Prediction of Cardiac Instability in Intensive Care

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

3069 participants

Study Classification

OBSERVATIONAL

Study Start Date

2022-06-01

Study Completion Date

2022-07-31

Brief Summary

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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.

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Detailed Description

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Conditions

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Hemodynamics

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

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Instability

Machine Learning Prediction

Intervention Type DIAGNOSTIC_TEST

Machine Learning Prediction

No Instability

Machine Learning Prediction

Intervention Type DIAGNOSTIC_TEST

Machine Learning Prediction

Interventions

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Machine Learning Prediction

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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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.