When treating cardiac arrest, rapid action can mean the difference between life and death.

Researchers from Osaka Metropolitan University have created a new scoring model using only prehospital resuscitation data to accurately predict the neurological outcomes of patients with out-of-hospital cardiac arrest (OHCA). This model allows healthcare providers to make quick and accurate decisions upon the patient's arrival at the hospital, thereby enhancing patient care and resource allocation.

Their findings were published in Resuscitation on May 31.

Figure 1. R-EDByUS. (Credit: Takenobu Shimada, Osaka Metropolitan University)

Cardiac arrest can result in death within minutes. OHCA is relatively common and often has low survival rates. In Japan, over 100,000 patients experience OHCA annually, with fewer than 10% returning to a normal life. Figure 1 shows R-EDByUS score features variables using only prehospital resuscitation data [1].

Rapid and accurate neurological prediction calculations are crucial in OHCA cases. Effective prediction models can save lives, reduce suffering, and cut down on unnecessary costs associated with futile resuscitation efforts.

“Current prognosis prediction models require complex calculations and blood test data, making them impractical for rapid use immediately after patient transport,” said Takenobu Shimada, a medical lecturer at Osaka Metropolitan University’s Graduate School of Medicine and lead author of the study.

To address this issue, the research team developed a scoring model that uses easily accessible prehospital data to predict unfavorable neurological outcomes. By analyzing data from the All-Japan Utstein Registry, they examined information collected between 2005 and 2019 on prehospital resuscitation and neurological recovery one month post-arrest for 942,891 adults with presumed cardiac-origin OHCA. Adverse outcomes include severe disability, vegetative state, or death.

The model, named the “R-EDByUS score,” is based on the initials of its five variables: age, duration to return of spontaneous circulation (ROSC) or time to hospital arrival, absence of bystander CPR, whether the arrest was witnessed, and initial heart rhythm (shockable versus non-shockable).

Patients were divided into two groups based on whether they achieved ROSC before hospital arrival or were still undergoing CPR upon arrival. The researchers developed detailed regression-based and simplified models to calculate R-EDByUS scores for each group.

Results showed that the R-EDByUS scores predicted neurological outcomes with high accuracy, achieving C-statistics values of approximately 0.85 for both groups. C-statistics measure a model's predictive accuracy, ranging from 0.5 (no predictive power) to 1.0 (perfect accuracy), with higher values indicating better performance.

“The R-EDByUS score enables high-precision prognosis prediction immediately upon hospital arrival, and its application via smartphone or tablet makes it suitable for everyday clinical use,” Shimada said.

This scoring model is expected to become a valuable tool for healthcare providers, aiding in the prompt assessment and management of patients undergoing resuscitation.

“In emergency care for OHCA, invasive procedures, such as mechanical circulatory support, can be lifesaving but are also highly burdensome,” Shimada said. “Our predictive model helps identify patients who are likely to benefit from intensive care while reducing unnecessary burdens on those with poor predicted outcomes.”

Source: Osaka Metropolitan University

References:

1. https://www.eurekalert.org/news-releases/1052543

Cite this article:

Hana M (2024), New Scoring Model Predicts Neurological Outcomes in Cardiac Arrest Patients Using Prehospital Data, AnaTechmaz, pp. 276