Adverse Drug Reactions in Elderly Hospitalized Patients: A 12-Year Population-Based Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Although research has identified some risk factors for first-time adverse drug reactions (ADRs), little is known about the risks associated with the number of ADRs. Modeling ADR counts is relatively complex because of the rarity of the events, requiring careful consideration of appropriate models that best present the observed data. OBJECTIVE: To determine the incidence of ADRs among elderly hospitalized patients, assess patient-related risk factors for the number of ADRs, and review drug classes commonly responsible for ADRs. METHODS: This retrospective cohort study used a population-based large administrative database on hospital separations from all acute care hospitals in the Canadian province of Newfoundland and Labrador. Patients aged 65 years or older with at least 1 hospital admission from April 1, 1995, to March 31, 2007, were included. Comorbidities, Charlson Comorbidity Index (CCI), and sociodemographic factors were assessed as predictors of ADR counts. A zero-inflated negative binomial regression model was used for analysis. RESULTS: The study cohort contained 64,446 patients. The incidence of ADRs was 15.2 per 1000 person-years (95% CI 14.8 to 15.7). Of those having an ADR, 15.4% had recurrent ADRs. The most common drug category implicated in ADRs was cardiovascular agents (17.7%). A dose-response relationship was found between CCI and ADR counts (rate ratio [RR] 1.67, 95% CI 1.41 to 1.98 for CCI 2-3; RR 2.38, 95% CI 1.98 to 2.87 for CCI 4-5; and RR 3.83, 95% CI 3.21-4.57 for CCI ≥6). Comorbid conditions including congestive heart failure (RR 1.58, 95% CI 1.33 to 1.89), diabetes (RR 2.42, 95% CI 1.64 to 3.56), and cancer (RR 3.12, 95% CI 2.58 to 3.76) were strong predictors. Rural areas (RR 1.22, 95% CI 1.01 to 1.46) were associated with increased risk for ADRs, whereas age and sex had no effect. CONCLUSIONS: Comorbidity from chronic diseases and severity of illness, rather than individual characteristics (advancing age and sex), increased the likelihood of ADRs. Changes in the delivery of care focusing on the monitoring of prescribed drugs in elderly patients with comorbidities could mitigate 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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".