Prediction of Cardiac Instability in Intensive Care (PRECAIN)

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.

Study Overview

• Status: Completed
• Conditions: Hemodynamics
• Intervention / Treatment: Diagnostic test: Machine Learning Prediction

• Study Type: Observational
• Enrollment (Actual): 3069

Contacts and Locations

Study Locations

• Austria
  • Upper Austria
    • Linz, Upper Austria, Austria, 4021
      • Kepler University Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

As described in the inclusion criteria.

Description

Inclusion Criteria:

• 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.

Exclusion Criteria:

• None.

Study Plan

How is the study designed?

Design Details

• Observational Models: Cohort
• Time Perspectives: Retrospective

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Instability	Diagnostic test: Machine Learning Prediction; Machine Learning Prediction
No Instability	Diagnostic test: Machine Learning Prediction; Machine Learning Prediction

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
AUROC for Classification of Instability	AUROC for Classification of Instability	2018-03-01 to 2020-10-31

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Confusion Matrix	Confusion Matrix Results: true positives, true negatives, false positive, false negatives and values calculated from these results.	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.	Descriptive Statistics (age in years, height in cm, weight in kg, gender as male/female, date of death, standard laboratory measurements (e. g. blood gas analysis, full blood count, liver function tests, kidney function tests), ICD 10-codes associated with the patient's admission, Glasgow Coma Scale) 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

Collaborators and Investigators

• Sponsor: Kepler University Hospital
• Investigators: Principal Investigator: Thomas Tschoellitsch, MD, Kepler University Hospital and Johannes Kepler University, Linz, Austria

Study record dates

Study Major Dates

• Study Start (Actual): June 1, 2022
• Primary Completion (Actual): July 31, 2022
• Study Completion (Actual): July 31, 2022

Study Registration Dates

• First Submitted: July 12, 2022
• First Submitted That Met QC Criteria: July 20, 2022
• First Posted (Actual): July 22, 2022

Study Record Updates

• Last Update Posted (Actual): August 17, 2022
• Last Update Submitted That Met QC Criteria: August 16, 2022
• Last Verified: August 1, 2022

More Information

Terms related to this study

• Other Study ID Numbers: PRECAIN

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: No

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No