Clinical characteristics and early identification of augmented renal clearance in PICU patients with severe sepsis associated with MRSA infection
Year: 2024
Type: article
Study design: Observational Study
Abstract: Objectives: To investigate the epidemiological characteristics of Augmented Renal Clearance (ARC) in severe sepsis children with MRSA infection and find risk factors to establish a model predicting ARC onset in PICU. Design: . Univariable and multivariable logistic regression analyses were performed to find the predictor for ARC. Multi-strategy modeling was used to form an early prediction model for ARC, which was evaluated by the area under the ROC curve (AUC), accuracy (ACC) and other indicators. Setting: One China PICU. Patients: Severe sepsis children with MRSA infection admitted to PICU from May 2017 to June 2022 at Children's Hospital of Nanjing Medical University. Interventions: None. Measurements and main results: 125 of 167 (74.9%) patients with severe sepsis with MRSA infection have occurred ARC during the hospitalization of PICU, of which 44% have an absolute decrease in vancomycin trough level (VTL), patients with ARC have a longer length of stay in both hospital and PICU, lower VTL and require longer anti-infective treatment. 20 different models were established for the early recognition of ARC. Among them, the best performer had an AUC of 0.746 and a high application prospect. Conclusion: ARC is a phenomenon significantly underestimated in pediatric patients with severe sepsis associated with MRSA infection, which can affect 74.9% of these patients and affects the process of anti-infection treatment and clinical outcomes. To achieve early prediction only by specific risk factors is unreliable, a model based on Multivariate Logistic Regression in this study was chosen to be used clinically.
Source: Frontiers in Pediatrics
Authors: Haonan Wang, Jiaqing Li, Xian Li, Han Li, Yinglang He, Rui Tan, Xuejian Mei, Haoyu Zha, Mingxing Fan, Shuangshuang Peng, Nan Hou, Zhe Li, Yue Wang, Chen Ji, Yao Liu, Hongjun Miao
Language: English
Subfield: Pharmacology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,295
OpenAlex ID: W4404664520
