Clinical trial NCT05466188: Prediction of Intrahospital Cardiac Arrest Outcomes
Official title: Prediction of Intrahospital Cardiac Arrest Outcomes
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Cardiac Arrest
Lead sponsor: Kepler University Hospital
Sponsor class: OTHER
First posted: 2022-07-20
Enrollment: 668
Sex: ALL
Minimum age (years): 18
Maximum age (years): 120
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: As described in the inclusion criteria.

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.


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.

Primary outcomes:
measure: AUROC for Classification of Outcome CPC
timeFrame: 2006-01-01 to 2018-12-31
Secondary outcomes:
measure: Confusion Matrix
timeFrame: 2006-01-01 to 2018-12-31
measure: Descriptive Statistics
timeFrame: 2006-01-01 to 2018-12-31
Other outcomes:
Locations:
city: Linz
country: Austria
facility: Kepler University Hospital
state: Upper Austria
Study officials:
affiliation: Kepler University Hospital and Johannes Kepler University, Linz, Austria
name: Thomas Tschoellitsch, MD
role: PRINCIPAL_INVESTIGATOR
References: