To address out-of-hospital cardiac arrest, researchers from Osaka Metropolitan University have developed a new scoring method that uses only data from pre-hospital CPRs to accurately predict neurological outcomes and enable doctors to make more informed decisions when a patient arrives at the hospital.
WHY IT IS IMPORTANT
After patient transport, OHCA patients face adverse neurological outcomes ranging from disability to death.
The new model, R-EDByUS, was developed by researchers at Osaka Metropolitan University and is named after the five key variables on which it is based: age, time to recovery of spontaneous circulation or time to hospital arrival, absence of bystander cardiopulmonary resuscitation, whether cardiac arrest was observed, and initial heart rate.
The model accurately predicted the neurological prognosis of cardiogenic OHCA upon hospital arrival, the research published in this month’s issue of Resuscitation.
“We hypothesize that a scoring model that includes only prehospital factors in the algorithm would be simple to use and could predict prognosis at the earliest stage of medical care,” the researchers said.
They took advantage of the unfavorable features of the American College of Cardiology algorithm:
- Arrest without witnesses
- Initial non-shockable rhythm
- No bystander resuscitation
- Time to ROSC of > 30 min
- Continuous resuscitation
- Blood pH of
- Lactate level of > 7 mmol/l
- Age > 85 years
- End-stage kidney disease
- Noncardiac causes in patients with cardiac arrest
They used data from the All-Japan Utstein Registry for 942,891 adults with suspected OHCA of cardiac origin, collected between 2005 and 2019, and categorized patients into two groups: those who achieved ROSC before hospital arrival or those who were still receiving resuscitation upon arrival. They then used detailed regression-based and simplified models to calculate R-EDByUS scores for each group.
Patients younger than 18 years, patients who had received bystander resuscitation, and a number of other factors were excluded.
In the prehospital ROSC group, 70.0% had adverse neurologic outcomes, while 55.7% experienced mortality. Of those who had EMS-continuous resuscitation upon arrival at the hospital, 99.4% had adverse neurologic outcomes and 98.2% died.
“Our predictive model helps identify patients who are likely to benefit from intensive care, while reducing unnecessary burden on patients with poor predicted outcomes,” said Takenobu Shimada, a medical professor at Osaka Metropolitan University Graduate School of Medicine and lead author of the study. MSN last week.
The article claims that the scoring model will become a valuable tool for healthcare providers, helping to quickly assess and treat patients undergoing resuscitation.
The researchers developed a web based calculator They say it is easy to use in a clinical setting and has potential for future validation.
THE BIGGER TREND
According to Piotr Orzechowski, founder and CEO of Infermedica, an AI-driven healthcare company focused on preliminary symptom analysis and digital triage, AI-based patient triage has the potential to create the right care channels, improving patient outcomes and experiences and optimizing resource utilization. However, it requires strict regulations to evaluate.
“AI tools are not authorized to diagnose patients,” Orzechowski said Healthcare IT News in December during a conversation about the relationship between healthcare and AI.
“Despite the remarkable advances in generative AI, we must remain cautious about its practical application in healthcare,” he said.
ON THE RECORD
“Using this free calculation tool, the predictive probability of adverse neurological outcomes and mortality can be easily estimated by checking each variable on the Internet instead of calculating with nomograms,” the researchers said.
Andrea Fox is Editor-in-Chief of Healthcare IT News.
Email address: afox@himss.org
Healthcare IT News is a publication of HIMSS Media.
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