Abstract 15256: Predicting Out-of-office Blood Pressure in United States Adults
Bibliographic record
Abstract
Introduction: The Predicting Out-of-Office Blood Pressure in the Clinic (PROOF-BP) algorithm accurately estimates out-of-office BP to guide ambulatory BP monitoring (ABPM) among adults in the UK and Canada with suspected high BP. We tested the validity of PROOF-BP in a diverse US population and developed a US-specific algorithm. Methods: We pooled data from four US studies (CARDIA, JHS, Masked Hypertension Study, and Improving Detection of Hypertension Study) that assessed both office BP and 24-hour ABPM. We included participants with >=2 office and >=10 daytime ambulatory BP readings. PROOF-BP estimates the difference between office systolic BP (SBP) and diastolic BP (DBP) and daytime ambulatory SBP and DBP using clinic BP measurements and patient characteristics. We examined the performance of PROOF-BP in US data and then used multivariable linear regression to develop a new algorithm optimized for the US population. We tested the ability of PROOF-BP to discriminate high awake ambulatory SBP and DBP (SBP/DBP >=130/80 mm Hg) using the area under the receiver-operator curve (AUROC). Models were developed in a 70% randomly selected derivation set and tested in a 30% validation set. The optimal predicted ambulatory BP thresholds were defined as those that resulted in the smallest proportion of individuals recommended for ABPM with an overall classification error <20% among those not screened. Results: We analyzed 3,080 individuals with a mean (SD) age of 52.0 (11.9) years, 38% were male, and 54% were black. Mean (SD) office SBP/DBP was 121.8 (16.6)/75.3 (9.8) mm Hg, mean (SD) awake ambulatory SBP/DBP was 127.3 (13.5)/78.6 (8.8) mm Hg, and 51% had awake ABPM >=130/80 mm Hg. The discrimination for high awake ABPM was similar between the existing (AUROC SBP = 0.77, DBP = 0.73) and US-specific models (AUROC SBP = 0.77, DBP = 0.72). Optimal predicted ambulatory BP thresholds with the US-specific algorithm were 125-134/75-84 mm Hg, resulting in 55% of the pooled cohort recommended for ABPM; compared to 66% recommended by the 2017 ACC/AHA guidelines. Conclusions: Both the original and US-specific PROOF-BP algorithms predicted high out-of-office BP among US adults. PROOF-BP may be used to guide clinical decisions and resource allocation among individuals considered for ABPM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".