Effects of the valsartan recall on heart failure patients: A nationwide analysis
Bibliographic record
Abstract
BACKGROUND: Valsartan is commonly used for cardiac conditions. In 2018, the Food and Drug Administration recalled generic valsartan due to the detection of impurities. Our objective was to determine if heart failure patients receiving valsartan at the recall date had a greater likelihood of unfavorable outcomes than patients using comparable antihypertensives. METHODS: We conducted a cohort study of Optum's de-identified Clinformatics® Datamart (July 2017-January 2019). Heart failure patients with commercial or Medicare Advantage insurance who received valsartan were compared to persons who received non-recalled angiotensin receptor blockers (ARBs) and angiotensin converting enzyme-inhibitors (ACE-Is) for 1 year prior and including the recall date. Outcomes included a composite for all-cause hospitalization, emergency department (ED), and urgent care (UC) use and a measure of cardiac events which included hospitalizations for acute myocardial infarction and hospitalizations/ED/UC visits for stroke/transient ischemic attack, heart failure or hypertension at 6-months post-recall. Cox proportional hazard models with propensity score weighting compared the risk of outcomes between groups. RESULTS: Of the 87 130 adherent patients, 15% were valsartan users and 85% were users of non-recalled ARBs/ACE-Is. Valsartan use was not associated with an increased risk of all-cause hospitalization/ED/UC use six-months post-recall (HR 1.00; 95% CI 0.96-1.03), compared with individuals taking non-recalled ARBs/ACE-Is. Similarly, cardiac events 6-months post-recall did not differ between individuals on valsartan and non-recalled ARBs/ACE-Is (HR 1.04; 95% CI 0.97-1.12). CONCLUSIONS: The valsartan recall did not affect short-term outcomes of heart failure patients. However, the recall potentially disrupted the medication regimens of patients, possibly straining the healthcare system.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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) | 0.001 | 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".