Vestesson, Emma Maria;
(2024)
Understanding and Optimising Antimicrobial Use in Children using Electronic Health Records Data: A Machine Learning and Causal Inference Approach.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
Antimicrobial resistance is a growing global health crisis, threatening the efficacy of treatments essential for modern medical practice, including infections, surgeries, and chemotherapy. This PhD thesis leverages electronic health records (EHRs) from the Clinical Practice Research Datalink (CPRD) and a specialist children’s hospital to improve antimicrobial stewardship in paediatric care. The first study described trends in therapeutic and prophylactic antibiotic prescribing at a tertiary children’s hospital. It showed that therapeutic prescribing was higher in neonates than in older children. The second study used interrupted time series models to assess changes in days of antibiotic therapy at a tertiary children’s hospital during the first year of the COVID-19 pandemic. There was an increase in antibiotic prescribing during the COVID-19 pandemic, but this was likely driven by changes in the patient population. The third study, a preliminary analysis, explored the potential of using EHR data to predict antibiotic resistance with the aim to reduce inappropriate prescribing. The predictive models did not show high accuracy and further research is needed. The fourth study used routine data from general practice to evaluate the effect of remote consultations on antibiotic prescribing for acute respiratory infections, finding no significant difference in prescribing rates for children but a 23% increase in the odds of antibiotics being prescribed in remote consultations for adults. Collectively, these studies provide important insights into antimicrobial use in children, highlighting both challenges and opportunities for improving antibiotic stewardship. By identifying data quality issues, evaluating the impact of external factors such as the COVID-19 pandemic, and exploring predictive models, this research contributes to a deeper understanding of how EHR data can be leveraged to optimise antimicrobial use in paediatric care. These findings underscore the potential for using data-driven approaches to refine stewardship strategies and ensure more targeted, appropriate antibiotic prescribing in both paediatric and broader healthcare contexts.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Understanding and Optimising Antimicrobial Use in Children using Electronic Health Records Data: A Machine Learning and Causal Inference Approach |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2024. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10201858 |
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