Global Heart Failure Epidemiology Versus Enrolment in Pivotal Trials: A Formidable Mismatch
Bibliographic record
Abstract
AIMS: Randomized clinical trials (RCTs) that inform international clinical practice guidelines should adequately represent regions burdened with disease. We aimed to assess the geographic representativeness of pivotal heart failure (HF) RCTs using two methodological approaches. METHODS AND RESULTS: We assessed the global geographic distribution of HF cases using the Global Burden of Disease 2021 dataset. We then assessed the geographic representativeness of pivotal phase 3 RCTs that have shaped international guidelines using two metrics: the representation index (RI), a ratio of regional trial sites to disease distribution, and the participant-to-prevalence ratio (PPR), a ratio of regional trial participants to disease distribution. In 2021, there were 55.4 million people with HF worldwide, with the greatest population in Asia (50%), followed by Europe (18%), Africa (14%), North America (10%), and Central & South America (8%). PPR estimates were limited by the variation in how trials classified regions when reporting participant enrolment. Yet, RI and PPR estimates revealed similar estimates of geographic representation. Europe (RI: 2.41, PPR: 2.69) and North America (RI: 3.25, PPR: 2.58) were over-represented in trials, while Asia (RI: 0.26, PPR: 0.22) and Africa (RI: 0.14, PPR: 0.05) were grossly under-represented. In contrast, Central & South America (RI: 1.29, PPR: 1.59) were adequately represented. CONCLUSIONS: Pivotal HF RCTs generate evidence primarily from Europe and North America, and grossly under-represent Africa and Asia. RI and PPR are correlated measures of regional representativeness, highlighting that regional participant enrolment is related to the number of trial sites in a region. Unlike PPR, RI can be estimated during trial planning and guide trial design for better regional representativeness.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".