Assessment of Organ Failure Risk Predictions in ICU

NCT06814327 | Active Not Recruiting

• 1 other identifier: BASEC-01046
• Study Type: observational
• Target: 499
• Locations: 1 country
• Sites: 1

Brief Summary

During this observational study, the investigators aim to assess the ability of ICU clinicians to predict the risk of impending organ failure and retrospectively compare it to the performance of previously published machine learning models. The central hypothesis of this study is that the treating physician can predict impending organ failure in adult ICU patients with similar accuracy as the best previously publishes machine learning models.

Conditions

Circulatory Failure; Respiratory Failure; Renal Failure

Keywords

Machine learning; Intensive care; artificial intelligence

Outcome Measures

Primary Outcomes (1)

• Clinician prediction of circulatory failure within 8 hours compared to published ML models

  This outcome compares the area under the receiver operating characteristic curve (auROC) for two methods of predicting circulatory failure within 8 hours of each assessment time point: (1) ICU clinicians' risk estimates, and (2) previously published machine learning (ML) models applied retrospectively. For each assessment, we compute the auROC separately for clinicians and for the ML model for the same time points and patients. The difference in auROC (clinician minus ML) is the main measure of interest, evaluated under a non-inferiority framework with a margin of 0.025.

  Assessments are collected within the first 72 hours following admission.

Secondary Outcomes (1)

• Clinician prediction of respiratory failure within 24 hours compared to published ML models

  Assessments are collected within the first 72 hours following admission.

Other Outcomes (6)

• Exploratory analysis of clinician prediction of renal failure within 48 hours compared to published ML models

  Assessments are collected within the first 72 hours following admission.

• Exploratory analysis of clinician mortality prediction compared to published ML models

  Assessments are collected within the first 72 hours following admission.

• Exploratory analysis of predictive accuracy of treating physicians versus treating nurses

  Assessments are collected within the first 72 hours following admission.

Study Arms (1)

Adult ICU patients

Adult ICU patients

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Adult Intensive Care Unit at the University Hospital Bern

You may qualify if:

• patient minimum age of 18 years
• emergency admission to the ICU
• arterial line in place

You may not qualify if:

• documented refusal (on the general consent form) to participate to clinical research
• patients with neurologic conditions that impair the patient's level of consciousness (including, but not limited to stroke, traumatic brain injury, intracranial hemorrhage, CNS infections; except polytrauma)
• patients on mechanical circulatory support systems (IABP, VA-ECMO, Impella, VAD) or extracorporeal membrane oxygenation (VV-ECMO) at any time during their ICU stay;
• patients receiving end-of-life care or are admitted for the sole purpose of evaluating organ donation

Sponsors & Collaborators

• ETH Zurich: lead
• Insel Gruppe AG, University Hospital Bern: collaborator

Study Sites (1)

University Hospital Inselspital, Berne

Bern, Canton of Bern, 3010, Switzerland

Location

MeSH Terms

Conditions

Shock; Respiratory Insufficiency; Renal Insufficiency

Condition Hierarchy (Ancestors)

Pathologic Processes; Pathological Conditions, Signs and Symptoms; Respiration Disorders; Respiratory Tract Diseases; Kidney Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases

Study Officials

• Martin Faltys, Dr. med.

  Insel Gruppe AG, University Hospital Bern

  PRINCIPAL INVESTIGATOR

Study Design

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

Study Record Dates

• First Submitted: November 18, 2024
• First Posted: February 7, 2025
• Study Start: November 18, 2024
• Primary Completion: May 15, 2025
• Study Completion (Estimated): May 15, 2026
• Last Updated: August 28, 2025

Record last verified: 2024-11

Locations

CH (1)