Can we predict sudden cardiac arrests in the general population?

The quest for a personalized risk model that can foretell sudden cardiac arrests (SCAs) in the general population remains, as the ESCAPE-NET* research project falls short in their pursuit to identify such a model that can predict SCA risk in this population.

“Most risk prediction models developed are for high-risk** groups … [As such,] we are working towards building a risk prediction model for SCA in the general population. I think this is … very important,” said project leader Dr Hanno Tan from the Amsterdam University Medical Centre AMC, the Netherlands, at EHRA 2022. “However, [our study] did not allow us to identify individuals who did suffer a cardiac arrest.”

Tan and colleagues established a harmonized dataset of >100,000 out-of-hospital cardiac arrest (OHCA) cases and a DNA bank of >10,000 samples. This included participants from the Copenhagen City Heart Study (n=10,100; 425 SCAs) and the Hoorn Study (n=2,464; 108 SCAs).

The team sought to evaluate whether they could replicate the ARIC*** risk prediction model. “The predicted and observed risks matched quite well. Our area under the curve (AUC) was quite consistent – it was not better but at least as equally good as [that observed] in ARIC (0.84 vs 0.82).”

However, it gets a ‘little less encouraging’ when looking at specificity, Tan pointed out. “Sensitivity was quite decent at 92 percent, but the specificity was a lot lower at 62 percent.”

“In view of the low specificity, this model serves primarily to screen, at an early stage, individuals who may have raised risk and require more detailed cardiac examination, rather than providing a direct means to utilize preventive measures such as implantable cardioverter defibrillators,” Tan explained.

Why is a risk score for SCA necessary?

SCA is a major cause of death that occurs unexpectedly and mostly out-of-hospital in the general population, said Tan. “Survival rates after OHCAs are very low (between 5 and 20 percent). [As such,] it is crucial that we are able to better recognize people who are at risk to institute timely preventative measures.”

Also, more than 50 percent of people who suffer from SCAs were not even known to have cardiovascular disease, Tan added. “For them, it is the first manifestation of cardiac disease.”

As with post-MI or atrial fibrillation patients, SCA in the general population is a very multifactorial thing, noted Tan. “To recognize and predict on an individual level, one needs a very rich dataset to work on … In the general population, we do not usually have very rich datasets because general population datasets are usually fairly limited in scale [and] are normally limited to data collected in routine care.”

Furthermore, SCA in the general population is fairly low, and case ascertainment is very difficult, Tan added.

“[As such,] prediction of SCA in the general population is needed,” said Tan. “If you can predict who is going to suffer from [SCA] and when, then you can take preventative measures … Early recognition may reduce the risk of SCA occurrence and increase the chances of surviving SCA.”