Applicability of Heart Failure Clinical Practice Guidelines in Low- and Middle-Income Countries
Bibliographic record
Abstract
AIMS: Clinical practice guidelines are commonly written by professional societies in high-income countries (HIC) with limited anticipation of implementation obstacles in other environments. We used heart failure (HF) guidelines as a paradigm to examine this concern, by conducting a survey to understand clinicians' ability to implement HF guidelines and their perceptions of the current HF guideline applicability in low- and middle-income countries (LMIC). METHODS AND RESULTS: An online survey of physicians in the database of the Translational Medicine Academy who treat HF patients was offered by email from 5 October to 27 November 2023, inquiring of participants' demographic information, experience, and views of HF guidelines as related to their practice. Of 2622 participating clinicians, 1592 partially completed, and an additional 1030 fully completed the survey. Participants were from 138 countries; 668 practiced in HIC, and 1954 in LMIC. Those from LMIC regarded HF guidelines to be less applicable in their country than did those from HIC (p = 0.0002). Of all those responding, 75.3% indicated that it was somewhat or mostly true that the HF guidelines were mostly applicable to HIC. Those from LMIC, but not HIC indicated that the greatest implementation obstacle was that the guidelines were for HIC (51.3% vs. 43.1%; p = 0.0387). A significantly higher proportion of respondents from LMIC indicated that resources for caring for their patients were somewhat or mostly limiting in most cases, than did those in HIC (41.6% vs. 32.5%, p = 0.0068). CONCLUSION: This survey examined the widely-held thought that HF guidelines are broadly applicable to all regions of the world, concluding that such a perception is incorrect. Clinicians from LMIC view the absence of consideration of local resource limitations as the greatest obstacle for guideline implementation. The results regarding HF guidelines likely also have implications for other guidelines and resultant patient outcomes.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".