Adverse drug reactions, multimorbidity and polypharmacy: a prospective analysis of 1 month of medical admissions
Bibliographic record
Abstract
OBJECTIVE: To ascertain the burden and associated cost of adverse drug reactions (ADRs), polypharmacy and multimorbidity through a prospective analysis of all medical admissions to a large university teaching hospital over a 1-month period. DESIGN: Prospective observational study. SETTING: Liverpool University Hospital Foundation National Health Service (NHS) Trust, England. PARTICIPANTS: All medical admissions with greater than 24-hour stay over a 1-month period. MAIN OUTCOME MEASURES: Prevalence of admissions due to an ADR and associated mortality, prevalence and association of multimorbidity and polypharmacy with ADRs, and estimated local financial cost of admissions where an ADR was a contributing or main reason for admission with projected costs for NHS in England. RESULTS: There were 218 identified patient admissions with an ADR giving a prevalence of 18.4%. The majority of these (90.4%) were ADRs that directly resulted in or contributed to admission. ADRs thus accounted for 16.5% of total admissions. Those with an ADR were on average taking more medicines (10.5 vs 7.8, p<0.01) and had more comorbidities than those without an ADR (6.1 vs 5.2, p<0.01). Drugs most commonly implicated were diuretics, steroid inhalers, anticoagulants and antiplatelets, proton pump inhibitors, chemotherapeutic agents and antihypertensives. 40.4% of ADRs were classified avoidable or possibly avoidable. The mortality rate due to an ADR was 0.34%. The average length of stay for those with an ADR was 6 days. Direct 1-month cost to the Trust from ADR admissions was £490 716. Extrapolated nationally, the projected annual cost to the NHS in England is 2.21 billion. CONCLUSION: The local prevalence of admission and mortality from ADRs is higher than previously reported. Important factors that could be contributing to this include polypharmacy and multimorbidity. ADRs place a significant burden on patients and healthcare services with associated financial implications. Reducing inappropriate polypharmacy should be a major aim for preventing ADRs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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".