ABSTRACT
Current guidelines published by the Centers for Disease Control and Prevention (CDC) and American Academy of Pediatrics (AAP) recommend empiric antibiotics for all neonates born to mothers with a diagnosis of chorioamnionitis due to the risk of early onset sepsis (EOS). EOS is difficult to diagnose due to nonspecific symptoms and a lack of reliable tests, can progress quickly, and is potentially fatal or have neurodevelopmental consequences for survivors.
Antibiotics are frequently prescribed in the hospital and are lifesaving in the setting of a serious infection. Conversely, overuse of antibiotics has potential negative effects to individuals and the population as a whole. Antibiotic resistant infections are a consequence of antibiotic misuse, are costly and difficult to treat, and pose a risk to patients hospitalized.
To examine this problem at The University of Vermont Medical Center (UVMMC) a retrospective chart review was preformed. Data on the maternal risk factors associated with EOS were collected in addition to clinical characteristics of their neonates and entered into a neonatal early onset sepsis (NEOS) calculator to determine the specific risk of infection to each infant. Treatment of the infant was compared to the NEOS calculator and CDC recommendations. Using posterior probability to determine a more specific risk profile better targets antibiotic therapy to ensure all infants that need treatment receive it, while reducing the number of infants treated empirically.
UVMMC currently treats 78% of infants according to CDC guidelines. Use of the NEOS calculator would reduce antibiotic treatment to 18% of term neonates born to mothers with a diagnosis of chorioamnionitis. Using a new tool to determine risk of EOS may safely reduce the number of infants receiving antibiotic treatment.
| Identifer | oai:union.ndltd.org:uvm.edu/oai:scholarworks.uvm.edu:graddis-1651 |
| Date | 01 January 2016 |
| Creators | Sooter, Rachel |
| Publisher | ScholarWorks @ UVM |
| Source Sets | University of Vermont |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate College Dissertations and Theses |
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