AI Clinician XP2 - A Study of the AI Clinician Running in Real Time in the ICU

Sepsis Clinical Trial

Official title:

AI Clinician XP2 - A Pilot Study of the AI Clinician Running in Real Time in the ICU

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT05748301
• Other study ID #: 22CX8050
• Status: Not yet recruiting
• Start date: February 28, 2023
• Est. completion date: June 30, 2023

Study information

• Verified date: February 2023
• Source: Imperial College London
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical 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. 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).

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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 64
• Est. completion date: June 30, 2023
• Est. primary completion date: June 30, 2023
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

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

• 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

Exclusion Criteria:

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

Related Conditions & MeSH terms

• Sepsis

Intervention

Other:
• AI Clinician
N/A - Study is observational study testing the feasibility of running the AI Clinician in real time.

Locations

Country	Name	City	State
United Kingdom	Imperial College Hospitals NHS Trust	London
United Kingdom	Univeristy College London Hospitals NHS Foundation Trust	London

Sponsors (3)

Lead Sponsor	Collaborator
Imperial College London	National Institute for Health and Care Research, NHS-X

Country where clinical trial is conducted

United Kingdom, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	6 months
Primary	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.	6 months
Primary	Usability data	Date and time of each system usage (each time it is used by clinicians).	6 months
Primary	Usability data	Data availability: what percentage of essential and optional data fields are available 24/7.	6 months
Primary	Anonymised patients' data	Patient demographics (age in years, gender, primary diagnosis)	6 months
Primary	Anonymised patients' data	Outcomes: organ function (hourly SOFA for up to 48 hours after the decision time), ICU and hospital mortality.	6 months
Primary	Anonymised patients' data	Vital signs and lab values including arterial blood gases.	6 months
Primary	Anonymised patients' data	Doses received of intravenous fluids and vasopressors , Urine output and fluid balance, Presence of sedation, mechanical ventilation, dialysis (binary)	6 months
Primary	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).	6 Months
Primary	Clinician Interviews	At the end of the study 2 participants will be qualitatively interviewed (with audio recording, for transcription +/- thematic analysis)	6 Months