A Retrospective Cohort Study of the 2018 Angiotensin Receptor Blocker (ARB) Recalls and Subsequent Drug Shortages on Patients with Hypertension
Bibliographic record
Abstract
ABSTRACT Purpose In July 2018, the Food and Drug Administration recalled valsartan due to carcinogenic impurities, leading to an unprecedented drug shortage leading to management challenges impacting a large population of valsartan users. However, the extent to which the valsartan recall impacted clinical outcomes is unknown. Our objective was to compare the risk of adverse events between patients with hypertension using valsartan and a propensity-score-matched group of patients using non-recalled angiotensin receptor blockers (ARBs) and angiotensin converting enzyme-inhibitors (ACE-Is). Methods We conducted a retrospective cohort analysis of Optum’s de-identified Clinformatics® Datamart (July 2017–January 2019). Adults with hypertension who received valsartan were compared to persons who received non-recalled ARBs and ACE-Is for 1 year prior to and on the recall date. The primary outcomes were measured in the 6 months following the recall date included: 1) a composite measure of all-cause hospitalization, all-cause emergency department (ED) and all-cause urgent care (UC) visit, 2) a composite cardiac event measure of hospitalizations for acute myocardial infarction and hospitalizations/ED/UC visits for stroke/transient ischemic attack, heart failure or hypertension. Cox proportional hazard models compared the risk of outcomes between propensity-score-matched treatment groups. Results Of adults with hypertension, 76,934 received valsartan at the time of the recall and 509,742 received a non-recalled ARB/ACE-I. Valsartan use at the time of the recall was associated with a combined increased risk of all-cause hospitalization, ED or UC use (HR 1.02; 95% CI: 1.00–1.04) and of the composite of cardiac events (HR 1.22; 95% CI: 1.15–1.29) within six-months after the recall. Conclusions The national valsartan recall and subsequent shortage had negative consequences for patients with hypertension. As the threat of shortages increases for drugs that treat common outpatient conditions, systems at the local- and national-levels need to be strengthened to protect patients from drug shortages by providing them with safe and reliable alternatives.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".