Clinical characteristics, antihypertensive medication use and blood pressure control among patients with treatment-resistant hypertension
Bibliographic record
Abstract
OBJECTIVE: We evaluated the characteristics of patients with treatment-resistant hypertension (TRH) and the prevalence of TRH in a large multicountry sample of specialist tertiary centres. METHODS: The Survey of PatIents with treatment ResIstant hyperTension (SPIRIT) study was a retrospective review of medical records of patients seen at tertiary centres located in Western Europe, Eastern Europe, North America, South America, Australia and Asia. Data on demographics, medical history and medication use were extracted from medical records. Prevalence and incidence of TRH were based upon estimated catchment populations. RESULTS: On thousand, five hundred and fifty-five patients from 76 centres were included, mostly from centres that specialize in hypertension (55%), cardiology (11%) or nephrology (19%). Mean age was 64, 60% were men, 62% were Caucasian, 36% had chronic kidney disease, 41% had diabetes, 12% were smokers and 31% had a previous cardiovascular event. Daytime and night-time ambulatory blood pressure (BP) was the most frequently used measurement for diagnosis (82%). Ninety-five percent of patients were prescribed diuretics, 93% an inhibitor of the renin-angiotensin system, 86% a calcium channel blocker, 74% a beta-blocker and 36% an aldosterone antagonist. The overall estimated mean incidence of TRH was 5.8 per 100 000 per year (ranging between 2.3 and 14.0 across regions) and the corresponding estimated mean prevalence of TRH was 23.9 per 100 000 (ranging between 7.6 and 90.5 across regions). CONCLUSION: Observed variation likely reflects real differences in patient characteristics and physician management practices across regions and specialities but may also reflect differences in patient selection and errors in estimation of catchment population across participating centres.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".