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Prediction of Intrahospital Cardiac Arrest Outcomes

NCT05466188 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 668

Last updated 2023-05-03

No results posted yet for this study

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

Interventions

DIAGNOSTIC_TEST

CPC

CPC

Sponsors & Collaborators

  • Kepler University Hospital

    lead OTHER

Principal Investigators

  • Thomas Tschoellitsch, MD · Kepler University Hospital and Johannes Kepler University, Linz, Austria

Eligibility

Min Age
18 Years
Max Age
120 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-06-01
Primary Completion
2022-07-31
Completion
2022-07-31

Countries

  • Austria

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05466188 on ClinicalTrials.gov