How Accurate Are Home Blood Pressure Devices in Use? A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Out of office blood pressure measurements, using either home monitors or 24 hour ambulatory monitoring, is widely recommended for management of hypertension. Though validation protocols, meant to be used by manufacturers, exist for blood pressure monitors, there is scant data in the literature about the accuracy of home blood pressure monitors in actual clinical practice. We performed a chart review in the blood pressure assessment clinic at a tertiary care centre. METHODS: We assessed the accuracy of home blood pressure monitors used by patients seen in the nephrology clinic in Ottawa between the years 2011 to 2014. We recorded patient demographics and clinical data, including the blood pressure measurements, arm circumference and the manufacturer of the home blood pressure monitor. The average of BP measurements performed with the home blood pressure monitor, were compared to those with the mercury sphygmomanometer. We defined accuracy based on a difference of 5 mm Hg in the blood pressure values between the home monitor and mercury sphygmomanometer readings. The two methods were compared using a Bland-Altman plot and a student's t-test. RESULTS: The study included 210 patients. The mean age of the study population was 67 years and 61% was men. The average mid-arm circumference was 32.2 cms. 30% and 32% of the home BP monitors reported a mean systolic and diastolic BP values, respectively, different from the mercury measurements by 5 mm Hg or more. There was no significant difference between the monitors that were accurate versus those that were not when grouped according to the patient characteristics, cuff size or the brand of the home monitor. CONCLUSIONS: An important proportion of home blood pressure monitors used by patients seen in our nephrology clinic were inaccurate. A re-validation of the accuracy and safety of the devices already in use is prudent before relying on these measurements for clinical decisions.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".