Adaptive Prediction of Antimicrobial Susceptibility and Its Implementation to Improve the Management of Urinary Tract Infection
Conditions: Urinary Tract Infections
Sponsor: Liverpool University Hospitals NHS Foundation Trust

i 
Version 1.1 June 2023 
 
 
TITLE PAGE 
 
FULL/LONG TITLE OF THE STUDY 
Adaptive Prediction of Antimicrobial Susceptibility and its Implementation to Improve the Management 
of Urinary Tract Infection 
 
SHORT STUDY TITLE / ACRONYM 
ADAPT-AST (Adaptive Antimicrobial Susceptibility Testing) 
 
PROTOCOL VERSION NUMBER AND DATE 
1.3 October 2023 
 
REFERENCE NUMBERS 
 
 
SPONSORS Number: LHS0205 
 
FUNDERS Number: 226691/Z/22/Z 
 
  IRAS Number: 330186 
 

 
ADAPT-AST Protocol 
 
  
 
ii 
Version 1.1 June 2023 
 
SIGNATURE PAGE 
 
The undersigned confirm that the following protocol has been agreed and accepted and that the Chief 
Investigator agrees to conduct the study in compliance with the approved protocol and will adhere to 
the principles outlined in the Declaration of Helsinki, the Sponsor’s SOPs, and other regulatory 
requirement. 
I agree to ensure that the confidential information contained in this document will not be used for any 
other purpose other than the evaluation or conduct of the investigation without the prior written 
consent of the Sponsor 
I also confirm that I will make the findings of the study publicly available through publication or other 
dissemination tools without any unnecessary delay and that an honest accurate and transparent 
account of the study will be given; and that any discrepancies from the study as planned in this 
protocol will be explained. 
 
For and on behalf of the Study Sponsor: 
Signature:  
 
...................................................................................................... 
 
Date: 
....../....../...... 
Name (please print): 
  
 
 
Position:  
 
 
 
 
Chief Investigator: 
Signature:  
 
...................................................................................................... 
 
Date:  
....../....../...... 
Name: (please print): 
 
 
 
 
 
 
 

 
ADAPT-AST Protocol 
 
  
 
iii 
Version 1.1 June 2023 
 
LIST OF CONTENTS 
 
TITLE PAGE ................................................................................................................................... i 
SIGNATURE PAGE ....................................................................................................................... ii 
LIST OF ABBREVIATIONS .......................................................................................................... iv 
KEY STUDY CONTACTS ............................................................................................................ vii 
STUDY SUMMARY .....................................................................................................................viii 
FUNDING AND SUPPORT IN KIND .............................................................................................. x 
ROLE OF STUDY SPONSOR AND FUNDER ............................................................................... x 
PROTOCOL CONTRIBUTORS .................................................................................................... xi 
KEY WORDS: ............................................................................................................................. xii 
STUDY PLAN .............................................................................................................................. xii 
STUDY PROTOCOL ..................................................................................................................... 1 
1 
BACKGROUND and RATIONALE ......................................................................................... 1 
1.1 Urinary Tract Infection and Current Laboratory Standard Operating Procedures ................... 1 
1.2 Bayesian causal inference, Prediction and Urinary Tract Infection ........................................ 3 
1.3 ADAPT-AST: An Adaptive Antimicrobial Susceptibility Testing Framework ........................... 5 
2 
PROJECT QUESTIONS/AIMS .............................................................................................. 6 
3 
METHODS and THEORETICAL FRAMEWORK ................................................................... 6 
3.1 Data Collection and Integration ............................................................................................. 6 
3.1.1 
Setting and Eligibility Criteria ............................................................................................. 6 
3.1.2 
Data Parameters of Interest............................................................................................... 7 
3.1.3 
Sample size ....................................................................................................................... 9 
3.1.4 
Pseudonymised Data Access ............................................................................................ 9 
3.1.5 
Data Cleaning ................................................................................................................. 10 
3.2 Development of an Adaptive Clinical Prediction Model ........................................................ 11 
3.2.1 
Bayesian causal inference Model Training ...................................................................... 11 
3.2.2 
Bayesian causal inference Model Testing ........................................................................ 14 
3.3 Development and Clinical Evaluation of ADAPT-AST in a simulated clinical setting ............ 14 
3.3.1 
AST Decision Making Algorithm Development................................................................. 14 
3.3.2 
Outcomes ........................................................................................................................ 16 
3.3.3 
Statistical Analysis ........................................................................................................... 18 
3.3.4 
Potential Limitations of Simulated Interventional Study .................................................... 18 

 
ADAPT-AST Protocol 
 
  
 
iv 
Version 1.1 June 2023 
 
3.4 Economic evaluation of ADAPT-AST .................................................................................. 18 
4 
ETHICAL AND REGULATORY CONSIDERATIONS .......................................................... 20 
4.1 Assessment and management of risk .................................................................................. 20 
4.2  
Regulatory Review and Amendments .............................................................................. 20 
4.2.1 
Ethical and Regulatory Review, Compliance and Reports ............................................... 20 
4.2.2 
Amendments ................................................................................................................... 20 
4.3  
Peer review ..................................................................................................................... 21 
4.4  
Patient & Public Involvement ........................................................................................... 21 
4.5 Protocol compliance ............................................................................................................ 21 
4.6 Consent, data protection and patient confidentiality ............................................................ 21 
4.7 Indemnity ............................................................................................................................ 24 
4.8 Access to the final study dataset ......................................................................................... 24 
5 
END OF STUDY ................................................................................................................. 24 
6 
DISSEMINATION POLICY .................................................................................................. 24 
6.1  
Dissemination policy ........................................................................................................ 24 
6.2  
Authorship eligibility guidelines and any intended use of professional writers .................. 25 
7 
ARCHIVING ........................................................................................................................ 25 
8 
REFERENCES .................................................................................................................... 25 
9 
APPENDICES ..................................................................................................................... 30 
9.1 Health Professional Questionnaire Consent Form ............................................................... 30 
9.2 Appendix 2 – Health Professional Questionnaire ................................................................. 31 
9.3  
Appendix 3 - Data Management plan .............................................................................. 32 
 
 
 
 
LIST OF ABBREVIATIONS 
 
AMR  
 
Antimicrobial Resistance 
APR 
 
 
Annual Progress Report 
AST 
 
 
Antimicrobial Susceptibility Testing 
AUC-ROC 
 
Area Under the Curve – Receiver Operator Characteristic 

 
ADAPT-AST Protocol 
 
  
 
v 
Version 1.1 June 2023 
 
AWaRe 
 
Access, Watch, Reserve 
BCI 
 
 
Bayesian causal inference 
BL 
 
 
Beta-Lactam 
BL-BLIC 
 
Beta-Lactam-Beta-Lactamase-Inhibitor Combination 
CAMO-NET 
 
Centres for Antimicrobial Optimisation Network 
CI 
 
 
Chief Investigator 
CIPHA  
 
Combined Intelligence for Public Health Action 
CDC 
 
 
Civic Data Co-operative 
COVID-19 
 
Coronavirus Disease 2019 
CQUIN 
 
Care Quality Indicator 
CTIMP  
 
Clinical Trial of an Investigational Medicinal Product 
DAAG  
 
Data Asset and Access Group 
DARG  
 
Data Action Research Group 
DSA 
 
 
Data Sharing Agreement 
DPIA  
 
Data Protection Impact Assessment 
eGFR  
 
Estimated Glomerular Filtration Rate 
EPMA  
 
Electronic Prescribing and Medicines Administration 
EMIS  
 
Egton Medical Information Systems 
EUCAST 
 
European Committee on Antimicrobial Susceptibility Testing 
GP 
 
 
General Practice 
IV 
 
 
Intravenous 
LCL 
 
 
Liverpool Clinical Laboratories 
LCH 
 
 
Liverpool Community Health 
LHP 
 
 
Liverpool Health Partners 
LMIC  
 
Low or Middle Income 
LR 
 
 
Logistic Regression 
LUHFT 
 
Liverpool University Hospitals NHS Foundation Trust 
LWfT  
 
Liverpool Womens’ NHS Foundation Trust 
MASCC 
 
Multinational Association of Supportive Care in Cancer 
MCMC  
 
Markov Chain Monte Carlo 
NHS 
 
 
National Health Service 

 
ADAPT-AST Protocol 
 
  
 
vi 
Version 1.1 June 2023 
 
NIHR  
 
National Institute for Health Research 
OPAT  
 
Outpatient Parenteral Antimicrobial Therapy 
R&D 
 
 
Research and Development 
SNOMED-CT  
Systematized Nomenclature of Medicine Clinical Terms 
SMG  
 
Study Management Group 
SOP 
 
 
Standard Operating Procedure 
TRE 
 
 
Trusted Research Environment 
UKRI  
 
United Kingdom Research and Innovation 
UTI 
 
 
Urinary Tract Infection 
WHO  
 
World Health Organisation 
 
 

 
ADAPT-AST Protocol 
 
  
 
vii 
Version 1.1 June 2023 
 
KEY STUDY CONTACTS 
 
Chief Investigator 
Alex Howard 
Medical Microbiology 
Department of Infection and Immunity 
Liverpool Clinical Laboratories 
Clinical Services Support Building 
Liverpool University Hospitals NHS Foundation Trust 
United Kingdom 
+44 7855 550 575 
Alexander.Howard@liverpool.ac.uk 
Sponsor 
Liverpool University Hospitals NHS Foundation Trust is the 
Sponsor for this Study. It is recognised that as an employee 
of the Trust the Chief Investigator has been delegated 
specific duties, as detailed in the Sponsorship Approval 
letter.  
Funder(s) 
Wellcome Trust 
Gibbs Building 
215 Euston Road 
London 
NW1 2BE 
020 7611 8888 
Key Protocol Contributors 
Dr Alex Howard 
Prof. William Hope 
Prof. Iain Buchan 
Dr David Hughes 
Prof. Simon Maskell 
Dr Beth Woods 
Liverpool AMR Citizens’ Jury 
 

 
ADAPT-AST Protocol 
 
  
 
viii 
Version 1.1 June 2023 
 
STUDY SUMMARY 
 
The aim of this study is to develop and evaluate ADAPT-AST, an adaptive informatics approach for 
laboratory antimicrobial susceptibility testing (AST) for urinary tract infection (UTI) pathogens 
compared with current practice, as a potential means 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, exposing patients to the associated risks of antimicrobial 
resistance (AMR), drug toxicity and other healthcare-associated infections. 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 train adaptive algorithms (including Bayesian causal inference [BCI] algorithms) to 
predict urine AST results and prioritise testing using patient demographics, prescribing, admission, 
and microbiology laboratory care data. The clinical utility of these 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 Title 
Adaptive Prediction of Antimicrobial Susceptibility and its 
Implementation to Improve the Management of Urinary Tract 
Infection 
Internal ref. no. (or short title) 
ADAPT-AST (Adaptive Antimicrobial Susceptibility Testing) 

 
ADAPT-AST Protocol 
 
  
 
ix 
Version 1.1 June 2023 
 
Study Design 
Retrospective observational study 
Study Participants 
Demographic, antimicrobial prescribing and microbiology 
specimen data from patients >18 years in the catchment area 
served by Liverpool University Hospitals NHS Foundation Trust 
and Liverpool Community Health 
Planned Size of Sample (if 
applicable) 
> 250,000 specimens estimated (the number of patients will be 
fewer because some patients will have had more than one 
specimen sent) 
Outcomes 
Primary outcome: 
 
The number of antimicrobial susceptibility results that 
‘should’ spur action by a clinician on the day that actual first-
line AST results were reported (in green on Figure 5). 
 
Secondary outcomes: 
 
1. The number of days until a result that ‘should’ spur 
action by a clinician (in green on Figure 5). 
2. The number of antimicrobial susceptibility results that 
‘could’ spur action by a clinician on the day that actual 
first-line AST results were reported (in yellow on 
Figure 5). 
3. The number of days until a result that ‘could’ spur 
action by a clinician (in yellow on Figure 5). 
4. The projected health economic cost per specimen, 
including laboratory (e.g., consumable cost) and 
patient (e.g., drug toxicity, clinical failure) measures 
guided by the above criteria. 
 
Follow up duration (if applicable) 
Not applicable 
Planned Study Period 
3 years 
Project Question/Aim(s) 
 
1. Can causal, explainable Bayesian causal inference (BCI) 
approaches be used to develop/(re)validate (train/test) 
clinical prediction models for prediction of urine organism 
antimicrobial susceptibility using NHS care data? 

 
ADAPT-AST Protocol 
 
  
 
x 
Version 1.1 June 2023 
 
2. Can the resulting ADAPT-AST adaptive, informatics-based 
(data-driven and system-wide) approach target AST for 
UTI better than existing approaches can, thereby 
improving timeliness of clinical decision making and saving 
resources through efficiency? 
 
 
FUNDING AND SUPPORT IN KIND 
 
FUNDER(S) 
 
FINANCIAL AND NON FINANCIALSUPPORT 
GIVEN 
Wellcome Trust 
Funding of all materials and staffing to conduct 
study 
 
ROLE OF STUDY SPONSOR AND FUNDER 
 
Study Sponsor (Liverpool University Hospitals NHS Foundation Trust): 
The sponsor will assume overall responsibility for proportionate, effective arrangements being in place 
to set up, run and report the project. They will have overall responsibility for the project, including: 
1. Identifying and addressing problems with the proposal, protocol and applications and ensuring 
that they take into account systematic reviews of relevant existing research evidence and other 
relevant projects in progress, make appropriate use of patient, service user and public 
involvement, and are scientifically sound (e.g. through independent expert review), safe, 
ethical, legal and feasible and remain so for the duration of the project, taking account of 
developments while the project is ongoing 
2. Satisfying itself that the investigators, project team and project sites are suitable 
3. Ensuring that roles and responsibilities of the parties involved in the project and any delegation 
by the sponsor of its tasks are agreed and documented 
4. Ensuring adequate provision is made for insurance or indemnity to cover liabilities which may 
arise in relation to the design, management and conduct of the project 
5. Ensuring appropriate arrangements are made for making information about the project publicly 
available before it starts 

 
ADAPT-AST Protocol 
 
  
 
xi 
Version 1.1 June 2023 
 
6. Agreeing appropriate arrangements for making data and tissue accessible, with adequate 
consent and privacy safeguards, in a timely manner after it has finished 
7. Ensuring arrangements for information about the findings of the project to be made available, 
including, where appropriate, to participants) 
8. Ensuring that, where expected or required, the project has approval from a research ethics 
committee (Whether outright or following a provisional opinion, re-submission, or appeal) and 
any other relevant approval bodies before it begins 
9. Verifying that regulatory and practical arrangements are in place, before permitting the project 
to begin in a safe and timely manner 
10. Putting and keeping in place arrangements for adequate finance and management of the 
project, including its competent risk management and data management 
11. Ensuring that effective procedures and arrangements are kept in place and adhered to for 
reporting (e.g. progress reports, safety reports) and for monitoring the project, including its 
conduct and the ongoing suitability of the approved proposal or protocol considering adverse 
events or other developments. 
 
Study funder (Wellcome Trust): 
The role of the Wellcome Trust as study funder is limited to provision of funds to conduct the project. It 
has no role in the design or implementation of the study. Grant reference number 226691/Z/22/Z. 
 
PROTOCOL CONTRIBUTORS 
 
Dr Alex Howard (Chief Investigator) – writing 
Prof. William Hope (Consultant in Infectious Diseases/Medical Microbiology) – review and editing 
Prof. Iain Buchan (Consultant in Public Health) – review and editing 
Dr David Hughes (Lecturer in Health Data Science) – review and editing 
Prof. Simon Maskell (Professor of Autonomous Systems) – review and editing 
Liverpool AMR Citizens’ Jury – Patient participation and Involvement in general principles of project 
design (see sections 1.3 and 4.4) 
Dr Beth Woods (Lecturer in Health Economics) – review and editing 
Prof Mark Sculpher (Lecturer in Health Economics) – review and editing 
Prof Marta Soares (Lecturer in Health Economics) – review and editing 

 
ADAPT-AST Protocol 
 
  
 
xii 
Version 1.1 June 2023 
 
 
KEY WORDS: 
Urinary tract infection, Antimicrobial Resistance, 
Susceptibility testing, Microbiology, Bayesian causal 
inference 
 
 
 STUDY PLAN 
 
 

 
ADAPT-AST Protocol 
 
  
 
1 
Version 1.1 June 2023 
 
STUDY PROTOCOL 
 
1 
BACKGROUND and RATIONALE 
 
The aim of this study is to develop and evaluate ADAPT-AST, an adaptive informatics (data-driven, 
whole-system) approach to improve decision-making over laboratory antimicrobial susceptibility 
testing (AST) for urinary tract infection (UTI) pathogens compared with current practice, and thereby 
examine the potential to improve patient outcomes, reduce AMR risks and reduce waste of laboratory 
resources. 
 
1.1 
Urinary Tract Infection and Current Laboratory Standard Operating Procedures 
 
Urinary tract infection (UTI) is one of the leading causes of serious community and hospital acquired 
infection(1,2). UTI is therefore a major driver of antimicrobial prescribing in primary and secondary 
care, with inherent risks of antimicrobial resistance (AMR), drug toxicity and other healthcare-
associated infections. The proliferation of AMR is also limiting treatment choices for many UTIs(3). 
Despite this, antimicrobial susceptibility testing (AST) of urine specimens is based on inflexible 
Standard Operating Procedures (SOPs) that are not fit for purpose in the era of AMR (see Figures 1 
and 2). 
 

 
ADAPT-AST Protocol 
 
  
 
2 
Version 1.1 June 2023 
 
 
Figure 1: Current laboratory workflows fail to make use of past information related to the patient or the patient’s immediate 
environment to make decisions about susceptibility testing, which may lead to delays in appropriate decision making. In low 
prevalence AMR settings, testing contributes little to clinical care. 
 
Figure 2: In highly-prelavent AMR settings, a second round of susceptibility testing is required before appropriate 
antimicrobials are identified and therapy is changed. Each additional panel of susceptibility testing typically incurs at least 24 
hours of delay. 
This standard practice results in the provision of susceptibility information for clinicians which is:  
 

 
ADAPT-AST Protocol 
 
  
 
3 
Version 1.1 June 2023 
 
a) Imprecise  
Some laboratories in high income settings can test very large panels of antimicrobials immediately at 
day one (for example, the ThermoFisher® SensititreTM Gram Negative MIC Plate tests 22 
antimicrobials), but more commonly, and particularly in low or middle income (LMIC) settings, a 
selected subset of antimicrobials is tested at day one to save resources (for example, the MAST URI® 
system used in Liverpool Clinical Laboratories [LCL] tests a 7-antimicrobial panel) (4–6). There is 
currently a ‘one-size-fits-all’ approach to this testing, where a standard panel of antimicrobials are 
initially tested against urinary pathogens. These panels do not adapt for previous resistance patterns, 
antimicrobial prescribing, or demographic information despite their potential impact on the likelihood of 
susceptibility to various agents(7). 
 
b) Inefficient  
When the standard panel tested yields no appropriate antibiotics for the clinician to use, a ‘second line’ 
standard panel of antibiotics will then be tested. This process unnecessarily consumes the time of 
laboratory staff and requires additional resources to those used to set up the initial unusable panel of 
antimicrobials. Lack of information related to appropriate oral or outpatient parenteral antimicrobial 
therapy (OPAT) treatment options delays early discharge with patients required to stay in hospital to 
receive high-frequency intravenous (IV) antibiotic regimens(8).  
  
c) Inequitable  
Current approaches of trial-and-error lead to a delay in actionable results being available to the 
clinician, and therefore a delay in the patient being changed to the appropriate antimicrobial therapy. 
The outcome of this may range from unnecessary adverse effects from the initial ineffective agent to 
progression of infection causing admission to hospital with sepsis(9). These complications are likely to 
disproportionately affect those at the highest risk of AMR, in whom the consequences of treatment 
failure are also often the most severe(10). 
 
1.2 
Bayesian causal inference, Prediction and Urinary Tract Infection 
 
Predictive statistical modelling is a key pathway to the achievement of ‘precision’ medicine, i.e., 
medical management tailored to an individual’s specific circumstances. Clinical prediction tools (for 
example, CURB-65 and MASCC [Multinational Association of Supportive Care in Cancer] scores in 

 
ADAPT-AST Protocol 
 
  
 
4 
Version 1.1 June 2023 
 
pneumonia and neutropenic fever respectively) have traditionally been limited to crude risk 
stratification of patients into groups based on statistical analysis of evidence harvested from pre-
existing observational studies(11,12). The quality and quantity of data available in clinical studies with 
relatively strict inclusion and exclusion criteria is often relatively low; this results in clinical tools that do 
not generalise well and cannot reliably be applied outside the study population in which they have 
been developed(13,14). 
Over the past decade, three main developments have increased the potential power of clinical 
prediction tools. Firstly, the proliferation of electronic health records in clinical care has significantly 
increased the volume of structured clinical data that is potentially accessible for statistical 
analyses(15). Secondly relevant statistical and computational method/resources, and their ease of 
application to clinical data/workflows, have advanced(16). Thirdly, adaptive approaches to clinical 
prediction have been developed through a combination of biostatistical, Bayesian causal inference 
(BCI) and clinical informatics research(17). These methods build on traditional statistical approaches, 
allowing a flexibility of methodology that can improve clinical prediction as core business of health 
systems(18). 
Infection management has been of particular interest in the field of precision medicine because the 
threat of sepsis combined with the ongoing proliferation of antimicrobial resistance means that 
antibiotic therapy needs to be not only efficacious, but also prompt and targeted(19). Several studies 
have employed statistical techniques to predict antimicrobial susceptibility testing in UTI. Methods for 
these applications have included logistic regression (LR), decision trees, random forest models, neural 
networks, and extreme gradient boosting. The predictive performance of models versus eventual 
culture(s) and clinical outcomes are evaluated using metrics such as area under the curve receiver 
operating characteristic (AUC-ROC). The objective of these studies, however, has been very different 
to that of ADAPT-AST; this prior work has focused solely on clinical decision support for clinician 
antimicrobial treatment selection at the bedside without consideration of the role of the laboratory. No 
studies to date have used adaptive prediction techniques to help personalise AST(20–24). 
The findings of previous attempts to predict antimicrobial susceptibility algorithmically have been 
promising. The value of a bedside clinical decision tool, however, is affected by what has been 
referred to as the ‘chasm’ between demonstrating algorithmic accuracy and clinical effectiveness. This 
is due to a range of factors, which can only be mitigated by deep a priori knowledge of the clinical 
subject matter; for example, predictions based on AMR datasets are particularly vulnerable to ‘concept 
shift’ of training datasets caused by changes in organism ecology and clinical laboratory processes 
over time(25). Prescriptively directing antimicrobial administration using predictive algorithms is also 
problematic in the clinical setting where there is inherent situational uncertainty and the impact of 

 
ADAPT-AST Protocol 
 
  
 
5 
Version 1.1 June 2023 
 
giving the wrong drug can be very high(26). Existing studies with limited specialist clinical involvement 
also suffer from an inability to separate causation from prediction, a particularly key distinction in 
medical microbiology where the complex host-microbe dynamic means that causal inference is not 
always straightforward(27). 
In the laboratory the potential value of BCI in automating and prioritising specimen workflow has been 
recognised; for instance, the potential of adaptive informatics systems to reduce workload by avoiding 
unnecessary urine culture has been explored(28,29). There has, however, been very little adaptive 
prediction modelling utilising medical microbiology’s unique position at the interface between the 
laboratory and the bedside; guiding diagnostic stewardship using methods which recognise and 
quantify inherent prediction uncertainty could provide a more tangible route to improving clinician 
decision making and driving resource efficiency in the real world(30). 
 
1.3 
ADAPT-AST: An Adaptive Antimicrobial Susceptibility Testing Framework 
 
This study will develop and evaluate ADAPT-AST, an adaptive AST system trained on healthcare data 
from patients over the age of 18 under the care of Liverpool University Hospitals NHS Foundation 
Trust (LUHFT) and/or Mersey Care NHS Foundation Trust Primary Care facilities in the Liverpool 
area. The study will advance understanding of how adaptive informatics approaches can be used to 
better target AST in microbiology laboratories, regardless of the level of resource available. If 
implementable, the immediate potential benefits would be faster appropriate treatment of UTI in 
patients with drug-resistant organisms, and better resource utilisation. The system developed from 
these models would be much more relevant to AMR, targeted and implementable than those 
developed by previous adaptive informatics work in UTI, which has predominantly examined support 
of decisions over whether to culture and which antimicrobial agent to administer. 
The project will examine how current causal BCI approaches perform this adaptive AST system. In a 
time of unprecedented resource pressures globally on healthcare and ever-increasing AMR, ADAPT-
AST could significantly increase the efficiency of urine testing and quality of UTI care in a range of 
healthcare settings. A revised informatics-based strategy would align with both local and global 
healthcare policy objectives, reflected by the UK Government’s 5-year National AMR Plan, NHS 
England’s 2022/23 Care Quality Indicator (CQUIN) on the diagnosis and management of UTI, NHS 
Standard Contract obligations on antimicrobial use aligned to the World Health Organisation’s 
(WHO’s) Access, Watch Reserve (AWaRe) classification, and the UK Government’s Carter Report on 
productivity in NHS hospitals(31–35). In 2022, an AMR Citizens’ Jury was commissioned by the 

 
ADAPT-AST Protocol 
 
  
 
6 
Version 1.1 June 2023 
 
University of Liverpool to explore public and patient perspectives on collaborative monitoring of AMR 
data for applications of this kind in the Liverpool area. The jury was broadly supportive of the use of 
integrated regional pseudonymised data for this process and raised important points about the legal, 
security and quality frameworks that will be required to underpin projects of this kind(36). 
 
2 
PROJECT QUESTIONS/AIMS 
 
The aim of this project is to develop and evaluate adaptive informatics approaches to supporting better 
clinical decisions over laboratory antimicrobial susceptibility testing for UTI pathogens compared with 
current practice, and thereby examine the potential to improve patient outcomes, reduce AMR risks 
and reduce waste of laboratory resources. The project questions are as follows: 
1. Can causal, explainable BCI approaches be used to develop/(re)validate (train/test) clinical 
prediction models for prediction of urine organism antimicrobial susceptibility using NHS care 
data? 
2. Can the resulting ADAPT-AST adaptive, informatics-based (data-driven and system-wide) 
approach target AST for UTI better than existing approaches can, thereby improving timeliness 
of clinical decision making (reducing unnecessary AMR selection pressure) and saving 
resources through efficiency? 
 
 
 
3 
METHODS and THEORETICAL FRAMEWORK 
 
The proposed approach is divided into 3 phases: 
 
3.1 
Data Collection and Integration 
3.1.1 Setting and Eligibility Criteria 
 
LUHFT and/or General Practices (GP) in the area served by Liverpool Clinical Laboratories have been 
chosen as the study setting to reflect the most representative range of patient types in the Liverpool 
area that data can become available for within the time frame of the study; other local Trusts have not 

 
ADAPT-AST Protocol 
 
  
 
7 
Version 1.1 June 2023 
 
been included because they have many different clinical and laboratory information systems that 
would pose insurmountable challenges to extract information in a timely manner. The eligibility criteria 
for the study are: 
 
Inclusion criteria: 
o The specimens for which AST predictions & recommendations will be made are 
urine specimens processed by LCL Microbiology laboratory taken from patients ≥ 
18 years old in LUHFT and/or GP locations that grew organisms within the period 
of the study dataset; these are the only specimens for which AST results will be 
available to train and test ADAPT-AST. Predictions will be made for all urine 
specimen types, including mid-stream urines, catheter specimens of urine and 
nephrostomy urine. 
 
Exclusion criteria: 
o Urine specimens processed by LCL that did not grow organisms within the period 
of the study dataset 
o Urine specimens taken from patients < 18 years old. These will be excluded 
because secondary care data is processed by Alder Hey Children’s Hospital and 
will therefore be missing for this cohort 
o Predictions will be made for asymptomatic bacteriuria screening specimens in 
pregnant women who have had specimens sent from a GP, but not those which 
have been sent from Liverpool Womens’ NHS Foundation Trust (LWfT). The 
reason for this is that some specimens sent from LWH will be from inpatients, for 
whom incomplete potentially important prescribing information close to the time 
point of sampling will be missing due to this data not being integrated. 
o Predictions for non-bacterial organisms grown in urine (i.e., fungi) will not be 
made; the small amount of susceptibility data available and the paucity of 
available agents will result in poorly-predictive algorithms with relatively little 
clinical impact. 
 
3.1.2 Data Parameters of Interest 
 
The pseudonymised data parameters of interest (see section 3.1.4) are listed in Table 1. 
Data Category 
Data Type 
Demographic 
Pseudonymised study number, pseudonymised postcode area, age group, sex 
 

 
ADAPT-AST Protocol 
 
  
 
8 
Version 1.1 June 2023 
 
Admission / 
Consultation 
Admission date & time, inpatient transfer date & time, pseudonymised inpatient transfer 
location type, discharge date and time, discharge location type, vital status 
(alive/deceased), date of change in vital status, general practitioner consultation date/time, 
previous general practitioner coded diagnoses 
 
Prescribing 
Drug name, pseudonymised prescription location type, route of administration, dose, 
dosing frequency, prescription start date & time, dates & times of administration, drug 
allergy (drug), drug allergy (reaction type), allergic reaction date 
 
Microbiology / Pathology 
Microbiology: Date & time of specimen collection, date & time of specimen receipt, data & 
time of specimen authorisation, specimen type, report code, comment code, specimen 
body site, pseudonymised location specimen was taken, pseudonymised specimen 
number, epithelial cells (urine specimens), white cell count (urine specimens), red cell 
count (urine specimens), organism count (urine specimens), organism code, organism 
name, antimicrobial susceptibilities for: phenoxymethylpenicillin, amoxicillin, ampicillin, 
oxacillin, methicillin, mecillinam, amoxicillin-clavulanate, piperacillin-tazobactam, temocillin, 
cefalexin, cefuroxime, cefpodoxime, ceftriaxone, cefoxitin, cefotaxime, ceftazidime, 
ceftazidime-avibactam, ceftolozane-tazobactam, ciprofloxacin, levofloxacin, gentamicin, 
amikacin, tetracycline, tigecycline, meropenem, ertapenem, teicoplanin, vancomycin, 
clindamycin, erythromycin, aztreonam, linezolid, co-trimoxazole, trimethoprim, 
nitrofurantoin, fosfomycin (IV), fosfomycin (oral), mupirocin, daptomycin, rifampicin, 
colistimethate sodium, metronidazole, chloramphenicol, fusidic acid.  
Biochemistry: Date of specimen collection, serum urea, serum creatinine, acute kidney 
injury alert flag 
 
Table 1: Data that will be collected and assessed for inclusion in the predictive model. 
 
The justifications for inclusion of the above data types are: 
 
Demographic data: Demographic features such as city area and sex may indirectly influence 
probability of antimicrobial resistance through factors such as socioeconomic status(37) 
 
Admission/consultation/past diagnoses data: Resistant organism colonisation is more likely in 
patients who have been exposed to healthcare(38). 
 
Prescribing data: Previous antimicrobial exposure is one of the key routes to development of 
drug resistance through induction of organism-level molecular resistance mechanisms and 
population-level selection of resistant organisms. Data on non-antimicrobial drugs will also be 
collected as this may be a surrogate marker of contact with healthcare that influences AMR 
risk e.g., drugs for benign prostatic hypertrophy(39) 
 
Microbiology data: Previous colonisation with an antimicrobial resistant organism is a risk 
factor for subsequent infection with an antimicrobial resistant organism; susceptibility data for 
all types of patient specimen will therefore be analysed (with appropriate algorithmic 
weighting), not just urine specimens(40). Specimen type and location type will also provide 
important information about risk factors (e.g., catheter specimen of urine, urology clinic 
specimens). 

 
ADAPT-AST Protocol 
 
  
 
9 
Version 1.1 June 2023 
 
Protected characteristics were also considered for inclusion to prevent unintentional algorithmic 
discrimination. Characteristics considered were those defined by UK law: pregnancy, marital 
status, race, sexual orientation, gender reassignment, disability, religion and nationality (41,42). 
Data on these characteristics will not be collected because: 1. There are no proven or plausible 
reasons why these characteristics should directly and significantly impact AMR risk; 2. protected 
characteristic data contained within the relevant systems is likely to be incomplete and unreliable; 
3. some protected characteristics (e.g., race or nationality) may increase the risk of participant 
identification due to the rarity of some characteristic types in the Liverpool area. 
 
3.1.3 Sample size 
 
The main sample size consideration is the volume of data required to train the BCI algorithm; this will 
be higher than the sample size required to provide 80% power to detect a significant (e.g., p <0.05) 
difference between groups in the simulated interventional study. For the binary LR techniques like that 
used to develop the predictive model, however, much larger sample sizes are required to provide 
predictive accuracy(43). Given that a range of BCI techniques are likely to be trialled and that most of 
the variation will be taking place in a small proportion of samples, data pertaining to a large number of 
specimens will be required. All eligible patients who have grown an organism in a urine specimen 
during the a five year period from 17th May 2017 will be recruited for the development and testing of 
the predictive algorithm. This period has been chosen to provide a balance between strength of 
model, the amount of data available, computational power required and ‘concept drift’ i.e., changes in 
susceptibility patterns and laboratory practice over time; the most recent AST practice change in LCL 
was implemented 17th May 2017(44). This period will provide an estimated sample size of at least 
250,000 specimens, which will pertain to fewer patients than that number as some will have had urine 
specimens sent more than once. 
 
3.1.4 Pseudonymised Data Access 
 
Patients do not exist solely within a single healthcare setting, but rather move through local healthcare 
systems according to their needs(45). The predictive value of the model will therefore theoretically be 
significantly improved by access to integrated data sets from across Liverpool primary and secondary 
care systems, to give an accurate picture of their care up to the point a urine sample is sent to the 
laboratory. This will be achieved using infrastructure provided by Mersey Care NHS Foundation 

 
ADAPT-AST Protocol 
 
  
 
10 
Version 1.1 June 2023 
 
Trust’s NHS Combined Intelligence for Population Health Action (CIPHA) programme supported by 
Liverpool City Region Civic Data Cooperative (CDC) and the CIPHA’s Data Action Research Group 
(DARG), which are entities based within NHS Cheshire and Merseyside Integrated Care board (ICB) 
(46,47). Following relevant approvals (see section 4.2), researchers will be given access to data within 
the NHS ICB/CIPHA regional secure data environment (SDE) that has been pseudonymised to a level 
that prevents re-identification using any means reasonably likely to be used by either researchers or 
any other person, according to NHS Data Services for Commission Regional Offices (DSCRO) 
standard operating procedures (48): 
 
Removal of patient name and hospital number 
 
Replacement of NHS number with generic study number 
 
Replacement of specimen number with generic study number 
 
Replacement of locations with dummy locations and location type (e.g. medical ward, surgical 
ward, GP practice), removal of patient address and postcode pseudonymisation 
Pseudonymisation code keys are kept within an encrypted drive within the NHS ICB/CIPHA secure 
data environment for 10 years from the end of the study, and are only accessible by ICB/CIPHA 
employees (they are not accessible by the research team). 
 
Access to linked, pseudonymised data is the optimum approach for two reasons: 
 
Regional integration of hospital and community data is critical to this project. CIPHA is an entity 
designed with that sole purpose that has already proven itself effective in the COVID-19 
pandemic(49). Its position as a link between different data controllers to generate social license 
for integrated data uses/intelligence is well placed to integrate hospital and community data 
into single pseudonymised datasets. 
 
LUHFT already has access to the necessary data science resource from University of 
Liverpool without depending on stretched Trust financial and human resource. 
 
3.1.5 Data Cleaning 
 
Once researchers have been given access to the data, cleaning of the dataset will be required 
because: 
 
Intrinsic resistance patterns will be populated in the dataset according to European Committee 
on Antimicrobial Susceptibility Testing (EUCAST) rules to enable proper interpretation (for 

 
ADAPT-AST Protocol 
 
  
 
11 
Version 1.1 June 2023 
 
instance, amoxicillin will not be tested on most Klebsiella species because of intrinsic 
resistance, but this will be a blank in the starting dataset, which will need to be filled in with an 
‘R’)(50) 
 
Organisms susceptible at increased antimicrobial exposure ‘I’ can be interpreted as 
susceptible in the context of urinary tract infection according to EUCAST rules(51). They will 
therefore be reclassified as ‘S’ to facilitate the use of binary LR (see below). 
 
Resistance data from multiple organisms grown in the same specimen will be inputted in the 
same way as for individual growth, but organisms will remain linked by pseudonymised 
specimen number to facilitate trial of composite ‘functional’ resistance of multiple organism 
growth as an input (i.e., if either organism in the specimen is resistant report the specimen as 
‘R’, if both are susceptible report as ‘S’). 
 
Some data elements (for instance specimen site) will have been recorded manually and there 
may therefore be typographical errors that require correction to make them statistically 
processable. 
 
Assessment of data completeness will be required and strategies for managing missing data 
developed. 
 
Other data transformations may need to occur to facilitate processability or economise on 
computing power, e.g., feature scaling or dimensionality reduction(52). 
 
3.2 
Development of an Adaptive Clinical Prediction Model 
3.2.1 Bayesian causal inference Model Training 
 
All data manipulation, analysis and algorithm writing will take place within the CIPHA TRE. The output 
of the adaptive clinical prediction model will be a quantification of the level of statistical probability of 
susceptibility to a specified antibiotic, as displayed in Figure 3. 

 
ADAPT-AST Protocol 
 
  
 
12 
Version 1.1 June 2023 
 
Figure 3: Laboratory workflow and susceptibility testing are reimagined by making use of prior information and prediction 
algorithms. In this situation, 4 antimicrobials are selected for testing from a panel of 8 for ease of illustration, instead of the 
panel of 7 used in the study. 
 
The antimicrobials for which susceptibility probability outputs will be created will be those with AST 
methods available which provide information about the likely activity of antimicrobials which are 
licensed for the treatment of UTI in the UK. These antimicrobials are listed in Table 2 according to 
their WHO AWaRe category.  
 
WHO AWaRe 
Category 
Access 
Watch 
Reserve 
Screening 
 
Antimicrobials 
 
 
Amikacin 
Amoxicillin 
Amoxicillin-Clavulanate 
Cefalexin 
Chloramphenicol 
Gentamicin 
Trimethoprim-Sulfamethoxazole 
Nitrofurantoin 
 
Ceftazidime 
Ciprofloxacin 
Ertapenem 
Erythromycin 
Fosfomycin (oral) 
Meropenem 
Piperacillin-tazobactam 
Teicoplanin 
 
Aztreonam 
Ceftazidime-avibactam 
Ceftolozane-tazobactam 
 
 
Cefoxitin 
Cefpodoxime 
 

 
ADAPT-AST Protocol 
 
  
 
13 
Version 1.1 June 2023 
 
Pivmecillinam 
Trimethoprim 
 
Temocillin 
Vancomycin 
 
Table 2: The group of antimicrobials from which ADAPT-AST will select a panel of 7 to test on each urine specimen, stratified 
by WHO AWaRe category. Cefpodoxime and cefoxitin are also part of the classification, but they are used purely in the 
laboratory for resistance phenotype screening purposes in Liverpool so have been left out of category as screening 
antimicrobials here. 
 
A range of different BCI techniques will need to be trialled and compared for this process using open-
source R, Stan, and Python coding languages on the open-source R-studio and PyCharm proprietary 
integrated development software platforms. A range of different BCI techniques will need to be trialled 
and compared for this process. However, binary LR with Markov Chain Monte Carlo (MCMC) 
estimation of the posterior distribution is likely to be the most appropriate initial trial technique for this 
because: 
 
A predictive model incorporating multiple input variables needs to be developed 
 
A supervised BCI statistical technique can be used here because the labels of the input 
variables are known 
 
The output variable only has two possible outcomes: ‘S’ for susceptible and ‘R’ for resistant 
 
The output variable can be expressed as a probability of outcome ‘S’ or ‘R’ 
 
Parameters will need to be estimated by random sampling from a posterior probability 
distribution which is influenced by random variables and in which there is probabilistic 
dependence between samples(53,54) 
Proposed models will be trained on data from a randomly selected 80% of eligible specimens from the 
retrospective data from between 17th May 2017 and the present. The models will be based on a 
consistent time frame prior to each urine specimen chosen. This is because firstly the influence of 
prior data on current antimicrobial susceptibility probability is likely to diminish with increasing time, 
and secondly it will avoid bias being introduced by more data being available for specimens at the end 
of the dataset(39). Different periods will be trialled for those which give the highest predictive value. 
Wherever possible, algorithms will be designed in a ‘modular’ way with intuitive exchangeable 
components. This will increase the potential scalability and reapplication of algorithms in other settings 
(e.g., LMICs) with minimal algorithm modification. 
 

 
ADAPT-AST Protocol 
 
  
 
14 
Version 1.1 June 2023 
 
3.2.2 Bayesian causal inference Model Testing 
 
The statistical performance of models will then be assessed ‘out of sample’ on the remaining 20% of 
specimens in the same retrospective dataset. The techniques used to assess and refine the initial LR-
derived model will be developed using R, Stan, and Python programming languages, and will consist 
of: 
 
Counterfactual analysis: Input variables will be sequentially removed from and returned to the 
model to ascertain their likely causative impact, and therefore determine whether they should 
be included in the final model(27). Where sufficient data are available, sensitivity analyses will 
also be performed to analyse to ensure that unjustified prediction inequalities do not occur(41). 
 
Scoring rule loss functions: Different performance assessments will be required depending on 
the models used. For LR, measures such as log loss score and AUC-ROC will provide 
measures of model accuracy by quantifying difference between the probability of antimicrobial 
susceptibility predicted by the model and observed antimicrobial susceptibility(55,56). 
 
3.3 
Development and Clinical Evaluation of ADAPT-AST in a simulated clinical setting 
3.3.1 AST Decision Making Algorithm Development 
 
The final stage of the project will be evaluating the potential clinical and resource impact of the testing 
framework on the same 20% of samples used in the testing dataset above. To do this, a clinically 
suitable AST decision making model first will need to be developed. Comparison of a range of 
algorithmic approaches is likely to be required, but the aim of the first decision model trialled will be to 
select 7 antimicrobials for testing (the number in the standard first-line panel currently tested under 
existing LCL SOPs). The antimicrobials that will be initially eligible for AST selection are those for 
which probability calculations will be outputted, listed above in Table 2. 
The first prioritisation approach trialled will be to rank antimicrobial choices in descending order of 
susceptibility uncertainty except for cefpodoxime and cefoxitin, then to apply an algorithm in Figure 4 
to ensure that a mixture of oral and intravenous agents is tested. 

 
ADAPT-AST Protocol 
 
  
 
15 
Version 1.1 June 2023 
 
 
Figure 4: Algorithm used to select 7 antimicrobials from the sorted shortlist.  
*Amoxicillin, Amoxicillin-Clavulanate, Aztreonam, Cefalexin, Ceftazidime, Phenoxymethylpenicillin, Piperacillin-tazobactam. 
 
Cefpodoxime or cefoxitin are not used as treatments in the local area, but resistance to either 
antimicrobial indicates a high probability of resistance mechanisms which preclude use of some beta 
lactams (BL) and beta-lactam-beta-lactamase-inhibitor combinations (BL-BLIC)(57,58). Therefore, the 
algorithm will also be designed for cefpodoxime and cefoxitin to only be tested when their 
susceptibility is sufficiently uncertain (a defined classification threshold will be calculated for this) and 
there is a chance of the affected BLs / BL-BLICs, cefpodoxime and cefoxitin all being tested as part of 
the panel of 7. 
No additional weighting, ranking or exclusion criteria will initially be applied, but depending on 
evaluation based on a priori microbiology / clinical / health economic knowledge, measures may need 
to be taken to further steward the AST choices. These may include exclusion of certain antimicrobial 
choices which are less practically useful for UTI but for which resistance uncertainty may be high (e.g., 
teicoplanin) or algorithmic prioritisation/exclusion/weighting measures for factors such as WHO 

 
ADAPT-AST Protocol 
 
  
 
16 
Version 1.1 June 2023 
 
AWaRe class, nephrotoxicity and risk of Clostridioides difficile diarrhoea. Stakeholder consultation by 
questionnaire will help to inform these sections of the algorithm. Participants in this element of the 
study will be: 
 
General Practitioners 
 
Antimicrobial Pharmacists 
 
Medical Microbiologists 
 
Infectious Diseases Physicians 
 
Acute Medicine Physicians 
 
Accident and Emergency Physicians 
 
Bacteriology senior biomedical scientist 
Invitations to participate will be disseminated by all-user trust email, with compensation offered for 
participant time (within sponsor allowances). Participation will be outside time dedicated for clinical 
care. Formal informed consent of participants will be sought in writing via email, with opportunities for 
participants to ask questions provided via email and Microsoft Teams. The participant consent form is 
enclosed in Appendix 1. Once consent has been obtained, participants will be provided with a 
questionnaire (enclosed in Appendix 2) asking them to rank pseudonymised antimicrobials by order of 
appropriateness based on several factors. These findings will be analysed by linear regression to 
output regression coefficients, which will be considered for incorporation into the selection algorithm. 
3.3.2 Outcomes 
 
Outcomes will be assessed and compared between the 7-result AST panels provided by ADAPT-AST 
algorithms and those provided by standard of care algorithms based on LCL current SOPs as of 2023, 
both of which will be run on every available specimen. Susceptibility information will be assessed 
alongside drug allergies, renal function, drug interactions to characterise results that ‘should’, 
‘could’, and cannot be actioned by the clinician. Computer algorithms will be written to 
perform this assessment in line with the schema in Figure 5 (amoxicillin provided as an 
example). 
 

 
ADAPT-AST Protocol 
 
  
 
17 
Version 1.1 June 2023 
 
 
Figure 5: Algorithmic schema for categorisation of antimicrobial results according to whether the clinician ‘should’, ‘could’, or cannot 
action them. In this example, amoxicillin is the result being assessed and antibiotic ‘X’ refers to any other antibiotic. 
 
 
Primary outcome: 
 
The number of antimicrobial susceptibility results that ‘should’ spur action by a clinician on 
the day that actual first-line AST results were reported (in green on Figure 5). 
 
Secondary outcomes: 
 
5. The number of days until a result that ‘should’ spur action by a clinician (in green on 
Figure 5). 
6. The number of antimicrobial susceptibility results that ‘could’ spur action by a clinician 
on the day that actual first-line AST results were reported (in yellow on Figure 5). 
7. The number of days until a result that ‘could’ spur action by a clinician (in yellow on 
Figure 5). 

 
ADAPT-AST Protocol 
 
  
 
18 
Version 1.1 June 2023 
 
8. The projected health economic cost per specimen, including laboratory (e.g., 
consumable cost) and patient (e.g., drug toxicity, clinical failure) measures guided by 
the above criteria. 
 
3.3.3 Statistical Analysis 
 
A chi square goodness of fit test will be conducted to determine whether the counts of, and number of 
days until, ‘should’ and ‘could’ action results follow a Poisson distribution; if so, a one-sample exact 
Poisson test will be run to determine whether the mean number of ‘should’ and ‘could’ action results 
per specimen and the number of days to these results using ADAPT-AST would be different from the 
expected number using the LCL SOP. The mean financial cost per specimen will be analysed either 
with a single-sample paired t-test or Wilcoxon signed-ranks test depending on whether the data are 
normally distributed. 
 
3.3.4 Potential Limitations of Simulated Interventional Study 
 
Given that the intervention in this study is simulated, there is no risk of harm to patients through 
incorrect testing decisions. However, the lack of intervention means that the impact on the eventual 
prescription cannot be assessed, because prescriber behaviour is not incorporated. Future 
randomised interventional studies will therefore need to be performed to assess impact before the 
approach could be deployed in the clinical setting. Another potential limitation is missing data, either 
from patients who have been transferred from outside of the region, or who have passed through 
areas without electronic prescribing data (e.g., Accident and Emergency, Intensive Care). Although 
this will be a small proportion of the antibiotics prescribed and administered, the potential impact of 
this will need to be considered when the model is explained. An ability to distinguish between transfer 
from out of area (for example, a high AMR setting) and patients presenting to healthcare for the first 
time may also affect the predictive value of an apparent lack of healthcare exposure for a lower AMR 
probability by diluting its effect. Some antimicrobial susceptibility data will also be missing due to 
agents not having been tested; the impact of this will need to be weighted within the process of 
algorithm development. 
 
3.4 
Economic evaluation of ADAPT-AST 
 

 
ADAPT-AST Protocol 
 
  
 
19 
Version 1.1 June 2023 
 
A decision model will be developed to estimate the cost and health outcomes associated with using 
ADAPT-AST to inform clinical decisions about the treatment of UTI in hospitals, compared to standard 
care (as observed within the Liverpool data). The introduction of ADAPT-AST is intended to allow 
patients to receive earlier access to appropriate therapy by better targeting the use of antibiotic 
susceptibility tests. This has the potential to identify early a larger set of antibiotics to which an 
individual is susceptible. This avoids the cost and health impacts of delayed appropriate therapy, and 
by expanding the choice of therapies allows clinical teams to prioritise between these therapies based 
on other important factors: namely toxicity profile; mode of administration; risk of AMR; and anticipated 
differences in efficacy. 
The economic evaluation will compare the AST Decision Making Algorithms evaluated within the 
simulated interventional trial to standard care. A conceptual model will be developed in consultation 
with relevant stakeholders (e.g. clinicians who treat UTIs, microbiologists) to identify how the AST 
information available under different AST Decision Making Algorithms (and under standard care) is 
likely to influence antibiotic prescription, and how this is likely to modify costs and outcomes amongst 
patients treated for UTIs in UK secondary care. The conceptual model will also identify which health 
and cost effects of introducing ADAPT-AST should be prioritised for modelling based on their likely 
importance for patient health and costs. This conceptual model will then form the basis for a more 
detailed model protocol documenting the model structure and data sources for the quantitative model.  
The susceptibility information expected to be available to inform clinical decision making under 
ADAPT-AST and standard care will be obtained from the clinical prediction model and simulated 
interventional study. An important consideration for the economic evaluation will be how this 
information influences clinical decision making. This will be informed by data from the Liverpool area 
dataset where feasible, supplemented by the literature and clinical opinion where necessary. The 
decision model will reflect the short-term costs of introducing ADAPT-AST in terms of clinical time, 
infrastructure and microbiology tests. The long-term cost implications of using ADAPT-AST to guide 
clinical decision making will be reflected accounting for impacts on length of stay and intensity of care 
within hospitals (e.g. requirement for IV antibiotics, significant treatments for antibiotic-related toxicity, 
use of ICU/HDU). The short- and long-term implications of using ADAPT-AST for patient health will be 
quantified as quality-adjusted life years (QALYs) which account for both morbidity and mortality 
effects, and can reflect both efficacy and toxicity effects of antibiotics.  We will separately quantify the 
expected impact of using ADAPT-AST and standard care on the proportion of patients receiving 
treatment with an antibiotic falling under the WHO Access, Watch and Reserve categories. 
Categorisation of patients receiving multiple agents will be established in discussion with experts. 
Cost, quality of life and mortality parameters will be obtained from a combination of the Liverpool area 
dataset, the literature and expert opinion where necessary.  
Model results will be summarised as incremental cost-effectiveness ratios (ICERs) and net health 
effects and an assessment of whether ADAPT-AST represents a cost-effective use of NHS resources 
will be provided, appropriately accounting for health opportunity costs. The sensitivity of the model 
results to important data inputs and assumptions will be tested using deterministic sensitivity analysis 
and probabilistic sensitivity analysis.  
 
 

 
ADAPT-AST Protocol 
 
  
 
20 
Version 1.1 June 2023 
 
4 
ETHICAL AND REGULATORY CONSIDERATIONS 
 
4.1 
Assessment and management of risk 
 
There is no interventional component to the study; there is therefore no risk of direct harm to 
individuals. The risks to consider at study outset are breaches in Information Governance, Data 
Protection and Data Security. The processes for obtaining appropriate regulatory approval and 
ensuring that such breaches do not occur are detailed in sections 4.2.1 and 4.6 of this protocol. Six-
monthly central monitoring milestones of risk will be put in place at which point the risk assessment 
will be updated. Risk assessments will also be updated if a protocol deviation occurs. If data quality or 
quantity is insufficient at the first annual milestone, a substantial amendment will be submitted to 
amend the route of data access. 
 
4.2  
Regulatory Review and Amendments 
 
4.2.1 Ethical and Regulatory Review, Compliance and Reports 
 
The study will be conducted in accordance with the principles of Good Clinical Practice (GCP). Before the 
start of the study, LUHFT Sponsorship, Cheshire and Merseyside ICB/CIPHA data asset and access 
group (DAAG – see Section and research ethics committee (REC) approval will be secured by the CI. 
 
4.2.2 Amendments  
 
A study management group (SMG) will be convened and chaired by the CI with representation from 
across the study team and a public lay representative to provide ongoing management and oversight, 
support amendments and deal with reporting, data issues and protocol deviations. It is the sponsor’s 
responsibility to decide whether an amendment is substantial or non-substantial. If a substantial 
amendment to the protocol is required, the chief investigator (CI) or delegate will submit a valid notice of 
amendment to the sponsor for consideration via the appropriate route. Amendment history will be tracked 
by keeping all protocol versions in a secure password-protected folder in the LUHFT computer system. 
Substantial amendments will not be implemented until all necessary regulatory approvals have been 
formally approved. 
 

 
ADAPT-AST Protocol 
 
  
 
21 
Version 1.1 June 2023 
 
4.3  
Peer review 
 
The study design was peer reviewed by Dr George Drusano, Director of the Institute for Therapeutic 
Innovation at the University of Florida, and Dr Ang Li, Consultant in Medical Microbiology at Liverpool 
University Hospitals NHS Foundation Trust. The reviewer(s) are not involved in the study in any way, 
and have sufficient knowledge of the clinical subject area to consider the protocol’s clinical, 
methodological and service aspects. 
 
4.4  
Patient & Public Involvement 
 
In 2022, an AMR Citizens’ Jury was commissioned by the University of Liverpool to explore public and 
patient perspectives on collaborative monitoring of AMR data for applications of this kind in the Liverpool 
area(36). Jurors were recruited by advertisement and selected to provide a representative cohort of the 
Liverpool city area. They were provided with subject matter background on AMR, data protection and 
information governance by domain experts over several days. They were then asked to discuss and 
provide their assessment of the acceptability, design, management, undertaking, research, analysis and 
dissemination of the results of integrated data systems used to facilitate clinical care, drug development 
and research. The jury was broadly supportive of the use of integrated regional pseudonymised data for 
this process and raised important points about the legal, security and quality frameworks that will be 
required to underpin healthcare data projects of this kind.  
 
4.5 
Protocol compliance  
 
All Protocol deviations will be recorded and reviewed as part of regular SMG meetings and reported to 
Sponsor where deemed appropriate.  
 
4.6 
Consent, data protection and patient confidentiality  
 
Pre-existing Population Health DPIAs have been signed by data controllers for healthcare data to be 
transferred to the ICB/CIPHA secure data environment (SDE) within Cheshire and Merseyside ICB 
firewalls, to perform routine data linkage and pseudonymisation. Only pseudonymised, non-identifiable 
data will be accessed by researchers with approval from ICB/CIPHA DAAG. Patients who opted out of 

 
ADAPT-AST Protocol 
 
  
 
22 
Version 1.1 June 2023 
 
data sharing for purposes other than direct care (Type 1 objections) are excluded from the flow of data 
into the ICB/CIPHA SDE. The study will adhere to LUHFT and CIPHA governance structures for data 
protection and patient confidentiality to ensure compliance to legal standards.  As study sponsor, 
Liverpool University Hospitals NHS Foundation Trust is the overriding governance structure. 
 
4.6.1 Data Access via CIPHA 
The data access and cleaning processes are detailed in Section 3.1. Core project datasets will be 
curated and maintained from the integrated healthcare records detailed in section 3.1.2, where 
available.  An application for access to the data within CIPHA will be made through the CIPHA Data 
Asset and Access Group (DAAG). CIPHA are an NHS Population Health platform established during 
the COVID-19 pandemic which will provide the software, tools, compute, and governance for project 
access. Approval will be obtained from DAAG for CIPHA to permit access of the ADAPT-AST team to 
pseudonymised data (pseudonymised by NHS Data Services for Commission Regional Offices 
[DSCRO] according to standard policies) in the CIPHA secure data environment. This environment is 
administered by CIPHA. CIPHA meets requirements of GDPR, The Data Protection Act 2018 and the 
NHS Data Security and Protection Toolkit, and is certified to ISO27001, ISO9001 and Cyber 
Essentials standards. 
The legal basis for the study under General Data Protection Regulation (GDPR) is Article 9(2)(j), in 
that data processing and access is necessary for the purpose of scientific or statistical purposes in 
accordance with Article 89(1).  Data controllers, the third party (NHS Cheshire and Merseyside 
ICB/CIPHA) and investigators must comply with the requirements of the Data Protection Act 1998 with 
regards to the collection, storage, processing, and disclosure of personal information and will uphold 
the Act’s core principles. Only the minimum amount of data pertaining to the minimum number of 
individuals required to facilitate the study methodology will be used. Pseudonymisation keys are kept 
secure within an encrypted, password protected ICB/CIPHA data warehouse and only accessible by 
an ICB/CIPHA engineer assigned to data linkage and pseudonymisation (not accessible by the 
research team). The details of the pseudonymisation process are listed in Section 3.1.4. The data flow 
diagram for ADAPT-AST is displayed in Figure 6. 
 
  

 
ADAPT-AST Protocol 
 
  
 
23 
Version 1.1 June 2023 
 
 
 
Figure 6: ADAPT-AST data flow diagram. DAAG and LUHFT approvals will facilitate portal access to pseudonymised LUHFT 
and GP data within the NHS CIPHA SDE. 
 
Access to the pseudonymised datasets will be limited to members and collaborators of the study 
group. Access will be password protected by NHS or UoL IT security. The pseudonymised data will be 
stored for 10 years after publication, in accordance with the UK Research and Innovation (UKRI) best 
practice recommendations (UK Research and Innovation, 2018). A data catalogue will be maintained. 
The data custodian will be the CI, and will be responsible for the safe custody, transport and storage 
of any aggregate data resulting from this project.   

 
ADAPT-AST Protocol 
 
  
 
24 
Version 1.1 June 2023 
 
 
4.6.2 
Confidentiality 
The Chief Investigator will preserve the confidentiality of participants taking part in the study and will abide 
by the Data Protection Act 2018 and the UK GDPR as amended from time to time and any successor 
legislation in the UK and any other directly applicable regulation relating to data protection and privacy. 
 
4.6.3  Audits 
The study may be subject to inspection and audit by LUHFT under their remit as sponsor and other regulatory 
bodies to ensure adherence to GCP and the UK Policy Framework for Health and Social Care Research (v3.2 10th 
October 2017).  
 
4.7 
Indemnity 
LUHFT holds Indemnity and insurance cover which will apply to this study. 
4.8 
Access to the final study dataset 
 
The final study dataset will be accessible to the CI and the data scientists performing the data 
analysis, and the CIPHA engineer(s) facilitating access to the pseudonymised data. 
 
5 
END OF STUDY 
 
The end of study will be when analysis of the simulated interventional study data on the final ADAPT-
AST algorithm version is complete, at which point an End of Study Declaration will be completed and 
submitted to the Sponsor. After the end of study is declared no study activity, other than final analysis 
of the data (following ‘lock’ of the study database) and report writing, will be undertaken. 
 
6 
DISSEMINATION POLICY 
 
6.1  
Dissemination policy 
 
The data arising from the study will be owned by the study authors. On completion of the study, the 
data will be analysed and tabulated, and a Final Study Report prepared, which will be accessible on 
the Centres for Antimicrobial Optimisation Network (CAMO-NET) website. Participating investigators 

 
ADAPT-AST Protocol 
 
  
 
25 
Version 1.1 June 2023 
 
can publish any of the study data with permission of the CI. Public participants of the Liverpool AMR 
Citizens’ Jury will be informed of the outcome of the study by provision of the publication. All statistical 
and BCI code used to generate the results will be made available open source, shared via a GitHub 
public repository following journal publication. 
 
6.2  
Authorship eligibility guidelines and any intended use of professional writers 
 
The main study results will be published as soon as a manuscript is completed, in the name of the 
study in a peer-reviewed journal, on behalf of all collaborators. The manuscript will be prepared by a 
writing group composed of the investigators. All investigators will be granted authorship on the final 
study report in line with International Committee for Medical Journal Editors criteria for individually 
named authors and group authorship. 
7 
ARCHIVING 
 
Data and all appropriate documentation will be stored for a minimum of 10 years after the completion 
of the study within the password-encrypted CIPHA secure data environment, including the follow‐up 
period.  
 
8 
REFERENCES 
 
1. 
Smith DRM, Pouwels KB, Hopkins S, Naylor NR, Smieszek T, Robotham JV. Epidemiology and 
health-economic burden of urinary-catheter-associated infection in English NHS hospitals: a 
probabilistic modelling study. Journal of Hospital Infection. 2019 Sep;103(1):44–54.  
2. 
Medina M, Castillo-Pino E. An introduction to the epidemiology and burden of urinary tract 
infections. Ther Adv Urol. 2019 Jan 2;11:175628721983217.  
3. 
Paul R. State of the globe: Rising antimicrobial resistance of pathogens in urinary tract infection. 
J Glob Infect Dis. 2018;10(3):117.  
4. 
Pokharel S, Raut S, Adhikari B. Tackling antimicrobial resistance in low-income and middle-
income countries. BMJ Glob Health. 2019 Nov 10;4(6):e002104.  
5. 
MAST. MAST URI®SYSTEM [Internet]. [cited 2022 Dec 21]. Available from: https://mast-
group.com/uk/products/mast-uri-system/ 
6. 
ThermoFisher. SensititreTM Gram Negative MIC Plate [Internet]. [cited 2022 Dec 21]. Available 
from: https://www.thermofisher.com/order/catalog/product/GN2F 
7. 
Opatowski M, Brun-Buisson C, Touat M, Salomon J, Guillemot D, Tuppin P, et al. Antibiotic 
prescriptions and risk factors for antimicrobial resistance in patients hospitalized with urinary 

 
ADAPT-AST Protocol 
 
  
 
26 
Version 1.1 June 2023 
 
tract infection: a matched case-control study using the French health insurance database 
(SNDS). BMC Infect Dis. 2021 Dec 14;21(1):571.  
8. 
Galar A, Leiva J, Espinosa M, Guillén-Grima F, Hernáez S, Yuste JR. Clinical and economic 
evaluation of the impact of rapid microbiological diagnostic testing. Journal of Infection. 2012 
Oct;65(4):302–9.  
9. 
Zhu H, Chen Y, Hang Y, Luo H, Fang X, Xiao Y, et al. Impact of inappropriate empirical 
antibiotic treatment on clinical outcomes of urinary tract infections caused by Escherichia coli: a 
retrospective cohort study. J Glob Antimicrob Resist. 2021 Sep;26:148–53.  
10. 
Dadgostar P. Antimicrobial Resistance: Implications and Costs. Infect Drug Resist. 2019 
Dec;Volume 12:3903–10.  
11. 
Barlow G, Nathwani D, Davey P. The CURB65 pneumonia severity score outperforms generic 
sepsis and early warning scores in predicting mortality in community-acquired pneumonia. 
Thorax. 2007 Mar 1;62(3):253–9.  
12. 
Klastersky J, Paesmans M, Rubenstein EB, Boyer M, Elting L, Feld R, et al. The Multinational 
Association for Supportive Care in Cancer Risk Index: A Multinational Scoring System for 
Identifying Low-Risk Febrile Neutropenic Cancer Patients. Journal of Clinical Oncology. 2000 
Aug 16;18(16):3038–51.  
13. 
Selby J v., Fireman BH. Building Predictive Models for Clinical Care—Where to Build and What 
to Predict? JAMA Netw Open. 2021 Jan 22;4(1):e2032539.  
14. 
Chen L. Overview of clinical prediction models. Ann Transl Med. 2020 Feb;8(4):71.  
15. 
Wood A, Denholm R, Hollings S, Cooper J, Ip S, Walker V, et al. Linked electronic health 
records for research on a nationwide cohort of more than 54 million people in England: data 
resource. BMJ. 2021 Apr 7;n826.  
16. 
Yamin M. Information technologies of 21st century and their impact on the society. International 
Journal of Information Technology. 2019 Dec 16;11(4):759–66.  
17. 
Shamout F, Zhu T, Clifton DA. Machine Learning for Clinical Outcome Prediction. IEEE Rev 
Biomed Eng. 2021;14:116–26.  
18. 
van Calster B, Verbakel JY, Christodoulou E, Steyerberg EW, Collins GS. Statistics versus 
machine learning: definitions are interesting (but understanding, methodology, and reporting are 
more important). J Clin Epidemiol. 2019 Dec;116:137–8.  
19. 
Fitzpatrick F, Tarrant C, Hamilton V, Kiernan FM, Jenkins D, Krockow EM. Sepsis and 
antimicrobial stewardship: two sides of the same coin. BMJ Qual Saf. 2019 Sep;28(9):758–61.  
20. 
Mancini A, Vito L, Marcelli E, Piangerelli M, de Leone R, Pucciarelli S, et al. Machine learning 
models predicting multidrug resistant urinary tract infections using “DsaaS”. BMC 
Bioinformatics. 2020 Aug 21;21(Suppl 10):347.  
21. 
Kim C, Park RW, Rhie SJ. 174. Development of a Machine Learning Prediction Model to Select 
Empirical Antibiotics in Patients with Clinically Suspected Urinary Tract Infection using Urine 
Culture Data. Open Forum Infect Dis. 2021 Dec 4;8(Supplement_1):S196–S196.  

 
ADAPT-AST Protocol 
 
  
 
27 
Version 1.1 June 2023 
 
22. 
Kanjilal S, Oberst M, Boominathan S, Zhou H, Hooper DC, Sontag D. A decision algorithm to 
promote outpatient antimicrobial stewardship for uncomplicated urinary tract infection. Sci 
Transl Med. 2020 Nov 4;12(568).  
23. 
Tandan M, Timilsina M, Cormican M, Vellinga A. Role of patient descriptors in predicting 
antimicrobial resistance in urinary tract infections using a decision tree approach: A 
retrospective cohort study. Int J Med Inform. 2019 Jul 1;127:127–33.  
24. 
Rich SN, Jun I, Bian J, Boucher C, Cherabuddi K, Morris JG, et al. Development of a Prediction 
Model for Antibiotic-Resistant Urinary Tract Infections Using Integrated Electronic Health 
Records from Multiple Clinics in North-Central Florida. Infect Dis Ther. 2022 Jul 31;  
25. 
Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering 
clinical impact with artificial intelligence. BMC Med. 2019 Dec 29;17(1):195.  
26. 
Lueangarun S, Leelarasamee A. Impact of inappropriate empiric antimicrobial therapy on 
mortality of septic patients with bacteremia: a retrospective study. Interdiscip Perspect Infect 
Dis. 2012;2012:765205.  
27. 
Prosperi M, Guo Y, Sperrin M, Koopman JS, Min JS, He X, et al. Causal inference and 
counterfactual prediction in machine learning for actionable healthcare. Nat Mach Intell. 2020 
Jul 13;2(7):369–75.  
28. 
Burton RJ, Albur M, Eberl M, Cuff SM. Using artificial intelligence to reduce diagnostic workload 
without compromising detection of urinary tract infections. BMC Med Inform Decis Mak. 2019 
Dec 23;19(1):171.  
29. 
Peiffer-Smadja N, Dellière S, Rodriguez C, Birgand G, Lescure FX, Fourati S, et al. Machine 
learning in the clinical microbiology laboratory: has the time come for routine practice? Clinical 
Microbiology and Infection. 2020 Oct;26(10):1300–9.  
30. 
Begoli E, Bhattacharya T, Kusnezov D. The need for uncertainty quantification in machine-
assisted medical decision making. Nat Mach Intell. 2019 Jan 7;1(1):20–3.  
31. 
Lord Carter of Coles. Operational productivity and performance in English NHS acute hospitals: 
Unwarranted variations – An independent report for the Department of Health by Lord Carter of 
Coles – February 2016 [Internet]. 2016 [cited 2022 Oct 28]. Available from: 
https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/fi
le/499229/Operational_productivity_A.pdf 
32. 
Sharland M, Cappello B, Ombajo LA, Bazira J, Chitatanga R, Chuki P, et al. The WHO AWaRe 
Antibiotic Book: providing guidance on optimal use and informing policy. Lancet Infect Dis. 2022 
Nov;22(11):1528–30.  
33. 
NHS England. NHS Standard Contract 2022/23 Technical Guidance [Internet]. 2022 [cited 2022 
Oct 28]. Available from: https://www.england.nhs.uk/wp-content/uploads/2022/03/nhs-standard-
contract-22-23-technical-guidance-march-25-update.pdf 
34. 
NHS England. Commissioning for Quality and Innovation (CQUIN): 2022/23 Guidance 
[Internet]. 2022 [cited 2022 Oct 28]. Available from: https://www.england.nhs.uk/wp-
content/uploads/2022/01/B1477-i-cquin-22-23-march-2022.pdf 

 
ADAPT-AST Protocol 
 
  
 
28 
Version 1.1 June 2023 
 
35. 
HM Government. Tackling antimicrobial resistance 2019–2024: The UK’s five-year national 
action plan. 2019.  
36. 
Center for Democratic Processes. Liverpool AMR Citizens’ Jury Full Report Liverpool 
Antimicrobial Resistance (AMR) Citizens’ Jury [Internet]. 2022 [cited 2022 Oct 28]. Available 
from: https://www.liverpool.ac.uk/media/livacuk/iib/AMR_Full,Report_Final,V1.4.pdf 
37. 
Allel K, García P, Labarca J, Munita JM, Rendic M, Grupo Colaborativo de Resistencia 
Bacteriana, et al. Socioeconomic factors associated with antimicrobial resistance of 
Pseudomonas aeruginosa, Staphylococcus aureus, and Escherichia coli in Chilean hospitals 
(2008–2017). Revista Panamericana de Salud Pública. 2020 Sep 23;44:1.  
38. 
Struelens MJ. The epidemiology of antimicrobial resistance in hospital acquired infections: 
problems and possible solutions. BMJ. 1998 Sep 5;317(7159):652–4.  
39. 
Costelloe C, Metcalfe C, Lovering A, Mant D, Hay AD. Effect of antibiotic prescribing in primary 
care on antimicrobial resistance in individual patients: systematic review and meta-analysis. 
BMJ. 2010 Jun 2;340(may18 2):c2096–c2096.  
40. 
Bonten MJM, Weinstein RA. The Role of Colonization in the Pathogenesis of Nosocomial 
Infections. Infect Control Hosp Epidemiol. 1996 Mar;17(3):193–200.  
41. 
Global Future Council on Human Rights 2016-2018. How to Prevent Discriminatory Outcomes 
in Machine Learning. Geneva; 2018 Mar. Report No.: REF 120318-case 00040065.  
42. 
HM UK Government. Discrimination: your rights: Types of Discrimination ('protected 
characteristics’) [Internet]. [cited 2023 Jan 23]. Available from: 
https://www.gov.uk/discrimination-your-rights 
43. 
Bujang MA, Sa’at N, Tg Abu Bakar Sidik TMI, Chien Joo L. Sample Size Guidelines for Logistic 
Regression from Observational Studies with Large Population: Emphasis on the Accuracy 
Between Statistics and Parameters Based on Real Life Clinical Data. Malaysian Journal of 
Medical Sciences. 2018;25(4):122–30.  
44. 
Lu J, Liu A, Dong F, Gu F, Gama J, Zhang G. Learning under Concept Drift: A Review. IEEE 
Trans Knowl Data Eng. 2018;1–1.  
45. 
Sampson R, Cooper J, Barbour R, Polson R, Wilson P. Patients’ perspectives on the medical 
primary–secondary care interface: systematic review and synthesis of qualitative research. BMJ 
Open. 2015 Oct 15;5(10):e008708.  
46. 
Combined Intelligence for Population Health Action. CIPHA [Internet]. [cited 2023 Jan 23]. 
Available from: https://www.cipha.nhs.uk 
47. 
Civic Data Cooperative. Liverpool City Region Civic Data Cooperative [Internet]. [cited 2023 Jan 
23]. Available from: https://civicdatacooperative.com 
48. 
Microsoft. Azure Data Factory Documentation [Internet]. [cited 2023 Jan 23]. Available from: 
https://learn.microsoft.com/en-us/azure/data-factory/ 
49. 
Green MA, Hungerford DJ, Hughes DM, Garcia-Fiñana M, Turtle L, Cheyne C, et al. Changing 
patterns of SARS-CoV-2 infection through Delta and Omicron waves by vaccination status, 

 
ADAPT-AST Protocol 
 
  
 
29 
Version 1.1 June 2023 
 
previous infection and neighbourhood deprivation: a cohort analysis of 2.7 M people. BMC 
Infect Dis. 2022 Nov 26;22(1):889.  
50. 
European Committee on Antimicrobial Susceptibility Testing. EUCAST Expected Resistant 
Phenotypes [Internet]. 2022 [cited 2022 Dec 14]. Available from: 
https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/2022/Expected_
Resistant_Phenotypes_v1.1_20220325.pdf 
51. 
European Committee on Antimicrobial Susceptibility Testing. EUCAST Redefining susceptibility 
testing categories S, I and R [Internet]. 2019 [cited 2022 Dec 15]. Available from: 
https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/EUCAST_Presentations/2018/
EUCAST_-_Intermediate_category_-_information_for_all.pdf 
52. 
Sarker IH. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN 
Comput Sci. 2021 May 22;2(3):160.  
53. 
Dreiseitl S, Ohno-Machado L. Logistic regression and artificial neural network classification 
models: a methodology review. J Biomed Inform. 2002 Oct;35(5–6):352–9.  
54. 
van Ravenzwaaij D, Cassey P, Brown SD. A simple introduction to Markov Chain Monte–Carlo 
sampling. Psychon Bull Rev. 2018 Feb 11;25(1):143–54.  
55. 
Vasandani J, Bharti S, Singh D, Priyadarshi S. Improving Model Accuracy with Probability 
Scoring Machine Learning Models. In 2021. p. 517–30.  
56. 
Bradley AP. The use of the area under the ROC curve in the evaluation of machine learning 
algorithms. Pattern Recognit. 1997 Jul;30(7):1145–59.  
57. 
Harris PNA, Tambyah PA, Lye DC, Mo Y, Lee TH, Yilmaz M, et al. Effect of Piperacillin-
Tazobactam vs Meropenem on 30-Day Mortality for Patients With E coli or Klebsiella 
pneumoniae Bloodstream Infection and Ceftriaxone Resistance. JAMA. 2018 Sep 
11;320(10):984.  
58. 
European Committee on Antimicrobial Susceptibility Testing. EUCAST guidelines for detection 
of resistance mechanisms and specific resistances of clinical and/or epidemiological importance 
[Internet]. 2013 [cited 2022 Dec 16]. Available from: 
https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Resistance_mechanisms/EUC
AST_detection_of_resistance_mechanisms_v1.0_20131211.pdf 
  
 
 

 
ADAPT-AST Protocol 
 
  
 
30 
Version 1.1 June 2023 
 
9 
APPENDICES 
 
9.1 
Health Professional Questionnaire Consent Form 
 
Title of Study: Adaptive Prediction of Antimicrobial Susceptibility and its Implementation to Improve 
the Management of Urinary Tract Infection 
 
Principal Investigator: Alex Howard 
 
Thank you for responding to the invitation to participate in ADAPT-AST as an expert healthcare 
professional. This questionnaire aims to explore your perception of the importance of various 
attributes of antimicrobials used in the management of urinary tract infection.  Before you decide 
whether to participate, it is important for you to understand the purpose, procedures, and risks of the 
study. Please read this form carefully and ask any questions you may have before agreeing to 
participate.  
 
Purpose of the study:  
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, as a potential means to improve patient outcomes, reduce AMR risks and reduce 
waste of laboratory resources. 
 
Procedures:  
If you agree to participate in this study, you will be asked to complete a questionnaire which will take 
up an estimated 15 to 30 minutes of your time This will be stored alongside a general description of 
your role (no personal identifying information will be stored). 
 
Risks:  
There are minimal risks associated with participating in this study. We will not specifically ask 
questions related to how you personally have managed individual patients. Please inform the 
researcher if you have any questions or concerns before participating in the study. 
 
Benefits:  
This study will give you the opportunity to contribute to medical research and the possibility of gaining 
a deeper understanding of perceptions related to antimicrobial treatment of UTI. 
 
Confidentiality:  
Responses will be confidential and pseudonymised. Only general area of professional expertise (e.g., 
Medical Microbiologist) will be disclosed in study findings. 
 
Voluntary participation:  
Participation in this study is completely voluntary. You may refuse to participate or withdraw your 
participation at any time without penalty or loss of benefits to which you are otherwise entitled. Study 
participation must be outside of time that is rostered for delivery of clinical care. 
 
Contact information:  

 
ADAPT-AST Protocol 
 
  
 
31 
Version 1.1 June 2023 
 
If you have any questions or concerns about the study, you may contact the chief investigator 
alex.howard@liverpoolft.nhs.uk 
 
Consent:  
By signing below, you indicate that you have read and understood the information provided in this 
consent form and agree to participate in this study. 
 
 
  
  
  
  
Participant Signature  
  
  
Date  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9.2 
Appendix 2 – Health Professional Questionnaire 
 

 
ADAPT-AST Protocol 
 
  
 
32 
Version 1.1 June 2023 
 
 
 
 
 
 
9.3  
Appendix 3 - Data Management plan 
 
What stage are you at in this project? 
● Post-award 
 
Which faculty do you belong to? 
● Health and Life Sciences 
 
Do you have, or will you be applying for Ethics approval for your project? 
● Yes 
 
Will be you collecting and storing personal or sensitive data as defined under the terms of 
GDPR? (this includes email addresses, phone numbers, etc) 
● No 
 
Will you require space on the Active DataStore? 
● No 
 
If you are not using the ADS, where will you store your data? 

 
ADAPT-AST Protocol 
 
  
 
33 
Version 1.1 June 2023 
 
● Cloud Store 
 
Will you be depositing your data in an open repository at the conclusion of your project? 
● No 
 
Your research data  
What types of data will be collected or created? 
The project will analyse linked, pseudonymised demographic, past medical, hospital admission, 
general practice consultation, prescribing, microbiology and other laboratory pathology specimen data. 
 
What formats will you use? 
Comma Separated Values files 
 
How much data do you estimate you will be collecting and storing? 
~ 80-100 columns 
~ 250,000-1,000,000 rows 
Estimated file size would be between 3GB and 4GB 
 
Documentation 
Are there any standards for organising, labelling or describing research data in your field of 
research. If so, detail below. 
Systematised Nomenclature of Medicine Clinical Terms (SNOMED-CT), an international clinical 
terminology metadata library. 
 
Ethics and Intellectual Property 
Who owns the data you will be using, creating or collecting? 
Data will be owned by Cheshire and Merseyside Integrated Care Board who manage the CIPHA 
service and access to this dataset. Prior data protection impact assessments and data sharing 
agreements have been completed by University Hospitals NHS Foundation Trust and all participating 
general practices. 
 
Are there any legal, ethical or commercial considerations? If so, how do you propose to deal 
with them? 
The possible risk of reidentification is negligible owing to the pseudonymisation process – project team 
members must take personal responsibility for data security, working in a way that prevents legal and 
procedural breaches.  Project team members will ensure that statistical disclosure controls are in 
place on output data to ensure that reidentification is not possible from results. 
 
Storage and Organisation 
 
Where will the data be stored during your project? If you are not using UoL managed drives, 
explain why. 
The NHS Cheshire and Merseyside Integrated Care board / Combined Intelligence for Public Health 
Action (CIPHA) secure secure data environment. 
 
Are there any security issues relating to the storage of the data? 
The data will be de-identified and will not leave the NHS-managed and provided secure environment. 
Only named and approved ADAPT-AST prroject members will be given access to the data. 

 
ADAPT-AST Protocol 
 
  
 
34 
Version 1.1 June 2023 
 
 
Who else will have access to this data during the project? 
NHS ICB-employed data engineers within the NHS ICB/CIPHA secure data environment. 
 
Data Sharing 
Will you be able to share any of your data in an open access repository? 
Statistically derived aggregate data may be shared in publication. 
 
If not 'open', who could have access to your data and how would this be facilitated? 
Any shareable data could be shared using the Liverpool Data Catalogue, with any share data directed 
to by a data statement in any publications.                      
 
What formats do you anticipate the data will be shared in? 
Not applicable 
 
Long Term Archiving 
Will there be any data that you cannot share but will need to be retained in the long term? 
Pseudonymised study data and code key will be archived within a password-encrypted secure data 
environment within the ICB/CIPHA SDE for 10 years. 
 
 
Where will the data be archived at the end of the project and how long will it be retained? 
The research team will request that data be archived for 10 years within the NHS CIPHA Secure Data 
Environment. 
 
 
What formats do you anticipate the data will be archived in? 
Not applicable - formatting will be set by CIPHA