Prediction of Cardiac Instability in Intensive Care

NCT05471193 | Completed DMC

• 1 other identifier: PRECAIN
• Study Type: observational
• Target: 3,069
• Locations: 1 country
• Sites: 1

Brief Summary

A large number of different organ functions are recorded in real time for patients who are monitored in an intensive care unit. On the one hand, the measured values collected in this way are used for continuous monitoring of vital parameters, but they are also evaluated several times a day in order to be able to make decisions regarding further diagnostics and therapy. In the first case, threshold values can be defined, and if these are exceeded or fallen short of, the treatment team is automatically alerted. If these limits are set too liberally, then the alert will only indicate an acute risk to the patient, where extensive pathophysiological changes have already occurred. If the limits are chosen too restrictively, then there are frequent false alarms, since the limits are exceeded in most cases due to natural fluctuation, without this having any pathological value. The consequence is a so-called "alarm fatigue", which in the worst case leads to ignoring correct alarms and thus endangers the patients. By design, all of these readings only show the status quo of a patient. It is the task of the treatment team to predict from the course of these readings whether a threatening situation is developing for the patient. For daily clinical practice, it would be better if dangerous changes in vital signs could be predicted. In this case, it would be possible to intervene therapeutically not only when a dangerous situation has arisen, but to try to avert this situation through adequate measures by changing the therapy strategy. In such a case, the treatment team would no longer be confronted with emergency alarms, but could counteract an impending deterioration with a long lead time. The first approaches for detecting a drop in blood pressure, for example, which are based on simple models, are already in clinical use.

Conditions

Hemodynamics

Outcome Measures

Primary Outcomes (1)

• AUROC for Classification of Instability

  AUROC for Classification of Instability

  2018-03-01 to 2020-10-31

Secondary Outcomes (2)

• Confusion Matrix

  2018-03-01 to 2020-10-31

• Descriptive Statistics This outcome measure will compare the individual feature (e. g. height in cm) in one group vs. the other. Significant difference will be described by p-value.

  2018-03-01 to 2020-10-31

Study Arms (2)

Instability

Diagnostic Test: Machine Learning Prediction

No Instability

Diagnostic Test: Machine Learning Prediction

Interventions

Machine Learning Prediction DIAGNOSTIC_TEST

Machine Learning Prediction

Instability; No Instability

Eligibility Criteria

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

Study Population

As described in the inclusion criteria.

You may qualify if:

• All adult patients that have been treated at the intensive care units of the Kepler University Hospital, Linz, Austria between 2018-03-01 and 2020-10-31.

You may not qualify if:

• None.

Sponsors & Collaborators

• Kepler University Hospital: lead

Study Sites (1)

Kepler University Hospital

Linz, Upper Austria, 4021, Austria

Location

Related Publications (1)

• Tschoellitsch T, Kaltenleithner S, Maletzky A, Moser P, Seidl P, Bock C, Thumfart S, Giretzlehner M, Hochreiter S, Meier J. Mean arterial pressure is all you need in a machine learning model for mean arterial pressure prediction. Eur J Anaesthesiol. 2025 Dec 1;42(12):1112-1122. doi: 10.1097/EJA.0000000000002238. Epub 2025 Jul 7.

  PMID: 40726206 DERIVED

Study Officials

• Thomas Tschoellitsch, MD

  Kepler University Hospital and Johannes Kepler University, Linz, Austria

  PRINCIPAL INVESTIGATOR

Study Design

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

Study Record Dates

• First Submitted: July 12, 2022
• First Posted: July 22, 2022
• Study Start: June 1, 2022
• Primary Completion: July 31, 2022
• Study Completion: July 31, 2022
• Last Updated: August 17, 2022

Record last verified: 2022-08

Data Sharing

• IPD Sharing: Will not share

Locations

AU (1)