Predicting Arrhythmic Event Score in Patients With Brugada Syndrome

The quest for the perfect crystal ball

Risk assessment is an integral part of the daily practice of medicine. When confronted with any disease, one of the questions universally asked by patients is: "What about the future? What is going to happen to me?" The answers remain probabilistic even for the simplest medical conditions: "We will cure you, but… there may be a chance that your situation recurs." That is an acceptable answer in dealing with straightforward, non–life-threatening problems. However, when medical issues are potentially deadly, an accurate evaluation of the future is desirable and expected by the patient and family. Unfortunately, we leave medical school with a diploma and a stethoscope, but they do not gratify our studies with a magic crystal ball.

To help us predict the future, we develop statistical models. While forecasting biological phenomena like symptoms, quality of life, and death with models, the responses are (by necessity) probabilistic, with certain sensitivity, specificity, and predictive values. A predictive model will never be perfect as no values can reach 100% in biology. In the search for the perfect crystal ball, it is thus understandable that each new model studied will push more and more toward excellent results. But the advancement curve is asymptotic, and it will never reach perfection.

Today, we can observe the progress made to assess the risk of arrhythmic events in patients with Brugada syndrome by creating a new risk score.¹ Rattanawong et al constructed this new score, Predicting Arrhythmic Event score, using worldwide polled analysis, a method with limitations. However, their score performs better than the three previously reported scores they used for comparison. While this is a positive finding, we must be aware of caveats. Most patients in the BRUGADA-RISK score data² did not undergo programmed ventricular stimulation, and the results could not be included in the analysis. Regarding the Shanghai score, this score is for diagnosing Brugada syndrome and is not a risk assessment tool.³ The comparison with the Sieira score⁴ is also a difficult exercise. The series by Sieira et al includes patients with previous cardiac arrest, and the events predicted by the score relate to first and recurrent events. The Predicting Arrhythmic Event score tries to predict the first event in a very different population.

Thus, the quest for the perfect crystal ball continues while we progress. Risk assessment is a temporal problem. The day we have a simple, effective, and safe treatment for Brugada syndrome, we will no longer discuss risk stratification. We may treat everybody and free them all from any potential danger.

References

1. Rattanawong P, Mattanapojanat N, Mead-Harvey C, et al. Predicting Arrhythmic Event Score in Brugada Syndrome: Worldwide Pooled Analysis With Internal and External Validation. Heart Rhythm. 2023 Jun 23. doi: 10.1016/j.hrthm.2023.06.013. Online ahead of print. https://www.heartrhythmjournal.com/article/S1547-5271(23)02398-6/fulltext
2. Honarbakhsh S, Providencia R, Garcia-Hernandez J, et al. A Primary Prevention Clinical Risk Score Model for Patients With Brugada Syndrome (BRUGADA-RISK). JACC Clin Electrophysiol. 2021;7(2):210-222. https://www.sciencedirect.com/science/article/pii/S2405500X20308197?via%3Dihub
3. Kawada S, Morita H, Antzelevitch C, et al. Shanghai Score System for Diagnosis of Brugada Syndrome: Validation of the Score System and System and Reclassification of the Patients. JACC Clin Electrophysiol. 2018;4(6):724-730. https://www.sciencedirect.com/science/article/pii/S2405500X18301695?via%3Dihub
4. Sieira J, Conte G, Ciconte G, et al. A Score Model to Predict Risk of Events in Patients With Brugada Syndrome. Eur Heart J. 2017;38(22):1756-1763. https://academic.oup.com/eurheartj/article/38/22/1756/3098019