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

NCT05748301 | Unknown

• 1 other identifier: 22CX8050
• Study Type: observational
• Target: 64
• Locations: 1 country
• Sites: 2

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).

Conditions

Sepsis

Outcome Measures

Primary Outcomes (10)

• 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

• 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

• Usability data

  Date and time of each system usage (each time it is used by clinicians).

  6 months

• Usability data

  Data availability: what percentage of essential and optional data fields are available 24/7.

  6 months

• Anonymised patients' data

  Patient demographics (age in years, gender, primary diagnosis)

  6 months

• Anonymised patients' data

  Outcomes: organ function (hourly SOFA for up to 48 hours after the decision time), ICU and hospital mortality.

  6 months

• Anonymised patients' data

  Vital signs and lab values including arterial blood gases.

  6 months

• Anonymised patients' data

  Doses received of intravenous fluids and vasopressors , Urine output and fluid balance, Presence of sedation, mechanical ventilation, dialysis (binary)

  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).

  6 Months

• Clinician Interviews

  At the end of the study 2 participants will be qualitatively interviewed (with audio recording, for transcription +/- thematic analysis)

  6 Months

Study Arms (2)

ICU Clinicians

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

Other: AI Clinician

Septic patients

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

Other: AI Clinician

Interventions

AI Clinician OTHER

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

ICU Clinicians; Septic patients

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Clinicians: ICU doctors at the senior registrar, ICU fellow or consultant level Patients: Patients with sepsis in ICU

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Imperial College London: lead
• National Institute for Health and Care Research: collaborator
• NHS-X: collaborator

Study Sites (2)

Univeristy College London Hospitals NHS Foundation Trust

London, NW1 2PG, United Kingdom

Location

Imperial College Hospitals NHS Trust

London, W2 1PG, United Kingdom

Location

MeSH Terms

Conditions

Sepsis

Condition Hierarchy (Ancestors)

Infections; Systemic Inflammatory Response Syndrome; Inflammation; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 18, 2023
• First Posted: February 28, 2023
• Study Start: February 28, 2023
• Primary Completion: June 30, 2023
• Study Completion: June 30, 2023
• Last Updated: February 28, 2023

Record last verified: 2023-02

Data Sharing

• IPD Sharing: Will not share

Locations

GB (2)