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Prediction of Cardiac Instability in Intensive Care

NCT05471193 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 3069

Last updated 2022-08-17

No results posted yet for this study

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

Interventions

DIAGNOSTIC_TEST

Machine Learning Prediction

Machine Learning Prediction

Sponsors & Collaborators

  • Kepler University Hospital

    lead OTHER

Principal Investigators

  • Thomas Tschoellitsch, MD · Kepler University Hospital and Johannes Kepler University, Linz, Austria

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-06-01
Primary Completion
2022-07-31
Completion
2022-07-31

Countries

  • Austria

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05471193 on ClinicalTrials.gov