Prediction of Intrahospital Cardiac Arrest Outcomes

NCT05466188 | Completed DMC

• 1 other identifier: PREDIHCA
• Study Type: observational
• Target: 668
• Locations: 1 country
• Sites: 1

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

Outcome Measures

Primary Outcomes (1)

• AUROC for Classification of Outcome CPC

  AUROC for Classification of Outcome CPC

  2006-01-01 to 2018-12-31

Secondary Outcomes (2)

• Confusion Matrix

  2006-01-01 to 2018-12-31

• Descriptive Statistics

  2006-01-01 to 2018-12-31

Study Arms (2)

Outcome CPC Positive

Outcome CPC Positive

Diagnostic Test: CPC

Outcome CPC Negative

Outcome CPC Negative

Diagnostic Test: CPC

Interventions

CPC DIAGNOSTIC_TEST

CPC

Outcome CPC Negative; Outcome CPC Positive

Eligibility Criteria

• Age: 18 Years - 120 Years
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

As described in the inclusion criteria.

You may qualify if:

• 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.

You may not qualify if:

• None.

Sponsors & Collaborators

• Kepler University Hospital: lead

Study Sites (1)

Kepler University Hospital

Linz, Upper Austria, 4021, Austria

Location

MeSH Terms

Conditions

Heart Arrest

Condition Hierarchy (Ancestors)

Heart Diseases; Cardiovascular Diseases

Study Officials

• Thomas Tschoellitsch, MD

  Kepler University Hospital and Johannes Kepler University, Linz, Austria

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 12, 2022
• First Posted: July 20, 2022
• Study Start: June 1, 2022
• Primary Completion: July 31, 2022
• Study Completion: July 31, 2022
• Last Updated: May 3, 2023

Record last verified: 2023-04

Data Sharing

• IPD Sharing: Will not share

Locations

AU (1)