Prediction of Cardiac Instability in Intensive Care

Hemodynamics Clinical Trial

— PRECAIN  

Official title:

Prediction of Cardiac Instability in Intensive Care

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05471193
• Other study ID #: PRECAIN
• Status: Completed
• Start date: June 1, 2022
• Est. completion date: July 31, 2022

Study information

• Verified date: August 2022
• Source: Kepler University Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 3069
• Est. completion date: July 31, 2022
• Est. primary completion date: July 31, 2022
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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.

Related Conditions & MeSH terms

• Hemodynamics

Intervention

Diagnostic Test:
• Machine Learning Prediction
Machine Learning Prediction

Locations

Country	Name	City	State
Austria	Kepler University Hospital	Linz	Upper Austria

Sponsors (1)

Lead Sponsor	Collaborator
Kepler University Hospital

Country where clinical trial is conducted

Austria, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	AUROC for Classification of Instability	AUROC for Classification of Instability	2018-03-01 to 2020-10-31
Secondary	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
Secondary	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