Digoxin prescribing for heart failure in elderly residents of long-term care facilities.
Bibliographic record
Abstract
BACKGROUND: Digoxin is often used in long-term care (LTC) residents with heart failure despite a high risk of toxicity associated with increased age, comorbidities and polypharmacy. This toxicity may occur at serum digoxin concentrations that are as low as 1.54 nmol/L. OBJECTIVES: To determine the prevalence of digoxin use, estimate the proportion at risk of toxicity and identify correlates of digoxin use in LTC residents with heart failure. METHODS: Cross-sectional survey in eight LTC facilities that lodge a total of 1223 residents. RESULTS: The prevalence of heart failure was 20%. Digoxin was prescribed for 32% of residents with heart failure and was associated with arrhythmia (primarily atrial fibrillation), anticoagulant and diuretic use, and higher serum thyroid-stimulating hormone. Digoxin doses higher than those that achieve the recommended therapeutic peak body stores of 6 microg/kg and 10 microg/kg were prescribed to 80% and 33% of residents with heart failure, respectively. Serum digoxin concentrations were greater than 1.5 nmol/L in 30% of patients. Comorbidities and concurrently prescribed medications that increase the risk of digoxin toxicity were prescribed to 26% of the patients. CONCLUSIONS: Approximately one-third of LTC residents with heart failure received digoxin. Atrial fibrillation was the most important determinant of use. At least 26% of these residents were exposed to an increased risk of digoxin toxicity. Studies are required to determine safe and effective digoxin dosing regimens for frail elderly heart failure patients. Clinicians should exercise caution when using digoxin in LTC residents.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".