## 4 Discussion
Our study applies unsupervised ML approaches to identify clusters among patients with SUC, enabling a preliminary, data-driven stratification that could potentially inform early-stage clinical decision-making, prior to the use of costly or invasive diagnostic tools. Five clusters with distinct demographic and clinical characteristics were identified, revealing specific profiles that may require personalized management strategies. A particular strength of this patient cohort is that all participants underwent overnight cardiorespiratory polygraphy with ECG recording and nocturnal HRV analysis, conducted within the scope of the SINCOSAS study [43], aimed at exploring autonomic and respiratory profiles in patients with SUC. This enabled the integration of polygraphic parameters in addition to demographic and clinical variables. Identifying clusters that combine clinical, demographic, and nocturnal respiratory event data and HRV may help determine which patients with syncope and sleep apnea could benefit from treatment for sleep-disordered breathing, with the goal of reducing syncope incidence. Since 24 h Holter monitoring is often part of the diagnostic work-up for patients with unexplained syncope, nocturnal HRV can be readily obtained by selecting sleep-time intervals from Holter data. HRV follows a circadian rhythm, characterized by parasympathetic predominance at night and sympathetic predominance during the day [44]. Disruptions to this circadian pattern can be observed in 24 h Holter recordings, which are routinely used in the assessment of syncope of unclear origin. SA [45], restless leg syndrome [46], insomnia [47], and other sleep disorders [43] may impact this circadian HRV cycle. Managing nocturnal autonomic nervous system (ANS) activity may contribute to greater stability of daytime ANS function. Cluster analysis identified five patient groups with distinct clinical and pathophysiological profiles. Importantly, respiratory polygraphy variables were not included in the initial clustering algorithm. This was a deliberate methodological decision, grounded in the clinical reality that cardiorespiratory sleep studies are not routinely performed as part of the initial evaluation of patients with syncope of uncertain cause. By excluding polygraphy data from the clustering inputs, we aimed to simulate a first-line, data-driven stratification process based on variables that are typically available during the early diagnostic workup--such as clinical history, demographics, and nocturnal HRV derived from Holter recordings. The goal was to identify latent patient profiles that might benefit from further testing, including respiratory polygraphy. This design avoids biasing the clustering process with post hoc diagnostic data and reflects a more pragmatic, stepwise clinical strategy. Nonetheless, we acknowledge that excluding polygraphy data from the unsupervised model may have limited the identification of certain pathophysiological patterns. The five identified clusters showed distinct demographic, clinical, autonomic, and respiratory profiles. Although this analysis does not support causal inference, the observed patterns suggest potentially relevant clinical groups that may inform future stratification strategies. Below, we provide exploratory interpretations for each cluster based on current clinical knowledge and pathophysiological plausibility. These hypotheses aim to generate future research directions and should not be construed as prescriptive recommendations. Cluster A represents young, overweight, non-smoking women with prominent daytime fatigue and sleepiness, no or mild sleep apnea, and no nocturnal oxygen desaturation. They present the lowest SDNN and RMSSD values, suggesting elevated nocturnal sympathetic activity. This cluster reflects a typical profile of hypersonnia or non-respiratory sleep disorders, in which full polysomnography may be more appropriate than simple respiratory polygraphy.