Passive Evaluation in Operational Environment of the AI Clinician Decision Support System for Sepsis Treatment
Conditions: Sepsis
Sponsor: Imperial College London

 
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AI Clinician XP1 
 
 
 
 
Passive evaluation in operational environment of the AI Clinician 
decision support system for sepsis treatment 
 
 
 
 
Version 1.1 – 18th March 2022 
 
 
 
 
MAIN SPONSOR: Imperial College London 
FUNDERS: NIHR/NHS-X 
 
IRAS Project ID:297800 
REC reference: 21/SC/0383 
NCT number: NCT05287477 
 
 
 
Protocol authorised by: 
 
Name & Role 
Date 
Signature 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
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Study Management Group 
 
Chief Investigator: Anthony Gordon 
 
Co-investigators: Aldo Faisal, Matthieu Komorowski 
 
Study Management: Elizabeth Fagbodun & Farah Al-Beidh 
 
 
 
 
Clinical Queries 
 
Clinical queries should be directed to Matthieu Komorowski who will direct the query 
to the appropriate person 
 
 
Sponsor 
 
Imperial College London is the main research Sponsor for this study.  For further 
information regarding the sponsorship conditions, please contact the Head of 
Regulatory Compliance at: 
 
 
 
Research Governance and Integrity Team 
Imperial College London and Imperial College Healthcare NHS Trust 
Room 215, Level 2, Medical School Building 
Norfolk Place 
London, W2 1PG 
Tel: 0207 594 9459/ 0207 594 1862 
http://www3.imperial.ac.uk/clinicalresearchgovernanceoffice  
 

 
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Funder 
 
NIHR/NHS-X 
 
This protocol describes the AI Clinician study and provides information about 
procedures for entering participants.  Every care was taken in its drafting, but 
corrections or amendments may be necessary. These will be circulated to investigators 
in the study.  Problems relating to this study should be referred, in the first instance, to 
the Chief Investigator.  
 
This study will adhere to the principles outlined in the UK Policy Frame Work for Health 
and Social Care Research. It will be conducted in compliance with the protocol, the 
Data Protection Act and other regulatory requirements as appropriate.  
 
Table of Contents                                                             Page No 
1. 
INTRODUCTION 
5 
1.1. 
BACKGROUND 
5 
1.2. 
RATIONALE FOR CURRENT STUDY 
6 
2. 
STUDY OBJECTIVES 
7 
3. 
STUDY DESIGN 
7 
3.1. 
STUDY OUTCOME MEASURES 
10 
4. 
PARTICIPANT ENTRY 
10 
 
Error! Bookmark not defined. 
4.1. 
INCLUSION CRITERIA 
10 
4.2. 
EXCLUSION CRITERIA 
11 
4.3. 
WITHDRAWAL CRITERIA 
11 
5. 
ADVERSE EVENTS 
11 
5.1. 
DEFINITIONS 
Error! Bookmark not defined. 
5.2. 
REPORTING PROCEDURES 
Error! Bookmark not defined. 
6. 
ASSESSMENT AND FOLLOW-UP 
11 
7. 
STATISTICS AND DATA ANALYSIS 
11 
8. 
REGULATORY ISSUES 
12 
8.1. 
ETHICS APPROVAL 
12 
8.2. 
CONSENT 
12 
8.3. 
CONFIDENTIALITY 
13 
8.4. 
INDEMNITY 
14 
8.5. 
SPONSOR 
14 
8.6. 
FUNDING 
14 
8.7. 
AUDITS 
14 
9. 
STUDY MANAGEMENT 
14 
10. 
PUBLICATION POLICY 
14 
11. 
REFERENCES 
14 
 
 
 

 
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GLOSSARY OF ABBREVIATIONS/DEFINITIONS 
Human 
evaluators 
ICU doctors (any level) assessing the AI in the background, 
without informing clinical practice 
ICU 
Intensive Care Unit 
Sepsis-3 
The current international definition of sepsis. Requires the 
presence of a suspected infection (administration of antibiotics 
and sampling of bodily fluids for bacteriological culture) and some 
degree of organ failure (an increase in SOFA score of 2 points or 
more) 
SOFA 
Sequential Organ Failure Assessment – an organ failure score 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
KEYWORDS 
Sepsis, decision support system, artificial intelligence, machine learning, electronic 
health record 
 
 
 
 
 
 
 
 
 

 
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STUDY SUMMARY 
 
TITLE Passive evaluation in operational environment of the AI Clinician decision 
support system for sepsis treatment 
DESIGN Prospective multi-centre observational study in 4 ICUs from 2 different NHS 
Trusts. 
AIMS Confirm operational robustness and technical reliability of the AI Clinician 
software when operating in real-time in an operational ICU environment. 
OUTCOME MEASURES System / usability data; anonymised patient time series data; doses of 
treatment chosen by clinicians; assessment data by human evaluators. 
POPULATION Patients: adult patients in intensive care; human evaluators: ICU doctors of 
any grade. 
ELIGIBILITY Adult patients with sepsis in intensive care 
DURATION Up to 24 months. 
 
 
 
1. INTRODUCTION  
 
1.1. 
BACKGROUND 
 
Sepsis is life-threatening organ dysfunction due to severe infection and affects 
250,000 patients annually in the UK (pre-COVID-19), of whom 48,000 die. In addition, 
virtually all COVID-19 intensive care unit (ICU) deaths had sepsis. It is a leading cause 
of death and the most expensive condition treated in hospitals. It was recognised as 
a top research priority by the James Lind Alliance, a partnership of patients and 
clinicians to prioritise the most pressing unanswered questions facing the NHS. 
The cornerstone of sepsis resuscitation is the administration of intravenous fluids 
and/or vasopressors (drugs that squeeze the blood vessels to increase blood 
pressure) to maintain blood flow to prevent organ failure. However, there is huge 
uncertainty around the individual dosing of these drugs in an individual patient, partially 
due to high sepsis heterogeneity. The current guidelines provide recommendations at 
a population-level but fail to individualise the decisions. Wrong decisions lead to poorer 
outcomes and increased ICU-resource use. A tool to personalise these medications 
could improve patient survival. 
We have developed a new method to automatically and continuously review and 
recommend the correct dose of these medications to doctors, which was created using 
artificial intelligence (AI) techniques applied to large medical databases. The method 
we used is called reinforcement learning. In this framework, we model patients with 
sepsis in the ICU as belonging to a large number of possible disease states, and we 
analyse what interventions are likely to help them transition to healthier states, and 

 
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eventually to survival. We demonstrated in our initial publication that the value of the 
AI selected strategy was on average reliably higher than human clinicians. In a large 
validation cohort independent from the training data, mortality was lowest in patients 
where clinicians’ actual doses matched the AI decisions: mortality rates rose, in a dose 
dependent manner, as the clinicians’ actual decisions diverged from the AI decisions. 
We have estimated that our AI algorithm could reduce mortality by 10% (in relative 
terms), which represents over 1,000 lives saved annually in the UK and would scale 
to hundreds of thousands of lives worldwide. Now, we intend to start clinical testing of 
this AI technology in the UK. 
The envisioned end-product will be a piece of software that will be accessible by 
clinicians (ICU doctors initially, then eventually to ICU nurses as well) at the bedside 
in intensive care. This software will be connected to the electronic patient record, 
which will be fed to the AI algorithm. In return, the AI will identify where the patient sits 
in the array of possible disease states, and which actions (a dose of intravenous fluids 
and vasopressors) are most likely to be beneficial. 
First, we will develop this software tool, capable of processing patient data within the 
electronic patient record of NHS hospitals in real-time to suggest a course of action. 
We will start by evaluating and refining this tool in simulation studies. We will then test 
the AI tool in two NHS Trusts in a “shadow mode” when the result is not provided to 
duty clinicians in charge of patient care (the purpose of this application). This will 
allow comparison of actual decisions made and recommended decisions from the AI 
system. Later on (this is not the purpose of this application), in the second stage 
of the clinical evaluation, we will display the recommendations to clinicians to assess 
the acceptability of the tool to clinicians and also confirm the technical feasibility to 
inform future large scale clinical trials. 
The long-term expected benefits of this project are numerous: improved patient 
survival, reduced use of precious intensive care resources and reduction in healthcare 
costs. 
 
 
1.2. 
RATIONALE FOR CURRENT STUDY 
 
 
Research question: 
How well does our real-time AI-based system for fluid and vasopressor therapy for 
sepsis in the ICU perform in an operational clinical environment? 
 
Hypotheses: 
• We are able to run the system with high availability, reliability and acceptable 
lag (near real-time) at the bedside. 
• The quality of AI suggestions, assessed by off-duty clinicians, is appropriate. 
• Patients’ outcomes are better when actual doctors’ decisions match the 
suggested AI decisions (which remain hidden from on duty doctors). 
 

 
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2. STUDY OBJECTIVES 
 
 
Primary objective: 
• Confirm operational robustness and technical reliability of the software when 
operating in real-time in an operational ICU environment. We will measure 
indicators of system reliability and availability (e.g. what percentage of time is the 
system available, at different times of the day and night, on weekdays versus 
weekends, etc.) and the lag of the system (e.g. delay between data recorded at the 
bedside and model producing an output). 
 
Secondary objectives: 
• In the shadow mode study, human evaluators will assess the AI decisions: off-
duty clinicians will evaluate the clinical correctness/appropriateness of AI 
suggested decisions. The evaluators will classify the AI suggested actions in 
different categories, such as “likely safe”, “likely inappropriate”, “possible too 
low”, or “possibly too high”. 
• We will document learning points and develop standard operating procedures 
for deploying the tool in various hospital systems 
• Finally, we will estimate patients’ outcomes (organ failure, ICU mortality and 
hospital mortality) if the AI decisions were followed and compare them to actual 
outcomes (which were linked with human decisions). 
 
 
 
3. STUDY DESIGN 
 
 
This study is a prospective multi-centre observational study that will include four 
intensive care units from two different NHS Trusts. 
 
The duration of the study will be up to 24 months. 
We will include 100 to 300 adult patients with early sepsis in ICU (within 24h of ICU 
admission). This number is a pragmatic, realistic estimate of how many patients will 
satisfy the inclusion criteria in the study period (assuming a conservative 60 
admissions per month on each unit (reality is closer to 80-100 pre-COVID-19), and 
20% sepsis at admission), and will allow us to deliver on the study objectives.  
 
With regards to human assessors, we will include 6 to 15 ICU doctors, mixing senior 
registrars, ICU fellows and consultants.  
 
The “system” will be a purpose built webapp accessed from a dedicated laptop, 
connected to an NHS ICT server, with secure login. It will be linked to a duplicate of 
the Philips ICCA database (Imperial College Healthcare NHS Trust) or the EPIC EHR 
(UCLH) which contains ICU patient data in near real time (estimated lag of 5 to 15 

 
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minutes). The system will be running the AI Clinician algorithm, which processes ICU 
patient data and outputs a suggested dose of intravenous fluids and vasopressors. 
Four times a day (every 6 hours), the system analyses newly admitted ICU patients 
and identify those who fulfil the sepsis-3 criteria. If they do, they will be added to a list 
of patients for potential inclusion that will be accessible from within the software (and 
assessed by human evaluator for enrolment). 
 
The system will contain several tabs:  
1- Patient screening. It displays a list of patients screened and highlighted by the 
system as fulfilling sepsis-3 criteria. The user (human evaluator) has the option 
to include them into the study, after verification of the inclusion/exclusion criteria 
(see sections 4.2 and 4.3 for these criteria). 
2- Individual patient data. This screen contains a drop-down list of patients 
included in the study. After selecting one, the data time series of this individual 
patient are displayed here, along with the doses of fluids and vasopressors 
actually given by human “on-duty” doctors and nurses. 
3- Evaluation tab. This is where the evaluation of the AI and human doctors’ 
decisions takes place. The option to scroll through the various previous time 
points is provided. The evaluation by the human evaluator takes place in 
several 
subsequent 
steps: 
First, 
the 
human 
evaluator 
makes 
a 
recommendation based on patient data alone (data point #1). Then, the human 
evaluator is presented with the AI recommendation (data point #2), along with 
other information (confidence in the suggested decision, explanation of why this 
action was recommended). They “rate” the AI decisions, by classifying the AI 
suggested actions in different categories, such as “likely safe”, “likely 
inappropriate”, “possible too low”, or “possibly too high”. Finally, the human 
evaluator is presented with the actual decision (made by human clinicians, data 
point #3) and the subsequent patient evolution (at least a few hours, but the 
final outcome of ICU death or survival will be presented if available). The human 
evaluators rate the actual decisions using the same scale, e.g. “likely safe”, 
“likely inappropriate”, etc. There will be a free-text box to enable the user to 
provide feedback on their decisions. 
 
 
The software will be accessed on the dedicated laptop from within the NHS hospitals 
by off-duty clinicians enrolled in the study, whom we call “human evaluators”. To 
explore system’s usability around the clock, there will be no restrictions with regards 
to timing of the research, so the human evaluators will be able to conduct the research 
at any time and on any day of the week. 
 
We intend to conduct the study during 1-hour sessions, during which the users will 
conduct these 2 actions: 
1) Identify new patients for inclusion from a pre-screened list  
2) Assess multiple decisions made on multiple included patients. Based on a 
conservative number of 5 patients with sepsis on each unit at any point in time, and 
between 4 - 10 sessions per user, we will gather a maximum of 600 to 1,500 data 
points (100 data points per user for 6 to 15 users). We expect to include an estimated 

 
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100 to 300 separate patients across 4 sites (based on 20 patients per month per unit 
and 27 months of total study period over the 4 units, which gives 540 potential eligible 
patients). This will enable to complete the study objectives. 
 
Data about the system’s operational reliability will be automatically collected: 
- 
Time elapsed between a clinical event (e.g., administration of a fluid bolus) and 
the record appearing in the database. This exploits the property of some clinical 
events to be time-stamped to quantify the lag in the system. 
- 
Date and time of all system accesses 
- 
Server status, down-time events, planned and unplanned outages. These 
events can be monitored remotely and logged by the ICT team. 
 
An independent online form (survey-type) will be created to log all technical issues 
that the users may encounter (e.g., system unavailable, login issues etc). This survey 
will be kept separate from the system, so it can’t be affected by server outage for 
example.  
 
The data about the system’s operational reliability and the technical issues reports do 
not contain patient sensitive information and will be available to researchers at Imperial 
College London, in the research groups of Prof Anthony Gordon and Prof Aldo Faisal. 
 
After running the study for 3 months on each study site, an interim analysis will be 
performed to test the data quality. A pseudo-anonymised dataset including the fields 
described in section 3.1 “study outcome measures” of all patients included so far in 
the study will be created. All patient identifiable information will be removed, patient 
NHS number will be replaced by an ad-hoc random number, and the mapping between 
the patients’ true identity and identifiers will be kept on the NHS server. No patient 
health records will leave NHS systems. 
 
The final analysis will occur at the end of the study period (after month 18, in August 
2022), once all data has been collected. The pseudo-anonymised dataset will be 
transferred encrypted to an Imperial College London secured server, within the Faisal 
Lab. Researchers in Anthony Gordon and Aldo Faisal groups will then conduct various 
modelling and analyses to assess the AI suggested decisions. 
 
This trial will be conducted across four intensive care units: Imperial College 
Healthcare Trust - Charing Cross Hospital, Hammersmith Hospital, St Marys Hospital 
and University College London Hospital. All four units are capable of treating critically 
ill patients.  
 
Routine clinical data (demographics, diagnosis, severity of illness, presence and 
duration of mechanical ventilation, ICU and hospital length of stay and hospital 
mortality, see appendix for more specific information) will be collected until hospital 
discharge from the recruiting centre. All outcome data will be collected from hospital 
databases including microbiology results. 
 

 
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To determine how the tool was implemented at each site, a trained researcher will 
accompany all site initiation visits to make observations and take field notes regarding 
discussions of planning and implementation at site. 
 
 
 
 
 
3.1. 
STUDY OUTCOME MEASURES 
 
 
 
Data of interest 
Study outcome measures 
System / usability 
data 
 
Data availability: what percentage of essential and optional 
data fields are available 24/7. 
System availability: delay in generating response 24/7. 
Number and nature of technical issues (drop-outs, freezes). 
Anonymised 
patients’ data 
 
Patient demographics (age in years, gender, primary 
diagnosis) 
Outcomes: organ function (hourly SOFA), ICU and hospital 
mortality  
Bedside physicians’ 
data 
 
Doses administered to patients of intravenous fluids and 
vasopressors, which are action chosen by on-duty clinicians 
 
Evaluators’ data 
(the doctors 
assessing the AI in 
the background) 
Evaluator’s grade and seniority 
A custom-made interface linked to a database will capture 
and record the following: 
- 
What dose they would recommend, before and after 
seeing the AI suggested dose. 
- 
Agreement with AI suggested dose: does it appear 
appropriate, too high or too low? 
 
 
 
4. PARTICIPANT ENTRY 
 
 
4.1. 
INCLUSION CRITERIA  
 
For patients: 
Adult patient > 18 years old 
Admitted to an intensive care unit 
Likely or confirmed diagnosis of sepsis as per sepsis-3 definition (as defined in the 
glossary) 
ICU length of stay > 24h 
 
For evaluators: 

 
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ICU doctors at the senior registrar, ICU fellow or consultant level 
 
4.2. 
EXCLUSION CRITERIA 
 
For patients: 
Not for full active care, e.g. not for vasopressors 
Not expected to survive more than 24h 
Elective surgical admission (these patients are regularly on antibiotics but given as a 
prophylaxis, with no sepsis) 
Opted-out for use of their data for research (NHS and NHS-X website) 
 
For both patients and evaluators: 
Declined participation 
No patient consent is required  
 
4.3. 
WITHDRAWAL CRITERIA  
 
For evaluators: 
Requested withdrawal from study. Human evaluators may request withdrawal by 
contacting the study coordinators. In the event that an evaluators request withdrawal 
from the study, no further data will be collected from them. 
 
 
5. ADVERSE EVENTS  
 
It is an observational study, so there is no direct risk for patients or the human 
evaluators. Adverse events involve data security questions, in particular data breach 
and loss of personal identifiable information. These aspects are addressed in section 
8.3 “confidentiality”. 
 
 
6. ASSESSMENT AND FOLLOW-UP  
 
 
There will be no follow-up. The study ends at each participating site, at the time of 
hospital discharge of the last recruited patient. 
 
 
 
7. STATISTICS AND DATA ANALYSIS 
 
 
Data and all appropriate documentation will be stored for a minimum of 10 years after 
the completion of the study, including the follow-up period.   
 
 
Data of interest 
Statistical analyses 

 
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System / usability 
data 
Simple descriptive statistics (e.g. average system lag, 
number of technical issues per week…) 
Anonymised 
patients’ data 
 
Simple descriptive statistics of the patient characteristics. 
Comparison of the training and prospective cohorts (simple 
parametric and non-parametric tests comparing 
demographics, severity of illness, etc). 
Evaluators’ data 
(the doctors 
assessing the AI in 
the background) 
Descriptive statistics from evaluators data: 
Agreement between initial human evaluator doses (data 
point #1) and AI decisions (data point #2), and between AI 
decisions (data point #2) and actual doses given to patients 
(data point #3). 
Proportion of AI decisions (data point #2) classified as 
appropriate / possibly too low / possibly too high 
Proportion of human decisions (data point #3) classified as 
appropriate / possibly too low / possibly too high 
AI decisions 
evaluation 
Quantitative analyses of AI decisions: estimate patients’ 
outcomes if the AI decisions were followed and compare 
them to actual outcomes (which were linked with human 
decisions). We will use 2 methods: 1) compare short-term 
outcomes between model-concordant treatment and model-
non-concordant treatment, with confounder adjustment; 2) 
off-policy policy evaluation of the AI policy in the prospective 
ICHT cohort. 
 
 
 
8. REGULATORY ISSUES  
 
8.1. 
ETHICS APPROVAL  
The Study Coordination Centre has obtained approval from the Health Regulatory 
Authority (HRA). The study must also receive confirmation of capacity and capability 
from each participating NHS Trust before accepting participants into the study or any 
research activity is carried out. The study will be conducted in accordance with the 
recommendations for physicians involved in research on human subjects adopted by 
the 18th World Medical Assembly, Helsinki 1964 and later revisions. 
 
8.2. 
CONSENT   
 
Consent will be sought from participating clinicians. A full explanation will be provided 
in the form of a written explanation covering the details of the study, their role and any 
potential risks of participating. Any remaining questions that participants have after 
reviewing the written documentation will be answered by the investigator team.  
 
All clinician participants are free to withdraw from the study at any stage upon written 
request to the investigator team without giving reasons. Their data will remain in the 
study unless they request for it to be removed. 
 

 
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Consent to enter the study must be sought from each participant only after a full 
explanation has been given, an information leaflet offered, and time allowed for 
consideration.  Signed participant consent should be obtained.  The right of the 
participant to refuse to participate without giving reasons must be respected. All 
participants are free to withdraw at any time from the protocol treatment without giving 
reasons and without prejudicing further treatment. 
 
Potential patients will only be identified by local NHS staff at the recruiting site and 
they will maintain the same duty of confidentiality owed to all patients by medical and 
nursing staff. All personal identifiable data will be kept within the NHS hospital and 
routine NHS databases. Pseudonymisation of patient data will be completed by the 
trusts research informatics team who form a part of the extended care team. Because 
of all of the above we will not seek specific patient consent for this phase of the study.  
 
Approval from the sponsor (Imperial College London / NHS Healthcare Trust) data 
controller has also been sought. 
 
 
 
8.3. 
CONFIDENTIALITY  
 
The Chief Investigator will preserve the confidentiality of participants taking part in the 
study as per the Data Protection Act. 
 
The General Data Protection Regulation (2016/679) broadly defines personal data 
breaches as a security incident that leads to the confidentiality, integrity or availability 
of personal data being affected. There will be a personal data breach whenever any 
personal data is lost, destroyed, corrupted or disclosed; if someone accesses the data 
or passes it on without proper authorisation; or if the data is made unavailable, for 
example, when it has been encrypted by ransomware, or accidentally lost or 
destroyed. Personal data breaches will be immediately reported to the Sponsor, the 
Data Protection Officer, and RGIT. Any breaches will be documented in the TMF/ISFs, 
and will follow Sponsor/Data Controller reporting processes. 
 
We have put measures in place to limit the risk of data confidentiality breach. 
Identifiable data will not leave trust servers. The study will only use pseudo-
anonymised data so confidentiality of patients and clinician participants will be 
maintained, however the Principal Investigator will ensure to preserve the 
confidentiality of participants and fulfil transparency requirements under the General 
Data Protection Regulation (GDPR). 
 
Data and all appropriate documentation will be stored for a minimum of 10 years after 
the completion of the study. Private health information such as patient identity, date of 
birth and date of death are not required. The lookup table mapping new patient 
identifiers and their initial identity will only be kept within trust systems. Identifiable 
information of clinician participants will likewise only be kept within trust systems. 
 

 
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Data will be kept encrypted in a secured lab accessible only (via swipe and pin code 
access) to members of the Imperial College Critical Care Research lab (under Prof. 
Anthony Gordon). The computer itself where the data will be kept is password 
protected, and its hard drives are encrypted. 
 
 
8.4. 
INDEMNITY 
Imperial College London holds negligent harm and non-negligent harm insurance 
policies which apply to this study. 
 
8.5. 
SPONSOR  
Imperial College London will act as the main Sponsor for this study.  Delegated 
responsibilities will be assigned to the NHS trusts taking part in this study.   
 
8.6. 
FUNDING  
NIHR-NHS X are funding this study. Participants will be remunerated up to £100 for 
time spent in the study.  
 
8.7. 
AUDITS   
The study may be subject to audit by Imperial College London under their remit as 
sponsor and other regulatory bodies to ensure adherence to GCP and the UK Policy 
Frame Work for Health and Social Care Research.  
 
 
9. STUDY MANAGEMENT  
 
The day-to-day management of the study will be co-ordinated through Dr Matthieu 
Komorowski. 
 
10. PUBLICATION POLICY  
 
The results of the project will be published in a peer-reviewed, high impact, and ideally 
fully open access journal.  
 
11. REFERENCES  
 
 
M. Komorowski, L. A. Celi, O. Badawi, A. C. Gordon, and A. A. Faisal, “The Artificial 
Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care,” 
Nature Medicine, vol. 24, no. 11, p. 1716, Nov. 2018. 
 
G Kennedy, B Gallego,Clinical prediction rules: A systematic review of healthcare 
provider opinions and preferences, International Journal of Medical Informatics, 
Volume 
123, 
2019, 
Pages 
1-10, 
ISSN 
1386-5056, 
https://doi.org/10.1016/j.ijmedinf.2018.12.003. 
 

 
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Appendix 1. Summary of investigations, treatment and assessments 
 
Investigations / Assessments  
 
 
Screening  
At each assessment session 
System Set-Up 
 
 
Medical History  
X 
 
Clinical Evaluation 
X 
X 
Operational Reliability 
checks 
 
X 
Informed consent 
(Clinician) 
X 
 
 
 
 
 
Appendix 2. Summary of required data fields. 
 
 
Category 
Items 
Format 
Unit 
Comment 
Cohort selection 
Patient 
unique 
identifier 
Integer/code - 
Cohort selection 
Date and time of 
administration of 
antibiotics 
Date 
- 
Any oral of IV antibiotic (no 
topical, e.g. cream or eye 
drops) 
Cohort selection 
Date and time any 
blood or urine 
culture sent for 
MCS 
Date 
- 

 
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Demographics 
Age 
Continuous 
days or 
years 
Demographics 
Gender 
Binary 
- 
Demographics 
Weight at the time 
of ICU admission 
Continuous 
kg 
Demographics 
Readmission 
to 
intensive care 
Binary 
- 
Has this patient already 
been admitted to ICU during 
this hospital stay ? 
Demographics 
Comorbidities/past 
medical 
history  
(ICD codes) 
Free text 
- 
if possible! 
Demographics 
Delay 
between 
hospital admission 
and ICU admission 
Date 
hours 
Demographics 
ICU and hospital 
mortality 
Binary 
- 
Vital signs 
Glasgow 
coma 
scale 
Continuous 
- 
Vital signs 
Heart rate 
Continuous 
bpm 
Vital signs 
Systolic 
blood 
pressure 
Continuous 
mmHg 
Vital signs 
Mean 
blood 
pressure 
Continuous 
mmHg 
Vital signs 
Diastolic 
blood 
pressure 
Continuous 
mmHg 
Vital signs 
Respiratory rate 
Continuous 
bpm 
Vital signs 
SpO2 
= 
pulse 
oxymetry 
Continuous 
% 
Vital signs 
Temperature 
Continuous 
Celsius 
Lab values 
Potassium 
Continuous 
meq/L 
Lab values 
Sodium 
Continuous 
meq/L 
Lab values 
Chloride 
Continuous 
meq/L 
Lab values 
Glucose 
Continuous 
mg/dL 
Lab values 
BUN = Blood Urea 
Nitrogen = Urea 
Continuous 
mg/dL 
Lab values 
Creatinine 
Continuous 
mg/dL 
Lab values 
Magnesium 
Continuous 
mg/dL 
Lab values 
Calcium 
Continuous 
mg/dL 
Lab values 
Ionised Calcium 
Continuous 
mg/dL 
Lab values 
CO2 : total CO2 
Continuous 
meq/L 
Lab values 
SGOT 
Continuous 
u/L 
Lab values 
SGPT 
Continuous 
u/L 
Lab values 
Total_bilirubin 
Continuous 
mg/dL 
Lab values 
Albumin 
Continuous 
g/dL 
Lab values 
Hb = Haemogobin 
Continuous 
g/dL 
Lab values 
WBC count 
Continuous 
E9/L 

 
Research Governance 
           and Integrity Team 
 
 
 
 
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Lab values 
Platelets count 
Continuous 
E9/L 
Lab values 
PTT = Partial 
Thromboplastin 
Time 
Continuous 
s 
Lab values 
PT = prothrombin 
time 
Continuous 
s 
Lab values 
INR = International 
Standardized Ratio 
Continuous 
- 
Blood gas 
Arterial_pH 
Continuous 
- 
Blood gas 
paO2 
Continuous 
mmHg 
Blood gas 
paCO2 
Continuous 
mmHg 
Blood gas 
Arterial base 
excess 
Continuous 
meq/L 
Blood gas 
Arterial lactate 
Continuous 
mmol/L 
Blood gas 
HCO3 = 
bicarbonate 
Continuous 
meq/L 
Ventilation 
parameters 
Mechanical 
Ventilation 
Binary 
- 
Is the patient mechanically 
ventilated? 
Ventilation 
parameters 
Interface for 
oxygen 
administration 
categorical 
- 
E.g. face mask, nasal 
cannulae… 
Ventilation 
parameters 
Flow of oxygen 
Continuous 
L/min 
Helps estimating the 
effective FiO2 
Ventilation 
parameters 
FiO2 
= 
inspired 
fraction of oxygen 
Continuous 
- 
May not be documented if 
there is a flow of O2 in 
L/min 
Medications 
and 
fluid balance 
Intravenous Fluid 
intake 
Continuous 
mL 
IV crystalloids given for 
resuscitation 
Medications 
and 
fluid balance 
Vasopressor 
Continuous 
mcg/kg/min 
Includes infusions of 
noradrenaline, adrenaline, 
phenylephrine, vasopressin, 
metaraminol, dopamine 
Other interventions 
Renal replacement 
therapy 
Categorical 
- 
any indication that the 
patient is undergoing acute 
dialysis for acute renal 
failure 
Other interventions 
Sedation 
Continuous 
- 
any continuous infusion of 
propofol, midazolam, 
fentanyl, remifentanil, 
thiopental, atracurium, 
rocuronium 

 
Research Governance 
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Medications 
and 
fluid balance 
Urine output 
Continuous 
mL 
Includes items such as -- 
"Foley" -- "Void -- "Condom 
Cath" -- "Ileoconduit" -- 
"Suprapubic" -- 
"Nephrostomy" -- "Ureteral 
Stent” 
Medications 
and 
fluid balance 
Cumulated 
fluid 
balance since ICU 
admission 
Continuous 
mL 
Preadmission data 
Total IV fluid intake 
prior 
to 
ICU 
admission 
Continuous 
mL 
Preadmission data 
Total urine output 
prior 
to 
ICU 
admission 
Continuous 
mL