treat Stage 1 hypertension, but often informs the intensity of therapy and follow-up intervals as part of comprehensive hypertension treatment algorithms.

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Comparing Risk-Based vs. BP-Based Treatment Strategies

Quantifying the comparative efficacy of risk-based versus blood pressure-based treatment approaches offers crucial insights for clinical decision-making. Meta-analyzes and outcomes research demonstrate measurable advantages of risk-stratified hypertension management across various clinical scenarios.

BPLTTC meta-analysis findings

The Blood Pressure Lowering Treatment Trialists’ Collaboration (BPLTTC) has provided some of the most compelling evidence supporting risk-based treatment strategies. In their landmark meta-analysis of 48 randomized trials involving 344,716 participants, followed for a median of 4.15 years, researchers found that relative treatment effects were proportional to the intensity of blood pressure reduction [13]. Throughout this extensive dataset, a 5 mm Hg reduction in systolic blood pressure reduced major cardiovascular event risk by approximately 10%, with hazard ratios of 0.91 (95% CI 0.89–0.94) for participants without previous cardiovascular disease and 0.89 (95% CI 0.86–0.92) for those with prior cardiovascular disease [13].

These findings remained consistent even at normal or high-normal blood pressure values, suggesting benefit across the entire blood pressure spectrum. According to these results, absolute risk reduction from a fixed blood pressure reduction increases proportionally with predicted cardiovascular risk [14]. This proportionality principle underpins risk-based treatment approaches.

Another meta-analysis by Law et al. demonstrated that blood pressure-lowering treatment effects in reducing cardiovascular outcomes appear largely independent of pre-randomization blood pressure values, with similar efficacy across major drug classes [14]. This underscores the value of considering overall risk rather than isolated blood pressure readings.

Number needed to treat (NNT) comparison

NNT calculations, defined as 1/ARR (absolute risk reduction), provide a practical clinical context for treatment decisions [15]. When stratified by baseline risk, NNT comparisons consistently favor risk-based approaches. For trials involving patients with relatively non-severe hypertension (entry DBP 90–110 mm Hg), the NNT to prevent one stroke over 5 years was 118, whereas for more severely hypertensive patients (entry DBP >115 mm Hg), the NNT was just 29 [16].

The Systolic Pressure Intervention Trial (SPRINT) demonstrated that stratification by cardiovascular risk level meaningfully affects treatment efficiency. Among quartiles of increasing baseline 10-year cardiovascular risk, the number needed to treat to prevent primary outcomes decreased dramatically from 91 in the lowest risk quartile to 38 in the highest risk quartile [17]. Simultaneously, the number needed to harm for all-cause serious adverse events increased from 62 to 250 [17].

Risk-based treatment strategies consistently yield lower NNTs than blood pressure-based approaches across broad treatment thresholds. In a meta-analysis comparing treatment strategies, the cardiovascular disease risk approach demonstrated superior efficiency until overlapping with the systolic blood pressure strategy at approximately the 80th percentile treatment rate [5].

Events avoided per 1000 treated patients

The clinical impact of different treatment strategies can be quantified by events avoided per 1000 treated patients. Achieving and maintaining the 2017 ACC/AHA guideline goals could prevent 71.9 cardiovascular events per 1000 treated patients (uncertainty range, 26.6–122.3) over 10 years compared to maintaining current blood pressure levels [18].

Direct comparisons of treatment strategies reveal meaningful differences in outcomes. Compared with treating everyone with SBP ≥ 150 mmHg, a cardiovascular risk-based approach would prevent 16% (95% CI 14%–18%) more cardiovascular events for the same number of persons treated [5]. For SBP ≥ 160 mmHg threshold comparisons, the advantage increases further, with risk-based strategies preventing 38% (95% CI 29%–40%) more cardiovascular events for equivalent numbers treated [5].

Even at lower thresholds, risk-based approaches maintain their advantage. Compared with treating everyone with SBP ≥ 140 mmHg, a risk-based strategy would prevent 3.1% (95% CI 1.5%–5.0%) more events for identical treatment numbers [5]. This benefit persists despite more modest relative advantages at lower treatment thresholds.

For high-risk patients, intensive treatment targeting systolic reduction from 135 to 120 mm Hg would yield approximately 70 patients needed to treat to prevent one cardiovascular event, while a more modest reduction to 130 mm Hg would increase this number to around 200 [19].

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Clinical Implications for Low-Risk and High-Risk Patients

Risk stratification reshapes clinical decision-making in hypertension management by providing a framework for balancing treatment intensity with expected benefits. This patient-centered approach allows for tailoring interventions to individual risk profiles rather than relying solely on blood pressure readings.

When to delay treatment in low-risk patients

For patients with stage 1 hypertension (BP 140/90-160/100 mmHg) and no additional risk factors, guidelines offer flexibility in treatment timelines. The International Society of Hypertension and British guidelines suggest that lifestyle changes can be followed for 6-12 months before implementing pharmacological therapy in these low-risk cases [20]. Nevertheless, this recommendation has evolved from earlier approaches in the 1980s and 1990s when 3-6 month periods of lifestyle intervention were standard before beginning therapy [20].

Current evidence indicates that extended treatment delays may not serve patients optimally. Indeed, recent analyzes suggest that delays in hypertension diagnosis and treatment initiation correlate with worse outcomes. Patients diagnosed more than one year after documented blood pressure elevations demonstrated a 29% higher risk of cardiovascular events compared to those diagnosed earlier [7]. Furthermore, delayed diagnosis was associated with substantially lower rates of antihypertensive medication prescription (30.6% vs 75.2%) [7].

When to intensify treatment in high-risk patients

For individuals with stage 2 hypertension or those with stage 1 hypertension plus other cardiovascular risk factors, a logical approach involves initiating pharmacological therapy simultaneously with lifestyle interventions [20]. The 2024 European Society of Cardiology guidelines recommend a systolic BP treatment target range of 120-129 mmHg for most patients receiving antihypertensive medication [8]. This represents a fundamental shift from prior European guidelines that generally recommended treating to <140/90 mmHg initially and only thereafter considering reduction to <130/80 mmHg [8].

In situations where standard treatments prove inadequate, the 2025 AHA/ACC hypertension guidelines support intensifying therapy based on quantitative risk assessment. For patients with persistent markedly elevated readings (systolic/diastolic BP >180/110-120 mmHg), initiating or intensifying medication during hospital admissions may offer benefits that outweigh risks [21].

Role of lifestyle interventions before medication

Lifestyle modification remains foundational in hypertension management despite the trend toward earlier pharmacological intervention. The DASH diet has demonstrated robust effects, reducing systolic BP by approximately 11 mmHg and diastolic BP by 6 mmHg in controlled studies—comparable to first-line antihypertensive agents [9]. Similarly, a 5 kg weight loss reduces systolic/diastolic BP by approximately 4.4/3.6 mmHg [9].

Primary lifestyle recommendations include:

• Maintaining body mass index between 20-25 kg/m² and waist circumference below 94 cm for men and 80 cm for women [9]
• Regular aerobic exercise, which can reduce systolic BP by up to 7-8 mmHg [9]
• Limiting alcohol intake to ≤100 g of pure alcohol weekly [9]
• Complete tobacco abstinence [9]

These non-pharmacological approaches should continue even when medications are prescribed [9]. In primary care settings, reduced salt intake and increased physical activity significantly correlate with improved BP control, whereas failure to implement these behaviors nearly doubles the likelihood of uncontrolled hypertension [9].

Implementation challenges remain, nonetheless. Calculating and evaluating cardiovascular risks for additional patients increases the workload for clinical practices [22]. Moreover, despite previous guideline recommendations to incorporate quantitative CVD risk assessment, real-world implementation in primary care settings remains inconsistent [23].

 

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Integrating Risk Assessment into Hypertension Treatment Algorithms

Modern hypertension management has evolved to incorporate quantitative risk assessment as a cornerstone of treatment algorithms. This approach transforms clinical decision-making beyond simple threshold-based interventions toward personalized care that considers a patient’s comprehensive cardiovascular profile.

How risk scores modify treatment thresholds

Risk assessment fundamentally alters the traditional blood pressure thresholds that trigger pharmacological intervention. The 2025 American Heart Association/American College of Cardiology (AHA/ACC) High Blood Pressure Guideline reaffirms this paradigm by recommending initiation of antihypertensive therapy based on predicted cardiovascular risk [4]. In practice, risk scores establish different treatment thresholds for patients with identical blood pressure readings. For those with stage 1 hypertension (130-139/80-89 mmHg), treatment decisions hinge on calculated cardiovascular risk alongside comorbidity assessment [4].

The 2025 guideline identifies four major benefit groups for whom antihypertensive therapy is recommended even at stage 1 hypertension levels:

• Patients with existing cardiovascular disease
• Individuals with diabetes
• Those with chronic kidney disease (eGFR <60 mL·min⁻¹·1.73 m⁻² or albuminuria >30 mg/g)
• People with an estimated 10-year total CVD risk ≥7.5% using PREVENT equations [4]

Practically, risk calculation effectively lowers treatment thresholds for high-risk patients while potentially raising them for those at lower risk—creating a dynamic intervention boundary that maximizes benefit across populations.

Examples from new hypertension guidelines

The 2025 AHA/ACC hypertension guideline introduces crucial modifications to risk assessment methodology. Foremost among these changes is adopting the PREVENT (Predicting Risk of CVD Events) equations to replace the previously used Pooled Cohort Equations (PCEs) [6]. This transition occurred primarily because PREVENT offers superior discrimination and calibration, was developed from a larger contemporary representative US sample, and expands predicted outcomes to include both atherosclerotic events and heart failure [4].

Importantly, the risk threshold for initiating pharmacological therapy has shifted from ≥10% with PCEs to ≥7.5% with PREVENT [6]. This adjustment maintains appropriate risk stratification despite the different calculation methodologies. The guidelines also newly recommend antihypertensive therapy for those with stage 1 hypertension and low predicted cardiovascular risk if blood pressure remains elevated after 3-6 months of lifestyle intervention [6].

Impact on treatment eligibility and intensity

The risk-based approach substantially reshapes the landscape of treatment eligibility. Data from 2017-2020 shows approximately 1.2% (3.0 million) of adults without existing antihypertensive medication were recommended treatment under the 2025 guidelines because they had stage 1 hypertension with high predicted CVD risk [6]. Even more remarkably, the 2025 guideline identifies an additional 10.8% (26.8 million) adults with stage 1 hypertension and low predicted CVD risk who may require pharmacological intervention if lifestyle modifications prove insufficient [6].

Half of this newly identified treatment group consists of adults aged 18-39 years [6], representing a substantial shift toward earlier intervention. This expansion reflects growing recognition that early blood pressure control yields long-term cardiovascular benefits.

 

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Challenges in Implementing Risk-Based Approaches

Despite the clear benefits of risk-based hypertension treatment approaches, the transition from theory to practice faces substantial hurdles in real-world clinical settings. Moving beyond traditional threshold-based care requires overcoming systematic barriers at multiple levels of healthcare delivery.

Data availability and EHR integration

Electronic health record systems offer tremendous potential for implementing risk-based hypertension treatment algorithms, yet face critical limitations. First, inconsistency in defining hypertension cases remains problematic—whether to use diagnosis codes, prescriptions for antihypertensive medications, elevated blood pressure measurements, or combinations thereof [24]. Second, handling patients with multiple, potentially conflicting blood pressure measurements in a single day creates analytical challenges that can affect treatment decisions [24]. Third, the lack of standardized data formats across different EHR systems complicates the integration of multisystem data for developing robust risk models [25].

For many patients, EHR data lacks the minimum information needed for a valid hypertension assessment. This occurs because specialists often defer blood pressure management to primary care physicians, who may not be part of participating data networks [24]. Moreover, patients interact with the health care system irregularly, resulting in incomplete cardiovascular risk profiles [25].

Clinician training and patient communication

Effective risk communication represents an essential yet often overlooked component of hypertension management. Many physicians struggle to balance empathy with systemic pressures, as workload and time constraints inhibit optimal discussions that impact health outcomes [26]. Patient factors additionally complicate communication, including literacy levels, language comprehension, and numerical ability—particularly relevant when explaining complex risk calculations [26].

Studies examining doctor-patient communication found that decision-making style (β = 0.20, p < 0.01) and proactive communication (β = 0.50, p < 0.001) directly impact hypertension control [27]. Nevertheless, communication training for primary care physicians has not consistently resulted in quantifiable improvements in blood pressure outcomes [27].

Overcoming inertia in primary care settings

Clinical inertia—the failure to initiate or intensify therapy when indicated—poses a major obstacle to implementing risk-based approaches. Early models suggest that 70% of clinical inertia stems from physician and health system factors related to time constraints, reactive care patterns, and inefficient EHR workflows, while 30% results from patient factors such as disease denial and medication attitudes [28].

Strikingly, a survey of primary care visits found treatment intensification occurred in only 16% of visits with diagnosed hypertensive patients, and medication initiation happened in merely 26.4% of visits [29]. Under these circumstances, standardized treatment protocols can help overcome inertia by identifying eligible patients, prompting medication initiation, standardizing follow-up, and empowering all clinical team members to engage in patient management [28].

 

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Future of Hypertension Management Beyond Cutoffs

Artificial intelligence (AI) and advanced monitoring technologies herald a paradigm shift in hypertension management, moving clinical practice toward truly personalized care models. This evolution represents the natural progression beyond current risk-based approaches.

Personalized treatment plans using AI and risk models

AI-based systems have demonstrated promising capabilities in developing precise treatment approaches for hypertension patients, who often exhibit diverse disease progression despite identical diagnoses based on blood pressure measurements [30]. Machine learning algorithms can accurately predict antihypertensive treatment success and help clinicians identify which patients are more or less likely to benefit from specific treatments [30]. One model developed by Boston University researchers generates custom hypertension prescriptions with an associated probability of success for each recommended medication [2]. In validation studies, this approach achieved a 70.3% larger reduction in systolic blood pressure than standard care [2]. Throughout clinical implementation, the emphasis remains on providing physicians with AI-generated insights while maintaining clinical judgment as paramount.

Continuous monitoring and dynamic risk recalculation

Remote blood pressure monitoring coupled with dynamic risk recalculation represents another frontier in precision hypertension care. Advanced AI titration platforms analyze longitudinal data—including daily blood pressure readings, prior drug responses, and patient characteristics—to generate dose recommendations that evolve in real time [1]. Digital twin technology, which creates virtual models of individual patients to simulate physiological responses under various interventions, may soon enable clinicians to compare treatment scenarios before initiation [1]. Heart rate variability (HRV), physical activity, and nighttime heart rate monitoring have shown promise as continuous risk assessment variables with significant associations to established risk factors [12].

Policy implications for guideline updates

Future guidelines will likely expand risk-based treatment to all individuals with elevated blood pressure [19]. This approach directs interventions toward those at the highest absolute risk while simultaneously informing treatment intensity [19]. The American Heart Association’s PREVENT equations, which consider 12 predictors including laboratory outcomes, comorbidities, lifestyle characteristics, and socioeconomic factors, have improved accuracy and precision compared to previous models [31]. As a result, future guidelines may classify blood pressure-lowering interventions by intensity—analogous to lipid-lowering therapies—with high-intensity regimens (≥20 mmHg reduction) reserved primarily for the highest-risk individuals [19]. Ongoing innovation in model development and implementation remains essential for realizing the promise of smarter, risk-tailored cardiovascular disease prevention [19].

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Conclusion   

The shift from threshold-based hypertension treatment toward continuous risk assessment represents a fundamental evolution in cardiovascular medicine. Blood pressure values exist on a continuum rather than at arbitrary cutpoints, therefore demanding a nuanced approach that considers overall cardiovascular risk. Clinical evidence overwhelmingly supports this paradigm shift, with multiple meta-analyzes demonstrating that absolute risk reduction from antihypertensive therapy increases proportionally with baseline cardiovascular risk despite similar relative risk reductions across risk strata.

Risk calculators such as PREVENT offer substantial advantages over traditional blood pressure thresholds. These tools incorporate multiple variables including age, sex, smoking status, cholesterol levels, and comorbidities to generate individualized treatment plans. Accordingly, physicians can identify patients most likely to benefit from intervention while avoiding unnecessary treatment in those with minimal absolute risk. The number needed to treat calculations consistently favor risk-based approaches, with studies showing three to sixteen percent more cardiovascular events prevented for the same number of patients treated.

Treatment decisions based solely on blood pressure thresholds fail to account for critical factors that modify cardiovascular outcomes. Patient-specific variables, such as blood pressure variability, diastolic J-curve phenomena, and coexisting risk factors, substantially influence prognosis beyond isolated readings. Additionally, the traditional binary approach creates paradoxical situations where high-risk individuals with readings just below arbitrary cutoffs receive no pharmacological intervention despite potentially substantial benefit.

Modern hypertension guidelines have progressively embraced risk-based management, culminating in the 2025 AHA/ACC recommendation to initiate therapy for stage 1 hypertension when 10-year cardiovascular risk exceeds 7.5%. Though implementing risk-based approaches faces challenges related to data availability, clinician training, and clinical inertia, these obstacles appear surmountable through improved electronic health record integration and standardized protocols.

Future hypertension management will likely go beyond current risk models through artificial intelligence, continuous monitoring technologies, and dynamic risk recalibration. Machine learning algorithms already show promise in developing personalized treatment plans with superior outcomes compared to standard care. Remote monitoring coupled with digital twin technology may soon enable physicians to simulate various interventions before implementation, further refining the precision of hypertension management.

The evolution toward continuous risk assessment ultimately transforms hypertension treatment from managing an isolated number to preventing cardiovascular events through targeted intervention. This approach aligns medical practice with the biological reality that cardiovascular risk exists along a spectrum rather than at arbitrary thresholds. Risk-based hypertension management thus represents not merely an incremental improvement but a fundamental reconceptualization of how physicians prevent cardiovascular disease through blood pressure control.

Key Takeaways

Modern hypertension management is evolving beyond simple blood pressure thresholds to embrace comprehensive cardiovascular risk assessment, leading to more personalized and effective treatment strategies.

• Risk-based treatment prevents more cardiovascular events than threshold-based approaches, with studies showing 16-38% more events prevented for the same number of patients treated.
• The 2025 AHA/ACC guidelines now recommend using the PREVENT risk calculator with a 7.5% 10-year cardiovascular risk threshold to guide treatment decisions for stage 1 hypertension.
• Blood pressure alone fails to predict outcomes accurately due to patient variability, J-curve phenomena in diastolic pressure, and the multifactorial nature of cardiovascular disease.
• AI and continuous monitoring technologies are transforming hypertension care, with machine learning models achieving 70% larger blood pressure reductions than standard approaches.
• Implementation challenges include EHR integration and clinical inertia, but standardized protocols and improved risk communication can overcome these barriers in primary care settings.

This paradigm shift represents a fundamental reconceptualization of hypertension management—from treating isolated numbers to preventing cardiovascular events through targeted, risk-stratified interventions that maximize benefit while minimizing unnecessary treatment.

 

 

Frequently Asked Questions:   

FAQs

Q1. What is continuous risk assessment in hypertension management? Continuous risk assessment in hypertension management is an ongoing process that evaluates a patient’s overall cardiovascular risk, rather than relying solely on blood pressure thresholds. It considers multiple factors such as age, sex, cholesterol levels, smoking status, and comorbidities to provide a more comprehensive view of a patient’s health status and guide personalized treatment decisions.

Q2. How does risk-based treatment compare to traditional blood pressure-based approaches? Risk-based treatment strategies have been shown to prevent more cardiovascular events than traditional blood pressure-based approaches. Studies indicate that risk-based strategies can prevent 16-38% more cardiovascular events for the same number of patients treated, making them more efficient in targeting those who will benefit most from intervention.

Q3. What are the key components of a cardiovascular risk assessment for hypertension? A comprehensive cardiovascular risk assessment for hypertension typically includes evaluation of blood pressure measurements, age, sex, smoking status, cholesterol levels, diabetes status, body mass index, and kidney function. Some risk calculators, like PREVENT, also consider factors such as social determinants of health and additional laboratory values to provide a more accurate risk prediction.

Q4. How have recent guidelines changed hypertension treatment recommendations? Recent guidelines, such as the 2025 AHA/ACC recommendations, have shifted towards a risk-based approach. They now recommend using the PREVENT risk calculator with a 7.5% 10-year cardiovascular risk threshold to guide treatment decisions for stage 1 hypertension. This represents a move away from relying solely on blood pressure thresholds for treatment initiation.

Q5. What role does artificial intelligence play in the future of hypertension management? Artificial intelligence is poised to transform hypertension management by enabling more personalized treatment plans. AI-based systems can analyze large amounts of patient data to predict treatment success, recommend optimal medications, and achieve greater blood pressure reductions than standard care. Future developments include continuous monitoring technologies and dynamic risk recalculation to further refine treatment strategies.

 

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References:  

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