Evaluating the Impact of a Blood Pressure Remote Telemonitoring Program (BP Connect)
Bibliographic record
Abstract
Background: The prevalence of hypertension is around 30-45% among the general population. Hypertension contributes to 1 out of 7 deaths in the United States and approximately 70% of persons who have a first heart attack or stroke. Timely treatment and optimal management of hypertension is associated with substantial reductions in stroke incidence (35-40%), myocardial infarction (20-25%), and heart failure (>50%). Additionally, remote monitoring with active intervention by medical professionals (telemonitoring) improves drug compliance and increase the target blood pressure (BP) achievement rate. Objective: BP Connect is a remote monitoring program to augment care and disease self-management in hypertensive patients. The program aims to engage patients in self-care by providing a secure web-based platform to record and track their BP. In addition, care providers can view the uploaded data, thereby, ensuring the continuum of care beyond the hospital setting. Methods: A total of 288 adult patients diagnosed with hypertension (baseline blood pressure of ≥140 mm Hg systolic or ≥90 mm Hg diastolic) were recruited from primary care and specialty clinics within the Partners Healthcare network of hospitals (Faulkner, Renal, Endocrinology and Cardiology departments at Brigham and Women’s Hospital and Massachusetts General Hospitals Women’s Health Association). The primary outcome was the impact of BP Connect on blood pressure as measured by the change in the proportion of patients with controlled BP (ie, 140/90 mm Hg) vs 64.0% at baseline (P=.01). At 3 months, there was a significant decline in the mean SBP (-10.1, P<.01) and the mean DBP (-4.3, P<.01). Among the 139 patients from specialty clinics who completed this program, BP was controlled at 3 months in 43.2% of patients compared with 11.2% patients at baseline (P=.001). A significant decrease in the mean SBP (-5.2, P<.001) and in the mean DBP (-2.5, P=.01) were also noted. Conclusions: Overall, the BP Connect program shows potential to improve clinical outcomes in hypertensive patients. The program provides an opportunity for participants to track their BP measurements on a web platform to foster patient participation in their own disease management that may improve patient outcomes and decrease burden on the care providers.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".