Quick action in treating cardiac arrest is crucial for survival. Researchers at Osaka Metropolitan University have created a new scoring model using prehospital data to predict outcomes for out-of-hospital cardiac arrest (OHCA) patients.
This model helps doctors make faster, more accurate decisions when patients arrive at the hospital, improving care and resource use. The study, published in Resuscitation on May 31, highlights that OHCA is common and often fatal, with less than 10% of patients in Japan returning to everyday life.
Rapid and accurate predictions are crucial for out-of-hospital cardiac arrest (OHCA) cases. Good prediction models can save lives, reduce suffering, and avoid unnecessary costs from ineffective resuscitations.
Takenobu Shimada from Osaka Metropolitan University says current models require complex calculations and blood tests, making them impractical for immediate use after transport.
To address this, researchers developed a new scoring model using simple prehospital data to predict poor neurological outcomes. They analyzed data from the All-Japan Utstein Registry, covering over 942,000 OHCA cases from 2005 to 2019, focusing on severe outcomes like disability, vegetative state, or death.
The “R-EDByUS score” is a new model for predicting outcomes in out-of-hospital cardiac arrest (OHCA). It uses five factors: age, time to return of spontaneous circulation (ROSC) or hospital arrival, absence of bystander CPR, whether the arrest was witnessed, and the initial heart rhythm.
Patients were split into two groups: those who achieved ROSC before hospital arrival and those still in CPR upon arrival. Researchers created detailed and simplified models to calculate R-EDByUS scores for both groups. The scores predicted neurological outcomes with high accuracy, with C-statistics of about 0.85 (where 1.0 is perfect accuracy).
According to Shimada, the R-EDByUS score can be used immediately upon hospital arrival and is accessible via smartphone or tablet for clinical use. This scoring model is expected to help healthcare providers quickly assess and manage resuscitation patients.
Shimada said, “In OHCA emergencies, invasive procedures can be life-saving but also very burdensome. Our model helps identify who needs intensive care and avoids putting unnecessary strain on those with poor outcomes.”
The new prediction model for cardiac arrest treatment is crucial because every minute matters. It quickly and accurately assesses patient outcomes, helping doctors make better decisions and focus resources where needed. This model improves patient care by guiding timely and effective treatment.
Journal reference :
- Takenobu Shimada, Ryota Kawai, et al., Neurological prognosis prediction upon arrival at the hospital after out-of-hospital cardiac arrest: R-EDByUS score. Resuscitation. DOI:10.1016/j.resuscitation.2024.110257.
