Manar A. Harbi

Objective: This study aims to identify the prevalence of drug–drug interactions among admitted pediatric patients, identify risk factors, and classify and identify the most common drug–drug interactions. Materials and Methods: A retrospective observational cross-sectional study was conducted on neonatal and pediatric patients, 12 years or younger, admitted in or during December 2022. The data were collected by reviewing the medical files of the patients; subsequently, the number of pDDIs of co-administered medications was assessed using Lexicomp version 8.2.0. Results: Out of 395 patients, 162 (41%) were associated with having at least one pDDI. About 37%, 20%, and 43% were admitted to the neonatal intensive care unit (NICU), the pediatric intensive care unit (PICU), and pediatric wards, respectively. The factors significantly linked with pDDIs were age, admission ward, diagnosis, length of stay, and the total number of drugs prescribed during admission. Moderate pDDIs were the most prevalent, 486 (72.1%); followed by minor pDDIs, 124 (18.4%); and then major pDDIs 64 (9.5%) with no contraindicated pDDIs encountered. The most prevalent major pDDIs were fentanyl + midazolam 15 (2.23%), while the most frequent moderate pDDIs were methylprednisolone + salbutamol 54 (8.01%), and ondansetron + paracetamol 32 (4.75%) was the most recurring minor pDDIs. Conclusion: The factors that were found to be significantly linked with pDDIs were age, admission ward, diagnosis, length of stay, and the total number of drugs prescribed during admission. Thus, further investigation of these factors is essential in mitigating pDDIs in admitted pediatrics.

The AUC_0–24 is the most accurate way to track the vancomycin level while the C_min is not an accurate surrogate. Most hospitals in Saudi Arabia are under-practicing the AUC-guided vancomycin dosing and monitoring. No previous work has been conducted to evaluate such practice in the whole kingdom. The current study objective is to calculate the AUC_0–24 using the Bayesian dosing software (PrecisePK), identify the probability of patients who receive the optimum dose of vancomycin, and evaluate the accuracy and precision of the Bayesian platform. This retrospective study was conducted at King Abdulaziz medical city, Jeddah. All adult patients treated with vancomycin were included. Pediatric patients, critically ill patients requiring ICU admission, patients with acute renal failure or undergoing dialysis, and febrile neutropenic patients were excluded. The AUC_0–24 was predicted using the PrecisePK platform based on the Bayesian principle. The two-compartmental model by Rodvold et al. in this platform and patients’ dose data were utilized to calculate the AUC_0–24 and trough level. Among 342 patients included in the present study, the mean of the estimated vancomycin AUC_0–24 by the posterior model of PrecisePK was 573 ± 199.6 mg, and the model had a bias of 16.8%, whereas the precision was 2.85 mg/L. The target AUC_0–24 (400 to 600 mg·h/L) and measured trough (10 to 20 mg/L) were documented in 127 (37.1%) and 185 (54%), respectively. Furthermore, the result demonstrated an increase in odds of AUC_0–24 > 600 mg·h/L among trough level 15–20 mg/L group (OR = 13.2, p < 0.05) as compared with trough level 10–14.9 mg/L group. In conclusion, the discordance in the AUC_0–24 ratio and measured trough concentration may jeopardize patient safety, and implantation of the Bayesian approach as a workable alternative to the traditional trough method should be considered.