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.
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: Clinical specimens from three Kathmandu hospitals were processed and S. aureus was identified using conventional microbiological procedures. MRSA was phenotypically identified with cefoxitin (30µg) disc diffusion, while vancomycin susceptibility was assessed using the Ezy MICTM stripes. The mecA and vanA genes were detected by polymerase chain reaction (PCR).
RESULTS: Out of 266 S. aureus samples from various clinical specimen subjected for analysis, 77 (28.9%) were found methicillin-resistant (MRSA) and 10 (3.8%) were observed vancomycin-resistant (VRSA). Vancomycin resistant isolates showed a significant correlation between resistance to ampicillin, chloramphenicol, and cefoxitin. The mecA gene was found in 39 of the MRSA isolates, having 50.64% of MRSA cases, while the vanA gene was detected in 4 of the VRSA cases, constituting 40% of VRSA occurrences.
CONCLUSIONS: The strains with higher vancomycin minimum inhibitory concentration values (≥ 1.5 μg/ml) displayed increased resistance rates to various antibiotics compared to strains with lower minimum inhibitory concentration values (< 1.5 μg/ml). The presence of vanA genes was strongly associated (100%) with vancomycin resistance, while the 10.3% mecA gene was identified from MRSA having resistance towards vancomycin also.
METHODS: This was an observational study conducted among sepsis patients presented to ED of a tertiary university hospital from 18th January 2021 until 28th February 2021. ED overcrowding status was determined using the National Emergency Department Overcrowding Score (NEDOCS) scoring system. Sepsis patients were identified using Sequential Organ Failure Assessment (SOFA) scores and their door-to-antibiotic time (DTA) were recorded. Patient outcomes were hospital length of stay (LOS) and in-hospital mortality. Statistical analysis was done using Statistical Package for Social Sciences (SPSS) version 26. P-value of less than 0.05 for a two-sided test was considered statistically significant.
RESULTS: Total of 170 patients were recruited. Among them, 33 patients presented with septic shock and only 15% (n = 5) received antibiotics within one hour. Of 137 sepsis patients without shock, 58.4% (n = 80) received antibiotics within three hours. We found no significant association between ED overcrowding with DTA time (p = 0.989) and LOS (p = 0.403). However, in-hospital mortality increased two times during overcrowded ED (95% CI 1-4; p = 0.041).
CONCLUSION: ED overcrowding has no significant impact on DTA and LOS which are crucial indicators of sepsis care quality but it increases overall mortality outcome. Further research is needed to explore other factors such as lack of resources, delay in initiating fluid resuscitation or vasopressor so as to improve sepsis patient care during ED overcrowding.
OBJECTIVES: The objective of this review is to compare the effects of different medical interventions in people diagnosed with cystic fibrosis and chronic rhinosinusitis.
SEARCH METHODS: We searched the Cochrane Cystic Fibrosis Trials Register, compiled from electronic database searches and hand searching of journals and conference abstract books. Date of last search of trials register: 09 September 2021. We also searched ongoing trials databases, other medical databases and the reference lists of relevant articles and reviews. Date of latest additional searches: 22 November 2021.
SELECTION CRITERIA: Randomized and quasi-randomized trials of different medical interventions compared to each other or to no intervention or to placebo.
DATA COLLECTION AND ANALYSIS: Two review authors independently assessed trials identified for potential inclusion in the review. We planned to conduct data collection and analysis in accordance with Cochrane methods and to independently rate the quality of the evidence for each outcome using the GRADE guidelines.
MAIN RESULTS: We identified no trials that met the pre-defined inclusion criteria. The most recent searches identified 44 new references, none of which were eligible for inclusion in the current version of this review; 12 studies are listed as excluded and one as ongoing.
AUTHORS' CONCLUSIONS: We identified no eligible trials assessing the medical interventions in people with cystic fibrosis and chronic rhinosinusitis. High-quality trials are needed which should assess the efficacy of different treatment options detailed above for managing chronic rhinosinusitis, preventing pulmonary exacerbations and improving quality of life in people with cystic fibrosis.