## 4 Discussion  
This study employs a novel and exploratory methodology based on unsupervised ML tools, which has proven highly useful for grouping patients with SUC. This clustering ability is especially relevant given that, despite advances in clinical guidelines and complementary testing, a high percentage of patients remain without a clear etiological diagnosis of syncope [9]. This underscores the need for innovative and efficient strategies to improve the management of these patients. In this context, AI represents a promising opportunity to optimize risk stratification, diagnosis, and clinical decision-making in patients with syncope [37; 38]. Previous studies have explored the use of AI algorithms to address the limitations of traditional risk stratification tools such as the Canadian Syncope Risk Score [19]. While these approaches have demonstrated performance comparable to conventional methods, integrating AI with broader clinical, demographic, and pathophysiological data could improve diagnostic accuracy and risk assessment in the future. More recent studies have applied AI to classify and stratify risk in patients with syncope. For example, a recent systematic review evaluated ML algorithms for syncope classification using hemodynamic parameters from the Head-Up Tilt Test (HUTT), reporting a sensitivity of 88.8%, specificity of 81.5%, and accuracy of 85.8% [20]. Additionally, AI has been used to predict prognosis and hospital admission [39], as well as to identify structural abnormalities in ECG [40]. Although AI has been used in syncope diagnosis [20], only two studies have applied clustering techniques. [PERSON] et al. [41] identified eight clusters using clinical data, but with limited translational impact. [PERSON] et al. [42] combined clinical and HUTT data for a more refined characterization, although the findings did not translate into changes in patient management. Unlike these prior studies, the present work innovatively incorporates autonomic parameters (such as heart rate variability) and respiratory data obtained via polygraphy. Notably, these respiratory variables were not used in the generation of clusters but were analyzed post hoc to explore pathophysiological differences among the predefined subgroups. This strategy enables a multidimensional phenotyping approach, suggesting that the interaction between autonomic dysfunction and nocturnal respiratory disturbances--particularly nocturnal sympathetic hyperactivity--may represent a relevant pathophysiological axis. This perspective complements the traditional clinical typology (reflex, cardiac, orthostatic) and opens new avenues for more precise and personalized stratification. Unlike these previous studies, our approach allows not only for a more robust clinical characterization but also for a pathophysiological perspective based on objective parameters. A key advantage of the unsupervised approach is its ability to identify latent subgroups without the need for predefined clinical labels, which is particularly useful in the context of SUC, where many patients remain undiagnosed despite standard testing. Thus, this model facilitates the detection of shared clinical patterns and may help generate pathophysiological hypotheses (e.g., autonomic dysfunction or significant sleep apnea) that could inform future studies aimed at developing more personalized stratification strategies. These hypotheses might eventually help prioritize diagnostic and therapeutic resources, pending further validation. In addition, in our model, HRV parameters were included directly as input variables in the clustering algorithm, along with clinical and demographic data. This decision is based on the fact that nocturnal HRV can be easily obtained from 24 h Holter recordings,making it an accessible and reproducible tool in clinical practice. It also enhances the model's ability to detect relevant autonomic patterns early in the diagnostic process.