Antimicrobial therapeutic drug monitoring in critically ill adult patients – An international perspective on access, utilisation, and barriers
Bibliographic record
Abstract
BACKGROUND: Therapeutic drug monitoring (TDM) is an effective method for individualising antimicrobial therapy in critically ill patients. The 2021 ADMIN-intensive care unit survey studied a wide range of intensive care unit clinicians worldwide to gain their perspectives on antimicrobial TDM. This article reports the responses from this survey relating to TDM access, utilisation, and barriers. METHODS: An online survey consisted of multiple-choice questions and 5-point Likert scales. The survey examined respondent's access to minimum inhibitory concentration (MIC) results, drug assays, and dosing software, as well as barriers to TDM. RESULTS: The survey included 538 clinicians from 409 hospitals in 45 countries, with 71% physicians and 29% pharmacists. Despite most respondents having access to assays, 21% and 26% of respondents lacked access to vancomycin and aminoglycosides, respectively. In lower-income countries, almost 40% reported no access. Delayed drug assay turnaround time was the most significant barrier to TDM, particularly in lower-income countries. Routine access to MIC results was unavailable for 41% of respondents, with 25% of lower-income country respondents having no access to MIC or susceptibility reports. CONCLUSIONS: This global survey indicated that consistent TDM usage is hindered by assay access in some sites and the timeliness of assay results in others. Addressing barriers to TDM, particularly in low-income countries, should be a priority to ensure equitable access to affordable TDM.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".