Identifying pseudo-resistant hypertension and optimizing diuretic therapy for confirmed resistant cases in primary care
Bibliographic record
Abstract
Background: Approximately 10% of individuals with hypertension are expected to have resistant hypertension (RH). Many have pseudo-resistant hypertension (p-RH) due to a variety of factors. To date, the prevalence of p-RH and optimal diuretic therapy in primary care have not been studied. Methods: A retrospective chart review was conducted, including patients referred to the hypertension clinic at the Centre for Family Medicine (CFFM) Family Health Team in Kitchener, ON, from January 2010 to September 2020. Individuals ≥18 years old referred to clinic by their family physician or other health care provider with 2 consecutive blood pressure (BP) readings of ≥140/90 mmHg despite using ≥3 antihypertensive agents were included. Results: Fifty-one patients taking ≥3 antihypertensive agents were referred during the study timeframe. Forty-one patients had ≥2 consecutive BP readings of ≥140/90 and were classified as having presumed RH. Of these, 24 patients (59%) had p-RH after BP was measured systematically in the hypertension clinic. Of the 17 with RH, 5 (29%) were prescribed optimal diuretic therapy upon referral. Most common clinic interventions included initiating or adjusting the dose of a diuretic (47%), adding a different antihypertensive agent (27%) or discontinuing an antihypertensive agent due to side effects (24%). Discussion: To our knowledge, this is the first time that the prevalence of p-RH and optimal diuretic therapy have been studied in primary care. p-RH was common and diuretic therapy was underused in RH. Conclusion: This study suggests that p-RH is prevalent and diuretic therapy underused in primary care. Systematic BP measurement and optimization of diuretic therapy should be prioritized prior to specialist referral.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".