Prediction of Intrahospital Cardiac Arrest Outcomes

NCT ID: NCT05466188

Last Updated: 2023-05-03

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 668 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-06-01
• Study Completion Date: 2022-07-31

Brief 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.

Conditions

Cardiac Arrest

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

Outcome CPC Positive

Outcome CPC Positive

CPC

Intervention Type: DIAGNOSTIC_TEST

CPC

Outcome CPC Negative

Outcome CPC Negative

CPC

Intervention Type: DIAGNOSTIC_TEST

CPC

Interventions

CPC

CPC

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• All adults patients suffering cardiac arrest and having been resuscitated by the medical emergency team of the Kepler University Hospital, Linz, Austria in the period of 2006-01-01 to 2018-10-31.

Exclusion Criteria

• None.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 120 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Kepler University Hospital

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Thomas Tschoellitsch, MD

Role: PRINCIPAL_INVESTIGATOR

Kepler University Hospital and Johannes Kepler University, Linz, Austria

Locations

Kepler University Hospital

Linz, Upper Austria, Austria

Countries

Austria

Other Identifiers

PREDIHCA

Identifier Type: -

Identifier Source: org_study_id