Clinical Validation of Cardiac Arrest Hospital Prognosis (CAHP) Score and MIRACLE2 Score to Predict Neurologic Outcomes after Out-of-Hospital Cardiac Arrest

MIRACLE2 score validation for neuroprognostication after out-of-hospital cardiac arrest: a district general hospital experience

INTRODUCTION AND OBJECTIVES

Decision-making regarding prognosticating out-of-hospital cardiac arrest (OHCA) remains challenging at the front door. The MIRACLE2 score provides a simple and practical tool for early neuroprognostication to aid decision-making. The study aims to validate the MIRACLE2 score in a district general hospital (DGH).

MATERIAL AND METHODS

This is a retrospective analysis of the patients with OHCA and return of spontaneous circulation (ROSC) in the community who attended the cardiac catheter laboratory in a DGH between 1 September 2021 and 25 September 2023. Patients with a Glasgow Coma Scale of 15/15 after ROSC were excluded. Medical notes were examined, and the MIRACLE2 score was calculated and correlated with the Cerebral Performance Category (CPC) on discharge and compared with other neuroprognostication risk scores. The primary outcome was poor neurological recovery at hospital discharge, and the secondary outcome included poor neurological recovery at 6 months.

RESULTS

A total of 46 patients satisfied the study criteria, of which 43 (93%) were males. The median age was 64; half had a CPC of 0-2 on discharge and at 6 months. The MIRACLE2 score was low (0-2) in 14 patients (30%), intermediate (3-4) in 16 patients (35%) and high (≥5) in 16 patients (35%). The MIRACLE2 score performed well in neuroprognostication as a MIRACLE2 score ≥5 had a positive predictive value of 91%, while a MIRACLE2 score ≤2 had a negative predictive value of 92% for poor neurological outcomes at discharge.

CONCLUSIONS

The MIRACLE2 score provides an accurate and practical neuroprognostication tool in patients with OHCA of cardiac origin presenting to this DGH.

Out-of-hospital cardiac arrest research progress and challenges in Lithuania

Aim

To present the evolution of data collection and analysis methods of out-of-hospital cardiac arrest (OHCA) research in Kaunas city, Lithuania, and discuss the challenges encountered.

Methods

In late 2016, data collection began with a focus on 2016 data, following the Utstein 2014 template. The Kaunas city emergency medical services (EMS) station, which has a protocol dispatch system, pioneered the use of electronic submissions for the national EMS data collection form, making the research process more efficient. Most OHCA patients were treated in a tertiary university hospital which transitioned to electronic health record system in 2017, improving data accessibility. Throughout data collection significant efforts have been directed towards enhancing process efficiency and simplifying operations. As a result, the expansion of the Excel data table led to the creation of the ''resuscitation registry form' 'in 2018, which became operational in 2020.

Results

The collected data were used in several observational studies to identify and better outcomes.

Conclusion

Engaging in research on OHCA is difficult and poses many unique challenges owning to the urgency of the condition, complexity of legal and ethical considerations, and implications of any research intervention. The lack of a connection between the EMS and hospital electronic health record systems poses challenges for data collection. Legal and ethical complexities, including mandatory initiation of resuscitation and challenges in obtaining ethical approval, highlight the need for a comprehensive framework. This study aims transition the accumulated expertise into a nationally recognised registry for OHCA.

Development of a Predictive Model for Survival Over Time in Patients With Out-of-Hospital Cardiac Arrest Using Ensemble-Based Machine Learning

As of now, a model for predicting the survival of patients with out-of-hospital cardiac arrest has not been established. This study aimed to develop a model for identifying predictors of survival over time in patients with out-of-hospital cardiac arrest during their stay in the emergency department, using ensemble-based machine learning. A total of 26 013 patients from the Korean nationwide out-of-hospital cardiac arrest registry were enrolled between January 1 and December 31, 2019. Our model, comprising 38 variables, was developed using the Survival Quilts model to improve predictive performance. We found that changes in important variables of patients with out-of-hospital cardiac arrest were observed 10 minutes after arrival at the emergency department. The important score of the predictors showed that the influence of patient age decreased, moving from the highest rank to the fifth. In contrast, the significance of reperfusion attempts increased, moving from the fourth to the highest rank. Our research suggests that the ensemble-based machine learning model, particularly the Survival Quilts, offers a promising approach for predicting survival in patients with out-of-hospital cardiac arrest. The Survival Quilts model may potentially assist emergency department staff in making informed decisions quickly, reducing preventable deaths.

Prediction performance of scoring systems after out-of-hospital cardiac arrest: A systematic review and meta-analysis

INTRODUCTION

Ongoing changes in post resuscitation medicine and society create a range of ethical challenges for clinicians. Withdrawal of life-sustaining treatment is a very sensitive, complex decision to be made by the treatment team and the relatives together. According to the guidelines, prognostication after cardiopulmonary resuscitation should be based on a combination of clinical examination, biomarkers, imaging, and electrophysiological testing. Several prognostic scores exist to predict neurological and mortality outcome in post-cardiac arrest patients. We aimed to perform a meta-analysis and systematic review of current scoring systems used after out-of-hospital cardiac arrest (OHCA).

MATERIALS AND METHODS

Our systematic search was conducted in four databases: Medline, Embase, Central and Scopus on 24th April 2023. The patient population consisted of successfully resuscitated adult patients after OHCA. We included all prognostic scoring systems in our analysis suitable to estimate neurologic function as the primary outcome and mortality as the secondary outcome. For each score and outcome, we collected the AUC (area under curve) values and their CIs (confidence iterval) and performed a random-effects meta-analysis to obtain pooled AUC estimates with 95% CI. To visualize the trade-off between sensitivity and specificity achieved using different thresholds, we created the Summary Receiver Operating Characteristic (SROC) curves.

RESULTS

24,479 records were identified, 51 of which met the selection criteria and were included in the qualitative analysis. Of these, 24 studies were included in the quantitative synthesis. The performance of CAHP (Cardiac Arrest Hospital Prognosis) (0.876 [0.853-0.898]) and OHCA (0.840 [0.824-0.856]) was good to predict neurological outcome at hospital discharge, and TTM (Targeted Temperature Management) (0.880 [0.844-0.916]), CAHP (0.843 [0.771-0.915]) and OHCA (0.811 [0.759-0.863]) scores predicted good the 6-month neurological outcome. We were able to confirm the superiority of the CAHP score especially in the high specificity range based on our sensitivity and specificity analysis.

CONCLUSION

Based on our results CAHP is the most accurate scoring system for predicting the neurological outcome at hospital discharge and is a bit less accurate than TTM score for the 6-month outcome. We recommend the use of the CAHP scoring system in everyday clinical practice not only because of its accuracy and the best performance concerning specificity but also because of the rapid and easy availability of the necessary clinical data for the calculation.