Ignite Creation Date:
2024-05-06 @ 8:12 PM
Last Modification Date:
2024-10-26 @ 3:23 PM
Study NCT ID:
NCT06297837
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-03-07
First Post:
2024-02-08
Brief Title:
ADAPT-AST Adaptive Antimicrobial Susceptibility Testing
Sponsor:
Liverpool University Hospitals NHS Foundation Trust
Organization:
Liverpool University Hospitals NHS Foundation Trust
Study Overview
Official Title:
Adaptive Prediction of Antimicrobial Susceptibility and Its Implementation to Improve the Management of Urinary Tract Infection
Status:
NOT_YET_RECRUITING
Status Verified Date:
2023-10
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
ADAPT-AST
Brief Summary:
The goal of this study is to improve the way urinary tract infections UTIs are tested for antibiotic resistance The main questions it aims to answer are
Can the investigators use a method called Bayesian causal inference to create or check clinical prediction models that help predict if certain antibiotics will work for a urinary infection using patient information from the National Health Service NHS
Can this new ADAPT-AST method which uses data and a smarter approach do a better job of testing for urinary infection than the old methods Will it help doctors make quicker decisions and save resources by being more efficient
Participants in this study will not be receiving treatments The study will involve
Using statistical methods to predict UTI test results based on patient data Evaluating whether this new approach can provide doctors with more timely and useful information for treating UTIs
Assessing whether it can help save money and resources in the lab and pharmacy
Can the investigators use a method called Bayesian causal inference to create or check clinical prediction models that help predict if certain antibiotics will work for a urinary infection using patient information from the National Health Service NHS
Can this new ADAPT-AST method which uses data and a smarter approach do a better job of testing for urinary infection than the old methods Will it help doctors make quicker decisions and save resources by being more efficient
Participants in this study will not be receiving treatments The study will involve
Using statistical methods to predict UTI test results based on patient data Evaluating whether this new approach can provide doctors with more timely and useful information for treating UTIs
Assessing whether it can help save money and resources in the lab and pharmacy
Detailed Description:
The aim of this study is to develop and evaluate an adaptive informatics approach for laboratory antimicrobial susceptibility testing AST for urinary tract infection UTI pathogens compared with current practice to improve patient outcomes reduce AMR risks and reduce waste of laboratory resources
UTI is a leading cause of community and hospital acquired infection and a major driver of antimicrobial prescribing in primary and secondary care The continued proliferation of AMR also increasingly limits treatment choices for many UTIs Despite the importance of UTI antimicrobial susceptibility testing AST of urine specimens is based on inflexible one-size-fits all standard operating procedures SOPs Either a very large unfocused panel of antimicrobials is immediately tested leading to wasted resources or more commonly and particularly in low or middle income LMIC settings a selected subset of antimicrobials is tested at day one prior to a second or even third panel of antimicrobials Such an approach does not adapt to prior information such as previous resistance patterns antimicrobial prescribing or demographic information despite these factors being powerful strong predictors of resistance This results in imprecise inefficient and inequitable provision of antimicrobial susceptibility information which provides suboptimal support of decisions for treatment of UTI
This project will use statistical techniques based on Bayesian causal inference to predict urine AST results and prioritise testing using patient demographics prescribing admission and microbiology laboratory care data The clinical utility of resulting algorithms will be evaluated in terms of their ability to increase the number timeliness and appropriateness of usable AST results available to clinicians and their ability to reduce laboratory resource costs through better test prioritisation The anticipated benefits of a successfully developed evaluated and implemented system are faster and more precise treatments of UTI in patients with drug-resistant organisms and more efficient resource management particularly in laboratory and pharmacy workflows
UTI is a leading cause of community and hospital acquired infection and a major driver of antimicrobial prescribing in primary and secondary care The continued proliferation of AMR also increasingly limits treatment choices for many UTIs Despite the importance of UTI antimicrobial susceptibility testing AST of urine specimens is based on inflexible one-size-fits all standard operating procedures SOPs Either a very large unfocused panel of antimicrobials is immediately tested leading to wasted resources or more commonly and particularly in low or middle income LMIC settings a selected subset of antimicrobials is tested at day one prior to a second or even third panel of antimicrobials Such an approach does not adapt to prior information such as previous resistance patterns antimicrobial prescribing or demographic information despite these factors being powerful strong predictors of resistance This results in imprecise inefficient and inequitable provision of antimicrobial susceptibility information which provides suboptimal support of decisions for treatment of UTI
This project will use statistical techniques based on Bayesian causal inference to predict urine AST results and prioritise testing using patient demographics prescribing admission and microbiology laboratory care data The clinical utility of resulting algorithms will be evaluated in terms of their ability to increase the number timeliness and appropriateness of usable AST results available to clinicians and their ability to reduce laboratory resource costs through better test prioritisation The anticipated benefits of a successfully developed evaluated and implemented system are faster and more precise treatments of UTI in patients with drug-resistant organisms and more efficient resource management particularly in laboratory and pharmacy workflows
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None