Assessment of Organ Failure Risk Predictions in ICU (AI4ICU-Obs)

ETH Zurich

Status: Active, not recruiting

Conditions: Renal Failure; Respiratory Failure; Circulatory Failure

Study type: Observational

Funder types: Other

Identifiers

NCT06814327

BASEC-01046

Details and patient eligibility

About

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.

Full description

In this observational study, clinician's (physicians and nurses) assessment of the estimated imminent organ failure risk in an ICU setting are prospectively collected. Circulatory failure is investigated in the primary objective, and respiratory failure, renal failure, and mortality are investigated in secondary objectives. These assessments investigate the predictive performance and influencing factors for clinician prediction. The assessments will be collected in questionnaires and be performed by the clinicians directly involved in the patient treatment and by clinicians who are not actively responsible for the patient treatment. Furthermore, this study aims to benchmark these risk assessments made by healthcare professionals against retrospectively generated AI risk scores for the same patients and timepoints. The AI risk scores will be calculated retrospectively from a set of models from a systematic search of the current literature. The AI models that will be employed for this analysis will be identified as indicated by a systematic review protocol and must satisfy the following two criteria: they do not require any data beyond what is routinely collected during an ICU stay and may be accessed as open source. Such a comparison is vital for the understanding of the relative accuracy and reliability of AI-based predictions in the context of organ failure risk compared to human performance. The data and findings from this study are anticipated to provide evidence for the clinical utility of AI-based risk scores and pave the way for future research into the optimization of AI systems for healthcare applications.

Enrollment: 499 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

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

Exclusion criteria

• 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

Trial design

499 participants in 1 patient group

Adult ICU patients

Description:

Adult ICU patients