Clinical Decision Rules to Improve the Detection of Adverse Drug Events in Emergency Department Patients
Bibliographic record
Abstract
OBJECTIVES: Adverse drug events (ADEs) are unintended and harmful consequences of medication use. They are associated with high health resource use and cost. Yet, high levels of inaccuracy exist in their identification in clinical practice, with over one-third remaining unidentified in the emergency department (ED). The study objective was to derive clinical decision rules (CDRs) that are sensitive for the detection of ADEs, allowing their systematic identification early in a patient's hospital course. METHODS: This was a prospective observational cohort study carried out in two Canadian tertiary care hospitals. Participants were adults presenting to the ED having ingested at least one prescription or over-the-counter medication within 2 weeks. Nurses and physicians evaluated patients for standardized clinical findings. A second evaluator performed interobserver assessments of predictor variables in a subset of patients. Pharmacists, who were blinded to the predictor variables, evaluated all patients for ADEs. An independent committee reviewed and adjudicated cases where the ADE assessment was uncertain or the pharmacist's diagnosis differed from the physician's working diagnosis. The primary outcome was an ADE that required a change in medical therapy, diagnostic testing, consultation, or hospital admission. CDRs were derived using kappa coefficients, chi-square statistics, and recursive partitioning. RESULTS: Among 1,591 patients, 131 (8.2%, 95% confidence interval [CI] = 7.0% to 9.7%) were diagnosed with the primary outcome. The following variables were associated with ADEs and were used to derive two CDRs: 1) presence of comorbid conditions, 2) antibiotic use within 7 days, 3) medication changes within 28 days, 4) age ≥ 80 years, 5) arrival by ambulance, 6) triage acuity, 7) recent hospital admission, 8) renal failure, and 9) use of three or more prescription medications. The more sensitive rule had a sensitivity of 96.7% (95% CI = 91.8% to 98.6%) and required 40.8% (95% CI = 37.7% to 42.9%) of patients to have medication review. The more specific rule had a sensitivity 90.8% (95% CI = 81.4% to 95.7%) and required 28.3% of patients to proceed to medication review. CONCLUSIONS: The authors derived CDRs that identified patients with ADEs with high sensitivity. These rules may improve the identification of ADEs early in a patient's hospital course while limiting the number of patients requiring a detailed medication review.
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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 | 0.011 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".