NCT05466188: PREDIHCA

Prediction of Intrahospital Cardiac Arrest Outcomes

Completed Last updated 3 May 2023

What this trial tests

trial testing CPC in Cardiac Arrest in 668 participants. Completed in 31 July 2022.

Timeline

1 June 2022

Primary endpoint
31 July 2022

31 July 2022

Quick facts

Lead sponsor	Kepler University Hospital
Status	Completed
Study type	OBSERVATIONAL
Enrollment	668
Start date	1 June 2022
Primary completion	31 July 2022
Estimated completion	31 July 2022
Sites	1 location across Austria

Drugs / interventions tested

• CPC

Conditions studied

• Cardiac Arrest — all drugs for Cardiac Arrest →

Sponsor

Kepler University Hospital

Who can join

Adults 18 to 120, any sex, with Cardiac Arrest. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

Intrahospital cardiovascular arrest is one of the most common causes of death in hospitalized patients. In contrast to extramural cases of cardiovascular arrest, hospitalized patients often have severe medical conditions that can affect the outcome of resuscitation. Nevertheless, survival rates from resuscitation are better in hospitals than outside, because there is often a rapid start of resuscitation measures and predefined resuscitation standards. Regular CPR training and the availability of defibrillators in all bedside units can also positively influence outcome. Despite these many efforts, survival rates, especially of patients with good neurological outcome, remained stable at low levels even within hospitals in recent years and did not improve.

Most outcome parameters are nowadays well known. (e.g., initial rhythm, age, early defibrillation, etc.) Nevertheless, we still do not know today how relevant the corresponding factors actually are, especially in relation to each other. One approach to this might be machine learning methods such as "random forest", which might be able to create a predictive model. However, this has not been attempted to date.

The hypothesis of this work is to find out if it is possible to accurately predict the probability of surviving an in-hospital resuscitation using the machine learning method "random forest" and if particularly relevant outcome parameters can be identified.

Design: retrospective data analysis of all data sets recorded in the resuscitation register of Kepler University Hospital.

Measures and Procedure: Review of the registry for missing data as well as false alarms of the CPR team and, if necessary, exclusion of these data sets; evaluation of the data sets using the machine learning method random forest.

Publications & conference data

No peer-reviewed publications indexed yet for this trial. Completed trials usually publish results within 12-18 months.