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. Author manuscript; available in PMC: 2026 Apr 1.
Published in final edited form as: Ann Intern Med. 2025 Aug 19;178(10):1409–1419. doi: 10.7326/ANNALS-25-00560

Effect of Systolic Blood Pressure Measurement Error on the Cost-Effectiveness of Intensive Blood Pressure Targets

Karen C Smith 1,2, Thomas A Gaziano 2,3,4, Alvin I Mushlin 5, David M Cutler 6, Nicolas A Menzies 3,7, Ankur Pandya 3,8
PMCID: PMC12875532  NIHMSID: NIHMS2127777  PMID: 40825205

Abstract

Background:

Analyses of clinical trials find that an intensive <120 mm Hg systolic blood pressure (SBP) target is cost-effective compared with a <140 mm Hg target for patients at high cardiovascular disease risk. However, guidelines from the American College of Cardiology and American Heart Association recommend a <130 mm Hg target, citing blood pressure measurement error in routine practice.

Objective:

To evaluate the effect of measurement error on the cost-effectiveness of intensive SBP targets.

Design:

Microsimulation model varying SBP measurement error.

Data Sources:

Systolic Blood Pressure Intervention Trial (SPRINT) data and published literature.

Target Population:

Patients at high cardiovascular risk.

Time Horizon:

Lifetime.

Perspective:

Health care sector.

Interventions:

SBP targets of <120, <130, and <140 mm Hg.

Outcome Measures:

Incremental cost-effectiveness ratios (ICERs).

Results of Base-Case Analysis:

With research-grade SBP measurement (mean error 0 mm Hg), the ICER for the <120 versus <130 mm Hg target was $24,400 per quality-adjusted life-year (QALY). With average measurement error (mean error 7.3 mm Hg in the <120 mm Hg target), the ICER increased to $42,000 per QALY.

Results of Sensitivity Analysis:

The ICER for the <120 mm Hg target was greater than $100,000 per QALY in scenarios with high error (mean error, ≥14.6 mm Hg in the <120 mm Hg target), when an inflection point for increasing risk for cardiovascular disease (CVD) was at or above 116 mm Hg, and in scenarios with a medication-taking disutility of at least 0.003 per antihypertensive medication.

Limitations:

Uncertainty in the relationship between low treated SBP (for example, <115 mm Hg) and cardiovascular risk.

Conclusion:

For SPRINT-eligible patients at high cardiovascular risk without diabetes or prior stroke, a <120 mm Hg target seems cost-effective across most settings with SBP measurement error. In scenarios with high error and an increase in CVD risk at low SBPs, a <130 mm Hg target may become cost-effective.

Primary Funding Source:

National Science Foundation and National Institute of Neurological Disorders and Stroke.

Introduction

Forty-eight percent of United States adults have hypertension (blood pressure ≥130/80 mm Hg), and hypertension contributes to about 1 in 6 deaths (1, 2). Yet, blood pressure measurement — the basis for hypertension diagnosis and treatment — is imprecise, and measurement methods vary (3–5). In the context of clinical trials, study staff measure blood pressure after 5 minutes of rest; use automatic, validated devices with proper cuff sizes; and take multiple measurements (6). In contrast, measurements in routine clinical practice are less standardized because of time and resource constraints, causing greater variability and higher average blood pressure values (7–10).

Blood pressure measurement issues have featured prominently in debates over the appropriate systolic blood pressure (SBP) target for patients at high cardiovascular risk. Although intensive SBP targets (such as <120 mm Hg) in clinical trials significantly decreased cardiovascular events and all-cause mortality compared with higher targets, intensive targets also increased treatment costs and adverse events, such as hypotension and syncope (11–13). Measurement error in routine practice could further increase an intensive target’s adverse events and costs because the upward bias may lead to excessive use of antihypertensive agents. In 2017, hypertension guidelines from the American College of Cardiology and American Heart Association (ACC/AHA) recommended a <130 rather than <120 mm Hg target, citing concerns about SBP measurements in routine practice (14).

In this study, we evaluated the effect of SBP measurement error on the cost-effectiveness of three SBP targets, <120, <130, and <140 mm Hg, for patients at high cardiovascular risk. Because clinical organizations use value assessments to develop guidelines and performance measures, the outcomes of this analysis are relevant to decisions about appropriate treatment thresholds (15, 16).

Methods

Analytic Overview

We developed a microsimulation model to simulate cost and quality-adjusted life-year (QALY) outcomes for individuals at high risk for cardiovascular disease (CVD). We compared average lifetime costs and QALYs across three SBP targets (<120, <130, and <140 mm Hg) varying the degree of SBP measurement error. Each month in the simulation, individuals could have an office visit. If an office visit occurred, blood pressure was measured, and new antihypertensive drugs could be prescribed, an existing antihypertensive’s dose could be increased or decreased, or an existing antihypertensive could be withdrawn. Antihypertensive agents lowered CVD risk but carried additional costs and adverse event risk (Figure 1).

Figure 1:

Figure 1:

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Model Diagram. The top panel (A) shows the health states included in the simulation model. Cardiovascular events include stroke, heart failure, myocardial infarction, and non-myocardial infarction acute coronary syndrome. Minor adverse events (e.g., dizziness, nausea) and serious adverse events (e.g., falls, kidney injury, hypotension, bradycardia, syncope, and electrolyte abnormalities) can occur in the hypertension state and the acute, chronic, or repeated CVD states. The bottom panel (B) shows the model process of updating blood pressure each time an office visit occurs. The parameters listed in each box are the model parameters that control that process. For example, observed SBP is a function of true SBP and the measurement error parameters (mean and variance). Abbreviations: CVD, cardiovascular disease; SBP, systolic blood pressure.

We evaluated cost-effectiveness using incremental cost-effectiveness ratios (ICERs), which are the ratio of incremental costs to incremental QALYs that result from different SBP targets. We used a threshold of $100,000 per QALY (17–19) and a health care sector perspective, and we discounted costs and QALYs at 3% annually (20). We include an impact inventory describing the costs and effects in Supplement Table 1.

Data Inputs

The simulation model used data from the 2013-to-2018 National Health and Nutrition Examination Surveys (NHANES), the Systolic Blood Pressure Intervention Trial (SPRINT), and published literature (Supplement Figure 1). We generated simulated individuals with NHANES data. We estimated transition probabilities using SPRINT data, the ACC/AHA Pooled Cohort Equations (PCEs), and meta-analyses (21, 22) (Table 1). We estimated calibration and validation targets from SPRINT and costs and health state utilities from published literature (24, 26–36).

Table 1:

Key Model Input Parameters

Parameter Base Case Value (Range)1 Source
Transition Probabilities (per month)
CVD event Calibrated Pooled Cohort Equations Goff et al, 2014 (21), model calibration
CVD mortality, % Derived from SPRINT study data2
 First month after a CVD event 14.6 (11.8, 17.8) –
 Subsequent months after a CVD event 0.3 (0.2, 0.4) –
Minor AE, % Derived from Law et al, 2003 (25) and Moran et al, 2015 (24)
 0.5 standardized antihypertensive doses 2.6 (1.8, 3.4) –
 1 standardized antihypertensive dose 5.1 (3.7, 6.6) –
 2 standardized antihypertensive doses 7.3 (5.2, 9.4) –
SAE – –
 SBP threshold (above this threshold, the probability of an SAE is 0%), mm Hg 148 (142, 164) Model calibration
 Probability per mm Hg below SBP threshold, % 0.005
(0.003, 0.007)		 Model calibration
 Mortality, % 2.1 (1.1, 3.2) Derived from SPRINT study data2
SBP Treatment
Relative risk for CVD events per 5-mm Hg SBP decrease 0.90 (0.88, 0.92) BPLTTC, 2021 (22)
SBP decrease, mm Hg3 – –
 0.5 standardized antihypertensive doses 6.70 (6.15, 7.25) Derived from Law et al, 2009 (23) and Law et al, 2003 (25)
 1 standardized antihypertensive dose 9.10 (8.85, 9.35)
 2 standardized antihypertensive doses 10.90 (10.65, 11.15)
SBP Measurement Error, mm Hg Derived from Drawz et al, 2020 (8)
Average error, <120 mm Hg target 7.30 (7.00, 7.60) –
Average error, <140 mm Hg target 4.60 (4.35, 4.85) –
High and Low Error Scenario Analyses –
 High error, <120 mm Hg target 9.00 (8.70, 9.30) –
 High error, <140 mm Hg target 6.00 (5.75, 6.25) –
 Low error, <120 mm Hg target 4.00 (3.70, 4.30) –
 Low error, <140 mm Hg target 3.00 (2.75, 3.25) –
Utility Values
Baseline utility for those with hypertension 0.816 (0.700, 0.932) Derived from Sullivan et al, 2006 (26)
Marginal Disutilities – –
 Acute CVD −0.103 (−0.121, −0.088) Derived from Morey et al, 2021 (27)
 Chronic CVD −0.052 (−0.060, −0.044) Derived from Morey et al, 2021 (27)
 Serious AE −0.067 (−0.140, −0.022) Derived from Bress et al, 2017 (28)
 Minor AE −0.008 (−0.009, −0.007) Derived from Moran et al, 2015 (24)
 First pill −0.009 (−0.011, −0.008) Derived from Hutchins et al, 2015 (29)
Costs, $
Acute CVD – Derived from HCUP (30)
 Age 45–64 y (one time) 23,300 (22,800, 23,800) –
 Age 65–84 y (one time) 22,900 (22,300, 23,400) –
 Age ≥85 y (one time) 16,300 (15,900, 16,600) –
Chronic CVD – Derived from Morey et al, 2021 (27)
 Age 45–64 y (annual) 3,800 (2,900, 4,900) –
 Age ≥65 y (annual) 3,800 (2,800, 4,900) –
SAE Derived from HCUP (30) and Hoffman et al, 2017 (31)
 Age 45–64 y (one time) 10,600 (10,300, 10,900) –
 Age 65–84 y (one time) 10,600 (10,300, 10,900) –
 Age ≥85 y (one time) 9,600 (9,400, 9,900) –
Antihypertensive medication, 1 standardized dose (monthly) 6 (3, 9) Derived from the FSS (32) and Ma et al, 2021 (33)
Office visit with laboratory tests 138 (113, 163) Derived from CMS fee schedules (34, 35)
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Abbreviations: AE, adverse event; BTLTTC, Blood Pressure Lowering Treatment Trialists Collaboration; CVD, cardiovascular disease; FSS, Federal Supply Schedule; HCUP, Healthcare Cost and Utilization Project; SAE, serious AE; SBP, systolic blood pressure; SPRINT, Systolic Blood Pressure Intervention Trial.

1

Parameter ranges are based on the 2.5th and 97.5th percentiles of the distributions reported in Supplement Table 10.

2

CVD and SAE mortality rates were estimated from SPRINT study data using time of death and whether the death was coded as a cardiovascular death or an SAE death.

3

The SBP decreases per standardized dose vary as a function of starting blood pressure, as described in Law et al. (23, 25) and Supplement section 1.4.5.

Model Cohort

We generated simulated individuals by following a previously published method to identify NHANES participants who met SPRINT inclusion criteria (Supplement 1.2, Supplement Figure 2, Supplement Table 2) (37). Our simulated cohort included adults who were aged 50 years or older, had an SBP of 130 to 180 mm Hg, were receiving 0 to 4 antihypertensive agents, and were at high CVD risk. High CVD risk was defined as 10-year atherosclerotic CVD risk of at least 15%; prior myocardial infarction, coronary heart disease, or angina; chronic kidney disease; or age 75 years or older. We excluded individuals with diabetes or prior stroke (full exclusion criteria are listed in Supplement Figure 2).

Blood Pressure Measurement

At each office visit, observed SBP was determined by an individual’s true SBP and an error term representing measurement error, which incorporated both an upward bias and random error. True SBP was based on the average of three SBP measurements from NHANES. We modeled the error term as being normally distributed with a mean and variance derived from a study comparing SBP measurements among SPRINT participants at trial visits (research-grade measurement) and nontrial outpatient visits (routine measurement) (Supplement 1.3) (8).

The error term mean was based on the average difference between research-grade and routine measurement across 49 SPRINT study sites: 7.3 and 4.6 mm Hg in the <120 and <140 mm Hg targets respectively. The variance was based on Bland-Altman agreement intervals, which were −30 to 45 mm Hg in both targets (8). This variability reflects the heterogeneity in the difference between research-grade and routine measurement and implies that for some participants, routine measurements will be lower than research-grade measurements.

We also present results based on study sites with low and high error. In these analyses, the average differences between research-grade and routine measurement were 4 and 3 mm Hg in the <120 and <140 mm Hg targets (low error) or 9 and 6 mm Hg in the <120 and <140 mm Hg targets (high error) (Supplement Table 3). In addition, we present sensitivity analyses with error estimated from alternative studies. In these analyses, error was the same across all targets (Supplement Table 4) (7, 38–43).

Blood Pressure Treatment

The simulation model included the probability that antihypertensive medications increased, decreased, or did not change during an office visit (Supplement Tables 5–8, Supplement Figures 3–4, Supplement 1.4). This probability was a function of observed SBP at the current and prior office visit, the SBP target, prior cardiovascular or adverse events, and medications already prescribed. We incorporated both number and dose of antihypertensive drugs by modeling standardized antihypertensive doses. The reduction in SBP for 0.5, 1, and 2 standard doses was a function of starting SBP from a meta-analysis of the effect of antihypertensives on SBP (23, 25). For a starting SBP of 150 mm Hg, the decreases for 0.5, 1, and 2 standard doses were 6.7, 8.7, and 10.5 mm Hg, respectively (Supplement 1.4.5). We assumed that 75% of patients would take an antihypertensive medication when first prescribed it (i.e., primary adherence) (23), and 69% would have continued adherence to prescribed antihypertensive treatment (44).

Risk for CVD without SBP treatment was calculated with the ACC/AHA’s PCEs to reflect cardiovascular risk in community-based cardiovascular cohort studies (21). Each 5 mm Hg decrease due to antihypertensive treatment lowered cardiovascular risk by 10% based on a 2021 meta-analysis (22). We applied the 10% decrease in risk for SBPs above 115 mm Hg because there is an established loglinear relationship between SBP and vascular mortality above 115 mm Hg (45). In the base case, we assumed that there was no additional reduction in CVD risk for SBPs lowered below 115 mm Hg. However, observational studies have found that low treated blood pressures may be associated with increased cardiovascular risk, termed the J-curve phenomenon (46, 47). We therefore included sensitivity analyses in which the relationship between SBP and cardiovascular risk was J-shaped, with cardiovascular risk increasing for SBPs treated to below 115 mm Hg (Supplement Figure 5, Supplement 1.5–1.6).

Lowering blood pressure also increased minor adverse events (e.g., dizziness, nausea) and serious adverse events (SAEs) (such as falls, kidney injury, hypotension, bradycardia, electrolyte abnormality, and syncope). Minor adverse event risk per 0.5, 1, and 2 standard doses was from a published meta-analysis (24, 25). Minor adverse events carried a decrement in health-related quality of life (i.e., disutility), and SAEs carried a disutility, cost, and mortality risk in the month they occurred.

Costs and Utilities

We included the cost of hypertension treatment, acute CVD events, chronic CVD, SAEs, non-cardiovascular health care, and mortality (Table 1, Supplement Table 9) (27, 30–36). We included disutilities for acute and chronic CVD, medication taking, and adverse events (24, 26–29). We adjusted costs to 2021 U.S. dollars using the Personal Consumption Expenditures price index and the Personal Health Care expenditure deflator (48, 49) (Supplement 1.7–1.8).

Model Calibration and Validation

The PCEs have overestimated risk in external validations (50) and do not include heart failure, which was part of the SPRINT composite outcome. We therefore calibrated PCE-predicted risk by simulating individuals in SPRINT and adjusting the estimated risk such that the overall CVD event rate matched the rate of primary outcome events in SPRINT. We also calibrated to SPRINT rates of intervention-related SAEs to estimate SAE risk as a function of SBP (Supplement 1.9–1.10).

To validate the model, we simulated <120 and <140 mm Hg targets with research-grade SBP measurement and compared simulated outcomes and outcomes observed in SPRINT. We conducted a cross-model validation, comparing our simulations of SPRINT and published analyses (28, 51).

Sensitivity Analyses

In one-way sensitivity analyses, we varied parameters, including measurement error mean and variance, antihypertensive cost, antihypertensive disutility, SAE rates, and office visit frequency, across plausible values. In probabilistic sensitivity analyses, which model uncertainty around model parameters simultaneously, we sampled 1000 parameter sets from distributions (Supplement Table 10, Supplement 1.11).

Study Oversight

The article was prepared using SPRINT research materials obtained from the Biologic Specimen and Data Repository Information Coordinating Center of the National Heart, Lung, and Blood Institute. The study was a secondary analysis of deidentified data and was determined not to be research involving human participants by the Harvard University Area Institutional Review Board.

Role of the Funding Source

This study was funded by the National Science Foundation and the National Institute of Neurological Disorders and Stroke, which had no role in the study design; the collection, analysis, or interpretation of the data; or the decision to submit the manuscript for publication.

Results

Model Validation

Simulations with research-grade SBP measurement replicated SPRINT outcomes well. All validation outcomes fell within the SPRINT 95% confidence intervals except for mean SBP in the <140 mm Hg target and mean standard doses in the <120 mm Hg target, both of which were within 2% of the lower bound (134.8 vs 135.5 mm Hg and 4.3 vs 4.4 standard doses). The cross-model validation replicated existing analyses well (Supplement Tables 11–13).

Clinical Results

Measurement error increased antihypertensive use and lowered average SBP (Supplement Table 14), resulting in lower CVD event rates but higher SAE rates. With research-grade measurement and a <140 mm Hg target, 7,020 lifetime CVD events and 910 SAEs occurred per 10,000 patients. The <130 mm Hg target averted 590 CVD events but caused 610 additional SAEs compared with the <140 mm Hg target, and the <120 mm Hg target averted 460 CVD events but caused 620 additional SAEs compared with the <130 mm Hg target. With average measurement error (mean error 7.3 and 4.6 mm Hg in the <120 and <140 mm Hg targets, respectively) and a <140 mm Hg target, 6,790 lifetime CVD events and 1,160 SAEs occurred per 10,000 patients. The <130 mm Hg target averted 600 CVD events but caused 670 additional SAEs compared with the <140 mm Hg target, and the <120 mm Hg target averted 360 CVD events but caused 660 additional SAEs compared with the <130 mm Hg target (Table 2).

Table 2:

Base Case Cost-Effectiveness Results

SBP Target Mean Error, mm Hg Median True SBP (IQR), mm Hg1 Median Observed SBP (IQR), mm Hg1 CVD Events per 10,0002 SAEs per 10,0002 Cost, $3 QALE3 Incremental Cost, $ Incremental QALE ICER, $/QALY
Research-Grade Measurement Error
<140 mm Hg 0 139 (133, 148) 137 (129, 150) 7,020 910 150,800 9.851 -- -- --
<130 mm Hg 0 131 (125, 144) 128 (120, 146) 6,430 1,520 151,800 9.908 1,000 0.057 17,900
<120 mm Hg 0 122 (116, 143) 119 (111, 144) 5,970 2,140 152,900 9.952 1,100 0.045 24,400
Low Measurement Error
<140 mm Hg 3 137 (131, 148) 137 (127, 154) 6,880 1,060 151,400 9.869 -- -- --
<130 mm Hg 3.5 129 (122, 144) 128 (119, 151) 6,310 1,680 152,400 9.920 1,000 0.051 19,300
<120 mm Hg 4 120 (113, 143) 119 (110, 148) 5,910 2,310 153,500 9.952 1,100 0.032 34,200
Average Measurement Error
<140 mm Hg 4.6 136 (130, 147) 137 (127, 155) 6,790 1,160 151,700 9.883 -- -- --
<130 mm Hg 5.9 127 (120, 144) 129 (119, 152) 6,190 1,830 152,700 9.931 1,000 0.048 20,500
<120 mm Hg 7.3 118 (111, 143) 120 (111, 151) 5,830 2,490 154,000 9.962 1,300 0.031 42,000
High Measurement Error
<140 mm Hg 6 135 (129, 147) 137 (128, 156) 6,700 1,230 151,700 9.883 -- -- --
<130 mm Hg 7.5 126 (119, 144) 129 (119, 153) 6,120 1,930 152,800 9.938 1,200 0.055 21,200
<120 mm Hg 9 116 (109, 143) 120 (111, 152) 5,810 2,580 154,300 9.966 1,400 0.028 52,300
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Abbreviations: CVD, cardiovascular disease; ICER, incremental cost-effectiveness ratio; QALE, quality-adjusted life-expectancy; QALY, quality-adjusted life-year; SAE, serious adverse event; SBP, systolic blood pressure.

1

True and observed SBP values are from 5 years into the simulation.

2

CVD events and SAEs per 10,000 simulated individuals are from lifetime projections and count multiple events per individual.

3

Costs and QALE are from lifetime projections. Uncertainty intervals for lifetime costs and QALE are shown in Supplement Table 18.

Cost-Effectiveness

With research grade measurement (mean error 0 mm Hg), the <120 mm Hg target was cost-effective at a threshold of $100,000 per QALY. Compared with the <140 target, the <130 mm Hg target increased costs by $1,000 and quality-adjusted life-expectancy (QALE) by 0.057 QALYs, leading to an ICER of $17,900 per QALY. Compared with the <130 target, the <120 mm Hg target increased costs by $1,100 and QALE by 0.045 QALYs, leading to an ICER of $24,400 per QALY.

With average measurement error (mean error 7.3 and 4.6 mm Hg in the <120 and <140 mm Hg target), the <120 mm Hg target’s ICER increased, but it remained cost-effective. Compared with the <140 target, the <130 mm Hg target increased costs by $1,000 and QALE by 0.048 QALYs, leading to an ICER of $20,500 per QALY. Compared with the <130 target, the <120 mm Hg target increased costs by $1,300 and QALE by 0.031 QALYs, leading to an ICER of $42,000 per QALY (Table 2).

Sensitivity Analyses

In the base case, the <120 mm Hg target’s cost-effectiveness was not sensitive to the degree of measurement error. In scenarios based on low error clinics (mean error 4 mm Hg in the <120 mm Hg target), the <130 vs <140 mm Hg and <120 vs <130 mm Hg ICERs were $19,300 per QALY and $34,200 per QALY respectively, and the <120 mm Hg target was cost-effective. In scenarios based on high error clinics (mean error 9 mm Hg in the <120 mm Hg target), these ICERs were $21,200 per QALY and $52,300 per QALY, respectively, and the <120 mm Hg target remained cost-effective (Table 2).

In sensitivity analyses varying measurement error, the <120 mm Hg target was cost-effective using a threshold of $100,000 per QALY when error was less than 14.6 mm Hg in the <120 target (Figure 2A). In scenarios using alternative measurement error values and assuming the same error across all targets, the ICER for the <120 mm Hg target ranged from $20,200 per QALY (mean error −3.7 mm Hg) to $223,500 per QALY (mean error 12.7 mm Hg). In these scenarios, the <120 mm Hg ICER was below $100,000 per QALY unless measured SBP was 10.3 or 12.7 mm Hg above research-grade SBP on average (Supplement Table 15).

Figure 2:

Figure 2:

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Sensitivity Analysis Varying Measurement Error. This figure shows the ICERs for the <120 vs <130 mm Hg targets varying measurement error in two scenarios. The y axis shows the percentage change in measurement error from the base case, and in parentheses we list the mean mm Hg error in the <120 mm Hg target. Panel A uses the base case assumption that there is no change in cardiovascular risk for SBPs lowered below 115 mm Hg. Panel B assumes that there is a J-curve relationship between systolic blood pressure and cardiovascular risk, with the inflection point at 115 mm Hg. In all scenarios where the <120 mm Hg target has an ICER above $100,000/QALY or is dominated (increased costs and decreased quality-adjusted life-expectancy), the <130 mm Hg target is cost-effective. The low and high measurement error scenario analyses are respectively approximated by the 60% and 120% analyses (marked with an asterisk). They do not correspond directly to a percentage change in average measurement error, as they were parameterized based on individual SPRINT clinics. The assumed threshold of $100,000/QALY is indicated with a dotted line. Abbreviations: ICER, incremental cost-effectiveness ratio; QALY, quality-adjusted life-year.

Cost-effectiveness was sensitive to assumptions about the relationship between SBP and CVD risk and variation in antihypertensive disutility. In analyses assuming a J-curve relationship, the <120 mm Hg target was cost-effective only when error was less than 8.8 mm Hg (Figure 2B) or when the inflection point was below 116 mm Hg (Supplement Figure 6). In scenarios with a disutility of 0.003 or higher for each additional antihypertensive, the <120 mm Hg target ICER was above $100,000 per QALY, and the <130 mm Hg target was cost-effective (Supplement Figure 7).

The <120 mm Hg target was consistently cost-effective in our analyses varying the rate of SAEs, office visit frequency, and measurement error variance (Supplement Tables 16–17, Supplement Figure 8). In threshold analyses varying use of brand-name antihypertensive drugs, the <130 mm Hg target only became cost-effective when more than 12% of antihypertensives were brand-name (Supplement Figure 9).

In probabilistic sensitivity analyses using a $100,000-per-QALY threshold and average error, the <120 mm Hg target was cost-effective in 99.7% of simulations, and the <130 mm Hg target was cost-effective in 0.3% of simulations (Figure 3). In simulations using the J-shaped relationship, the <120 mm Hg target was cost-effective in 71.9% of simulations, whereas the <130 mm Hg target was cost-effective in 28.1% (Supplement Figure 10).

Figure 3:

Figure 3:

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Cost-Effectiveness Acceptability Curve. The figure shows the probability that each target is cost-effective in the average measurement error scenario, varying the cost-effectiveness threshold on the x-axis. The base-case threshold of $100,000/QALY is shown with the dotted line. Abbreviations: QALY, quality-adjusted life-year; SBP, systolic blood pressure.

Discussion

In our simulation study, we found that an intensive <120 mm Hg target for patients at high cardiovascular risk without diabetes or prior stroke was cost-effective across many plausible levels of SBP measurement error. With error based on the average across 49 SPRINT study sites, the <120 mm Hg target reduced cardiovascular events, improved QALE, and was cost-effective under a wide range of scenarios. The <130 mm Hg target was cost-effective in scenarios with very high measurement error (≥14.6 mm Hg in the <120 mm Hg target) or high measurement error and a J-shaped relationship between SBP and cardiovascular risk.

Previous studies of SBP targets have found that a <120 mm Hg target would be cost-effective in trial settings (28, 51, 52). Cost-effectiveness analyses of SPRINT estimated that the <120 mm Hg target had an ICER below $50,000 per QALY (28, 51), and a simulation study of adding a <120 mm Hg target to guidelines from the 2014 Joint National Committee found that the <120 mm Hg target would be cost-saving or cost-effective (53). Our work suggests that these findings would hold outside of trials in settings with typical SBP measurement quality. Our results also affirm the 2017 ACC/AHA decision to lower the recommended target for patients with high CVD risk. Compared with <140, a <130 mm Hg target was cost-effective across scenarios with a wide range of costs, adverse event probabilities, and measurement error levels.

Programs to improve SBP measurement (54–56) could allow an intensive target to be cost-effective in very-high-error clinics. Errors in measurement, such as inaccurate cuff sizing, omission of quiet rest before measurement, and incorrect patient positioning, can affect measurement by up to 20 mm Hg and are generally associated with overestimation of SBP (8–10, 57). In randomized crossover trials, using a too-small blood pressure cuff increased measured SBP by 5 to 20 mm Hg (9) and measuring SBP with nonstandard arm positions increased measured SBP by 4 to 7 mm Hg (10). In contrast, measuring blood pressure in a noisy public space had little effect on accuracy (58). Ambulatory blood pressure monitoring and home blood pressure monitoring by patients may provide more stable measurements (59, 60). We did not model ambulatory blood pressure monitoring because it is infrequently used in the United States, and randomized evidence on setting SBP targets with this measurement technique is limited (61–64). Additional research on the efficacy of targets based on ambulatory or home blood pressure monitoring could alleviate measurement error concerns.

We found that a <130 mm Hg target would be cost-effective for patients who have a strong preference for taking fewer medications. Studies of utilities associated with medication-taking find that patient preferences are variable (65). In one study, utilities associated with taking pills for cardiovascular prevention ranged from 1.0 (i.e., no disutility) to 0.9 (29). Physicians should consider variability in patient preferences and use shared decision making to select a target for patients who want to avoid additional medications. A <130 mm Hg target was also cost-effective when more than 12% of patients were prescribed higher cost, brand-name antihypertensives. However, this threshold is substantially higher than typical estimates of brand-name antihypertensive use: Across states, estimates ranged from 1.6% to 4.8% (66).

Compared with the <130 mm Hg target, we found that a <120 mm Hg target would increase adverse events related to antihypertensive treatment, such as falls, kidney injury, and hypotension. Consistent with previous analyses of SPRINT, we assumed that adverse events would carry a disutility for one month (28, 51). However, omission bias, the preference for harms that occur due to inaction rather than action (67), could also cause patients and clinicians to place greater disutility on adverse events compared with CVD events. To mitigate harms from adverse events, clinicians using an intensive target could consider methods for prevention, such as increased patient monitoring, laboratory testing, and fall prevention programs. Future research could evaluate the value of such programs or identify when a <130 mm Hg target would be inappropriate for patients with higher-than-usual concern about or risk for adverse events.

Our analysis has limitations. First, measurement error may not be normally distributed, and error among SPRINT participants may not represent error in other clinics. To incorporate real-world variability, we evaluated a range of measurement error levels.

Second, post-hoc analyses of clinical trials suggest that benefits and harms from intensive SBP treatment may differ across patients (68). We did not incorporate treatment heterogeneity across patient characteristics because the effect of intensive SBP treatment did not differ across prespecified subgroups in SPRINT. We also did not include heterogeneity in SAE-related and CVD-related mortality because there were few deaths in SPRINT. However, this does not rule out the possibility of heterogeneity. Our model did include two well-established sources of treatment heterogeneity. The SBP reduction per standard dose decreased with each additional antihypertensive agent. In addition, because we decreased CVD risk by 10% per 5-mm Hg decrease, a given SBP reduction provided a greater absolute risk reduction to patients with high baseline CVD risk than to patients with low CVD risk.

Third, the effect of lowering SBP below 115 mm Hg is uncertain. Observational studies show a J-shaped association between treated blood pressure and CVD risk (46, 47). However, the J-shaped association has not been established in randomized settings. In SPRINT, the benefit from intensive SBP treatment did not differ by baseline blood pressure (11, 69). In addition, patient-level factors associated with being below target, rather than the SBP value itself, may explain the J-curve phenomena (70–72). Given this, we modeled a J-shaped relationship in sensitivity analyses. Even with a J-shaped relationship, a <120 mm Hg target was cost-effective with average levels of error.

Fourth, we calibrated SAE rates to the rate of intervention-related SAEs in SPRINT. This could overestimate SAEs at lower SBPs because SPRINT participants in the <120 mm Hg target had more study visits than those in the <140 mm Hg target and therefore more opportunities to report adverse events (73). To address this, we included a sensitivity analysis with SAE risk estimated from published literature, and the results were consistent with our base case findings.

Fifth, our results apply only to SPRINT-eligible patients. Future work should consider the effect of intensive SBP targets for other populations, such as patients with diabetes.

In conclusion, a <120 mm Hg target was cost-effective in our simulations except in scenarios with high measurement error (≥14.6 mm Hg in the <120 mm Hg target), higher-than-expected cardiovascular risk at low SBPs, and higher-than-expected disutility from antihypertensive medications. This suggests that a <120 mm Hg target for SPRINT-eligible patients at high cardiovascular risk without diabetes or prior stroke may provide good value in many clinical settings.

Supplementary Material

Supplement

Grant Support

By the National Science Foundation Graduate Research Fellowship Program (DGE-1745303) and the National Institute of Neurological Disorders and Stroke (R01NS104143).

Footnotes

Reproducible Research Statement

Study protocol: Not available. Statistical code: Available on request from Dr. Smith (ksmith81@bwh.harvard.edu). Data set: SPRINT trial data are available via request to the Biologic Specimen and Data Repository Information Coordinating Center of the National Heart, Lung, and Blood Institute. NHANES data are publicly available.

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