Scientists have created the R-EDByUS score, an innovative model for predicting neurological outcomes in patients who experience out-of-hospital cardiac arrest (OHCA) based solely on prehospital data. This model facilitates quick decision-making when patients arrive at the hospital, ultimately improving patient care and resource management. This development represents a major step forward in emergency medicine.
In the case of cardiac arrest, swift action can be life-saving.
A team of researchers from Osaka Metropolitan University has developed a new scoring system that accurately forecasts neurological outcomes for patients suffering from out-of-hospital cardiac arrest (OHCA), using only prehospital resuscitation information. This new model provides healthcare professionals with the ability to make prompt and precise decisions as patients arrive at the hospital, which can enhance both patient treatment and resource distribution.
Their research findings were shared in the journal Resuscitation on May 31.
Cardiac arrest can quickly lead to fatality, with OHCA cases being quite common and often resulting in low survival figures. In Japan, more than 100,000 individuals face OHCA every year, with fewer than 10% managing to return to a normal life.
Quick and accurate calculations for predicting neurological outcomes are essential in cases of OHCA. Efficient prediction models can save lives, alleviate suffering, and minimize unnecessary expenses associated with ineffective resuscitation attempts.
“Existing prognosis prediction models involve complex calculations and require blood test results, rendering them impractical for rapid use right after patient transport,” stated Takenobu Shimada, a medical lecturer at Osaka Metropolitan University’s Graduate School of Medicine and the lead author of the study.
The research team responded to this issue by creating a scoring model utilizing easily accessible prehospital data for predicting negative neurological outcomes. They analyzed data from the All-Japan Utstein Registry, scrutinizing information gathered from 2005 to 2019 regarding prehospital resuscitation and neurological recovery one month following arrest for 942,891 adults with presumed cardiac-origin OHCA. Negative outcomes include severe disability, a vegetative state, or death.
Called the “R-EDByUS score,” this model is derived from the first letters of its five key factors: age, the time taken to achieve return of spontaneous circulation (ROSC) or the time to reach the hospital, the presence or absence of bystander CPR, whether the arrest was witnessed, and the initial heart rhythm (shockable or non-shockable).
Patients were categorized into two groups based on whether they reached ROSC before arriving at the hospital or if they were still receiving CPR upon arrival. The study team created both detailed regression-based and simplified models to determine R-EDByUS scores for each group.
The findings indicated that the R-EDByUS scores effectively predicted neurological outcomes with remarkable accuracy, achieving C-statistics values close to 0.85 for both groups. The C-statistics indicate the predictive capability of a model, ranging from 0.5 (no predictive ability) to 1.0 (perfect accuracy), with higher values representing better performance.
“The R-EDByUS score permits precise prognosis predictions immediately upon hospital arrival, and its application via smartphones or tablets makes it practical for daily clinical use,” noted Shimada.
This scoring model is anticipated to be a vital asset for healthcare providers, assisting with the swift evaluation and management of patients undergoing resuscitation.
“In emergency situations involving OHCA, invasive methods like mechanical circulatory support can be lifesaving yet highly demanding,” Shimada explained. “Our predictive model aids in identifying patients who are likely to gain from intensive care while alleviating unnecessary stress on those expected to have poor outcomes.”
