Precision antibiotic prescribing for the treatment of third-generation cephalosporin resistant Enterobacterales infections
File(s)
Author(s)
Vasikasin, Vasin (Paul)
Type
Thesis or dissertation
Abstract
Background: Third-generation cephalosporin-resistant Enterobacterales (3GCRE) are critical priority pathogens. Treatment options remain limited to predominantly carbapenems, resulting in the increasing emergence of carbapenem resistant organisms (CRO). Precision antibiotic prescribing, through improvement in antibiotic selection and dosing, has been proposed as a method to tackle CRO emergence and treatment.
Hypothesis: This thesis aimed to explore the hypothesis that precision antibiotic prescribing for 3GCRE infections can be improved to increase the chance of better clinical outcomes and potentially reduce risk of carbapenem resistance.
Methods: Different research methods were used to investigate this hypothesis. To best identify antibiotics with least effect on the emergence of CRO, machine learning methods were compared to classical statistical analysis. To compare the efficacy of alternative treatment to broad-spectrum carbapenems for empirical treatment of 3GCRE bacteraemia, a prospective non-inferiority propensity score matching study was conducted. To enable improved dosing through therapeutic drug monitoring, a novel lateral flow assay (LFA) for meropenem quantification was developed together with an image processing algorithm for accurate interpretation of the LFA readings with simple smart phone camera.
Results: Machine learning models could define the effect of antibiotics overtime on the emergence of CRO and could identify ertapenem as a carbapenem with least resistance pressure. Moreover, ertapenem was found to be non-inferior to other carbapenems as empirical treatments for 3GCRE bacteraemia. A novel LFA for meropenem quantification was developed which holds significant promise for potential development into a point-of-care test.
Conclusion: Precision antibiotic prescribing for 3GCRE infections can be improved through better antibiotic selection and dosing. Options for antibiotic selection could be explored. Optimised dosing could be supported by point of care test where a novel LFA developed shows promise.
Hypothesis: This thesis aimed to explore the hypothesis that precision antibiotic prescribing for 3GCRE infections can be improved to increase the chance of better clinical outcomes and potentially reduce risk of carbapenem resistance.
Methods: Different research methods were used to investigate this hypothesis. To best identify antibiotics with least effect on the emergence of CRO, machine learning methods were compared to classical statistical analysis. To compare the efficacy of alternative treatment to broad-spectrum carbapenems for empirical treatment of 3GCRE bacteraemia, a prospective non-inferiority propensity score matching study was conducted. To enable improved dosing through therapeutic drug monitoring, a novel lateral flow assay (LFA) for meropenem quantification was developed together with an image processing algorithm for accurate interpretation of the LFA readings with simple smart phone camera.
Results: Machine learning models could define the effect of antibiotics overtime on the emergence of CRO and could identify ertapenem as a carbapenem with least resistance pressure. Moreover, ertapenem was found to be non-inferior to other carbapenems as empirical treatments for 3GCRE bacteraemia. A novel LFA for meropenem quantification was developed which holds significant promise for potential development into a point-of-care test.
Conclusion: Precision antibiotic prescribing for 3GCRE infections can be improved through better antibiotic selection and dosing. Options for antibiotic selection could be explored. Optimised dosing could be supported by point of care test where a novel LFA developed shows promise.
Version
Open Access
Date Issued
2024-05-03
Date Awarded
2024-09-01
Copyright Statement
Creative Commons Attribution NonCommercial Licence
License URL
Advisor
Holmes, Alison
Rawson, Timothy
Publisher Department
Department of Infectious Disease
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)