Researchers develop model that can predict outcomes of OHCA
Using R-ED By US score data to predict outcomes in out-of-hospital cardiac arrest
image for illustrative purpose
Cardiac arrest, which can result in death within minutes, is a critical emergency with low survival rates. Accurate early prediction models are crucial in OHCA cases, potentially saving lives and reducing unnecessary healthcare costs
New Delhi: Japanese researchers have developed a new model that can help predict the outcomes of patients who suffer from out-of-hospital cardiac arrest (OHCA).
In cardiac arrest cases, immediate action is crucial, as it can determine the patient's survival. Timely intervention not only improves the chances of recovery but also minimises the risk of severe complications.
Researchers from Osaka Metropolitan University developed the R-EDByUS score, a scoring model that uses prehospital resuscitation data to predict outcomes in out-of-hospital cardiac arrest patients.
The R-EDByUS score comprises 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).
Cardiac arrest, which can result in death within minutes, is a critical emergency with low survival rates. Accurate early prediction models are crucial in OHCA cases, potentially saving lives and reducing unnecessary healthcare costs. "Current prognosis prediction models require complex calculations and blood test data, making them impractical for rapid use immediately after patient transport," explained Takenobu Shimada, a medical lecturer at the University’s Graduate School of Medicine.
The model demonstrated high predictive accuracy, with C-statistics values around 0.85, indicating excellent 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 noted.
This new tool is expected to be invaluable for healthcare providers, aiding in the prompt assessment and management of patients undergoing resuscitation.
