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Doctors can now access an AI-based decision support tool to assist in managing cardiac arrest patients. By entering relevant data in a , appropriately skilled physicians can discover how thousands of similar patients have fared, thanks to researchers at the University of Gothenburg, Sweden.

Three systems of decision support have been developed and it is hoped that these may make a significant difference to doctors鈥� work in the future. The clinical prediction model, known as SCARS-1, is presented in and available to download free of charge from the .

The app accesses data from the on 55,000 patient cases. The Gothenburg researchers used an advanced form of machine learning to teach clinical prediction models to recognise various factors that have affected previous outcomes. The algorithms take into account numerous factors relating to the cardiac arrest, such as treatment provided, previous ill health, medication and socioeconomic status.

Research head Araz Rawshani, a researcher at Gothernburg鈥檚 Sahlgrenska Academy and resident physician in cardiology at Sahlgrenska University 黑料吃瓜群网, said, 鈥淏oth I and several of my colleagues who treat emergency patients with cardiac arrest have already started using the prediction models as part of our process for deciding on the level of care.

鈥淭he answer from these tools often means we get confirmation of views we鈥檝e already arrived at. Still, it helps us not to subject patients to painful treatment that is very unlikely to be of benefit to the patient, while saving care resources.鈥�

The model indicates whether a new patient case resembles previous cases and offers information about whether the previous patient had survived or died 30 days after their cardiac arrest.

The researchers said the model鈥檚 accuracy is unusually high. Based on the 10 most significant factors, the model has a sensitivity of 95% and a specificity of 89%.

The 鈥楢UC-ROC value鈥� (ROC being the receiver operating characteristic curve for the model and AUC the area under the ROC curve) for this model is 0.97. The highest possible AUC-ROC value is 1.0 and the threshold for a clinically relevant model is 0.7.

This decision support element was developed by Fredrik Hessulf, a doctoral student at the Sahlgrenska Academy and anaesthesiologist at Sahlgrenska University 黑料吃瓜群网/M枚lndal.

鈥淭his decision support is one of several pieces in a big puzzle: the doctor鈥檚 overall assessment of a patient. We have many different factors to consider in deciding whether to go ahead with cardiopulmonary resuscitation,鈥� Hessulf said.

This form of support is based on 393 factors affecting patients鈥� chances of surviving their cardiac arrest for 30 days after the event, but 10 factors have been found to be most significant in predicting survival; the most important was whether the heart regained a viable cardiac rhythm again after the patient鈥檚 admission to the emergency department.

The second decision support tool published, SCARS-2, has been presented in the journal and will be launched shortly. This tool is based on data from patients who survived their out-of-hospital cardiac arrest until they were discharged from hospital.

The predictive models are based on 886 factors in 5098 patient cases from the Swedish Cardiopulmonary Resuscitation Register. This tool, developed by research doctor Gustaf Hells茅n, is partly aimed at helping doctors identify which patients are at risk of another cardiac arrest or death within a year of discharge from hospital.

It also aims to highlight which factors are important for long-term survival after cardiac arrest 鈥� an aspect of the subject area that has not been well studied.

鈥淭he accuracy of this tool is reasonably good. It can predict with about 70% reliability whether the patient will die, or will have had another cardiac arrest, within a year. Like Fredrik鈥檚 tool, this one has the advantage that just a few factors can predict outcome almost as well as the model with several hundred variables,鈥� Hells茅n said.

A third decision support tool, SCARS-3, is also planned and this will offer support for doctors treating patients who experience an in-hospital cardiac arrest.

Image credit: iStock.com/Marcus Millo