Prediction of Cardiac Instability in Intensive Care (PRECAIN)

K

Kepler University Hospital

Status

Completed

Conditions

Hemodynamics

Treatments

Diagnostic Test: Machine Learning Prediction

Study type

Observational

Funder types

Other

Identifiers

NCT05471193
PRECAIN

Details and patient eligibility

About

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.

Enrollment

3,069 patients

Sex

All

Ages

18+ years old

Volunteers

No Healthy Volunteers

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.

Trial design

3,069 participants in 2 patient groups

Instability
Treatment:
Diagnostic Test: Machine Learning Prediction
No Instability
Treatment:
Diagnostic Test: Machine Learning Prediction

Trial contacts and locations

0

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Data sourced from clinicaltrials.gov

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