Researchers at Osaka Metropolitan University have developed a new scoring method called R-EDByUS to improve the prediction of neurological outcomes for patients experiencing out-of-hospital cardiac arrest (OHCA).
This method uses data available from prehospital resuscitations, which allows clinicians to make better-informed decisions upon the patient’s arrival at the hospital.
The R-EDByUS model is based on five key variables: the patient’s age, the duration to return of spontaneous circulation or time to hospital arrival, whether bystander CPR was administered, if the arrest was witnessed, and the initial heart rhythm.
The importance of this model lies in its ability to predict unfavorable neurological outcomes, which are common in OHCA patients after they are transported to the hospital.
These outcomes can range from severe disability to death, making early and accurate prognosis critical for determining the level of care required. The model is designed to be easy to use in clinical settings, as it only requires prehospital data, which are typically available before the patient reaches the hospital.
The development of the R-EDByUS model involved analyzing data from the All-Japan Utstein Registry, which includes information from 942,891 adults with presumed cardiac-origin OHCA between 2005 and 2019.
The researchers categorized patients into two groups: those who achieved return of spontaneous circulation (ROSC) before hospital arrival and those who were still receiving CPR upon arrival.
The model was tested using both regression-based and simplified calculations, showing that it could accurately predict the likelihood of poor neurological outcomes and mortality.
Results from the study highlighted the model’s effectiveness, particularly in the group still receiving CPR upon hospital arrival, where 99.4% experienced unfavorable neurological outcomes, and 98.2% died.
The R-EDByUS model can help clinicians identify patients who may benefit from intensive care and reduce the burden on those with poor predicted outcomes. The researchers also developed a web-based calculator to make the model easily accessible in clinical settings, suggesting its potential for future validation.
The broader implications of this research touch on the use of AI and other technological tools in healthcare. While AI-powered patient triage systems offer promising ways to improve patient outcomes and optimize resources, there is a need for careful regulatory oversight to ensure their safe and effective use.
The R-EDByUS model represents a step forward in utilizing prehospital data to improve patient care, with the potential to become a valuable tool for healthcare providers dealing with OHCA cases.
