CLINICALLY SIGNIFICANT DRUG-DRUG INTERACTION IN A LARGE ANTIRETROVIRAL TREATMENT CENTRE IN LAGOS, NIGERIA
Bibliographic record
Abstract
Background: An important cause of treatment failure to antiretroviral therapy (ART) is the potential interaction between the antiretroviral (ARV) drugs and co-prescribed drugs used concomitantly for the treatment of opportunistic infections and co-morbid ailments in HIV-infected patients. Objectives: The study evaluated potential clinically significant drug interactions (CSDIs) occurring between recommended ART regimens and their co-prescribed non-antiretroviral drugs (CPD) Method: This study was carried out in a large HIV treatment centre (APIN clinic) in a Nigerian teaching hospital, in Lagos Nigeria, caring for over 20,000 registered patients. Electronic Medical Records (EMR) of 500 patients who received treatment between 2005 and 2015, were selected using systematic random sampling, reviewed retrospectively, and evaluated for potential CSDIs using Liverpool HIV Pharmacology Database and other similar databases. Results: Majority of patients, 421 (84%) were at risk of CSDIs, of which 410, (82%) were moderate and frequently involved co-trimoxazole + zidovudine (or stavudine) /lamivudine (386, 77.2%) and NNRTIs or PIs + artemisinin-based combination therapies (ACTs) [296, 59.2%]. Age (p=0.131), sex (p=0.316) and baseline CD4+ cell counts (p>0.05) were not significantly associated with CSDIs. The interactions, however, were significantly associated with the development of antiretroviral treatment failure (p <0.001) which occurred in nearly a third (139; 27.8%) of the patients. Conclusion: There is a high prevalence of CSDIs between ART and CPDs most of which were categorized as moderate. Further studies are required to evaluate the pharmacokinetic and clinical relevance of these interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".