Electronic health record (EHR)-detectable statin intolerance phenotypes: Prevalence and validation in real-world general practice
Bibliographic record
Abstract
ABSTRACT Aims This study focused on patients who were prescribed statins as primary prevention of cardiovascular diseases. This study aimed to identify statin intolerant patients and determine the prevalence of statin intolerance by implementing electronic health record (EHR)-detectable statin intolerance electronic phenotyping algorithms, and to validate these algorithms. Methods This study used the Electronic Practice Based Research Network (ePBRN) dataset. The methodology took place in four stages: (1) literature review to identify electronic phenotypes, (2) implementation of electronic phenotypes on ePBRN, (3) development and implementation of reference standard, (4) validation of electronic phenotypes. Results Six EHR-detectable statin intolerance electronic phenotypes were identified, including the Minnesota Combined Rule-Based algorithm, Japan-Statin induced myopathy (SIMs), USA-SIMs, Singapore-SIMs (algorithms A, B, C, and D), Japan-Statin-associated muscle toxicity (SAMT), and NHS-UK-Statin intolerance pathway. The prevalence of statin intolerance among those prescribed statins in ePBRN was 5.09%. The Singapore SIMs-B algorithm showed the highest accuracy (57.05%), sensitivity (92.95%), negative predictive value (43.43%), and F1 (71.51%) scores, while the Japan SAMT algorithm showed the highest specificity (99.13%), positive predictive value (76.19%), and correlation coefficient (0.05%). Conclusion The prevalence of statin intolerance in ePBRN is at the low end of the 5–15% range reported in Australia and globally. The differences in prevalence calculations may be due to the varying definitions of intolerance. Our findings suggest that EHR-detectable phenotypes should be used as decision-support aid rather than as definitive diagnostic tools and that clinical judgement and patient engagement are necessary for the management of suspected statin intolerance. Key points This study found that: The prevalence of statin intolerance among those prescribed statins in the ePBRN dataset was 5.09%, which is at the low end of the 5–15% range reported in Australia and globally. Different phenotyping algorithms show various prevalence estimations, which may be due to the varying definitions of intolerance. EHR-detectable phenotypes should be used as decision-support aids rather than as definitive diagnostic tools and that clinical judgement and patient engagement is necessary for the management of suspected statin intolerance.
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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 | 0.015 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| 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".