Prediction of Cardiac Instability in Intensive Care | NCT05471193

Studies
Prediction of Cardiac Instability in Intensive Care

NCT05471193

Sponsor

Kepler University Hospital

Status

Completed

Last Update Posted

Aug 17, 2022Actual

Enrollment

3,069Actual

Phase

Not provided

Conditions

Hemodynamics

Interventions

Machine Learning Prediction

Countries

Austria

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Aug 2022

Overall Recruitment Status or Expanded Access Status

Completed

Last Known Status

Not provided

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Jun 1, 2022Actual

Primary Completion Date

Jul 31, 2022Actual

Completion Date

Jul 31, 2022Actual

First Submitted Date

Jul 12, 2022

First Submission Date that Met QC Criteria

Jul 20, 2022

First Posted Date

Jul 22, 2022Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Aug 16, 2022

Last Update Posted Date

Aug 17, 2022Actual

Oversight Module

Has Data Monitoring Committee (DMC)

Yes

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

Brief 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.

Detailed Description

Not provided

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Target Follow-Up Duration

Not provided

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

3,069Actual

Arms/Interventions Module

Arm Groups

Label	Type	Description	Intervention Names
Instability			Diagnostic Test: Machine Learning Prediction
No Instability			Diagnostic Test: Machine Learning Prediction

Interventions

Name	Type	Description	Arm Group Labels	Other Names
Machine Learning Prediction	Diagnostic Test	Machine Learning Prediction	Instability No Instability

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
AUROC for Classification of Instability	AUROC for Classification of Instability	2018-03-01 to 2020-10-31

Secondary Outcomes

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.

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

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.

Accepts Healthy Volunteers

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

18 Years

Maximum Age

Not provided

Standard Ages

AdultOlder Adult

Study Population

As described in the inclusion criteria.

Sampling Method

Non-Probability Sample

Contacts/Locations Module

Central Contacts

Not provided

Overall Officials

Name	Affiliation	Role
Thomas Tschoellitsch, MD	Kepler University Hospital and Johannes Kepler University, Linz, Austria	Principal Investigator

Locations

Facility	Status	City	State	ZIP	Country	Contacts
Kepler University Hospital		Linz	Upper Austria	4021	Austria

References Module

Citations

PubMed Identifier	Type	Citation	Retractions
40726206	Derived	Tschoellitsch T, Kaltenleithner S, Maletzky A, Moser P, Seidl P, Bock C, Thumfart S, Giretzlehner M, Hochreiter S, Meier J. Mean arterial pressure is all you need in a machine learning model for mean arterial pressure prediction. Eur J Anaesthesiol. 2025 Dec 1;42(12):1112-1122. doi: 10.1097/EJA.0000000000002238. Epub 2025 Jul 7.