A Retrospective Cohort Study of the 2018 Angiotensin Receptor Blocker Recalls and Subsequent Drug Shortages in Patients With Hypertension
Bibliographic record
Abstract
BACKGROUND: Valsartan was recalled by the US Food and Drug Administration in July 2018 for carcinogenic impurities, resulting in a drug shortage and management challenges for valsartan users. The influence of the valsartan recall on clinical outcomes is unknown. We compared the risk of adverse events between hypertensive patients using valsartan and a propensity score-matched group using nonrecalled angiotensin receptor blockers and angiotensin-converting enzyme inhibitors. METHODS AND RESULTS: We used Optum's deidentified Clinformatics Datamart (July 2017-January 2019). Hypertensive patients who received valsartan or nonrecalled angiotensin receptor blockers/angiotensin-converting enzyme inhibitors for 1 year before and on the recall date were compared. Primary outcomes were measured in the 6 months following the recall and included: (1) a composite measure of all-cause hospitalization, all-cause emergency department visit, and all-cause urgent care visit, and (2) a composite cardiac event measure of hospitalizations for acute myocardial infarction and hospitalizations/emergency department visits/urgent care visits for stroke/transient ischemic attack, heart failure, or hypertension. We compared the risk of outcomes between treatment groups using Cox proportional hazard models. Of the hypertensive patients, 76 934 received valsartan, and 509 472 received a nonrecalled angiotensin receptor blocker/angiotensin-converting enzyme inhibitor. Valsartan use at the time of recall was associated with a higher risk of all-cause hospitalization, emergency department use, or urgent care use (hazard ratio [HR], 1.02 [95% CI, 1.00-1.04]) and the composite of cardiac events (HR, 1.22 [95% CI, 1.15-1.29]) within 6 months after the recall. CONCLUSIONS: The valsartan recall and shortage affected hypertensive patients. Local- and national-level systems need to be enhanced to protect patients from drug shortages by providing safe and reliable medication alternatives.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".