Association of a calcium channel blocker and diuretic prescribing cascade with adverse events: A population‐based cohort study
Bibliographic record
Abstract
BACKGROUND: Prescribing cascades occur when a drug adverse event is misinterpreted as a new medical condition and a second, potentially unnecessary drug, is prescribed to treat the adverse event. The population-level consequences of prescribing cascades remain unknown. METHODS: This population-based cohort study used linked health administrative databases in Ontario, Canada. The study included community-dwelling adults, 66 years of age or older with hypertension and no history of heart failure (HF) or diuretic use in the prior year, newly dispensed a calcium channel blocker (CCB). Individuals subsequently dispensed a diuretic within 90 days of incident CCB dispensing were classified as the prescribing cascade group, and compared to those not dispensed a diuretic, classified as the non-prescribing cascade group. Those with and without a prescribing cascade were matched one-to-one on the propensity score and sex. The primary outcome was a serious adverse event (SAE), which was the composite of emergency room visits and hospitalizations in the 90-day follow-up period. We estimated hazard ratios (HRs) with 95% confidence intervals (CI) for SAE using an Andersen-Gill recurrent events regression model. RESULTS: Among 39,347 older adults with hypertension and no history of HF who were newly dispensed a CCB, 1881 (4.8%) had a new diuretic dispensed within 90 days after CCB initiation. Compared to the non-prescribing cascade group, those in the prescribing cascade group had higher rates of SAEs (HR: 1.21, 95% CI: 1.02-1.43). CONCLUSIONS: The CCB-diuretic prescribing cascade was associated with an increased rate of SAEs, suggesting harm beyond prescribing a second drug therapy. Our study raises awareness of the downstream impact of the CCB-diuretic prescribing cascade at a population level and provides an opportunity for clinicians who identify this prescribing cascade to review their patients' medications to determine if they can be optimized.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".