Functional Mapping Identifies Early Arrhythmogenic Substrate in Recurrent Atrial Fibrillation

Functional Mapping Identifies Early Arrhythmogenic Substrate in Recurrent Atrial Fibrillation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Functional Mapping Identifies Early Arrhythmogenic Substrate in Recurrent Atrial Fibrillation Chiara Valeriano, David Soto-Iglesias, Diego Penela, Giulio Falasconi, and 21 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8007116/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Feb, 2026 Read the published version in Journal of Interventional Cardiac Electrophysiology → Version 1 posted You are reading this latest preprint version Abstract Background Pulmonary vein isolation (PVI) often fails to prevent atrial fibrillation (AF) recurrence, particularly in persistent AF, where the atrial substrate plays a critical role. Endocardial scar reflects late and irreversible remodeling. Hidden slow conduction (HSC), unmasked by short-coupled extrastimuli, may represent an early functional marker of the arrhythmogenic substrate. Objective This pilot study aimed to define the prevalence and distribution of HSC sites in recurrent AF and examine their relationship with structural remodeling markers, including low-voltage areas (LVAs), intramyocardial fat (inFAT), and left atrial wall thickness (LAWT). Methods Consecutive AF patients (41% persistent, 59% paroxysmal) underwent multidetector CT with ADAS 3D LA™ segmentation of inFAT and LAWT, merged with left atrial voltage maps created using a contact-force ablation catheter. HSC sites were identified as fragmented or double electrograms evoked by triple extrastimuli. Results A total of 960 points were analyzed, with 14.5% testing HSC+. HSC + sites clustered in the septum (34%) and anterior wall (14%). Compared with HSC– sites, they showed greater inFAT (dense: 79% vs 40%; admixture: 89% vs 67%; both p < 0.001) and lower voltage (0.80 vs 1.13 mV, p < 0.001), but no significant association with LVAs or LAWT. AF duration (p = 0.004) and AF type (p = 0.018) were independent predictors of increased fat infiltration. Conclusion HSC + sites cluster within fat-rich atrial regions, suggesting they may represent an early substrate component that promotes conduction slowing before the development of overt scar. Integrating HSC mapping with inFAT imaging may refine substrate characterization and guide targeted ablation strategies. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia worldwide, and its prevalence is projected to more than double by 2060, affecting nearly 18 million adults ( 1 ). The identification of the pulmonary veins as the predominant trigger source for AF established pulmonary vein isolation (PVI) as the cornerstone of catheter ablation ( 2 ). Nevertheless, PVI alone does not prevent AF recurrence in a substantial proportion of patients, particularly those with persistent AF. While pulmonary vein triggers remain central to AF initiation, the atrial substrate is increasingly recognized as a key determinant of AF maintenance and recurrence ( 3 ). Substrate modification strategies, such as targeting complex fractionated atrial electrograms (CFAEs) or creating linear lesions, have provided inconsistent results due to their limited specificity for arrhythmogenic tissue ( 4 – 6 ). Ablation of low-voltage areas (LVAs) has shown more promising outcomes in persistent AF ( 7 , 8 ); however, endocardial scar likely represents a late and largely irreversible stage of atrial remodeling. Accordingly, there is growing focus on identifying new markers capable of detecting early arrhythmogenic substrate. Epicardial adipose tissue (EAT) has been identified as an independent predictor of AF development and recurrence after ablation, it may contribute to arrhythmogenesis both indirectly, through pro-inflammatory and pro-oxidative mediators, and directly, via myocardial infiltration ( 9 , 10 ). Left atrial wall thickness (LAWT) has also been implicated as a marker of atrial remodeling and may influence arrhythmia recurrence ( 11 , 12 ). Recently, we found that employing short-coupled atrial extrastimuli revealed highly fragmented or double atrial evoked electrograms (EGMs) in AF patients, termed as hidden slow conduction (HSC). HSC sites were more prevalent among patients with persistent AF and were not consistently found within areas of complex EGMs during AF. Importantly, identifying HSC sites may provide insight into the early identification of the arrhythmogenic substrate, offering a potential target for ablation ( 13 ). This pilot study aimed to characterize the prevalence and anatomical distribution of HSC sites in patients with recurrent AF undergoing repeat ablation, and to investigate their relationship with structural remodeling markers, including LVAs, intramyocardial fat (inFAT), and LAWT. Methods Study design and imaging We enrolled consecutive patients with AF undergoing a repeat ablation procedure. All participants underwent pre-procedural multi-detector cardiac tomography (MDCT), processed with ADAS 3D LA™ software to generate 3D LAWT and inFAT maps (dense and admixture) as previously described ( 10 ). Summarizing, endocardial and epicardial shells were segmented semi-automatically, with minor manual adjustments when required. LAWT was computed as the distance between the two shells, inFAT was defined as tissue with radiodensity between − 194 and − 5 HU, subdivided into dense (− 194 to − 50 HU) and admixture (− 50 to − 5 HU). inFAT volumes were automatically quantified, normalized to LA volume, and their regional distribution assessed using a semi-automatic 17-segment atrial model (Fig. 1 ). A custom MATLAB script was applied to calculate segment-specific inFAT volumes and relative percentages per segment. Segments including the PVs (1a, 1b, and 2) were excluded from the analysis. During the procedure, a contact‑force ablation catheter was used to create a left atrial voltage map, which was merged with the CT‑derived segmentation. LVAs were defined as regions with bipolar voltage < 0.5 mV. This study was conducted in accordance with the principles of the Declaration of Helsinki, the protocol was approved by the Local Ethics Committee, and all participants signed the written informed consent. Mapping and HSC identification HSC mapping was performed in sinus rhythm. Triple extrastimuli were delivered from the right atrial appendage at the following intervals relative to the atrial effective refractory period (AERP): (i) AERP + 60 ms, (ii) AERP + 40–20 ms, and (iii) AERP + 30–20 ms. HSC + sites were defined as highly fragmented or double electrograms (EGMs, showing an isoelectric line) elicited by triple extrastimuli. Mapping points were manually acquired and annotated in the electroanatomical map (CARTO3) as green (HSC+) or orange (HSC–), using a 10-mm interpolation for the color threshold (Fig. 2 ). Spatial correlation between HSC sites and underlying tissue characteristics (inFAT, LAWT) was achieved by merging the electroanatomical map with CT-derived segmentations. Annotated points were projected onto the CT derived 3D shell using the transformation matrix applied in the navigation system for image registration.PVs reconnection was assessed during mapping and ablated afterward. Statistical analysis Continuous variables were summarised as mean ± standard deviation (SD) and categorical variables as frequencies and percentages. Data distribution was assessed using the Shapiro–Wilk test. Between‑group comparisons used two‑sample t‑tests or Mann–Whitney U tests for continuous variables and χ² or Fisher’s exact tests for categorical variables. Paired comparisons of HSC‑positive versus HSC‑negative sites were performed using paired t‑tests. Associations between continuous variables were evaluated using Pearson or Spearman correlation coefficients as appropriate. Multivariable linear regression was employed to identify independent predictors of atrial structural remodeling parameters. A two‑sided p‑value < 0.05 was considered statistically significant. Results Baseline and procedural characteristics A total of 22 consecutive AF patients (41% persistent, 59% paroxysmal) underwent HSC mapping. The mean age was 65 ± 8 years, with an average AF duration of 5.1 ± 4.8 years and 1.3 prior ablation procedures (Table 1 ). Procedure duration was 74 ± 14 min, with HSC mapping time of 14 ± 5 min. PV reconnection occurred in 19 of the 22 patients (86.4%). The sites most frequently reconnected were the anterior carina of the right PVs and the antero‑superior ridge of the left PVs, each seen in 9 of the 22 patients (≈ 41%). Table 1 Baseline Characteristics Baseline Characteristics N = 22 AF Type Persistent 41% ( 9 ) Paroxysmal 59% ( 13 ) AF duration (months) 131.1 ± 309.4 N. previous procedure 1.3 ± 0.5 Age 65.4 ± 7.9 Sex Female 27.3% ( 6 ) BMI 28.3 ± 4.6 CHA 2 DS 2 -VASc 2.0 ± 1.5 LVEF 57.9 ± 5.6 Hypertension 72.7% ( 16 ) Diabetes Mellitus 9.1% ( 2 ) Continuous variables are presented as mean ± standard deviation. Categorical variables are shown as number of patients (N) and percentage (%). AF = Atrial Fibrillation; BMI = Body Mass Index; LVEF = Left Ventricular Ejection Fraction. Distribution and tissue characteristics of HSC + sites A total of 960 sites (44 ± 11 per patient) were tested. The overall positive rate was 14.5% (140/960 sites), with HSC + sites predominantly located in the septum (73/216 sites, 33.8%) and the anterior wall (40/289, 13.8%) as shown in Fig. 2 . HSC + sites correlated with higher inFAT content (dense: 78.6% vs 39.7%; admixture: 88.7% vs 66.7%; both p < 0.001) and lower voltage (0.80 ± 0.44 mV vs 1.13 ± 0.57 mV, p < 0.001), without a significant association with LVAs (31.8% vs 9.1%, p = 0.13). No significant differences were observed in LAWT (1.38 ± 0.42 mm vs 1.52 ± 0.37 mm, p = 0.07; Fig. 3 ). HSC + predictors In univariate analysis, AF duration was the only variable showing a borderline inverse association with HSC burden (β ≈ − 0.06; p ≈ 0.07); age, BMI, AF type, mean voltage, LAWT and inFAT volume were not significant predictors. At the segmental level, both dense (β = 1.41, 95% CI 0.54–2.29, p = 0.004, R² = 0.51) and admixture inFAT (β = 1.50, 95% CI 0.46–2.53, p = 0.008, R² = 0.45) were significantly associated with the proportion of HSC + sites (Fig. 4 ). Left atrial segmental analysis In our segmental analysis of left atrial tissue composition, the septal region (segment 8) demonstrated the highest fat infiltration, with mean dense fat of around 21.2% and admixture fat of 19.3%. The thickest atrial wall was observed in segment 4a, corresponding to the antero‑superior septal segment, with a mean LAWT of 2.21 ± 0.53 mm. The segment with the lowest mean voltage was segment 4c, the antero‑inferior septal segment, at 0.68 ± 0.52 mV (Fig. 5 ). Clinical correlates of left atrial fat infiltration, wall thickening and voltage - InFAT: both AF duration (Pearson r = 0.509, p = 0.019) and AF type (persistent vs paroxysmal: 4.74 ± 0.48% vs 4.11 ± 0.44%; p = 0.007) were associated with higher fat infiltration. In the multivariable model, AF duration remained a strong independent predictor (β = 0.0053, p = 0.004), as did AF type (β = 0.4613, p = 0.018). This model explained about 63% of the variability (R² = 0.629), indicating that longer-standing and persistent AF is strongly linked to increased atrial fat infiltration. - LAWT: in multivariable analysis, age (β = 0.0164, p = 0.039) and AF type (β = 0.2847, p = 0.021) were significant predictors of wall thickness. The model’s R² was 0.538, suggesting that wall thickening is mainly driven by age and by the presence of persistent AF. - Voltage: Neither age, BMI, AF duration, nor AF type significantly predicted average voltage in either univariate or multivariable analyses (Table 2 ). Table 2 Multivariate linear regression analysis to identify clinical predictors of atrial substrate Outcome Age β (p-value) BMI β (p-value) AF duration β (p-value) AF type β (p-value) Model R² InFAT 0.0188 (0.122) 0.0402 (0.070) 0.0053 (0.004) 0.4613 (0.018) 0.629 LAWT 0.0164 (0.039) 0.0273 (0.056) -0.0003 (0.777) 0.2847 (0.021) 0.538 Voltage -0.0060 (0.694) 0.0072 (0.794) -0.0019 (0.367) -0.1131 (0.627) 0.104 Outcomes included dense intramyocardial fat infiltration (inFAT, %), left atrial wall thickness (LAWT, mm), and average bipolar voltage (mV). Independent variables were age, body mass index (BMI), atrial fibrillation (AF) duration, and AF type (paroxysmal vs. persistent). Values are reported as regression coefficients (β) with corresponding p-values. Model performance is expressed as the coefficient of determination (R²). Discussion The main findings of our study are the following: HSC + sites are not uniformly distributed across the LA but are preferentially located in the septum (34%) and anterior wall region (14%) HSC + sites are strongly associated with greater myocardial fat infiltration and did not systematically overlap with classical LVAs Persistent AF and AF duration are significant predictors of increased fat infiltration HSC as a marker of early functional substrate The atrial substrate underlying AF is not static but evolves over time, with functional alterations often preceding fixed structural remodeling. In this context, HSC may represent a marker of dynamic conduction abnormalities. The role of slow conduction as a contributor to AF maintenance is well established ( 14 , 15 ), but the use of short-coupled extrastimuli to unmask “hidden” conduction delay introduces a novel mapping approach. Jadidi et al. demonstrated how dynamic pacing protocols can reveal functional conduction abnormalities not apparent at baseline ( 16 ), underscoring the potential value of cycle-length dependent mapping strategies. More recently, Frontera et al. showed that extrastimuli with short coupling intervals can uncover rhythm-dependent conduction abnormalities at sites with preserved voltage, and that these sites predict AF recurrence after ablation ( 17 ). In our cohort, the burden of HSC sites showed a negative trend with AF duration, suggesting that HSC is more evident in the earlier stages of remodeling and tends to diminish as progressive fibrosis consolidates the substrate. This interpretation is reinforced by the observation that HSC⁺ sites were associated with lower voltage values but did not consistently overlap with LVAs. Taken together, these findings support the existence of a transitional period in the natural history of AF substrate evolution, during which conduction slowing is present without fixed scarring. In later stages, as remodeling advances, the substrate may become relatively fixed and less sensitive to changes in pacing cycle length or activation directionality ( 18 ). HSC + sites cluster in fat-rich atrial regions HSC + sites were predominantly located in the septum (34%) and anterior wall (14%), consistent with the distribution reported by Silva et al., who also described a 0.47 mV (0.33mV in our cohort) difference in local voltage between HSC⁺ and HSC⁻ sites during pacing ( 13 ). We extend these observations by showing that HSC⁺ sites were associated with significantly greater intramyocardial fat infiltration. To our knowledge, this is the first direct link between functional conduction abnormalities and underlying fat infiltration. These findings suggest that adiposity within the atrial wall may represent an early substrate component that promotes conduction slowing before overt scar formation. Unlike LVAs, which largely reflect end-stage fibrosis, HSC appears to capture regions where conduction abnormalities occur despite preserved voltage but in the presence of increased fat content. Additionally, when analyzing HSC predictors we found that overall atrial fat content was not associated with HSC burden, whereas regional fat distribution at the segmental level strongly correlated with the proportion of HSC⁺ sites. The lack of correlation at the patient level likely reflects the heterogeneous distribution of fat, supporting the concept that local rather than global adipose infiltration is more relevant in determining conduction abnormalities. InFAT: a novel structural correlate of AF substrate Emerging evidence indicates that EAT actively contributes to the AF substrate. The unicity of EAT lies not only in its proximity to the atrial myocardium without fascial separation, but also in its distinctive transcriptome profile, which differs from other visceral and subcutaneous depots ( 19 ). Evidence from basic science and translational studies suggest that EAT arrhythmogenic mechanisms may involve pro-fibrotic and pro-inflammatory paracrine effects, oxidative stress, and other pathways, but also direct adipocyte infiltration of the myocardium. However, the role of this last potential mechanism (intramyocardial fat infiltration = inFAT) has been poorly investigated, most likely due to the difficulty in defining atrial myocardial fat by a standardized and reproducible approach. We recently demonstrated that pre-procedural MDCT-derived images can be post-processed to create patient-specific three-dimensional left inFAT maps. In line with our results, persistent AF has been associated with greater left atrial inFAT independently of BMI, with a predilection for the interatrial septum and anterior wall ( 10 ). In addition, recent data from our group suggest that inFAT may contribute to pulmonary vein reconnection after PVI ( 20 ), underscoring its potential relevance for substrate characterization and for tailoring ablation strategies, such as prolonged applications in fat-rich sites. Clinical implications and future directions The association between HSC + sites and inFAT highlights the potential value of multimodal strategies that integrate functional testing with advanced imaging to refine substrate characterization. Incorporating HSC mapping and inFAT segmentation could guide more targeted ablation strategies, particularly in the septum and anterior wall, and may help improve outcomes in persistent AF. Future studies are needed to assess whether ablating HSC + sites or tailoring ablation strategies according to inFAT distribution can translate into improved procedural success. Further mechanistic research is also warranted to clarify how intramyocardial fat modulates conduction at the cellular level and to explore whether interventions targeting adiposity—through metabolic modulation or anti-inflammatory therapies—could modify the progression of the AF substrate. Clinical evidence supports this concept: the REVERSE-AF ( 21 ) and LEGACY ( 22 ) studies demonstrated that weight loss not only reduces AF burden but can also reverse disease progression. These benefits may be mediated in part by regression of epicardial fat, as shown in pharmacological studies with GLP-1 receptor agonists such as liraglutide, which induce rapid reductions in EAT volume ( 23 ). Notably, liraglutide has also been associated with improved outcomes after PVI, independent of weight loss ( 24 ). Limitations This study has several limitations. First, the sample size was relatively small. However, the per-point analysis provided a larger dataset that partially compensated for this limitation and increased the statistical power. Nonetheless, the patient-level evaluation of clinical predictors may still be prone to bias. Second, triple extrastimuli were delivered exclusively from the right atrial appendage to create a non-physiological activation wavefront and maximize sensitivity for detecting areas of functional slow conduction; whether alternative pacing sites or protocols would have revealed additional abnormalities remains unknown. Third, high-density mapping catheters were not used, resulting in lower spatial resolution and fewer points collected per anatomical segment. Finally, we did not assess long-term procedural outcomes after targeting or ignoring HSC + regions, so the clinical implications of incorporating HSC mapping into ablation strategies remain to be tested in prospective trials. Conclusions Highly fragmented or double atrial electrograms evoked by triple extrastimuli (HSC+) consistently cluster within the septum and the anterior wall. Their presence correlates with local fat infiltration and may represent an early substrate component that promotes conduction slowing before the development of overt scar. Integrating HSC mapping with inFAT segmentation may enable more targeted ablation strategies and potentially improve outcomes in persistent AF. These results should be considered hypothesis-generating, and future studies are warranted to determine whether ablating HSC + sites or tailoring ablation to regional inFAT distribution can translate into improved procedural success. Declarations Disclosures: Antonio Berruezo is stockholder of Galgo Medical. David Soto-Iglesias and Paula Franco are employees of Biosense Webster. Juan Fernández-Armenta is consultant for Biosense Webster. All remaining authors have declared no conflicts of interest. Funding: Chiara Valeriano received a grant from the CardioPaTh PhD Program Competing Interests Antonio Berruezo is stockholder of Galgo Medical. David Soto-Iglesias and Paula Franco are employees of Biosense Webster. Juan Fernández-Armenta is consultant for Biosense Webster. All remaining authors have declared no conflicts of interest. Author Contribution CV designed the study, collected and analyzed the data, and drafted the manuscript. AB and JFA supervised the project, contributed to the study design, and critically revised the manuscript. DSI extracted and analyzed the data, and provided methodological and technical support. GF and DP contributed to the revision of the manuscript. 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14:50:10","extension":"xml","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":86740,"visible":true,"origin":"","legend":"","description":"","filename":"aa097e22b277425c81fa8141059eabe81structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8007116/v1/ca7e8ec7be122d33d6604325.xml"},{"id":96241756,"identity":"d5f9ed4e-2469-4be7-b54b-44322ee3b3f4","added_by":"auto","created_at":"2025-11-19 07:11:19","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":98224,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8007116/v1/410d7c547393ca56e19793db.html"},{"id":96240468,"identity":"a53005cb-f677-4397-aa7d-313c6bbbb54c","added_by":"auto","created_at":"2025-11-19 07:08:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":393343,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSegmentation of the left atrium (LA).\u003c/strong\u003e LA map with wall thickness segmentation (color code corresponding to different thickness : red \u0026lt;1mm, yellow 2-3mm, green 3-4mm, purple\u0026gt;4mm) is shown in three views (from left to right: antero-posterior, postero-anterior, and left-lateral) illustrating the standardized segmental model. Segment 8 = septum; 4a–d = anterior wall; 6 = mitral annulus; 7a–d = low posterior wall; 3a–d = high posterior wall; 1–2 = pulmonary vein antra.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8007116/v1/1f8acc1bb37851c35f5505fe.png"},{"id":95845583,"identity":"ffc2cb1c-2779-460c-939f-5f80a5bcfe6d","added_by":"auto","created_at":"2025-11-13 14:50:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":285074,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHidden slow conduction (HSC) sites map.\u003c/strong\u003e The left atrium is shown with inFAT segmentation (fat infiltration in red) and projected HSC sites. HSC+ points are displayed in green and HSC- points in orange. Representative electrograms are highlighted with white arrows.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8007116/v1/2d319f459046b968238cbd0d.png"},{"id":95845580,"identity":"35a90f86-6e7a-47ac-b848-ed72465447b3","added_by":"auto","created_at":"2025-11-13 14:50:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":35980,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of hidden slow conduction positive (HSC+) sites across the left atrium \u003c/strong\u003e(A) Bar graph showing the percentage of HSC+ sites across individual left atrial segments, ordered from highest to lowest. The greatest burden was observed in segment 8, followed by the antero-septal segments (4a–c).\u003cstrong\u003e \u003c/strong\u003e(B) Distribution of HSC⁺ sites grouped by main atrial regions. The highest prevalence was found in the septum, followed by the anterior wall. Region 8 = septum, 4 = anterior wall, 6 = mitral annulus, 7 = low posterior wall, 3 = high posterior wall.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8007116/v1/af719e83b0eaabf184c8cc4e.png"},{"id":95845581,"identity":"377761c4-8515-4e6e-8a12-522c2cc113f2","added_by":"auto","created_at":"2025-11-13 14:50:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":57742,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacteristics of sites with and without hidden slow conduction (HSC).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoxplots compare percentage of dense and admixture fat infiltration (top left and right), left atrial wall thickness (bottom left) and voltage (bottom right), between HSC+ and HSC- sites. Central lines represent medians, boxes indicate interquartile range and individual sites are shown as dots. Significant differences were observed for fat infiltration and voltage (p \u0026lt; 0.05, unpaired t-test).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8007116/v1/af2baee839c540914e55708e.png"},{"id":96239851,"identity":"8d13ab9e-af39-4749-bc9d-f90b5d725de9","added_by":"auto","created_at":"2025-11-19 07:07:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":87306,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between segmental fat infiltration and HSC+ burden.\u003c/strong\u003e\u003cbr\u003e\nScatter plots show the relationship between the mean percentage of HSC⁺ sites per segment and segmental fat content. Left: admixture fat (%). Right: dense fat (%). Each dot represents one atrial segment. Red line = linear regression with 95% confidence interval (shaded). Pearson’s correlation coefficient (r) and p-value are reported.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8007116/v1/0128a542e94d59e8aa3d35f8.png"},{"id":95845592,"identity":"3d626d1d-8049-466a-89ab-c4f4b87cece4","added_by":"auto","created_at":"2025-11-13 14:50:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":90802,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSegmental composition of the left atrium.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeatmaps illustrate the distribution of structural parameters across atrial segments. Voltage (mV): mean bipolar voltage per segment. LAWT (mm): mean left atrial wall thickness. InFAT (%): percentage of intramyocardial fat infiltration, shown separately for admixture and dense components. Higher colour intensity corresponds to greater values in each parameter.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8007116/v1/d291a75d0e8fc93ad2fffe1a.png"},{"id":103251150,"identity":"d732f947-1a41-4a2a-8c6e-f4f397aa55ab","added_by":"auto","created_at":"2026-02-23 16:05:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2022052,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8007116/v1/1696f692-4068-474b-917f-ab856f4902de.pdf"}],"financialInterests":"Competing interest reported. Antonio Berruezo is stockholder of Galgo Medical. David Soto-Iglesias and Paula Franco are employees of Biosense Webster. Juan Fernández-Armenta is consultant for Biosense Webster. All remaining authors have declared no conflicts of interest.","formattedTitle":"Functional Mapping Identifies Early Arrhythmogenic Substrate in Recurrent Atrial Fibrillation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAtrial fibrillation (AF) is the most prevalent cardiac arrhythmia worldwide, and its prevalence is projected to more than double by 2060, affecting nearly 18\u0026nbsp;million adults (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The identification of the pulmonary veins as the predominant trigger source for AF established pulmonary vein isolation (PVI) as the cornerstone of catheter ablation (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Nevertheless, PVI alone does not prevent AF recurrence in a substantial proportion of patients, particularly those with persistent AF. While pulmonary vein triggers remain central to AF initiation, the atrial substrate is increasingly recognized as a key determinant of AF maintenance and recurrence (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Substrate modification strategies, such as targeting complex fractionated atrial electrograms (CFAEs) or creating linear lesions, have provided inconsistent results due to their limited specificity for arrhythmogenic tissue (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Ablation of low-voltage areas (LVAs) has shown more promising outcomes in persistent AF (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e); however, endocardial scar likely represents a late and largely irreversible stage of atrial remodeling. Accordingly, there is growing focus on identifying new markers capable of detecting early arrhythmogenic substrate.\u003c/p\u003e\u003cp\u003eEpicardial adipose tissue (EAT) has been identified as an independent predictor of AF development and recurrence after ablation, it may contribute to arrhythmogenesis both indirectly, through pro-inflammatory and pro-oxidative mediators, and directly, via myocardial infiltration (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Left atrial wall thickness (LAWT) has also been implicated as a marker of atrial remodeling and may influence arrhythmia recurrence (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRecently, we found that employing short-coupled atrial extrastimuli revealed highly fragmented or double atrial evoked electrograms (EGMs) in AF patients, termed as hidden slow conduction (HSC). HSC sites were more prevalent among patients with persistent AF and were not consistently found within areas of complex EGMs during AF. Importantly, identifying HSC sites may provide insight into the early identification of the arrhythmogenic substrate, offering a potential target for ablation (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis pilot study aimed to characterize the prevalence and anatomical distribution of HSC sites in patients with recurrent AF undergoing repeat ablation, and to investigate their relationship with structural remodeling markers, including LVAs, intramyocardial fat (inFAT), and LAWT.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and imaging\u003c/h2\u003e\u003cp\u003eWe enrolled consecutive patients with AF undergoing a repeat ablation procedure. All participants underwent pre-procedural multi-detector cardiac tomography (MDCT), processed with ADAS 3D LA\u0026trade; software to generate 3D LAWT and inFAT maps (dense and admixture) as previously described (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Summarizing, endocardial and epicardial shells were segmented semi-automatically, with minor manual adjustments when required. LAWT was computed as the distance between the two shells, inFAT was defined as tissue with radiodensity between \u0026minus;\u0026thinsp;194 and \u0026minus;\u0026thinsp;5 HU, subdivided into dense (\u0026minus;\u0026thinsp;194 to \u0026minus;\u0026thinsp;50 HU) and admixture (\u0026minus;\u0026thinsp;50 to \u0026minus;\u0026thinsp;5 HU). inFAT volumes were automatically quantified, normalized to LA volume, and their regional distribution assessed using a semi-automatic 17-segment atrial model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A custom MATLAB script was applied to calculate segment-specific inFAT volumes and relative percentages per segment. Segments including the PVs (1a, 1b, and 2) were excluded from the analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDuring the procedure, a contact‑force ablation catheter was used to create a left atrial voltage map, which was merged with the CT‑derived segmentation. LVAs were defined as regions with bipolar voltage\u0026thinsp;\u0026lt;\u0026thinsp;0.5 mV. This study was conducted in accordance with the principles of the Declaration of Helsinki, the protocol was approved by the Local Ethics Committee, and all participants signed the written informed consent.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMapping and HSC identification\u003c/h3\u003e\n\u003cp\u003eHSC mapping was performed in sinus rhythm. Triple extrastimuli were delivered from the right atrial appendage at the following intervals relative to the atrial effective refractory period (AERP): (i) AERP\u0026thinsp;+\u0026thinsp;60 ms, (ii) AERP\u0026thinsp;+\u0026thinsp;40\u0026ndash;20 ms, and (iii) AERP\u0026thinsp;+\u0026thinsp;30\u0026ndash;20 ms.\u003c/p\u003e\u003cp\u003eHSC\u0026thinsp;+\u0026thinsp;sites were defined as highly fragmented or double electrograms (EGMs, showing an isoelectric line) elicited by triple extrastimuli. Mapping points were manually acquired and annotated in the electroanatomical map (CARTO3) as green (HSC+) or orange (HSC\u0026ndash;), using a 10-mm interpolation for the color threshold (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Spatial correlation between HSC sites and underlying tissue characteristics (inFAT, LAWT) was achieved by merging the electroanatomical map with CT-derived segmentations. Annotated points were projected onto the CT derived 3D shell using the transformation matrix applied in the navigation system for image registration.PVs reconnection was assessed during mapping and ablated afterward.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eContinuous variables were summarised as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and categorical variables as frequencies and percentages. Data distribution was assessed using the Shapiro\u0026ndash;Wilk test. Between‑group comparisons used two‑sample t‑tests or Mann\u0026ndash;Whitney U tests for continuous variables and χ\u0026sup2; or Fisher\u0026rsquo;s exact tests for categorical variables. Paired comparisons of HSC‑positive versus HSC‑negative sites were performed using paired t‑tests. Associations between continuous variables were evaluated using Pearson or Spearman correlation coefficients as appropriate. Multivariable linear regression was employed to identify independent predictors of atrial structural remodeling parameters. A two‑sided p‑value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eBaseline and procedural characteristics\u003c/h2\u003e\n \u003cp\u003eA total of 22 consecutive AF patients (41% persistent, 59% paroxysmal) underwent HSC mapping. The mean age was 65\u0026thinsp;\u0026plusmn;\u0026thinsp;8 years, with an average AF duration of 5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8 years and 1.3 prior ablation procedures (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Procedure duration was 74\u0026thinsp;\u0026plusmn;\u0026thinsp;14 min, with HSC mapping time of 14\u0026thinsp;\u0026plusmn;\u0026thinsp;5 min. PV reconnection occurred in 19 of the 22 patients (86.4%). The sites most frequently reconnected were the anterior carina of the right PVs and the antero‑superior ridge of the left PVs, each seen in 9 of the 22 patients (\u0026asymp;\u0026thinsp;41%).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline Characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBaseline Characteristics\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;22\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAF Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePersistent 41% (\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eParoxysmal 59% (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAF duration (months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131.1\u0026thinsp;\u0026plusmn;\u0026thinsp;309.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN. previous procedure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.3% (\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLVEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.7% (\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes Mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.1% (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. Categorical variables are shown as number of patients (N) and percentage (%). AF\u0026thinsp;=\u0026thinsp;Atrial Fibrillation; BMI\u0026thinsp;=\u0026thinsp;Body Mass Index; LVEF\u0026thinsp;=\u0026thinsp;Left Ventricular Ejection Fraction.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eDistribution and tissue characteristics of HSC\u0026thinsp;+\u0026thinsp;sites\u003c/h2\u003e\n \u003cp\u003eA total of 960 sites (44\u0026thinsp;\u0026plusmn;\u0026thinsp;11 per patient) were tested. The overall positive rate was 14.5% (140/960 sites), with HSC\u0026thinsp;+\u0026thinsp;sites predominantly located in the septum (73/216 sites, 33.8%) and the anterior wall (40/289, 13.8%) as shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eHSC\u0026thinsp;+\u0026thinsp;sites correlated with higher inFAT content (dense: 78.6% vs 39.7%; admixture: 88.7% vs 66.7%; both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and lower voltage (0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44 mV vs 1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57 mV, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), without a significant association with LVAs (31.8% vs 9.1%, p\u0026thinsp;=\u0026thinsp;0.13). No significant differences were observed in LAWT (1.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42 mm vs 1.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37 mm, p\u0026thinsp;=\u0026thinsp;0.07; Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eHSC\u0026thinsp;+\u0026thinsp;predictors\u003c/h3\u003e\n\u003cp\u003eIn univariate analysis, AF duration was the only variable showing a borderline inverse association with HSC burden (\u0026beta; \u0026asymp; \u0026minus;\u0026thinsp;0.06; p\u0026thinsp;\u0026asymp;\u0026thinsp;0.07); age, BMI, AF type, mean voltage, LAWT and inFAT volume were not significant predictors. At the segmental level, both dense (\u0026beta;\u0026thinsp;=\u0026thinsp;1.41, 95% CI 0.54\u0026ndash;2.29, p\u0026thinsp;=\u0026thinsp;0.004, R\u0026sup2; = 0.51) and admixture inFAT (\u0026beta;\u0026thinsp;=\u0026thinsp;1.50, 95% CI 0.46\u0026ndash;2.53, p\u0026thinsp;=\u0026thinsp;0.008, R\u0026sup2; = 0.45) were significantly associated with the proportion of HSC\u0026thinsp;+\u0026thinsp;sites (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eLeft atrial segmental analysis\u003c/h3\u003e\n\u003cp\u003eIn our segmental analysis of left atrial tissue composition, the septal region (segment 8) demonstrated the highest fat infiltration, with mean dense fat of around 21.2% and admixture fat of 19.3%. The thickest atrial wall was observed in segment 4a, corresponding to the antero‑superior septal segment, with a mean LAWT of 2.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53 mm. The segment with the lowest mean voltage was segment 4c, the antero‑inferior septal segment, at 0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52 mV (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical correlates of left atrial fat infiltration, wall thickening and voltage\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e- InFAT: both AF duration (Pearson r\u0026thinsp;=\u0026thinsp;0.509, p\u0026thinsp;=\u0026thinsp;0.019) and AF type (persistent vs paroxysmal: 4.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48% vs 4.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44%; p\u0026thinsp;=\u0026thinsp;0.007) were associated with higher fat infiltration. In the multivariable model, AF duration remained a strong independent predictor (\u0026beta;\u0026thinsp;=\u0026thinsp;0.0053, p\u0026thinsp;=\u0026thinsp;0.004), as did AF type (\u0026beta;\u0026thinsp;=\u0026thinsp;0.4613, p\u0026thinsp;=\u0026thinsp;0.018). This model explained about 63% of the variability (R\u0026sup2; = 0.629), indicating that longer-standing and persistent AF is strongly linked to increased atrial fat infiltration.\u003c/p\u003e\n\u003cp\u003e- LAWT: in multivariable analysis, age (\u0026beta;\u0026thinsp;=\u0026thinsp;0.0164, p\u0026thinsp;=\u0026thinsp;0.039) and AF type (\u0026beta;\u0026thinsp;=\u0026thinsp;0.2847, p\u0026thinsp;=\u0026thinsp;0.021) were significant predictors of wall thickness. The model\u0026rsquo;s R\u0026sup2; was 0.538, suggesting that wall thickening is mainly driven by age and by the presence of persistent AF.\u003c/p\u003e\n\u003cp\u003e- Voltage: Neither age, BMI, AF duration, nor AF type significantly predicted average voltage in either univariate or multivariable analyses (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate linear regression analysis to identify clinical predictors of atrial substrate\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003cp\u003e\u0026beta; (p-value)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003cp\u003e\u0026beta; (p-value)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAF duration\u003c/p\u003e\n \u003cp\u003e\u0026beta; (p-value)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAF type\u003c/p\u003e\n \u003cp\u003e\u0026beta; (p-value)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel R\u0026sup2;\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInFAT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0188 (0.122)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0402 (0.070)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0053 (0.004)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.4613 (0.018)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.629\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLAWT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0164 (0.039)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0273 (0.056)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0003 (0.777)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.2847 (0.021)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eVoltage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0060 (0.694)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0072 (0.794)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0019 (0.367)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.1131 (0.627)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eOutcomes included dense intramyocardial fat infiltration (inFAT, %), left atrial wall thickness (LAWT, mm), and average bipolar voltage (mV). Independent variables were age, body mass index (BMI), atrial fibrillation (AF) duration, and AF type (paroxysmal vs. persistent). Values are reported as regression coefficients (\u0026beta;) with corresponding p-values. Model performance is expressed as the coefficient of determination (R\u0026sup2;).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe main findings of our study are the following:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eHSC\u0026thinsp;+\u0026thinsp;sites are not uniformly distributed across the LA but are preferentially located in the septum (34%) and anterior wall region (14%)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eHSC\u0026thinsp;+\u0026thinsp;sites are strongly associated with greater myocardial fat infiltration and did not systematically overlap with classical LVAs\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePersistent AF and AF duration are significant predictors of increased fat infiltration\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eHSC as a marker of early functional substrate\u003c/h2\u003e\u003cp\u003eThe atrial substrate underlying AF is not static but evolves over time, with functional alterations often preceding fixed structural remodeling. In this context, HSC may represent a marker of dynamic conduction abnormalities.\u003c/p\u003e\u003cp\u003eThe role of slow conduction as a contributor to AF maintenance is well established (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), but the use of short-coupled extrastimuli to unmask \u0026ldquo;hidden\u0026rdquo; conduction delay introduces a novel mapping approach. Jadidi et al. demonstrated how dynamic pacing protocols can reveal functional conduction abnormalities not apparent at baseline (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), underscoring the potential value of cycle-length dependent mapping strategies. More recently, Frontera et al. showed that extrastimuli with short coupling intervals can uncover rhythm-dependent conduction abnormalities at sites with preserved voltage, and that these sites predict AF recurrence after ablation (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn our cohort, the burden of HSC sites showed a negative trend with AF duration, suggesting that HSC is more evident in the earlier stages of remodeling and tends to diminish as progressive fibrosis consolidates the substrate. This interpretation is reinforced by the observation that HSC⁺ sites were associated with lower voltage values but did not consistently overlap with LVAs. Taken together, these findings support the existence of a transitional period in the natural history of AF substrate evolution, during which conduction slowing is present without fixed scarring. In later stages, as remodeling advances, the substrate may become relatively fixed and less sensitive to changes in pacing cycle length or activation directionality (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eHSC\u0026thinsp;+\u0026thinsp;sites cluster in fat-rich atrial regions\u003c/h2\u003e\u003cp\u003eHSC\u0026thinsp;+\u0026thinsp;sites were predominantly located in the septum (34%) and anterior wall (14%), consistent with the distribution reported by Silva et al., who also described a 0.47 mV (0.33mV in our cohort) difference in local voltage between HSC⁺ and HSC⁻ sites during pacing (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). We extend these observations by showing that HSC⁺ sites were associated with significantly greater intramyocardial fat infiltration. To our knowledge, this is the first direct link between functional conduction abnormalities and underlying fat infiltration. These findings suggest that adiposity within the atrial wall may represent an early substrate component that promotes conduction slowing before overt scar formation. Unlike LVAs, which largely reflect end-stage fibrosis, HSC appears to capture regions where conduction abnormalities occur despite preserved voltage but in the presence of increased fat content. Additionally, when analyzing HSC predictors we found that overall atrial fat content was not associated with HSC burden, whereas regional fat distribution at the segmental level strongly correlated with the proportion of HSC⁺ sites. The lack of correlation at the patient level likely reflects the heterogeneous distribution of fat, supporting the concept that local rather than global adipose infiltration is more relevant in determining conduction abnormalities.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eInFAT: a novel structural correlate of AF substrate\u003c/h2\u003e\u003cp\u003eEmerging evidence indicates that EAT actively contributes to the AF substrate. The unicity of EAT lies not only in its proximity to the atrial myocardium without fascial separation, but also in its distinctive transcriptome profile, which differs from other visceral and subcutaneous depots (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Evidence from basic science and translational studies suggest that EAT arrhythmogenic mechanisms may involve pro-fibrotic and pro-inflammatory paracrine effects, oxidative stress, and other pathways, but also direct adipocyte infiltration of the myocardium. However, the role of this last potential mechanism (intramyocardial fat infiltration\u0026thinsp;=\u0026thinsp;inFAT) has been poorly investigated, most likely due to the difficulty in defining atrial myocardial fat by a standardized and reproducible approach. We recently demonstrated that pre-procedural MDCT-derived images can be post-processed to create patient-specific three-dimensional left inFAT maps. In line with our results, persistent AF has been associated with greater left atrial inFAT independently of BMI, with a predilection for the interatrial septum and anterior wall (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In addition, recent data from our group suggest that inFAT may contribute to pulmonary vein reconnection after PVI (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), underscoring its potential relevance for substrate characterization and for tailoring ablation strategies, such as prolonged applications in fat-rich sites.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eClinical implications and future directions\u003c/h2\u003e\u003cp\u003eThe association between HSC\u0026thinsp;+\u0026thinsp;sites and inFAT highlights the potential value of multimodal strategies that integrate functional testing with advanced imaging to refine substrate characterization. Incorporating HSC mapping and inFAT segmentation could guide more targeted ablation strategies, particularly in the septum and anterior wall, and may help improve outcomes in persistent AF. Future studies are needed to assess whether ablating HSC\u0026thinsp;+\u0026thinsp;sites or tailoring ablation strategies according to inFAT distribution can translate into improved procedural success. Further mechanistic research is also warranted to clarify how intramyocardial fat modulates conduction at the cellular level and to explore whether interventions targeting adiposity\u0026mdash;through metabolic modulation or anti-inflammatory therapies\u0026mdash;could modify the progression of the AF substrate. Clinical evidence supports this concept: the REVERSE-AF (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and LEGACY (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) studies demonstrated that weight loss not only reduces AF burden but can also reverse disease progression. These benefits may be mediated in part by regression of epicardial fat, as shown in pharmacological studies with GLP-1 receptor agonists such as liraglutide, which induce rapid reductions in EAT volume (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Notably, liraglutide has also been associated with improved outcomes after PVI, independent of weight loss (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eThis study has several limitations. First, the sample size was relatively small. However, the per-point analysis provided a larger dataset that partially compensated for this limitation and increased the statistical power. Nonetheless, the patient-level evaluation of clinical predictors may still be prone to bias. Second, triple extrastimuli were delivered exclusively from the right atrial appendage to create a non-physiological activation wavefront and maximize sensitivity for detecting areas of functional slow conduction; whether alternative pacing sites or protocols would have revealed additional abnormalities remains unknown. Third, high-density mapping catheters were not used, resulting in lower spatial resolution and fewer points collected per anatomical segment. Finally, we did not assess long-term procedural outcomes after targeting or ignoring HSC\u0026thinsp;+\u0026thinsp;regions, so the clinical implications of incorporating HSC mapping into ablation strategies remain to be tested in prospective trials.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eHighly fragmented or double atrial electrograms evoked by triple extrastimuli (HSC+) consistently cluster within the septum and the anterior wall. Their presence correlates with local fat infiltration and may represent an early substrate component that promotes conduction slowing before the development of overt scar. Integrating HSC mapping with inFAT segmentation may enable more targeted ablation strategies and potentially improve outcomes in persistent AF. These results should be considered hypothesis-generating, and future studies are warranted to determine whether ablating HSC\u0026thinsp;+\u0026thinsp;sites or tailoring ablation to regional inFAT distribution can translate into improved procedural success.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosures:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAntonio Berruezo is stockholder of Galgo Medical. David Soto-Iglesias and Paula Franco are employees of Biosense Webster.\u0026nbsp;Juan Fernández-Armenta is\u0026nbsp;consultant for Biosense Webster. All remaining authors have declared no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChiara Valeriano received a grant from the CardioPaTh PhD Program\u003c/p\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cp\u003eAntonio Berruezo is stockholder of Galgo Medical. David Soto-Iglesias and Paula Franco are employees of Biosense Webster. Juan Fern\u0026aacute;ndez-Armenta is consultant for Biosense Webster. All remaining authors have declared no conflicts of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eCV designed the study, collected and analyzed the data, and drafted the manuscript. AB and JFA supervised the project, contributed to the study design, and critically revised the manuscript. DSI extracted and analyzed the data, and provided methodological and technical support. GF and DP contributed to the revision of the manuscript. All authors contributed to data collection and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKrijthe BP, Kunst A, Benjamin EJ, Lip GY, Franco OH, Hofman A, et al. Projections on the number of individuals with atrial fibrillation in the European Union, from 2000 to 2060. Eur Heart J. 2013;34(35):2746\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHa\u0026iuml;ssaguerre M, Ja\u0026iuml;s P, Shah DC, Takahashi A, Hocini M, Quiniou G, et al. Spontaneous initiation of atrial fibrillation by ectopic beats originating in the pulmonary veins. N Engl J Med. 1998;339(10):659\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcCauley MD, Iacobellis G, Li N, Nattel S, Goldberger JJ. Targeting the Substrate for Atrial Fibrillation: JACC Review Topic of the Week. J Am Coll Cardiol. 2024;83(20):2015\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVerma A, Sanders P, Macle L, Deisenhofer I, Morillo CA, Chen J, et al. Substrate and Trigger Ablation for Reduction of Atrial Fibrillation Trial-Part II (STAR AF II): design and rationale. Am Heart J. 2012;164(1):1\u0026ndash;e6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVogler J, Willems S, Sultan A, Schreiber D, L\u0026uuml;ker J, Servatius H, et al. Pulmonary Vein Isolation Versus Defragmentation: The CHASE-AF Clinical Trial. J Am Coll Cardiol. 2015;66(24):2743\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWynn GJ, Panikker S, Morgan M, Hall M, Waktare J, Markides V, et al. Biatrial linear ablation in sustained nonpermanent AF: Results of the substrate modification with ablation and antiarrhythmic drugs in nonpermanent atrial fibrillation (SMAN-PAF) trial. Heart Rhythm. 2016;13(2):399\u0026ndash;406.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJunarta J, Siddiqui MU, Riley JM, Dikdan SJ, Patel A, Frisch DR. Low-voltage area substrate modification for atrial fibrillation ablation: a systematic review and meta-analysis of clinical trials. Europace. 2022;24(10):1585\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSalih A, Sutaria A, Montaser Z, Magar TP, El Ashal G, Zaghloul S, et al. Fibrosis-Guided Ablation in Patients With Atrial Fibrillation: A Meta-Analysis of Randomized Controlled Trials. J Cardiovasc Electrophysiol. 2025;36(8):2025\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWong CX, Ganesan AN, Selvanayagam JB. Epicardial fat and atrial fibrillation: current evidence, potential mechanisms, clinical implications, and future directions. Eur Heart J. 2017;38(17):1294\u0026ndash;302.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaglietto A, Falasconi G, Soto-Iglesias D, Francia P, Penela D, Alderete J et al. Assessing left atrial intramyocardial fat infiltration from computerized tomography angiography in patients with atrial fibrillation. Europace. 2023;25(12).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTeres C, Soto-Iglesias D, Penela D, J\u0026aacute;uregui B, Ordo\u0026ntilde;ez A, Chauca A, et al. Left atrial wall thickness of the pulmonary vein reconnection sites during atrial fibrillation redo procedures. Pacing Clin Electrophysiol. 2021;44(5):824\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBai Y, Jia R, Wang X, Chan J, Cui K. Association of left atrial wall thickness with recurrence after cryoballoon ablation of paroxysmal atrial fibrillation. J Interv Card Electrophysiol. 2024;67(3):657\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSilva Garcia E, Lobo-Torres I, Fern\u0026aacute;ndez-Armenta J, Penela D, Fernandez-Garcia M, Gomez-Lopez A et al. Functional mapping to reveal slow conduction and substrate progression in atrial fibrillation. Europace. 2023;25(11).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarkides V, Schilling RJ, Ho SY, Chow AW, Davies DW, Peters NS. Characterization of left atrial activation in the intact human heart. Circulation. 2003;107(5):733\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoberts-Thomson KC, Stevenson IH, Kistler PM, Haqqani HM, Goldblatt JC, Sanders P, et al. Anatomically determined functional conduction delay in the posterior left atrium relationship to structural heart disease. J Am Coll Cardiol. 2008;51(8):856\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJadidi AS, Duncan E, Miyazaki S, Lellouche N, Shah AJ, Forclaz A, et al. Functional nature of electrogram fractionation demonstrated by left atrial high-density mapping. Circ Arrhythm Electrophysiol. 2012;5(1):32\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFrontera A, Villella F, Cristiano E, Comi F, Latini A, Ceriotti C, et al. The functional substrate in patients with atrial fibrillation is predictive of recurrences after catheter ablation. Heart Rhythm. 2025;22(6):1401\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWong GR, Nalliah CJ, Lee G, Voskoboinik A, Prabhu S, Parameswaran R, et al. Dynamic Atrial Substrate During High-Density Mapping of Paroxysmal and Persistent AF: Implications for Substrate Ablation. JACC Clin Electrophysiol. 2019;5(11):1265\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIacobellis G. Epicardial adipose tissue in contemporary cardiology. Nat Rev Cardiol. 2022;19(9):593\u0026ndash;606.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLandra F, Saglietto A, Falasconi G, Penela D, Soto-Iglesias D, Curti E et al. Left atrial intramyocardial fat at pulmonary vein reconnection sites during atrial fibrillation redo ablation. Europace. 2025;27(2).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiddeldorp ME, Pathak RK, Meredith M, Mehta AB, Elliott AD, Mahajan R, et al. PREVEntion and regReSsive Effect of weight-loss and risk factor modification on Atrial Fibrillation: the REVERSE-AF study. Europace. 2018;20(12):1929\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePathak RK, Middeldorp ME, Meredith M, Mehta AB, Mahajan R, Wong CX, et al. Long-Term Effect of Goal-Directed Weight Management in an Atrial Fibrillation Cohort: A Long-Term Follow-Up Study (LEGACY). J Am Coll Cardiol. 2015;65(20):2159\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIacobellis G, Villasante Fricke AC. Effects of Semaglutide Versus Dulaglutide on Epicardial Fat Thickness in Subjects with Type 2 Diabetes and Obesity. J Endocr Soc. 2020;4(4):bvz042.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoldberger JJ, Mitrani RD, Fishman J, Baez-Garcia C, Zaatari G, Aguilar V, et al. PO-01-030 LONG-TERM ABLATION OUTCOMES IN THE LIRAGLUTIDE EFFECT ON ATRIAL FIBRILLATION (LEAF) STUDY. Heart Rhythm. 2025;22(4):S137\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8007116/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8007116/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePulmonary vein isolation (PVI) often fails to prevent atrial fibrillation (AF) recurrence, particularly in persistent AF, where the atrial substrate plays a critical role. Endocardial scar reflects late and irreversible remodeling. Hidden slow conduction (HSC), unmasked by short-coupled extrastimuli, may represent an early functional marker of the arrhythmogenic substrate.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThis pilot study aimed to define the prevalence and distribution of HSC sites in recurrent AF and examine their relationship with structural remodeling markers, including low-voltage areas (LVAs), intramyocardial fat (inFAT), and left atrial wall thickness (LAWT).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eConsecutive AF patients (41% persistent, 59% paroxysmal) underwent multidetector CT with ADAS 3D LA\u0026trade; segmentation of inFAT and LAWT, merged with left atrial voltage maps created using a contact-force ablation catheter. HSC sites were identified as fragmented or double electrograms evoked by triple extrastimuli.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 960 points were analyzed, with 14.5% testing HSC+. HSC\u0026thinsp;+\u0026thinsp;sites clustered in the septum (34%) and anterior wall (14%). Compared with HSC\u0026ndash; sites, they showed greater inFAT (dense: 79% vs 40%; admixture: 89% vs 67%; both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and lower voltage (0.80 vs 1.13 mV, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but no significant association with LVAs or LAWT. AF duration (p\u0026thinsp;=\u0026thinsp;0.004) and AF type (p\u0026thinsp;=\u0026thinsp;0.018) were independent predictors of increased fat infiltration.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eHSC\u0026thinsp;+\u0026thinsp;sites cluster within fat-rich atrial regions, suggesting they may represent an early substrate component that promotes conduction slowing before the development of overt scar. Integrating HSC mapping with inFAT imaging may refine substrate characterization and guide targeted ablation strategies.\u003c/p\u003e","manuscriptTitle":"Functional Mapping Identifies Early Arrhythmogenic Substrate in Recurrent Atrial Fibrillation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 14:50:05","doi":"10.21203/rs.3.rs-8007116/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9b594252-9ed5-40bc-90cd-9772df0f83fd","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:02:22+00:00","versionOfRecord":{"articleIdentity":"rs-8007116","link":"https://doi.org/10.1007/s10840-026-02254-5","journal":{"identity":"journal-of-interventional-cardiac-electrophysiology","isVorOnly":false,"title":"Journal of Interventional Cardiac Electrophysiology"},"publishedOn":"2026-02-20 15:57:50","publishedOnDateReadable":"February 20th, 2026"},"versionCreatedAt":"2025-11-13 14:50:05","video":"","vorDoi":"10.1007/s10840-026-02254-5","vorDoiUrl":"https://doi.org/10.1007/s10840-026-02254-5","workflowStages":[]},"version":"v1","identity":"rs-8007116","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8007116","identity":"rs-8007116","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}