Abstract P317: Are Home Blood Pressure Devices Accurate? A Systematic Review of the Evidence
Bibliographic record
Abstract
Background: Out of office blood pressure (BP) measurements, especially home BP monitoring, are recommended for diagnosis and management of hypertension by all professional societies and guidelines. Though validation protocols exist for individual monitors, little data exists on the real world accuracy of home BP monitors, in actual use. We undertook this systematic review to estimate the accuracy of home BP monitors as reported in the literature. Methods: We undertook a literature search of MEDLINE and EMBASE from 1946 and 1947 until April 2023 respectively. We included studies that evaluated the accuracy of home BP devices against manual BP considered as the gold standard. Two reviewers independently selected studies, extracted data and assessed quality. Disagreements between the two reviewers were resolved by a third author. Results: Our search revealed 1284 non-duplicate citations. After applying selection criteria, nineteen studies, involving 4954 patient-devices (median 91, interquartile range 69, 489) were included in the systematic review. The reported inaccuracy of home BP monitors compared to mercury sphygmomanometer ranged from 10% to 72% for systolic BP, with each study using different thresholds for inaccuracy. The absolute mean difference for systolic BP between home BP monitor and the standard ranged from 2.4 mm Hg to 10.4 mm Hg and for diastolic BP from 1 to 8.7 mm Hg. Conclusion: The existing literature reports a relatively high degree of inaccuracy in home BP monitors being used. Data is limited by varying definitions being used for reporting inaccuracy. Consideration should be given to standardised definitions of accuracy, and individual patient validation of accuracy as home BP monitors use for clinical decision making becomes widespread.
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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 | 0.037 | 0.206 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".