AI to Improve Outcome From Traumatic Cardiac Arrest

Overview

This study evaluates data from patients in The Danish Medical Service electronical registry over a 6-year period from 2016 to 2021 with traumatic cardiac arrest. The objective of this study is to use artificial intelligence to evaluate reversible causes and relevant circumstances regarding traumatic OHCA in order to improve treatment and survival.

Background Traumatic cardiac arrest (TCA) is the leading cause of death among young individuals, but in cases where Return of Spontanous Circulation (ROSC) can be achieved, outcome appears to be more favorable than in other causes of cardiac arrest. Data from registry based studies have shown varying survival rates between 1.5 - 31.7%. This wide range could be reflected by heterogeneity in inclusion, study design and health care systems in different countries or regions. Further, this regional diversity might be driven by lack of evidence regarding treatment on-scene, transportation, but also within the prehospital decisions of with-holding treatment. Reversible causes of TCA includes hemorrhage, tension pneumothorax, asphyxia and pericardial tamponade, with uncontrollable hemorrhage accounting for around 50% of the insults. Specific algorithms has been established by the European Resuscitation Council in order to address and handle the reversible causes of TCA. Management of TCA is very time-dependent and depends on advanced prehospital treatment and further specialized care in the setting of a trauma centre.The Danish Emergency Medical System (EMS) introduced a nationwide registry of electronic medical reports in 2016.This report system allows electronic searches and thereby the opportunity to identify subgroups of OHCA's. With aid of machine learning, the hypothesis is that advances text searches will lead to improvement of quality of data from the Danish registry of Out-of-Hospital Cardiac Arrest. With this pioneering approach, this project might contribute to amend the management of TCA. Further, we speculate that this novel data from the EMS reports provide new and central data on reversible causes, which presumably are linked to enhanced survival of TCA. Thus, this study aims to: * Investigate descriptives of traumatic out-of-hospital cardiac arrest * Assess outcomes of cardiac arrests of traumatic origin * Evaluate inter- and intrasystem comparisons Through artificial intelligence, this study proposes an innovative, inexpensive, high-quality approach to substitute the manual validation of the Danish Cardiac Arrest registry, Whereas the registry at its present format requires manual perusal of 9000 cases per year in order to ensure the validity and quality of the national registry. Materials and Methods This registry-based follow-up study includes data from patients in The Danish Medical Service electronical registry over a 6-year period from 2016 to 2021 with traumatic cardiac arrest. OHCA data Data on OHCA's with attempted resuscitation in Denmark have been collected in the electronic based Danish Medical Service reporting system since 2016. The registry covers detailed data including the EMS report. The data consist of executive entries and advanced text searches of prehospital charts in conjunction, augmenting the identification and collecting all OHCA's in Denmark. All cases have been through an elaborate validation process of which all identified events were read through manually. This was conducted by an external verification team to corroborate high quality of data throughout the approximately 5200 cases of OHCA in Denmark annually. Within this practice of verification, supplementary sources of data have been linked to each individual case of OHCA. Further, data consists of death certificates and autopsy records from the Danish cause of death registry. Information of certain interest was survival, localization, initiation of bystander CPR, actions from EMS personnel and cause of death. Identification of Traumatic Cardiac Arrest Within this diverse entity of OHCA's further investigation of subgroups of trauma are required. Based on traditional machine learning, this study targets on extracting defined features from natural language, from the national registry of OHCA using a bag-of-words model. This approach is a natural language processing method for converting text to numbers. The text will be prepared since the method requires conversion into lowercase letters, removal of stop words and punctuation and further standardisation of used acronyms. Finally, words will be reduced to the root, often by removal of suffixes. Using the pre-defined trigger-words this processing allows text mining and thus the ability to derive high-quality information on OHCA related to trauma from the registry. The identified cases will be coupled to the national registry after external manual validation. Variables included * Age: Age will be defined as the subject age at the time of the event. * Gender: Gender will be defined as either male, female or undetermined, and derived from personal identification numbers. Gender will be characterized based on EMS-charts in those cases without an identification number. * Initial rhythm: The initial rhythm will be defined as the first rhythm observed by EMS personnel, and categorized as either shockable, non-shockable (asystole), non-shockable (other) or undetermined. * Location of Incidence: Location will be characterized as either residential area, outdoors and nature, private home or other. * Observation of occurrence: Cardiac arrests will be defined as either unwitnessed, bystander witnessed or EMS-witnessed. * Cardio-pulmonary-resuscitation (CPR): CPR will include bystander-initiated CPR and EMS treatment with CPR. * Defibrillation and use automatic external defibrillators (AEDs): Defibrillation will include defibrillation by bystanders and/or EMS personnel. Use of AED's includes using the device also in the case it did not deliver shocks. * EMS-response time: This will be defined as the time between a dispatcher receiving the emergency call and the arrival of the first EMS-personnel. * Hospitalization: Hospitalization will be considered as either; transported to hospital or declared dead by EMS-personnel. * Return-of-spontaneous-circulation (ROSC): ROSC will be classified as the achievement of ROSC anytime between recognition of the event and termination (defined as either hospital admission or declaration of death by EMS-personnel). * State at hospital admission: State of the case upon arrival at the hospital will be defined as either ROSC or ongoing CPR. * Survival: Survival will be defined as ROSC at the time of hospital admission, additionally 30-day survival will be included, this derived from data from the National Patient Registry. * Airway management: This includes the airway manoeuvres performed on each case. It is defined by listing the use of airway adjuncts such as nasopharyngeal airway, oropharyngeal airway as well as endo-tracheal intubation, supraglottic airway device and isolated bag-valve-mask ventilation. Analysis All data will be pseudo-anonymized, and all analyses will be accomplished on an aggregated nationwide level. Data is collected using the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) statement. The objective of this study is to clarify etiology, descriptive data and prehospital interventions of OHCA's related to trauma. Secondary an update of the annual incidence and survival rate is carried out. Descriptive statistics includes the above-mentioned variables labelled with absolute numbers and percentages. Comparative analyses will be carried out using non-parametric testing to examine subgroups. Forward logistic regression analysis will be performed for multivariate analysis. Within this multivariate logistic regression analysis both known and unknown variables will be processed. Odds ratio for survival will be calculated stratified by etiology, localization, bystander and EMS-actions. Data storage Data is stored on secure drive according to the regional instructions for safe conduct of data management. Ethical considerations GDPR will be followed according to danish law and the study will be registered at the Danish Data Protection Agency, capital region of Denmark. Since it is a registry-based study, no ethical approval is required. Perspectives This study gives unique information on TCA in the general population of Denmark; the descriptive statistics provides relevant data based on a reviewed, high-quality database. Furthermore, throughout analyses, a better understanding of the preceding circumstances and etiology might contribute to improve handling this type of arrests. Lastly, it proposes improvement of quality and development of observational health research. Publication The final results are targeted for publication in an international peer reviewed journal. Participation as coauthors will be decided according to the Vancouver criteria or acknowledged for providing access to data. All Danish regional EMS regions will receive this manuscript prior to publication for eventual comments. List of Abbreviations TCA Traumatic Cardiac Arrest ROSC Return of Spontanous Circulation OHCA Out-of-Hospital Cardiac Arrest EMS Emergency Medical Service STROBE STrengthening the Reporting of OBservational studies in Epidemiology CPR Cardiopulmonary Resuscitation GDPR General Data Protection Regulation

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Outcomes