Prediction model for cardiac arrest treatment

In Australia, more than 26,000 people have a cardiac arrest out of hospital every year, and only about 10% of these people survive, according to The Heart Foundation.

While a cardiac arrest can happen anywhere, at any time, almost 80% of cardiac arrests that occur out of hospital occur in people’s homes, research suggests.

When treating cardiac arrests, quick action can improve the chance of survival.

Researchers from Osaka Metropolitan University have developed a new scoring model with an aim to allow healthcare providers to make quick and accurate decisions upon the patient’s arrival at the hospital.

The new model claims to enhance early prognosis prediction using only prehospital resuscitation data that accurately predicts neurological outcomes of patients with out-of-hospital cardiac arrest (OHCA). Effective neurological prediction can save lives, reduce suffering and cut down unnecessary costs associated with futile resuscitation efforts.

Named the ‘R-EDByUS score’, the model is derived from 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, finally, initial heart rhythm (shockable versus non-shockable).

Image caption: R-EDByUS score features variables using only prehospital resuscitation data. Credit: Takenobu Shimada, Osaka Metropolitan University, CC BY 4.0.

“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.

The research team addressed this gap by constructing a scoring model that uses readily available pre-hospital data to predict unfavourable neurological outcomes. Analysing 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.

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.

The results demonstrated that the R-EDByUS scores predicted neurological outcomes with high precision, achieving C-statistics values of approximately 0.85 for both groups. C-statistics measure the predictive accuracy of a model, ranging from 0.5 (no predictive power) to 1.0 (perfect accuracy), with higher values indicating superior 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.

“In emergency care for OHCA, invasive procedures, such as mechanical circulatory support, can be lifesaving but are also highly burdensome.

“Our predictive model helps identify patients who are likely to benefit from intensive care while reducing unnecessary burdens on those with poor predicted outcomes,” Shimada said.

Top image credit: iStock.com/Jacob Wackerhausen