The Prevalence of Non-Adherence in Patients with Resistant Hypertension: a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
ABSTRACT Background Resistant hypertension is quite prevalent and a risk factor for cardiovascular events. Patients with suspected resistant hypertension undergo more screening intensity for secondary hypertension, despite some of them being non-adherent to prescribed pharmacotherapy. The prevalence of non-adherence in this setting varies from about 5 to 80% in the published literature. Apart from the wide range, the relation between method of assessment and prevalence is not well established. Our objective was to establish the overall prevalence of non-adherence in the apparent treatment resistant hypertension population, explore causes of heterogeneity, and evaluate the effect of the method of assessment on the estimate of non-adherence. Methods We performed a systematic review and meta-analysis. MEDLINE, EMBASE Classic+EMBASE, Cochrane, CINAHL, and Web of Science were searched for relevant articles. Details about the method of adherence assessment were extracted from each included article and grouped into direct and indirect. Pooled analysis was performed using the random effects model and heterogeneity was explored with metaregression and subgroup analyses. Results The literature search yielded 1428 studies, of which 36 were included. The pooled prevalence of non-adherence was 35% (95% confidence interval 25 – 46 %). For indirect methods of adherence assessment, it was 25% (95% CI 15 – 39 %), whereas for direct methods of assessment, it was 44% (95% CI 32 – 57 %). Metaregression suggested gender, age, and time of publication as potential factors contributing to the heterogeneity. Conclusions Non-adherence to pharmacotherapy is quite common in resistant hypertension, with the prevalence varying with the methods of assessment. Brief Summary Resistant hypertension is known to be a risk factor for cardiovascular events. These patients also undergo higher screening intensity for secondary hypertension. However, not all patients with apparent treatment resistant hypertension have true resistant hypertension, with some of them being non-adherent to prescribed pharmacotherapy. This systematic review aims to establish the overall prevalence of non-adherence in the apparent treatment resistant hypertension population and assess the relative contributions of non-adherence assessed with direct and indirect measures.
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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 | 0.023 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.049 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".