Self-organized criticality, dragon kings, black swans, and the prediction of vasovagal syncope

Vasovagal events occur across a spectrum, from minor events to severe episodes resulting in syncope. The intensity distribution of these events follows a power law, as described by the universal theory of self-organized criticality (SOC). Extreme SOC events may manifest as unpredictable “black swans” or predictable outliers called “dragon kings.” This study aimed to characterize the nature of the most significant events leading to (near)syncope.

We analyzed RR-interval time series from eleven healthy individuals who experienced vasovagal (near)syncope during a head-up tilt test. Bradycardia sequences were evaluated based on their intensity (number of beats) to construct Zipf's distributions and calculate the regression coefficient (r). Following the latest three-step approach for identifying event types, we first compared Zipf's distributions with and without the vasovagal (VV) event. Next, we applied time rescaling, and finally, we refined the definition of bradycardia sequences.

The first step failed to demonstrate vasovagal syncope as a dragon king (r without VV vs. r with VV: 0.98 ± 0.00 and 0.98 ± 0.00, respectively). The second step proved inconclusive, as time rescaling disrupted the power law distribution. However, the third step revealed that the largest events acted as outliers or predictable dragon kings, significantly altering the correlation coefficient of event distribution (r without vs. with outliers: 0.98 ± 0.01 and 0.86 ± 0.02, respectively, p = 0.001).

Our findings suggest that vasovagal syncope represents a dragon king in a self-organized system poised at criticality. These results pave the way for predicting vasovagal syncope based on recent advancements in SOC theory.