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Abstract TH127: Detecting Potential Threshold Bias in Recorded Blood Pressure in Clinical Practice and Research: A study of 7 International Datasets

Kathryn Foti, Matti Marklund, Di Zhao, Chathurangi H Pathiravasan, Ziling Shen, Elena Blasco‐Colmenares, Edgar R. Miller, Cara Nordberg, Annemarie G. Hirsch, Alex R. Chang, Katie Harris, Mark Woodward, Mohammad Robed Amin, Syed Akhter Hossain, Mahfuzur Rahman Bhuiyan, Shamim Jubayer, Sohel Reza Choudhury, Dessie Girma, Betsegaw Dereje, Reena Gupta, Lawrence J. Appel, Kunihiro Matsushita
Hypertension · 2025;82(Suppl_1)
DOI10.1161/hyp.82.suppl_1.th127

Abstract

Introduction: Threshold bias, a tendency to record blood pressure (BP) measurements at values below the threshold for BP control in adults with hypertension, may be an underrecognized issue in clinical practice and research and could result in undertreatment of hypertension. Aim: To develop a method for detecting potential threshold bias using population-based surveillance data as a reference and apply it to research cohort, clinical practice, and clinical trial datasets. Hypothesis: Threshold bias may be present in settings with a BP control goal (e.g., hypertension programs and some clinical trials), but not in those without (e.g., research cohorts). Methods: In national surveillance datasets (i.e., reference datasets) from the US and Bangladesh, we examined the systolic blood pressure (SBP) distributions in adults with treated hypertension and calculated the expected SBP threshold ratios, defined as the number of individuals with SBP 130-139 vs. 140-149 mmHg. We used weighted bootstrapping with 10,000 random draws of 100 observations to obtain the probability of observing prespecified threshold ratios (e.g., ≥2.0, ≥2.5, ≥3.0) in the reference datasets. We then calculated the SBP threshold ratios in 7 research cohort, clinical practice, and clinical trial datasets and compared them to the bootstrap probabilities from the reference datasets from the same country as a scale to indicate the likelihood of threshold bias. Results: The SBP threshold ratios in the reference datasets for the US and Bangladesh were 1.2 and 1.3, respectively. In each reference dataset, the probability of observing ratios ≥2.5 was <5% and ≥3.0 was <2%; thus, ratios exceeding these values in other datasets may indicate potential threshold bias. When we calculated threshold ratios in 2 research cohorts, there was no evidence of threshold bias ( Table ). Among clinical datasets, we observed threshold ratios of 2.6 in public clinics in Bangladesh, which may indicate threshold bias. One clinical trial (ACCORD) with a BP goal had a threshold ratio of 2.3; another trial without a BP goal (ADVANCE) had a threshold ratio of 1.2. Conclusion: Using our proposed method, we identified multiple clinical practice and trial datasets with BP control goals with potential threshold bias. The results highlight the need for routine monitoring of threshold bias and renewed emphasis on obtaining high-quality BP measurements, including accurate recording.

Keywords

Clinical PracticeBlood pressureBootstrapping (finance)Clinical trialThreshold modelDetection thresholdScale (ratio)

Author affiliations

Kathryn Foti
University of Alabama at Birmingham
iD0000-0002-6380-2735
Matti Marklund
Johns Hopkins University
iD0000-0002-3320-796X
Di Zhao
Johns Hopkins University
iD0000-0002-9978-6773
Chathurangi H Pathiravasan
Johns Hopkins University
iD0000-0003-2170-1247
Ziling Shen
Johns Hopkins University
Elena Blasco‐Colmenares
Johns Hopkins University
iD0000-0001-5694-2628
Edgar R. Miller
Johns Hopkins University
Cara Nordberg
Geisinger Medical Center
iD0000-0003-2317-5332
Annemarie G. Hirsch
Geisinger Medical Center
iD0000-0001-7699-2171
Alex R. Chang
Geisinger Medical Center
iD0000-0002-8114-7447
Katie Harris
The George Institute for Global Health
Mark Woodward
The George Institute for Global Health
iD0000-0001-9800-5296
Mohammad Robed Amin
Directorate General of Health Services
Syed Akhter Hossain
Directorate General of Health Services
iD0000-0001-6546-9692
Mahfuzur Rahman Bhuiyan
National Heart Foundation Hospital & Research Institute
iD0000-0001-6962-7264
Shamim Jubayer
National Heart Foundation Hospital & Research Institute
iD0000-0002-8595-1993
Sohel Reza Choudhury
National Heart Foundation Hospital & Research Institute
iD0000-0002-7498-4634
Dessie Girma
Save the Children
Betsegaw Dereje
Save the Children
Reena Gupta
University of California, San Francisco
iD0009-0004-7116-7278
Lawrence J. Appel
Johns Hopkins University Applied Physics Laboratory
iD0000-0002-0673-6823
Kunihiro Matsushita
Johns Hopkins University
iD0000-0002-7179-718X

Article history

Published
01 Sept 2025
How to cite this
Kathryn Foti, Matti Marklund, Di Zhao, Chathurangi H Pathiravasan, Ziling Shen, Elena Blasco‐Colmenares, Edgar R. Miller, Cara Nordberg, Annemarie G. Hirsch, Alex R. Chang, Katie Harris, Mark Woodward, Mohammad Robed Amin, Syed Akhter Hossain, Mahfuzur Rahman Bhuiyan, Shamim Jubayer, Sohel Reza Choudhury, Dessie Girma, Betsegaw Dereje, Reena Gupta, Lawrence J. Appel, & Kunihiro Matsushita. (2025). Abstract TH127: Detecting Potential Threshold Bias in Recorded Blood Pressure in Clinical Practice and Research: A study of 7 International Datasets.  Hypertension, 82(Suppl_1). https://doi.org/10.1161/hyp.82.suppl_1.th127
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Abstract TH127: Detecting Potential Threshold Bias in Recorded Blood Pressure in Clinical Practice and Research: A study of 7 International Datasets | NHFB Dept. of Epidemiology