Drug-Drug Interactions Associated with Length of Stay and Cost of Hospitalization
Bibliographic record
Abstract
PURPOSE: To evaluate the prevalence of drug-drug interactions (DDI) in prescriptions of hospitalized patients and to identify risk factors associated. METHODS: A retrospective cross-sectional analysis of prescription data and medical records from a public hospital in Brazil was conducted to identify potential DDI. An inappropriate drug combination was identified and classified with a standard drug interaction source. The main diagnoses were classified with Charlson Comorbidity Index (CCI). Sex, age, polypharmacy and length of stay, among other variables, were correlated with the frequency of potential DDI. RESULTS: The study included 589 patients and 3,585 prescriptions. Thirty-seven percent of the patients were exposed to at least one potential interaction during their stay in the hospital. The most frequent interacting pair was Digoxin+Furosemide (11%). In univariate analysis, several variables were associated with DDI, including sex, age, number of prescribed drugs, length and cost of hospitalization and CCI. Multivariate analysis showed that the adjusted odds of being prescribed a potential DDI among patients in polypharmacy was almost five-fold that of patients taking less than five drugs. Further, length of stay, CCI and cost of hospitalization were independently associated with DDI. CONCLUSION: Analysis of prescription data found that a substantial number of potential DDI were identified. Results of this study indicate that DDI is associated with number of prescribed drugs, increased duration of stay in the hospital and cost, which suggest that DDI are a significant clinical and economic problem. Potential damage to patients could be avoided.
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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 | 0.004 |
| 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) | 0.002 | 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".