A Proof of Concept of the Role of TDM-Based Clinical Pharmacological Advices in Optimizing Antimicrobial Therapy on Real-Time in Different Paediatric Settings
Bibliographic record
Abstract
Introduction: Antimicrobial treatment is quite common among hospitalized children. The dynamic age-associated physiological variations coupled with the pathophysiological alterations caused by underlying illness and potential drug-drug interactions makes the implementation of appropriate antimicrobial dosing extremely challenging among paediatrics. Therapeutic drug monitoring (TDM) may represent a valuable tool for assisting clinicians in optimizing antimicrobial exposure. Clinical pharmacological advice (CPA) is an approach based on the correct interpretation of the TDM result by the MD Clinical Pharmacologist in relation to specific underlying conditions, namely the antimicrobial susceptibility of the clinical isolate, the site of infection, the pathophysiological characteristics of the patient and/or the drug-drug interactions of cotreatments. The aim of this study was to assess the role of TDM-based CPAs in providing useful recommendations for the real-time personalization of antimicrobial dosing regimens in various paediatric settings. Materials and methods: Paediatric patients who were admitted to different settings of the IRCCS Azienda Ospedaliero-Universitaria of Bologna, Italy (paediatric intensive care unit [ICU], paediatric onco-haematology, neonatology, and emergency paediatric ward), between January 2021 and June 2021 and who received TDM-based CPAs on real-time for personalization of antimicrobial therapy were retrospectively assessed. Demographic and clinical features, CPAs delivered in relation to different settings and antimicrobials, and type of dosing adjustments were extracted. Two indicators of performance were identified. The number of dosing adjustments provided over the total number of delivered CPAs. The turnaround time (TAT) of CPAs according to a predefined scale (optimal, <12 h; quasi-optimal, between 12–24 h; acceptable, between 24–48 h; suboptimal, >48 h). Results: Overall, 247 CPAs were delivered to 53 paediatric patients (mean 4.7 ± 3.7 CPAs/patient). Most were delivered to onco-haematological patients (39.6%) and to ICU patients (35.8%), and concerned mainly isavuconazole (19.0%) and voriconazole (17.8%). Overall, CPAs suggested dosing adjustments in 37.7% of cases (24.3% increases and 13.4% decreases). Median TAT was 7.5 h (IQR 6.1–8.8 h). Overall, CPAs TAT was optimal in 91.5% of cases, and suboptimal in only 0.8% of cases. Discussion: Our study provides a proof of concept of the helpful role that TDM-based real-time CPAs may have in optimizing antimicrobial exposure in different challenging paediatric scenarios.
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 | 0.031 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".