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

NCT ID: NCT05748301

Last Updated: 2023-02-28

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 64 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2023-02-28
• Study Completion Date: 2023-06-30

Brief 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).

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

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: PROSPECTIVE

Study Groups

ICU Clinicians

ICU doctors at the senior registrar, ICU fellow or consultant level will evaluate the AI Clinician system.

AI Clinician

Intervention Type: OTHER

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

Septic patients

Septic patients meeting the inclusion criteria will be included on the system.

AI Clinician

Intervention Type: OTHER

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

Interventions

AI Clinician

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

Intervention Type: OTHER

Eligibility Criteria

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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

National Institute for Health and Care Research

UNKNOWN

Sponsor Role: collaborator

NHS-X

UNKNOWN

Sponsor Role: collaborator

Imperial College London

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Univeristy College London Hospitals NHS Foundation Trust

London, , United Kingdom

Imperial College Hospitals NHS Trust

London, , United Kingdom

Countries

United Kingdom

Facility Contacts

TBC TBC

Role: primary

Matthieu Komorowski, MD, PhD

Role: primary

Other Identifiers

22CX8050

Identifier Type: -

Identifier Source: org_study_id