SepsisNCT05748301
AI Clinician XP2 - A Study of the AI Clinician Running in Real Time in the ICU
An observational study of Sepsis, run by Imperial College London at 2 sites in United Kingdom.
Trial summary
The cornerstone of sepsis resuscitation is the administration of intravenous fluids (IVF) and/or vasopressors (drugs that squeeze the blood vessels to increase blood pressure) to maintain blood flow to prevent organ failure. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Sepsis
- What is being tested?
- AI Clinician
- How many people
- 64 participants
- Where
- 2 sitesUnited Kingdom
- Study dates
- 28 Feb 2023to 30 Jun 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Clinicians: ICU doctors at the senior registrar, ICU fellow or consultant level Patients: Patients with sepsis in ICU
Who may be able to join
- For patients:
- Adult > 18yr
- Admitted to an ICU in a participating centre
- With early (within 24 of onset) sepsis (as defined by the sepsis-3 definition)
- For full escalation (no ceiling of care, e.g. patient "not for vasopressors")
- Expected to survive more than 24h
Show all 9Show fewer
- Has not opted-out for use of their data for research (NHS and NHS-X website)
- For clinician participants:
- ICU doctors at the senior registrar, ICU fellow or consultant level
Who cannot join
- For patients:
- Not for full active care, e.g. not for vasopressors
- Not expected to survive more than 24hr
- 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 clinician participants:
- Declined participation
Trial locations and contacts
2 sites in 1 country.
United Kingdom2 sites
Univeristy College London Hospitals NHS Foundation Trust
London, NW1 2PG
TBC TBC
David Brealey
Imperial College Hospitals NHS Trust
London, W2 1PG
Matthieu Komorowski, MD, PhD, 07578 975 175, m.komorowski14@imperial.ac.uk
Matthieu Komorowski, MD, PhD
About this study
Official title: AI Clinician XP2 - A Pilot Study of the AI Clinician Running in Real Time in the ICU
The cornerstone of sepsis resuscitation is the administration of intravenous fluids (IVF) 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.
The investigators 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 used is called reinforcement learning, and we call the technology the "AI Clinician".
Read more
In the AI Clinician XP1, the investigators tested the safety of the AI Clinician when running in "shadow mode", i.e. in pseudonymised batches of patient data presented to off-duty ICU clinicians. This enabled the investigators to 1) develop methods and software to connect to real-time electronic health records (EHR); 2) check the safety of the algorithm when used in a contemporary UK ICU patient cohort.
In XP2, the AI Clinician will be running in real-time on dedicated computers at the bedside of actual patients in 4 ICUs across 2 NHS Trusts (Three ICUs at ICHT and one ICU at UCLH).
Read the detailed description
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 (IVF) 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.
The investigators 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 used is called reinforcement learning, and we call the technology the "AI Clinician".
In the AI Clinician XP1, the investigators tested the safety of the AI Clinician when running in "shadow mode", i.e. in pseudonymised batches of patient data presented to off-duty ICU clinicians. This enabled the investigators to 1) develop methods and software to connect to real-time electronic health records (EHR); 2) check the safety of the algorithm when used in a contemporary UK ICU patient cohort.
In XP2, the AI Clinician will be running in real-time on dedicated computers at the bedside of actual patients in 4 ICUs across 2 NHS Trusts (Three ICUs at ICHT and one ICU at UCLH).
This present experiment will test the feasibility of running the AI Clinician in real-time in operational ICUs, in preparation for a future large scale multicentre randomised trial that will test for an improvement in clinically relevant outcomes. At this stage and in the interest of focusing on prescribers first, we will only be testing the use of the system by ICU doctors. Studies with nurses will be conducted in the future.
- Conditions
- Sepsis
- Being tested
- AI Clinician
- Medical subject headings
- Sepsis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI ClinicianOther
N/A - Study is observational study testing the feasibility of running the AI Clinician in real time.
| Study group | Role | What they receive |
|---|---|---|
| ICU Clinicians | ICU doctors at the senior registrar, ICU fellow or consultant level will evaluate the AI Clinician system. Receives: Other: AI Clinician | |
| Septic patients | Septic patients meeting the inclusion criteria will be included on the system. Receives: Other: AI Clinician |
What is being measured
The primary outcome is the main question the study is designed to answer.
Patient identification
Number of subjects identified and presented to a bedside doctor each week in each centre
Number of times the system is used for each patient
Measured: 6 months
System data
System availability: success/failure of generating a response. Delay in generating response when the system is triggered.
Number and nature of technical issues (drop-outs, freezes). 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 on the same research laptop, but separate from the AI Clinician application, so it can't be affected by server outage for example.
Server status, down-time events, planned and unplanned outages. These events can be monitored remotely and logged by the ICT team.
Measured: 6 months
Usability data
Date and time of each system usage (each time it is used by clinicians).
Measured: 6 months
Usability data
Data availability: what percentage of essential and optional data fields are available 24/7.
Measured: 6 months
Anonymised patients' data
Patient demographics (age in years, gender, primary diagnosis)
Measured: 6 months
Anonymised patients' data
Outcomes: organ function (hourly SOFA for up to 48 hours after the decision time), ICU and hospital mortality.
Measured: 6 months
Anonymised patients' data
Vital signs and lab values including arterial blood gases.
Measured: 6 months
Anonymised patients' data
Doses received of intravenous fluids and vasopressors , Urine output and fluid balance, Presence of sedation, mechanical ventilation, dialysis (binary)
Measured: 6 months
Evaluators' data
Clinicians Gender, grade and seniority
At each evaluation of the AI the database will capture and record the following:
Agreement with AI suggested dose: does it appear appropriate, too high or too low? Will you modify your prescription based on the AI suggestion? (yes/no) Would you intervene if the AI dose was to be administered automatically? (yes/no).
Measured: 6 Months
Clinician Interviews
At the end of the study 2 participants will be qualitatively interviewed (with audio recording, for transcription +/- thematic analysis)
Measured: 6 Months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Feb 2023
- Study start28 Feb 2023 (estimated)
- Primary completion30 Jun 2023 (estimated)
- Study completion30 Jun 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for SepsisStudies of the same condition, open and under way.
- More trials run by Imperial College LondonThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 64 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Imperial College London
- Sponsor type
- University, hospital or other
- Collaborators
- National Institute for Health and Care Research, NHS-X
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05748301
- Sponsor's protocol ID
- 22CX8050
- Status last verified
- Feb 2023
- First submitted
- 18 Jan 2023
- Last updated
- 28 Feb 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be shared.
Study at a glance
From the registry record, last updated by the sponsor on 28 Feb 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI Clinician XP2 - A Pilot Study of the AI Clinician Running in Real Time in the ICU
- ClinicalTrials.gov ID
- NCT05748301
- Acronym / sponsor ID
- 22CX8050
- Conditions
- Sepsis
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 64 participants (estimated)
- Lead sponsor
- Imperial College LondonUniversity, hospital or other
- Collaborators
- National Institute for Health and Care Research, NHS-X
- Study start
- 28 Feb 2023 (estimated)
- Primary completion
- 30 Jun 2023 (estimated)
- Study completion
- 30 Jun 2023 (estimated)
- First posted
- 28 Feb 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The cornerstone of sepsis resuscitation is the administration of intravenous fluids (IVF) 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.
The investigators 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 used is called reinforcement learning, and we call the technology the "AI Clinician".
In the AI Clinician XP1, the investigators tested the safety of the AI Clinician when running in "shadow mode", i.e. in pseudonymised batches of patient data presented to off-duty ICU clinicians. This enabled the investigators to 1) develop methods and software to connect to real-time electronic health records (EHR); 2) check the safety of the algorithm when used in a contemporary UK ICU patient cohort.
In XP2, the AI Clinician will be running in real-time on dedicated computers at the bedside of actual patients in 4 ICUs across 2 NHS Trusts (Three ICUs at ICHT and one ICU at UCLH).
Detailed description
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 (IVF) 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.
The investigators 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 used is called reinforcement learning, and we call the technology the "AI Clinician".
In the AI Clinician XP1, the investigators tested the safety of the AI Clinician when running in "shadow mode", i.e. in pseudonymised batches of patient data presented to off-duty ICU clinicians. This enabled the investigators to 1) develop methods and software to connect to real-time electronic health records (EHR); 2) check the safety of the algorithm when used in a contemporary UK ICU patient cohort.
In XP2, the AI Clinician will be running in real-time on dedicated computers at the bedside of actual patients in 4 ICUs across 2 NHS Trusts (Three ICUs at ICHT and one ICU at UCLH).
This present experiment will test the feasibility of running the AI Clinician in real-time in operational ICUs, in preparation for a future large scale multicentre randomised trial that will test for an improvement in clinically relevant outcomes. At this stage and in the interest of focusing on prescribers first, we will only be testing the use of the system by ICU doctors. Studies with nurses will be conducted in the future.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| ICU Clinicians | – | Other: AI ClinicianICU doctors at the senior registrar, ICU fellow or consultant level will evaluate the AI Clinician system. |
| Septic patients | – | Other: AI ClinicianSeptic patients meeting the inclusion criteria will be included on the system. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Clinicians: ICU doctors at the senior registrar, ICU fellow or consultant level Patients: Patients with sepsis in ICU
Key inclusion criteria (9 listed)
- Adult > 18yr
- Admitted to an ICU in a participating centre
- With early (within 24 of onset) sepsis (as defined by the sepsis-3 definition)
- For full escalation (no ceiling of care, e.g. patient "not for vasopressors")
- Expected to survive more than 24h
Key exclusion criteria (7 listed)
- Not for full active care, e.g. not for vasopressors
- Not expected to survive more than 24hr
- 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)
- Declined participation
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI ClinicianN/A - Study is observational study testing the feasibility of running the AI Clinician in real time. | Other | ICU Clinicians, Septic patients |
Primary outcome
Patient identification
Time frame: 6 months
Number of subjects identified and presented to a bedside doctor each week in each centre Number of times the system is used for each patient
System data
Time frame: 6 months
System availability: success/failure of generating a response. Delay in generating response when the system is triggered. Number and nature of technical issues (drop-outs, freezes). 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 on the same research laptop, but separate from the AI Clinician application, so it can't be affected by server outage for example. Server status, down-time events, planned and unplanned outages. These events can be monitored remotely and logged by the ICT team.
Usability data
Time frame: 6 months
Date and time of each system usage (each time it is used by clinicians).
Usability data
Time frame: 6 months
Data availability: what percentage of essential and optional data fields are available 24/7.
Anonymised patients' data
Time frame: 6 months
Patient demographics (age in years, gender, primary diagnosis)
Anonymised patients' data
Time frame: 6 months
Outcomes: organ function (hourly SOFA for up to 48 hours after the decision time), ICU and hospital mortality.
Anonymised patients' data
Time frame: 6 months
Vital signs and lab values including arterial blood gases.
Anonymised patients' data
Time frame: 6 months
Doses received of intravenous fluids and vasopressors , Urine output and fluid balance, Presence of sedation, mechanical ventilation, dialysis (binary)
Evaluators' data
Time frame: 6 Months
Clinicians Gender, grade and seniority At each evaluation of the AI the database will capture and record the following: Agreement with AI suggested dose: does it appear appropriate, too high or too low? Will you modify your prescription based on the AI suggestion? (yes/no) Would you intervene if the AI dose was to be administered automatically? (yes/no).
Clinician Interviews
Time frame: 6 Months
At the end of the study 2 participants will be qualitatively interviewed (with audio recording, for transcription +/- thematic analysis)
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Feb 2023.
- Individual participant dataSharing planned: No. Individual participant data will not be shared.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Imperial College Hospitals NHS Trust
London, United Kingdom
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Sepsis, run by Imperial College London at 2 sites in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 9 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 28 Feb 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Patient identification (time frame: 6 months). The study lists 10 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 28 Feb 2023.