Incidence and Predictors of Adverse Drug Reactions Caused by Drug-Drug Interactions in Elderly Outpatients: A Prospective Cohort Study
Bibliographic record
Abstract
PURPOSE: The primary objective of this study was to investigate the incidence of drug-drug interactions (DDIs) related to adverse drug reactions (ADRs) in elderly outpatients who attended public primary healthcare units in a southeastern region of Brazil. The secondary objective was to investigate the possible predictors of DDI-related ADRs. METHODS: A prospective cohort study was conducted between November 1, 2010, and November 31, 2011, in the primary public healthcare system in the Ourinhos micro-region in Brazil. Patients who were at least 60 years old, with at least one potential DDI, were eligible for inclusion in the study. Eligible patients were assessed by clinical pharmacists for DDI-related ADRs for 4 months. The causality of DDI-related ADRs was assessed independently by four clinicians using three decisional algorithms. The incidence of DDI-related ADRs during the study period was calculated. Logistic regression analysis was used to study DDI-related ADR predictors. RESULTS: A total of 433 patients completed the study. The incidence of DDI-related ADRs was 6.5%. A multivariate analysis indicated that the adjusted odds ratios (ORs) rose from 0.91 (95% confidence interval [CI] = 0.75-1.12, p = 0.06) in patients aged 65-69 years to 4.40 (95% CI = 3.00-6.12, p < 0.01) in patients aged 80 years or older. Patients who presented two to three diagnosed diseases presented lower adjusted ORs (OR = 0.93 [95% CI = 0.68-1.18, p = 0.08]) than patients who presented six or more diseases (OR = 1.12 [95% CI = 1.02-2.01, p < 0.01]). Elderly patients who took five or more drugs had a significantly higher risk of DDI-related ADRs (OR = 2.72 [95% CI = 1.92-3.12, p < 0.01]) than patients who took three to four drugs (OR = 0.93 [95% CI = 0.74-1.11, p = 0.06]). No significant difference was found with regard to sex (OR = 1.08 [95% CI 0.48-2.02, p = 0.44]). CONCLUSION: The incidence of DDI-related ADRs in elderly outpatients was significant, and most of the events presented important clinical consequences. Because clinicians still have difficulty managing this problem, highlighting the factors that increase the risk of DDI-related ADRs is essential. Polypharmacy was found to be a significant predictor of DDI-related ADRs in our sample.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".