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Prediction of Intrahospital Cardiac Arrest Outcomes — PREDIHCA  

Cardiac Arrest Clinical Trial

Official title:
Prediction of Intrahospital Cardiac Arrest Outcomes

Clinical Trial Summary

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.

Clinical Trial Description

n/a

Study Design

Related Conditions & MeSH terms

Clinical Trial Details
NCT number NCT05466188
Study type Observational
Source Kepler University Hospital
Contact
Status Completed
Phase
Start date June 1, 2022
Completion date July 31, 2022
View Details
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