AREAS COVERED: Procalcitonin (PCT)-guided antibiotic use was discussed in various clinical conditions across initiation, management, and discontinuation stages. Most experts strongly recommended using PCT-driven antibiotic therapy among patients with lower respiratory tract infections, sepsis, and COVID-19. However, additional research is required to understand the optimal use of PCT in patients with organ transplantation and cancer patients with febrile neutropenia. Implementation of the solutions discussed in this review can help improve PCT utilization in guiding AMS in these regions and reducing challenges.
EXPERT OPINION: Experts strongly support the inclusion of PCT in AMS. They believe that PCT in combination with other clinical data to guide antibiotic therapy may result in more personalized and precise targeted antibiotic treatment. The future of PCT in antibiotic treatment is promising and may result in effective utilization of this biomarker.
METHODS: A retrospective cohort study was performed between October 1st, 2018, and October 31st, 2020, in Farwaniya Hospital PICU, a 20-bed unit. All pediatric patients who were admitted to PICU and received systemic antimicrobials during the study period were included and followed until hospital discharge. The ASP team provided weekly prospective audit and feedback on antimicrobial use starting October 8th, 2019. A pediatric infectious diseases specialist joined the ASP rounds remotely. Descriptive analyses and a pre-post intervention comparison of days of therapy (DOT) were used to assess the effectiveness of the ASP intervention.
RESULTS: There were 272 and 156 PICU admissions received systemic antimicrobial before and after the initiation of ASP, respectively. Bronchiolitis and pneumonia were the most common admission diagnoses, together compromising 60.7% and 61.2% of cases pre- and post-ASP. The requirement for respiratory support was higher post-ASP (76.5% vs. 91.5%, p
METHODS: C0 were retrieved from a large neonatal vancomycin dataset. Individual estimates of AUC0-24 were obtained from Bayesian post hoc estimation. Various ML algorithms were used for model building to C0 and AUC0-24. An external dataset was used for predictive performance evaluation.
RESULTS: Before starting treatment, C0 can be predicted a priori using the Catboost-based C0-ML model combined with dosing regimen and nine covariates. External validation results showed a 42.5% improvement in prediction accuracy by using the ML model compared with the population pharmacokinetic model. The virtual trial showed that using the ML optimized dose; 80.3% of the virtual neonates achieved the pharmacodynamic target (C0 in the range of 10-20 mg/L), much higher than the international standard dose (37.7-61.5%). Once therapeutic drug monitoring (TDM) measurements (C0) in patients have been obtained, AUC0-24 can be further predicted using the Catboost-based AUC-ML model combined with C0 and nine covariates. External validation results showed that the AUC-ML model can achieve an prediction accuracy of 80.3%.
CONCLUSION: C0-based and AUC0-24-based ML models were developed accurately and precisely. These can be used for individual dose recommendations of vancomycin in neonates before treatment and dose revision after the first TDM result is obtained, respectively.