Prediction of Cardiac Instability in Intensive Care

Study Overview
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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.

Official Title
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Prediction of Cardiac Instability in Intensive Care

Conditions
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Hemodynamics

Intervention / Treatment
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* Diagnostic Test: Machine Learning Prediction


Participation Criteria
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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. Exclusion Criteria: None.

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Instability<br> | Diagnostic Test: Machine Learning Prediction<br>* Machine Learning Prediction<br>|
| No Instability<br> | Diagnostic Test: Machine Learning Prediction<br>* Machine Learning Prediction<br>|

What is the study measuring?
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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 |