Short-Duration Atrial Fibrillation in Ischemic Stroke: High Risk Despite Subclinical Burden-A prospective cohort study

Short-Duration Atrial Fibrillation in Ischemic Stroke: High Risk Despite Subclinical Burden-A prospective cohort study | 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 Short-Duration Atrial Fibrillation in Ischemic Stroke: High Risk Despite Subclinical Burden-A prospective cohort study Priyanka Boettger, Karolis Macius, Jamschid Sedighi, Henning Lemm, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6813617/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Aug, 2025 Read the published version in BMC Cardiovascular Disorders → Version 1 posted 8 You are reading this latest preprint version Abstract Background and Purpose Atrial fibrillation (AF) episodes ≥30 seconds are currently considered clinically relevant in stroke diagnostics. However, shorter AF episodes may signal a significant embolic risk, especially in patients with embolic stroke of undetermined source (ESUS). This study investigates the prevalence, risk profile, and stroke severity associated with short-duration AF (SDAF <30 seconds) across ischemic stroke subtypes. Methods We prospectively enrolled 714 consecutive patients with ischemic stroke or Transient ischemic attacks who underwent ≥48-hour ECG monitoring. AF episodes were classified as 0–14 s, 15–29 s, or ≥30 s. Stroke subtypes were defined using TOAST and ESUS criteria. Risk profiles, NIH Stroke Scale scores, and CHA₂DS₂-VASc scores were analyzed by AF duration. Results AF of any duration was detected in 53.8% of patients; 22.8% had episodes ≥30 seconds and 29.9% had SDAF. Among ESUS patients, 35.7% exhibited SDAF, and 80.2% of these had CHA₂DS₂-VASc scores ≥2. Stroke severity and risk scores were significantly higher in patients with SDAF than those without AF. SDAF was more prevalent in women (37.0%) and in individuals aged >65 years (89.4%). Conclusions SDAF is common across stroke subtypes—particularly ESUS—and is associated with elevated thromboembolic risk despite falling below current diagnostic thresholds. These findings highlight a diagnostic blind spot in stroke workup and support reevaluation of duration-based criteria for post-stroke AF detection and risk profiling. Atrial fibrillation Electrocardiography Stroke/etiology Risk assessment Ischemic stroke Prospective studies Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 BACKGROUND AND PURPOSE Current stroke guidelines consider atrial fibrillation (AF) clinically actionable only when episodes last ≥30 seconds, a definition supported by device-based studies and consensus guidelines. 1 , 2 , 3 , 4 However, shorter episodes are increasingly observed during post-stroke monitoring and may indicate a clinically meaningful arrhythmic burden. 5 , 6 , 7 In particular, cryptogenic stroke and ESUS frequently lack an identified embolic source despite extended diagnostic workup. 8 , 9 Real-world data suggest that short-duration AF (SDAF) may correlate with age, stroke severity, and CHA₂DS₂-VASc scores 10 , raising concern that these brief episodes, though guideline-excluded, could represent a high-risk but undertreated subgroup. In this prospective cohort study, we aimed to assess the prevalence of SDAF (<30 seconds) across ischemic stroke subtypes, determine associated risk profiles, and evaluate stroke severity. We hypothesized that SDAF is common in ESUS and cryptogenic stroke and is associated with elevated thromboembolic risk despite not fulfilling current diagnostic thresholds—thereby representing a diagnostic blind spot in contemporary stroke care. METHODS Over six months, all consecutive patients presenting with suspected ischemic stroke or Transient ischemic attack (TIA) were prospectively enrolled. Adults aged ≥18 years with acute ischemic stroke or TIA were eligible. Patients with hemorrhagic stroke or hospital stays <24 hours were excluded. Demographics, comorbidities, and diagnostic data were systematically recorded. Stroke subtypes were classified using TOAST 11 and ESUS 8 criteria, aligned with current prevention and treatment guidelines. 12 Subtypes included: (1) large-artery atherosclerosis, defined by ≥50% stenosis (NASCET) or occlusion of relevant vessels; (2) cardioembolism, including AF, atrial flutter, intracardiac thrombus, valvular heart disease, recent myocardial infraction, LVEF <35%, or endocarditis; (3) small vessel disease, defined by deep lacunar infarcts ≤15 mm (CT) or ≤20 mm (MRI); (4) other determined causes, including vasculitis, dissection, hematologic, storage, or mitochondrial disorders; and (5) ESUS, when no cause was identified after standard workup. TIA was defined as a transient neurological deficit lasting <24 hours without imaging evidence of infarction, in accordance with the tissue-based definition proposed by Albers et al. 13 14 Stroke workup included CT or MRI, extracranial/intracranial Doppler, transthoracic or transesophageal echocardiography, ≥48-hour ECG monitoring, and labs. Neurological status was assessed using the NIH Stroke Scale (NIHSS) at admission and every six hours.²⁵All patients received a 12-lead ECG at admission, followed by ≥48-hour Holter ECG or continuous telemetry. 15 No implantable loop recorders were used. AF episodes were categorized as <30 seconds (0–14 s, 15–29 s) or ≥30 seconds. AF episodes lasting ≥30 seconds were considered diagnostic of AF, based on current guideline recommendations. 2 16 Cardiovascular risk factors and CHA₂DS₂-VASc scores were recorded. STATISTICS This descriptive study was not powered for hypothesis testing. Categorical variables are presented as counts and percentages; continuous variables as means ± SD or medians with interquartile range (IQR, 25th–75th percentile), depending on distribution. Group comparisons were exploratory and used chi-square or Fisher’s exact test for categorical data, and t-test or Mann–Whitney U test for continuous variables. Odds ratios (OR) with 95% confidence intervals (CI) and p-values were calculated for key subgroup associations using SPSS (IBM). Post-hoc power analysis was performed using observed proportions, sample sizes, and chi-square tests via the statsmodels package in Python. Descriptive graphics were generated using Microsoft Excel®. In addition, an exploratory multivariable logistic regression analysis was performed to identify independent predictors of short-duration atrial fibrillation (SDAF), including age, sex, stroke subtype, and hypertension. RESULTS During the six-month period, 771 patients with suspected stroke were admitted. After excluding 57 with hemorrhagic stroke, 714 patients were included in the final analysis. The mean age of the cohort was 73.2 ± 9.1 years, and the mean NIHSS on admission was 4.8 ± 3.6. Of these, 185 (25.9%) had TIA, 209 (29.3%) cardioembolic stroke, 110 (20.8%) large-artery atherosclerotic stroke, 40 (7.6%) lacunar stroke, and 163 (22.8%) cryptogenic stroke according to TOAST, with 98 (13.7%) meeting criteria for ESUS. The prevalence of hypertension was significantly higher among patients with short-duration AF compared to those without (82% vs. 71%, p = 0.002), a difference that remained significant after adjustment for age and sex (adjusted OR 1.75; 95% CI, 1.20–2.55; p = 0.004). Further baseline characteristics can be found in Table 1. Table 1. Baseline characteristics of patients with ischemic stroke , stratified by stroke subtype. Continuous variables are presented as mean ± standard deviation (SD) or median with interquartile range (IQR); categorical variables as absolute numbers and percentages. ESUS = embolic stroke of undetermined source; TIA = transient ischemic attack; NIHSS = National Institutes of Health Stroke Scale; BMI = body mass index; ECG = electrocardiogram. Stroke population TIA cryptogenic ESUS Athero-sclerotic cardioembolic lacunar n=714 n=185 (25.9%) n=163 (30.8%) n=98 (18.5%) n=110 (20.8%) n=209 (29.3%) n= 40 (7.6%) Age, mean ± SD (range) 71 ± 9.2 (20–99) 72 ± 8.5 (20–95) 67 ± 10.1 (29–99) 67 ± 9.8 (29–81) 70 ± 8.7 (39–96) 75 ± 7.9 (31–98) 72 ± 8.3 (29–92) Age, median (IQR) 74 (67–81) 75 (68–82) 69 (62–76) 70 (64–77) 69 (61–75) 77 (71–83) 77 (70–84) NIHSS at admission, median (IQR) 7 (3–11) 2 (1–4) 7 (4–10) 6 (3–9) 8 (5–12) 11 (6–16) 4 (2–6) NIHSS at discharge, median (IQR) 3 (1–5) 0 (0–1) 3 (1–4) 2 (1–3) 4 (2–6) 5 (3–7) 1 (0–2) Comorbidities Previous TIA/stroke 181 (25.4%) 41 (22.2%) 37 (22.7%) 11 (11,2%) 36 (32.7%) 55 (26.3%) 10 (25.0%) BMI, mean ± SD 29.1 ± 4.2 28.7 ± 4.5 30.2 ± 3.9 30.4 ± 4.1 28.9 ± 3.8 29.5 ± 4.4 29.3 ± 3.6 Obesity (BMI>30 kg/m2) 334 (46,8%) 75 (40.5%) 84 (51.5%) 53 (54,2%) 45 (41.0%) 106 (50.7%) 19 (47,5%) Hypertension 537 (75.2%) 151 (81.6%) 116 (71%) 71 (72,2%) 83 (72.4%) 156 (74.6%) 29(72.5%) Diabetes mellitus 212 43 (23.2%) 47 (28.8%) 31 (31,6%) 39 (35.5%) 66 (31.6%) 16 (40.0%) Atrial fibrillation 163 (22.8%) 42 (22.7%) 0 (0.0%) 0 (0%) 14 (12.7%) 101 (48.3%) 5 (12.5%) Coronary artery disease 191 (26.8%) 41 (22.2%) 46 (28.2%) 27 (27,5%) 30 (27.3%) 64 (30.6%) 9 (22.5%) Artificial heart valve 41 (5.7%) 6 (3.2%) 0 (0.0%) 0 (0%) 4 (3.6%) 30 (14.3%) 1 (2.5%) Heart failure 66 (9%) 6 (2.7%) 14 (7.3%) 6 (6,1%) 5 (4.5%) 40 (19.3%) 1 (2.5%) Hyper-cholesterinemia 274 (38.4%) 68 (36.8%) 66 (40.5%) 42 (42,9%) 59 (53.64%) 66 (31.6%) 16 (40.0%) ECG monitoring Stroke unit monitoring, mean ± SD (h) 37.1 ± 6.8 30.6 ± 5.4 37.9 ± 7.1 36.0 ± 6.2 39.9 ± 7.5 42.5 ± 6.9 34.1 ± 5.7 Stroke Unit Monitoring 651 (91.2%) 165 (89.2%) 151 (92.6%) 94 (96,0%) 101 (91.8%) 191 (91.4%) 36 (90%) Distribution of atrial fibrillation by stroke subtype Atrial fibrillation (AF) of any duration was identified in 384 of 714 patients (53.8%). AF >30 seconds occurred in 163 patients (22.8%), while 107 (15.0%) had AF episodes of 15–29 seconds and 114 (16.0%) had episodes of 0–14 seconds. AF distribution varied markedly across stroke subtypes (Figure 1). In the cardioembolic stroke group (n = 209; mean age 78.2 ± 7.6 years), 94 patients (45.0%) exhibited AF >30 seconds, 42 (20.1%) had episodes of 15–29 seconds, and 46 (22.0%) had 0–14 second episodes. Thus, 182 patients (87.1%) had AF of any duration—significantly more than in all other subtypes (OR 9.2; 95% CI, 6.2–13.6; p < 0.001). While the overall distribution of SDAF differed significantly across stroke subtypes (p < 0.001), the higher prevalence in ESUS compared to large-artery stroke did not reach statistical significance in direct comparison (p = 0.08), suggesting a trend that warrants further investigation. In cryptogenic stroke (n = 163; mean age 70.4 ± 8.1 years), no patient had AF >30 seconds. However, 28 (17.2%) had AF episodes of 15–29 seconds and 30 (18.4%) of 0–14 seconds, yielding a short-duration AF prevalence of 35.6%. Similarly, among ESUS patients (n = 98), 18 (18.4%) had 15–29 second episodes and 17 (17.3%) had 0–14 second episodes, for a combined prevalence of 35.7%. No significant difference was found between cryptogenic and ESUS groups (p = 0.98). In TIA patients (n = 185; mean age 73.5 ± 9.4 years), AF >30 seconds was seen in 41 (22.2%), while 13 (7.0%) and 18 (9.7%) had AF episodes of 15–29 and 0–14 seconds, respectively. Compared with the cardioembolic group, short-duration AF was significantly less common (OR 0.37; 95% CI, 0.23–0.60; p 30 seconds occurred in 13 patients (11.8%), with 17 (15.4%) and 10 (9.1%) having AF episodes of 15–29 and 0–14 seconds, respectively. Although short-duration AF was less frequent than in ESUS (25.4% vs. 35.7%), this difference was not statistically significant (p = 0.09). The lacunar stroke group (n = 40) had the lowest AF burden overall: 5 (12.5%) with AF >30 seconds, 3 (7.5%) with 15–29 second episodes, and 2 (5.0%) with 0–14 second episodes. Compared to all other stroke subtypes, this difference reached statistical significance (p 65 years (mean age 78.1 ± 6.9 years), compared to 282 of 507 (55.6%) without short-duration AF (mean age 70.3 ± 8.4 years), yielding an odds ratio of 6.7 (95% CI, 4.3–10.5; p < 0.001). This age-related gradient was further supported by subgroup analysis: short-duration AF was present in 36.8% of individuals aged 65–74 (49/133), 54.3% in those aged 75–84 (95/175), and 69.0% in those ≥85 years (40/58) (Figure 3). The trend across age strata was statistically significant (χ² for trend, p < 0.001). Sex differences in short-duration AF When assessing short-duration AF by sex, it was found to be more prevalent among female patients. Specifically, 115 out of 311 women (37.0%) experienced short-duration AF compared to 92 out of 403 men (22.8%). This difference was statistically significant, with an odds ratio of 2.0 (95% CI: 1.4–2.9, p < 0.001), indicating that women were nearly twice as likely to exhibit short-duration arrhythmias following stroke (Figures 5a and 5b). Stroke severity and short-duration AF (NIHSS) Patients with any AF had more severe strokes than those without (median NIHSS 6 [IQR 3–11] vs. 3 [IQR 1–6]; p < 0.001). Severity increased with AF duration: NIHSS was higher in long-duration AF (≥30 sec) than short-duration AF (median 7 [IQR 4–12] vs. 4 [IQR 2–8]; p = 0.003). In short-duration AF, NIHSS and CHA₂DS₂-VASc scores were modestly correlated (Spearman’s ρ = 0.23; p = 0.001). Stroke severity in short-duration AF varied by subtype (Figure 4). TIA patients (n = 18, 9.7%) were typically neurologically intact (NIHSS 0), while most cryptogenic/ESUS patients (n = 58, 35.6%) had mild deficits (NIHSS 1–5). Cardioembolic stroke patients (n = 88, 42.1%) showed broader severity; 65.2% of all short-duration AF cases (n = 135) had NIHSS scores between 1 and 14. Mean NIHSS was higher in cardioembolic vs. ESUS patients with short-duration AF (8.3 ± 4.2 vs. 4.2 ± 2.8; p < 0.01). Premorbid CHA₂DS₂-VASc scores in short-duration AF and AF patients Patients with AF of any duration had significantly higher premorbid CHA₂DS₂-VASc scores than those without AF (median 4 [IQR 3–5] vs. 3 [IQR 2–4]; p < 0.001). A score ≥2 was present in 91.4% of AF patients versus 70.3% without AF, and scores ≥5 were observed in 51.8% vs. 22.4% (both p < 0.001) (Figure 2). No significant difference was observed between long- and short-duration AF (median score 4 [IQR 3–5] in both; score ≥5 in 48.8% vs. 51.5%; p = 0.58). Among short-duration AF patients (n = 207), 166 (80.2%) had scores ≥2 and 101 (48.8%) had scores ≥5. Low-risk scores (0–1) were found in 18 patients (8.7%), primarily within the small-vessel subgroup. The proportion of CHA₂DS₂-VASc <2 was higher in small-vessel stroke than in other subtypes (40% vs. 8.1%; p = 0.002). In sex-stratified analysis, women with short-duration AF had significantly higher scores than men (median 5 [IQR 4–6] vs. 4 [IQR 3–5]; p < 0.001). Clinical distinction between known and newly detected AF Among the 384 patients with AF of any duration, 126 (32.8%) had a documented history of AF prior to stroke onset (premorbid AF), while 258 (67.2%) were newly diagnosed during in-hospital ECG monitoring (AF after stroke). Short-duration AF (<30 s) was significantly more common in patients with newly detected AF compared to those with premorbid AF (72.1% vs. 18.3%; OR, 11.5; 95% CI, 6.7–19.7; P <0.001). In contrast, AF ≥30 s occurred more frequently in patients with premorbid AF (81.7% vs. 27.9%; OR, 10.9; 95% CI, 6.3–18.8; P <0.001). Stroke severity at admission, assessed using the NIHSS, was significantly lower in patients with newly detected AF than in those with premorbid AF (median NIHSS 4 [IQR, 2–7] vs. 7 [IQR, 4–12]; P =0.002). Among patients with ESUS, all AF episodes were newly detected after stroke, and 35.7% had SDAF. These findings support the notion that AF after stroke may represent a distinct, frequently subclinical and less severe phenotype, underscoring the need for refined post-stroke AF classification and individualized management. Premorbid antithrombotic therapy and atrial fibrillation subtype Premorbid antithrombotic use was prevalent in this cohort, with 94.9% of patients receiving antithrombotic therapy prior to stroke onset. Among patients with coronary or peripheral artery disease, acetylsalicylic acid (ASA) monotherapy was near-universal (>90%). In contrast, antiplatelet use among patients without known vascular disease was markedly lower (49.5%; OR, 12.3; 95% CI, 7.0–21.6; P<0.001), underscoring the strong influence of documented atherosclerotic disease on preventive strategies. Among patients with short-duration atrial fibrillation (SDAF), premorbid ASA use was disproportionately high (71.5%) compared to AF-negative patients (53.1%; OR, 2.2; 95% CI, 1.6–3.2; P<0.001), suggesting a partial recognition of underlying vascular or embolic risk. Nevertheless, SDAF patients experienced a median NIHSS of 4 (IQR, 2–8), indicating that ASA may be insufficient in mitigating embolic stroke severity in this group. AF detection patterns differed markedly between known and newly diagnosed cases. Of 384 patients with AF of any duration, 67.2% were diagnosed post-stroke, predominantly with SDAF (<30 seconds; 72.1% vs. 18.3% in known AF; OR, 11.5; 95% CI, 6.7–19.7; P<0.001). Conversely, sustained AF was more common in the premorbid AF group (81.7% vs. 27.9%; OR, 10.9; 95% CI, 6.3–18.8; P<0.001), correlating with higher anticoagulation rates (87.3%) prior to stroke. Among older patients with known AF, NOACs were the predominant anticoagulant choice (91.2%), while VKAs were limited to patients with specific comorbidities. Strikingly, none of the patients with newly detected AF were anticoagulated at stroke onset (0.0% vs. 87.3%; OR, ∞; 95% CI, 49.9–∞; P<0.001), yet this group exhibited lower stroke severity (median NIHSS 4 [IQR, 2–7]) compared to those with known AF (median NIHSS 7 [IQR, 4–12]; P=0.002). This contrast suggests divergent pathophysiologic mechanisms and risk profiles between incident and chronic AF, with potential implications for post-stroke rhythm monitoring and treatment thresholds. Among patients with prior cerebrovascular events (n = 181), 97.8% were receiving secondary prevention at baseline—primarily ASA (80.7%) or NOACs (17.1%). Despite this, recurrent ischemic events occurred, particularly in those with underlying AF (35.4%), including 39 with SDAF. This subgroup had elevated CHA₂DS₂-VASc scores and moderate stroke severity, underscoring the limitations of current antithrombotic strategies and the need for refined post-stroke risk stratification, especially in patients with occult or subclinical AF. Exploratory multivariable regression analysis To explore factors independently associated with short-duration atrial fibrillation (SDAF), we conducted a multivariable logistic regression including age >65 years, female sex, hypertension, CHA₂DS₂-VASc score, and stroke subtype (with ESUS as reference category). SDAF was used as the dependent variable (1 = SDAF present, 0 = no AF or only longer-duration AF). In the final model, age >65 years (OR 3.7; 95% CI 2.4–5.9; p < 0.001), female sex (OR 1.9; 95% CI 1.4–2.7; p < 0.001), and hypertension (OR 1.8; 95% CI 1.2–2.7; p = 0.004) emerged as independent predictors of SDAF. A higher CHA₂DS₂-VASc score was also significantly associated with the presence of SDAF (per point increase: OR 1.4; 95% CI 1.2–1.6; p < 0.001). Importantly, the ESUS subtype remained significantly associated with SDAF compared to other stroke categories (OR 1.5; 95% CI 1.0–2.3; p = 0.049). Full model results are presented in Supplementary Table 1. These findings support the descriptive results and suggest that traditional vascular risk factors and embolic stroke subtype are independently associated with the detection of short-duration atrial fibrillation. Echocardiographic characteristics by SDAF status Transthoracic echocardiography was available for all patients. Patients with short-duration atrial fibrillation (SDAF) exhibited a distinct atrial profile compared to those without SDAF. Notably, left atrial volume index (LAVI) was significantly higher in the SDAF group (mean 39.5 ± 10.6 mL/m² vs. 33.4 ± 9.1 mL/m²; p = 0.002), and atrial reservoir strain was markedly reduced (–17.1 ± 4.3% vs. –21.5 ± 5.2%; p = 0.001). Measures of diastolic function, including E/e′ ratio and LAVI/a′ ratio, also differed significantly between groups. While left ventricular ejection fraction (LVEF) was numerically lower in the SDAF group, this difference did not reach statistical significance. Similarly, mitral regurgitation ≥ mild was more common among SDAF patients, though the association was not statistically significant (p = 0.083). These findings suggest a subclinical atrial cardiomyopathy pattern in patients with SDAF. ( Corresponding data are summarized in Table 2) Table 2. Echocardiographic Characteristics by Presence of Short-Duration Atrial Fibrillation This table summarizes echocardiographic differences between patients with and without SDAF. Those with SDAF had significantly larger left atria, higher LAVI, impaired atrial strain, and more advanced diastolic dysfunction, while LVEF and mitral regurgitation rates were similar between groups. Values are presented as mean ± SD or number (percentage). Echocardiographic parameter SDAF present (n=154) SDAF absent (n=375) p-value Left ventricular ejection fraction, % (mean ± SD) 58.3 ± 7.1 59.8 ± 6.3 0.117 Left atrial enlargement ≥42 mm, n (%) 105 (68.2%) 159 (42.5%) <0.001 Left atrial volume index (LAVI), mL/m² (mean ± SD) 39.5 ± 10.6 33.4 ± 9.1 0.002 Septal PA-TDI, ms (mean ± SD) 131.2 ± 12.5 102.3 ± 10.2 <0.001 LAVI/a′ ratio (mean ± SD) 4.3 ± 1.1 3.1 ± 0.9 0.004 Diastolic dysfunction ≥ grade II, n (%) 64 (41.5%) 96 (25.6%) 0.009 E/e′ ratio (mean ± SD) 12.8 ± 4.1 10.4 ± 3.7 0.006 Mitral regurgitation ≥ mild, n (%) 41 (26.5%) 67 (17.8%) 0.083 Left atrial strain, % (mean ± SD) –17.1 ± 4.3 –21.5 ± 5.2 0.001 Values are presented as mean ± standard deviation or number (percentage), as appropriate. LAVI = left atrial volume index; LA strain = left atrial reservoir strain; E/e′ = ratio of early mitral inflow velocity to mitral annular early diastolic velocity. Bold p-values indicate statistically significant differences (p < 0.05). DISCUSSION This study identifies a clinically important subgroup of stroke and TIA patients with short-duration atrial fibrillation (SDAF <30 seconds) who remain undetected or untreated under current diagnostic thresholds. Although these brief episodes fall below the guideline-defined cut-off for atrial fibrillation, 9 , 17 , 18 they were observed in nearly one-third of our cohort—particularly among patients with embolic stroke of undetermined source and cryptogenic stroke. Most affected individuals had elevated CHA₂DS₂-VASc scores, mild to moderate stroke severity, and a disproportionate representation of older adults and women. These findings suggest that SDAF may represent an overlooked marker of embolic potential, highlighting a diagnostic blind spot in current stroke evaluation. In this prospective cohort of patients with ischemic stroke or TIA, short-duration AF (<30 seconds) was detected in 29% of all cases and 35.7% of ESUS. Over 80% of these patients had CHA₂DS₂-VASc scores ≥2, and nearly half had scores ≥5, indicating substantial thromboembolic risk despite not meeting the diagnostic AF threshold of ≥30 seconds ( Figures 1 & 2) . NIHSS scores were significantly higher among patients with elevated CHA₂DS₂-VASc scores, and a modest but significant correlation between stroke severity and thromboembolic risk was observed (ρ = 0.23, p = 0.001). Higher CHA₂DS₂-VASc scores are independently associated with increased embolic risk even in patients without atrial fibrillation, with a consistent stepwise increase in risk across large population-based studies 19,20 . Nonetheless, its moderate discriminatory power limits clinical utility, and current guidelines do not support its use for anticoagulation decisions in non-AF populations. Our findings reinforce this association, demonstrating that elevated CHA₂DS₂-VASc scores may reflect relevant embolic risk even in patients without guideline-defined AF 21 . In light of this, the adequacy of the 30-second threshold for defining clinically relevant AF may require re-evaluation in high-risk patients with brief arrhythmic episodes. While not all short AF episodes require treatment, the 30-second threshold remains a long-established diagnostic convention endorsed by major societies including the ESC, AHA/ASA, and HRS/EHRA. 2 12 22 23 12,24 This cutoff has been consistently applied in key stroke trials such as CRYSTAL-AF, EMBRACE, and STROKE-AF 25 26 27 and is used to guide post-stroke rhythm monitoring and classification in international and national guidelines. In our study, the 30-second threshold was used solely for detection and classification—not to imply a treatment indication. We also acknowledge that in real-world device-based monitoring, episode durations ≥6 minutes are commonly used to guide oral anticoagulation, as recently demonstrated in the ARTESiA trial 28 . Our findings aim to inform early risk stratification and generate hypotheses, not to redefine treatment thresholds. Emerging evidence suggests that even AF episodes <30 seconds may predict future clinically manifest AF and should not be dismissed as benign—particularly in high-risk stroke populations. 29 30 The ESUS classification seeks to identify embolic strokes without an identifiable cause, yet our findings demonstrate that short-duration AF is common in both ESUS and cryptogenic strokes. While the overall difference across stroke subtypes was statistically significant (p < 0.001), the higher SDAF prevalence in ESUS compared to large-artery stroke did not reach statistical significance (p = 0.08), suggesting a possible trend. Only 23% of our cohort met the guideline-defined AF threshold (≥30 seconds), while a larger subset had shorter AF episodes. Among ESUS patients with short-duration AF, 80% had CHA₂DS₂-VASc ≥2 and nearly 50% had scores ≥5, suggesting a considerable embolic risk. Prior work indicates that CHA₂DS₂-VASc scores >2 are predictive of incident AF, 31 32 supporting the idea that these brief arrhythmias may reflect a clinically relevant underlying substrate. 33 Short-duration AF disproportionately affected older adults and women—groups known to carry a higher baseline stroke risk. While these findings offer meaningful insights into the prevalence and clinical profile of short-duration AF in ischemic stroke, they should be interpreted in the context of the study’s observational design. Although causality cannot be firmly established, the associations observed are clinically relevant and underscore the need for prospective studies to further explore the potential pathophysiological and therapeutic implications of these brief arrhythmias. In our study, 89.4% of patients with short-duration AF were over 65 years, and over two-thirds were 75 or older. Women were more frequently affected than men (37.0% vs. 22.8%; OR 2.0; 95% CI, 1.4–2.9; p < 0.001) and had more severe strokes, with a higher median NIHSS on admission (5 [IQR 3–10] vs. 3 [IQR 2–7]; p = 0.002), in line with epidemiologic data showing rising AF prevalence with age and greater stroke vulnerability in women. 14,31 These findings raise concerns about the recent removal of female sex as a standalone CHA₂DS₂-VASc component. 34,35 Additionally CHA₂DS₂-VASc scores correlated significantly with stroke severity (ρ = 0.23; p = 0.001), indicating a non-negligible risk of recurrence even in patients without guideline-defined AF. Stroke severity also varied by AF status and stroke subtype. Cardioembolic strokes were associated with the highest NIHSS scores and most severe deficits. Patients with AF—regardless of episode duration—had significantly more severe strokes than those without AF (Figure 6). Notably, the severity patterns observed in patients with short-duration AF did not clearly mirror those of classic cardioembolic strokes. Instead, the median NIHSS scores in this group fell within a moderate range and more closely resembled the distribution seen in atherosclerotic strokes (Figure 4). This suggests that stroke severity alone may not reliably distinguish embolic mechanisms in the presence of SDAF and supports the view that ESUS encompasses heterogeneous pathophysiologies—including occult AF and non-stenotic atherosclerosis The RE-SPECT ESUS and NAVIGATE ESUS trials failed to demonstrate a benefit of DOAC therapy in unselected ESUS populations 36 37 but subgroup analyses by AF duration were not performed. Given that 30–33% of cryptogenic and ESUS strokes in our study exhibited short-duration AF, this subgroup may merit separate consideration in future anticoagulation trials. Prolonged ECG monitoring, cardiac imaging, and biomarkers may improve detection of AF-related stroke risk. The SAFAS study showed that multimodal diagnostic approaches enhance post-stroke AF detection. 38 According to current studies, AF predictors in ESUS include older age, high CHA₂DS₂-VASc scores, rhythm irregularity burden (HR 3.12), elevated NT-proBNP, left atrial enlargement, NSAT, prolonged PR interval, and specific imaging patterns (e.g. non-lacunar, bihemispheric, multifocal) . 17 , 39,40 and elevated CHA₂DS₂-VASc and CHA₂DS₂ scores have been linked to delayed post-stroke AF onset. 31,41 Recent data from the ARTESIA trial demonstrated that anticoagulation with apixaban reduces stroke risk in patients with subclinical AF lasting 6 minutes to 24 hours, albeit with an increased bleeding risk. 28 A subgroup analysis found particular benefit in patients with prior stroke or TIA and subclinical AF, demonstrating potential benefits of individualized anticoagulation decisions based on risk profile rather than fixed duration thresholds. 42,43 Our findings identified a clinically relevant subgroup of stroke patients with short-duration AF and elevated risk profiles. Future studies should explore the prognostic relevance and long-term outcomes of SDAF in high-risk stroke populations. Our results underscore the need for burden-adapted follow-up strategies that account for arrhythmia duration, patient risk profile, and stroke subtype. LIMITATIONS AND STRENGTHS This single-center design may limit generalizability. Data on prior anticoagulant or antiplatelet therapy were not available, and long-term follow-up was not performed, precluding assessment of stroke recurrence or AF progression. Strengths include the prospective design, standardized data collection, and structured neurological assessments during hospitalization. Daily clinical evaluation minimized missing data, and detailed ECG analysis enhanced detection and classification of atrial fibrillation. As with all descriptive observational studies, causality cannot be inferred; however, the consistent patterns observed in high-risk subgroups underscore the need for prospective validation, to see whether or not short-duration AF is a predictor for developing longer episodes of AF or recurrent strokes. Furthermore, the use of a 30-second threshold for AF detection, while standardized in trials and guidelines, may not align with real-world treatment practices, which often apply longer duration cutoffs (e.g., ≥6 minutes) when considering anticoagulation initiation CONCLUSIONS Short-duration atrial fibrillation occurred across all ischemic stroke subtypes, with the highest prevalence in cardioembolic, cryptogenic, and ESUS cases. It was more frequent in older adults and women and associated with elevated CHA₂DS₂-VASc scores, indicating substantial thromboembolic risk despite falling below current AF diagnostic thresholds. These findings suggest that SDAF may signal a clinically relevant embolic source and define a high-risk, undertreated subgroup. This diagnostic blind spot underscores the need to reconsider fixed duration thresholds in post-stroke AF detection. Risk-based approaches incorporating age, sex, and CHA₂DS₂-VASc may better guide monitoring and secondary prevention. Prospective trials are needed to determine whether targeted treatment of SDAF can improve outcomes in this overlooked population. DECLARATIONS Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Medical Association of Westphalia-Lippe (file number 2015-091-fS). Written informed consent was obtained from all participants. Consent for publication: Not applicable. Availability of data and materials The datasets generated and/or analyzed during the current study are not publicly available due to patient confidentiality and institutional data protection policies but are available from the corresponding author upon reasonable request and with appropriate ethical approval. Competing interests The authors declare that they have no competing interests. Funding This study received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Clinical trial number: not applicable. Disclosures Language editing assistance was supported by AI-based tools (e.g., Microsoft Copilot, and DeepL Write) for grammar and style refinement. No AI tools were used for data analysis, interpretation, or manuscript drafting beyond language support. All content and conclusions were developed by the authors. Authors' contributions Priyanka Boettger and Karolis Macius contributed to the conception and design of the study, data interpretation, and drafting of the manuscript. Jamschid Sedighi and Omar Alhaj Omar were involved in patient recruitment, data acquisition, and clinical assessments. Martin Juenemann and Bernhard Unsoeld contributed to statistical analysis, data interpretation, and manuscript revision. Henning Lemm and Kerstin Piayda supported the analysis and clinical validation of arrhythmia findings in the intensive care cohort. Michael Buerke and Samuel Sossalla supervised the project, reviewed all data critically, and provided senior oversight throughout the manuscript development. All authors read and approved the final manuscript. Acknowledgements We sincerely thank our colleagues at the University Hospital of Gießen for their valuable contributions to this study. 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The Lancet Neurology . 2025;24:140–151. doi: 10.1016/S1474-4422(24)00475-7 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx Cite Share Download PDF Status: Published Journal Publication published 20 Aug, 2025 Read the published version in BMC Cardiovascular Disorders → Version 1 posted Editorial decision: Accepted 23 Jul, 2025 Reviews received at journal 22 Jul, 2025 Reviews received at journal 20 Jul, 2025 Reviewers agreed at journal 14 Jul, 2025 Reviewers agreed at journal 14 Jul, 2025 Reviewers invited by journal 14 Jul, 2025 Submission checks completed at journal 14 Jul, 2025 First submitted to journal 13 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6813617","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":488089715,"identity":"dd76f2a8-e972-4184-a2ea-3ba9b28c9545","order_by":0,"name":"Priyanka Boettger","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABJElEQVRIiWNgGAWjYBAC+wbGBiCVwMAgARNiBwkYWODUYnAAWcsBIM3AcwAkLoFHC5hC1iIBIhjwaLl9uE3i5440eQbp5oOPP/6olTOf+fzqhh8FEgz87d0JWP3Sl9gm2Xsmx7BB5liywYGE48Yyt3PKbvYAHSZx5uwGrLbwMLZJ8LZVMDZI5JhJHEg4ljhDOiftBg9Qi4FELk4tkn/bKuyBWsx/ALXUz5A8k3bzDwEt0rxtOYkgW4Der0mQkGA/dpuALc3Wsm1pyW1Av0icSTtgOIMnh+22jIEED26/sD+8+bYt2bYfGGIfKmzq5CXYjz+7+eaPjRx/ey9WLUDAAo4CNgjnMBDzGIBYPDiUgwDzByROHRCzP8CjehSMglEwCkYgAACsHmSWlL8NiwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Giessen","correspondingAuthor":true,"prefix":"","firstName":"Priyanka","middleName":"","lastName":"Boettger","suffix":""},{"id":488089716,"identity":"c325d568-408f-4508-ac98-306bd698b6fe","order_by":1,"name":"Karolis Macius","email":"","orcid":"","institution":"University of Giessen","correspondingAuthor":false,"prefix":"","firstName":"Karolis","middleName":"","lastName":"Macius","suffix":""},{"id":488089719,"identity":"8471bd93-243e-47d1-9a14-9062f0a985e3","order_by":2,"name":"Jamschid Sedighi","email":"","orcid":"","institution":"University of Giessen","correspondingAuthor":false,"prefix":"","firstName":"Jamschid","middleName":"","lastName":"Sedighi","suffix":""},{"id":488089720,"identity":"4bcafc27-7b03-451e-a461-deb647e24cbe","order_by":3,"name":"Henning Lemm","email":"","orcid":"","institution":"St. Marien-Hospital","correspondingAuthor":false,"prefix":"","firstName":"Henning","middleName":"","lastName":"Lemm","suffix":""},{"id":488089723,"identity":"15ac6941-aece-4608-bec4-ec0c98ceee56","order_by":4,"name":"Kerstin Piayda","email":"","orcid":"","institution":"University of Giessen","correspondingAuthor":false,"prefix":"","firstName":"Kerstin","middleName":"","lastName":"Piayda","suffix":""},{"id":488089724,"identity":"863ecf4d-b5df-4aa3-a74e-31a6a82e7576","order_by":5,"name":"Bernhard Unsoeld","email":"","orcid":"","institution":"University of Giessen","correspondingAuthor":false,"prefix":"","firstName":"Bernhard","middleName":"","lastName":"Unsoeld","suffix":""},{"id":488089725,"identity":"9a93710e-611c-4a34-ba2c-caf62e31f2ea","order_by":6,"name":"Samuel Sossalla","email":"","orcid":"","institution":"University of Giessen","correspondingAuthor":false,"prefix":"","firstName":"Samuel","middleName":"","lastName":"Sossalla","suffix":""},{"id":488089726,"identity":"70388f3b-f8b4-4612-8bef-3a8a7a09efa5","order_by":7,"name":"Omar Alhaj Omar","email":"","orcid":"","institution":"University of Giessen","correspondingAuthor":false,"prefix":"","firstName":"Omar","middleName":"Alhaj","lastName":"Omar","suffix":""},{"id":488089727,"identity":"d9b8af2c-b305-453b-aeb5-7ea2975e0fbb","order_by":8,"name":"Martin Juenemann","email":"","orcid":"","institution":"University of Giessen","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Juenemann","suffix":""},{"id":488089728,"identity":"958525e7-189b-458d-98f0-4bf4bb70ecfc","order_by":9,"name":"Michael Buerke","email":"","orcid":"","institution":"St. Marien-Hospital","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Buerke","suffix":""}],"badges":[],"createdAt":"2025-06-03 17:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6813617/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6813617/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12872-025-05080-1","type":"published","date":"2025-08-20T16:29:29+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87370031,"identity":"5f520022-209c-4a27-ae92-787b4344f065","added_by":"auto","created_at":"2025-07-23 07:05:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":20059,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of atrial fibrillation episodes by ischemic stroke subtype.\u003c/strong\u003e Prevalence of atrial fibrillation (AF) episodes ≥30 seconds, 15–29 seconds, and 0–14 seconds across stroke subtypes. AF of any duration was detected in 87.1% of cardioembolic strokes (n = 182/209), compared with 35.6% in cryptogenic stroke and 35.7% in ESUS. AF was least common in lacunar strokes (25.0%), with statistically significant differences across subtypes (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). No patient with cryptogenic or ESUS stroke had AF ≥30 seconds.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6813617/v1/f63644b93445093898b6dec9.png"},{"id":87370032,"identity":"16a2f3c3-1e8f-49fb-a939-f2b6cc01d2d9","added_by":"auto","created_at":"2025-07-23 07:05:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":22044,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCHA₂DS₂-VASc score distribution in patients with short-duration atrial fibrillation (SDAF).\u003c/strong\u003e Among patients with SDAF (\u0026lt;30 seconds, n = 207), 80.2% had a CHA₂DS₂-VASc score ≥2 and 48.8% had scores ≥5, compared with 70.3% and 22.4%, respectively, in patients without AF (both \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). There was no significant difference in median CHA₂DS₂-VASc score between patients with SDAF and those with AF ≥30 seconds (median 4 [IQR 3–5] in both groups; \u003cem\u003ep\u003c/em\u003e = 0.58).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6813617/v1/147a173dac9a9c073911d7ed.png"},{"id":87370033,"identity":"ac3bc853-2069-49d1-9329-2144737bbdec","added_by":"auto","created_at":"2025-07-23 07:05:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":23848,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAge distribution of patients with short-duration atrial fibrillation.\u003c/strong\u003e Short-duration AF was significantly more frequent in older patients: 36.8% in those aged 65–74 (49/133), 54.3% in those aged 75–84 (95/175), and 69.0% in those aged ≥85 years (40/58), with a significant trend across age strata (χ² for trend, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001). Overall, 89.4% of patients with SDAF were aged \u0026gt;65 years (OR 6.7; 95% CI, 4.3–10.5; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6813617/v1/73e786f56e053d75eb2923fe.png"},{"id":87371288,"identity":"d3ebc2f3-2573-4ed1-9489-ea5996c1d37f","added_by":"auto","created_at":"2025-07-23 07:13:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":20171,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStroke severity by atrial fibrillation status and duration. \u003c/strong\u003eMedian NIHSS scores on admission were significantly higher in patients with AF (median 6 [IQR 3–11]) compared to those without AF (3 [IQR 1–6]; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Stroke severity increased with AF duration: median NIHSS was 7 [IQR 4–12] in AF ≥30 s and 4 [IQR 2–8] in SDAF (\u0026lt;30 s) (\u003cem\u003ep\u003c/em\u003e = 0.003). A modest but significant correlation was observed between NIHSS and CHA₂DS₂-VASc scores in patients with SDAF (Spearman ρ = 0.23; \u003cem\u003ep\u003c/em\u003e = 0.001).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6813617/v1/2fdb046be8c8811eb281d46b.png"},{"id":87370035,"identity":"4589821b-8744-421b-bdee-dc05f7ce90a3","added_by":"auto","created_at":"2025-07-23 07:05:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":49848,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSex-specific prevalence of short-duration atrial fibrillation after ischemic stroke.\u003c/strong\u003e Panel (a) shows that 115 of 311 women (37.0%) exhibited SDAF, compared to 92 of 403 men (22.8%) in panel (b), yielding an odds ratio of 2.0 (95% CI, 1.4–2.9; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Women with SDAF also had significantly higher CHA₂DS₂-VASc scores than men (median 5 [IQR 4–6] vs. 4 [IQR 3–5]; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6813617/v1/f8c6f7288bc740d8ec4bd5b2.png"},{"id":87370038,"identity":"019d6568-5681-4340-aa2d-8bb9c5525394","added_by":"auto","created_at":"2025-07-23 07:05:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":276159,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eShort-Duration Atrial Fibrillation in Ischemic Stroke: Subtype Distribution, Risk Stratification, and Clinical Overlap\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel A: SDAF Prevalence by Stroke Subtype \u003c/strong\u003eStacked bar chart showing the prevalence of SDAF episodes of 0–14 seconds and 15–29 seconds across stroke subtypes. SDAF was most common in cardioembolic strokes, followed by cryptogenic and ESUS, and least common in lacunar strokes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel B: High CHA₂DS₂-VASc in SDAF Patients by Sex \u003c/strong\u003eBar chart comparing the proportion of SDAF patients with CHA₂DS₂-VASc scores ≥2 and ≥5 by sex. A higher percentage of women with SDAF had CHA₂DS₂-VASc ≥5, indicating elevated stroke risk despite short AF duration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel C: NIHSS by AF Duration \u003c/strong\u003eBar chart depicting the median NIHSS score (with interquartile range) at admission by AF status. Stroke severity was highest in patients with AF ≥30 seconds, followed by SDAF \u0026lt;30s, and lowest in those without AF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel D: Clinical Overlap of ESUS, SDAF, and High Stroke Risk \u003c/strong\u003eVenn diagram illustrating the intersection of ESUS, SDAF \u0026lt;30s, and CHA₂DS₂-VASc ≥2. A notable subset of patients exhibit all three features, highlighting a high-risk ESUS group with occult atrial fibrillation and elevated stroke risk.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6813617/v1/974d759f0e9d92bae03415e9.png"},{"id":89847198,"identity":"c455e4f4-3b70-4921-992f-1f3caee0256d","added_by":"auto","created_at":"2025-08-25 16:41:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1365129,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6813617/v1/d960894e-a81d-41a7-980f-16c420964e44.pdf"},{"id":87370036,"identity":"a7fa84ab-c0f2-42f7-93e8-c301fe1b0666","added_by":"auto","created_at":"2025-07-23 07:05:47","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":15336,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6813617/v1/009be670dbf6cf33e47c30e1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Short-Duration Atrial Fibrillation in Ischemic Stroke: High Risk Despite Subclinical Burden-A prospective cohort study","fulltext":[{"header":"BACKGROUND AND PURPOSE","content":"\u003cp\u003eCurrent stroke guidelines consider atrial fibrillation (AF) clinically actionable only when episodes last \u0026ge;30 seconds, a definition supported by device-based studies and consensus guidelines.\u003csup\u003e1\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e2\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e3\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e4\u003c/sup\u003e However, shorter episodes are increasingly observed during post-stroke monitoring and may indicate a clinically meaningful arrhythmic burden.\u003csup\u003e5\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e6\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e7\u003c/sup\u003e In particular, cryptogenic stroke and ESUS frequently lack an identified embolic source despite extended diagnostic workup.\u0026nbsp;\u003csup\u003e8\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e9\u003c/sup\u003e Real-world data suggest that short-duration AF (SDAF) may correlate with age, stroke severity, and CHA₂DS₂-VASc scores\u003csup\u003e10\u003c/sup\u003e, raising concern that these brief episodes, though guideline-excluded, could represent a high-risk but undertreated subgroup. In this prospective cohort study, we aimed to assess the prevalence of SDAF (\u0026lt;30 seconds) across ischemic stroke subtypes, determine associated risk profiles, and evaluate stroke severity. We hypothesized that SDAF is common in ESUS and cryptogenic stroke and is associated with elevated thromboembolic risk despite not fulfilling current diagnostic thresholds\u0026mdash;thereby representing a diagnostic blind spot in contemporary stroke care.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eOver six months, all consecutive patients presenting with suspected ischemic stroke or Transient ischemic attack (TIA) were prospectively enrolled. Adults aged \u0026ge;18 years with acute ischemic stroke or TIA were eligible. Patients with hemorrhagic stroke or hospital stays \u0026lt;24 hours were excluded. Demographics, comorbidities, and diagnostic data were systematically recorded.\u003c/p\u003e\n\u003cp\u003eStroke subtypes were classified using TOAST\u003csup\u003e11\u003c/sup\u003e and ESUS\u003csup\u003e8\u003c/sup\u003e criteria, aligned with current prevention and treatment guidelines.\u003csup\u003e12\u003c/sup\u003e Subtypes included: (1) large-artery atherosclerosis, defined by \u0026ge;50% stenosis (NASCET) or occlusion of relevant vessels; (2) cardioembolism, including AF, atrial flutter, intracardiac thrombus, valvular heart disease, recent myocardial infraction, LVEF \u0026lt;35%, or endocarditis; (3) small vessel disease, defined by deep lacunar infarcts \u0026le;15 mm (CT) or \u0026le;20 mm (MRI); (4) other determined causes, including vasculitis, dissection, hematologic, storage, or mitochondrial disorders; and (5) ESUS, when no cause was identified after standard workup.\u003c/p\u003e\n\u003cp\u003eTIA was defined as a transient neurological deficit lasting \u0026lt;24 hours without imaging evidence of infarction, in accordance with the tissue-based definition proposed by Albers et al.\u0026nbsp;\u003csup\u003e13\u003c/sup\u003e \u003csup\u003e14\u003c/sup\u003e Stroke workup included CT or MRI, extracranial/intracranial Doppler, transthoracic or transesophageal echocardiography, \u0026ge;48-hour ECG monitoring, and labs. Neurological status was assessed using the NIH Stroke Scale (NIHSS) at admission and every six hours.\u0026sup2;⁵All patients received a 12-lead ECG at admission, followed by \u0026ge;48-hour Holter ECG or continuous telemetry.\u003csup\u003e15\u003c/sup\u003e No implantable loop recorders were used. AF episodes were categorized as \u0026lt;30 seconds (0\u0026ndash;14 s, 15\u0026ndash;29 s) or \u0026ge;30 seconds. AF episodes lasting \u0026ge;30 seconds were considered diagnostic of AF, based on current guideline recommendations.\u003csup\u003e2\u003c/sup\u003e \u003csup\u003e16\u003c/sup\u003e Cardiovascular risk factors and CHA₂DS₂-VASc scores were recorded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSTATISTICS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis descriptive study was not powered for hypothesis testing. Categorical variables are presented as counts and percentages; continuous variables as means \u0026plusmn; SD or medians with interquartile range (IQR, 25th\u0026ndash;75th percentile), depending on distribution. Group comparisons were exploratory and used chi-square or Fisher\u0026rsquo;s exact test for categorical data, and t-test or Mann\u0026ndash;Whitney U test for continuous variables. Odds ratios (OR) with 95% confidence intervals (CI) and p-values were calculated for key subgroup associations using SPSS (IBM). Post-hoc power analysis was performed using observed proportions, sample sizes, and chi-square tests via the statsmodels package in Python. Descriptive graphics were generated using Microsoft Excel\u0026reg;. In addition, an exploratory multivariable logistic regression analysis was performed to identify independent predictors of short-duration atrial fibrillation (SDAF), including age, sex, stroke subtype, and hypertension.\u0026nbsp;\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eDuring the six-month period, 771 patients with suspected stroke were admitted. After excluding 57 with hemorrhagic stroke, 714 patients were included in the final analysis. The mean age of the cohort was 73.2 \u0026plusmn; 9.1 years, and the mean NIHSS on admission was 4.8 \u0026plusmn; 3.6. Of these, 185 (25.9%) had TIA, 209 (29.3%) cardioembolic stroke, 110 (20.8%) large-artery atherosclerotic stroke, 40 (7.6%) lacunar stroke, and 163 (22.8%) cryptogenic stroke according to TOAST, with 98 (13.7%) meeting criteria for ESUS. The prevalence of hypertension was significantly higher among patients with short-duration AF compared to those without (82% vs. 71%, p = 0.002), a difference that remained significant after adjustment for age and sex (adjusted OR 1.75; 95% CI, 1.20\u0026ndash;2.55; p = 0.004). Further baseline characteristics can be found in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 1. Baseline characteristics of patients with ischemic stroke\u003c/strong\u003e, stratified by stroke subtype. Continuous variables are presented as mean \u0026plusmn; standard deviation (SD) or median with interquartile range (IQR); categorical variables as absolute numbers and percentages. ESUS = embolic stroke of undetermined source; TIA = transient ischemic attack; NIHSS = National Institutes of Health Stroke Scale; BMI = body mass index; ECG = electrocardiogram.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"652\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStroke population\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTIA\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecryptogenic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eESUS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAthero-sclerotic\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecardioembolic\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elacunar\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003en=714\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003en=185 (25.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003en=163 (30.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003en=98\u003c/p\u003e\n \u003cp\u003e(18.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003en=110 (20.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003en=209\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(29.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003en= 40 (7.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAge, mean \u0026plusmn; SD (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e71 \u0026plusmn; 9.2 (20\u0026ndash;99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e72 \u0026plusmn; 8.5 (20\u0026ndash;95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e67 \u0026plusmn; 10.1 (29\u0026ndash;99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e67 \u0026plusmn; 9.8 (29\u0026ndash;81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e70 \u0026plusmn; 8.7 (39\u0026ndash;96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e75 \u0026plusmn; 7.9 (31\u0026ndash;98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e72 \u0026plusmn; 8.3 (29\u0026ndash;92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAge, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e74 (67\u0026ndash;81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e75 (68\u0026ndash;82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e69 (62\u0026ndash;76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e70 (64\u0026ndash;77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e69 (61\u0026ndash;75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e77 (71\u0026ndash;83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e77 (70\u0026ndash;84)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eNIHSS at admission, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e7 (3\u0026ndash;11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e2 (1\u0026ndash;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e7 (4\u0026ndash;10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e6 (3\u0026ndash;9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e8 (5\u0026ndash;12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e11 (6\u0026ndash;16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e4 (2\u0026ndash;6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eNIHSS at discharge, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3 (1\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0 (0\u0026ndash;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e3 (1\u0026ndash;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e2 (1\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e4 (2\u0026ndash;6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e5 (3\u0026ndash;7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1 (0\u0026ndash;2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eComorbidities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003ePrevious TIA/stroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e181 (25.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e41 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e37 (22.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e11 (11,2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e36 (32.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e55 (26.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e10 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eBMI, mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e29.1 \u0026plusmn; 4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e28.7 \u0026plusmn; 4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e30.2 \u0026plusmn; 3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e30.4 \u0026plusmn; 4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e28.9 \u0026plusmn; 3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e29.5 \u0026plusmn; 4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e29.3 \u0026plusmn; 3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eObesity (BMI\u0026gt;30 kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e334 (46,8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e75 (40.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e84 (51.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e53 (54,2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e45 (41.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e106 (50.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e19 (47,5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e537 (75.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e151 (81.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e116 (71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e71 (72,2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e83 (72.4%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e156 (74.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e29(72.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e43 (23.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e47 (28.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e31 (31,6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e39 (35.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e66 (31.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e16 (40.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAtrial fibrillation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e163 (22.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e42 (22.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e14 (12.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e101 (48.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e5 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eCoronary artery disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e191 (26.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e41 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e46 (28.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e27 (27,5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e30 (27.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e64 (30.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e9 (22.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eArtificial heart valve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e41 (5.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e6 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e4 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e30 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eHeart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e66 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e6 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e14 (7.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e6 (6,1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e5 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e40 (19.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eHyper-cholesterinemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e274 (38.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e68 (36.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e66 (40.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e42 (42,9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e59 (53.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e66 (31.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e16 (40.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eECG monitoring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eStroke unit monitoring, mean \u0026plusmn; SD (h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e37.1 \u0026plusmn; 6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e30.6 \u0026plusmn; 5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e37.9 \u0026plusmn; 7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e36.0 \u0026plusmn; 6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e39.9 \u0026plusmn; 7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e42.5 \u0026plusmn; 6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e34.1 \u0026plusmn; 5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eStroke Unit Monitoring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e651 (91.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e165 (89.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e151 (92.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e94 (96,0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e101 (91.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e191 (91.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e36 (90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDistribution of atrial fibrillation by stroke subtype\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAtrial fibrillation (AF) of any duration was identified in 384 of 714 patients (53.8%). AF \u0026gt;30 seconds occurred in 163 patients (22.8%), while 107 (15.0%) had AF episodes of 15\u0026ndash;29 seconds and 114 (16.0%) had episodes of 0\u0026ndash;14 seconds. AF distribution varied markedly across stroke subtypes (Figure 1). In the cardioembolic stroke group (n = 209; mean age 78.2 \u0026plusmn; 7.6 years), 94 patients (45.0%) exhibited AF \u0026gt;30 seconds, 42 (20.1%) had episodes of 15\u0026ndash;29 seconds, and 46 (22.0%) had 0\u0026ndash;14 second episodes. Thus, 182 patients (87.1%) had AF of any duration\u0026mdash;significantly more than in all other subtypes (OR 9.2; 95% CI, 6.2\u0026ndash;13.6; p \u0026lt; 0.001). While the overall distribution of SDAF differed significantly across stroke subtypes (p \u0026lt; 0.001), the higher prevalence in ESUS compared to large-artery stroke did not reach statistical significance in direct comparison (p = 0.08), suggesting a trend that warrants further investigation. In cryptogenic stroke (n = 163; mean age 70.4 \u0026plusmn; 8.1 years), no patient had AF \u0026gt;30 seconds. However, 28 (17.2%) had AF episodes of 15\u0026ndash;29 seconds and 30 (18.4%) of 0\u0026ndash;14 seconds, yielding a short-duration AF prevalence of 35.6%. Similarly, among ESUS patients (n = 98), 18 (18.4%) had 15\u0026ndash;29 second episodes and 17 (17.3%) had 0\u0026ndash;14 second episodes, for a combined prevalence of 35.7%. No significant difference was found between cryptogenic and ESUS groups (p = 0.98). In TIA patients (n = 185; mean age 73.5 \u0026plusmn; 9.4 years), AF \u0026gt;30 seconds was seen in 41 (22.2%), while 13 (7.0%) and 18 (9.7%) had AF episodes of 15\u0026ndash;29 and 0\u0026ndash;14 seconds, respectively. Compared with the cardioembolic group, short-duration AF was significantly less common (OR 0.37; 95% CI, 0.23\u0026ndash;0.60; p \u0026lt; 0.001). In atherosclerotic stroke (n = 110), AF \u0026gt;30 seconds occurred in 13 patients (11.8%), with 17 (15.4%) and 10 (9.1%) having AF episodes of 15\u0026ndash;29 and 0\u0026ndash;14 seconds, respectively. Although short-duration AF was less frequent than in ESUS (25.4% vs. 35.7%), this difference was not statistically significant (p = 0.09). The lacunar stroke group (n = 40) had the lowest AF burden overall: 5 (12.5%) with AF \u0026gt;30 seconds, 3 (7.5%) with 15\u0026ndash;29 second episodes, and 2 (5.0%) with 0\u0026ndash;14 second episodes. Compared to all other stroke subtypes, this difference reached statistical significance (p \u0026lt; 0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAge-related distribution of short-duration AF\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAge was significantly associated with the presence of short-duration AF. Among the 207 patients with short-duration AF, 185 (89.4%) were aged \u0026gt;65 years (mean age 78.1 \u0026plusmn; 6.9 years), compared to 282 of 507 (55.6%) without short-duration AF (mean age 70.3 \u0026plusmn; 8.4 years), yielding an odds ratio of 6.7 (95% CI, 4.3\u0026ndash;10.5; p \u0026lt; 0.001). This age-related gradient was further supported by subgroup analysis: short-duration AF was present in 36.8% of individuals aged 65\u0026ndash;74 (49/133), 54.3% in those aged 75\u0026ndash;84 (95/175), and 69.0% in those \u0026ge;85 years (40/58) (Figure 3). The trend across age strata was statistically significant (\u0026chi;\u0026sup2; for trend, p \u0026lt; 0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSex differences in short-duration AF \u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWhen assessing short-duration AF by sex, it was found to be more prevalent among female patients. Specifically, 115 out of 311 women (37.0%) experienced short-duration AF compared to 92 out of 403 men (22.8%). This difference was statistically significant, with an odds ratio of 2.0 (95% CI: 1.4\u0026ndash;2.9, p \u0026lt; 0.001), indicating that women were nearly twice as likely to exhibit short-duration arrhythmias following stroke (Figures 5a and 5b).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStroke severity and short-duration AF (NIHSS)\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePatients with any AF had more severe strokes than those without (median NIHSS 6 [IQR 3\u0026ndash;11] vs. 3 [IQR 1\u0026ndash;6]; p \u0026lt; 0.001). Severity increased with AF duration: NIHSS was higher in long-duration AF (\u0026ge;30 sec) than short-duration AF (median 7 [IQR 4\u0026ndash;12] vs. 4 [IQR 2\u0026ndash;8]; p = 0.003). In short-duration AF, NIHSS and CHA₂DS₂-VASc scores were modestly correlated (Spearman\u0026rsquo;s \u0026rho; = 0.23; p = 0.001). Stroke severity in short-duration AF varied by subtype (Figure 4). TIA patients (n = 18, 9.7%) were typically neurologically intact (NIHSS 0), while most cryptogenic/ESUS patients (n = 58, 35.6%) had mild deficits (NIHSS 1\u0026ndash;5). Cardioembolic stroke patients (n = 88, 42.1%) showed broader severity; 65.2% of all short-duration AF cases (n = 135) had NIHSS scores between 1 and 14. Mean NIHSS was higher in cardioembolic vs. ESUS patients with short-duration AF (8.3 \u0026plusmn; 4.2 vs. 4.2 \u0026plusmn; 2.8; p \u0026lt; 0.01).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePremorbid CHA₂DS₂-VASc scores in short-duration AF and AF patients\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePatients with AF of any duration had significantly higher premorbid CHA₂DS₂-VASc scores than those without AF (median 4 [IQR 3\u0026ndash;5] vs. 3 [IQR 2\u0026ndash;4]; p \u0026lt; 0.001). A score \u0026ge;2 was present in 91.4% of AF patients versus 70.3% without AF, and scores \u0026ge;5 were observed in 51.8% vs. 22.4% (both p \u0026lt; 0.001) (Figure 2). No significant difference was observed between long- and short-duration AF (median score 4 [IQR 3\u0026ndash;5] in both; score \u0026ge;5 in 48.8% vs. 51.5%; p = 0.58). Among short-duration AF patients (n = 207), 166 (80.2%) had scores \u0026ge;2 and 101 (48.8%) had scores \u0026ge;5. Low-risk scores (0\u0026ndash;1) were found in 18 patients (8.7%), primarily within the small-vessel subgroup. The proportion of CHA₂DS₂-VASc \u0026lt;2 was higher in small-vessel stroke than in other subtypes (40% vs. 8.1%; p = 0.002). In sex-stratified analysis, women with short-duration AF had significantly higher scores than men (median 5 [IQR 4\u0026ndash;6] vs. 4 [IQR 3\u0026ndash;5]; p \u0026lt; 0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eClinical distinction between known and newly detected AF\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 384 patients with AF of any duration, 126 (32.8%) had a documented history of AF prior to stroke onset (premorbid AF), while 258 (67.2%) were newly diagnosed during in-hospital ECG monitoring (AF after stroke). Short-duration AF (\u0026lt;30 s) was significantly more common in patients with newly detected AF compared to those with premorbid AF (72.1% vs. 18.3%; OR, 11.5; 95% CI, 6.7\u0026ndash;19.7; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). In contrast, AF \u0026ge;30 s occurred more frequently in patients with premorbid AF (81.7% vs. 27.9%; OR, 10.9; 95% CI, 6.3\u0026ndash;18.8; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eStroke severity at admission, assessed using the NIHSS, was significantly lower in patients with newly detected AF than in those with premorbid AF (median NIHSS 4 [IQR, 2\u0026ndash;7] vs. 7 [IQR, 4\u0026ndash;12]; \u003cem\u003eP\u003c/em\u003e=0.002). Among patients with ESUS, all AF episodes were newly detected after stroke, and 35.7% had SDAF. These findings support the notion that AF after stroke may represent a distinct, frequently subclinical and less severe phenotype, underscoring the need for refined post-stroke AF classification and individualized management.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePremorbid antithrombotic therapy and atrial fibrillation subtype\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePremorbid antithrombotic use was prevalent in this cohort, with 94.9% of patients receiving antithrombotic therapy prior to stroke onset. Among patients with coronary or peripheral artery disease, acetylsalicylic acid (ASA) monotherapy was near-universal (\u0026gt;90%). In contrast, antiplatelet use among patients without known vascular disease was markedly lower (49.5%; OR, 12.3; 95% CI, 7.0\u0026ndash;21.6; P\u0026lt;0.001), underscoring the strong influence of documented atherosclerotic disease on preventive strategies.\u003c/p\u003e\n\u003cp\u003eAmong patients with short-duration atrial fibrillation (SDAF), premorbid ASA use was disproportionately high (71.5%) compared to AF-negative patients (53.1%; OR, 2.2; 95% CI, 1.6\u0026ndash;3.2; P\u0026lt;0.001), suggesting a partial recognition of underlying vascular or embolic risk. Nevertheless, SDAF patients experienced a median NIHSS of 4 (IQR, 2\u0026ndash;8), indicating that ASA may be insufficient in mitigating embolic stroke severity in this group.\u003c/p\u003e\n\u003cp\u003eAF detection patterns differed markedly between known and newly diagnosed cases. Of 384 patients with AF of any duration, 67.2% were diagnosed post-stroke, predominantly with SDAF (\u0026lt;30 seconds; 72.1% vs. 18.3% in known AF; OR, 11.5; 95% CI, 6.7\u0026ndash;19.7; P\u0026lt;0.001). Conversely, sustained AF was more common in the premorbid AF group (81.7% vs. 27.9%; OR, 10.9; 95% CI, 6.3\u0026ndash;18.8; P\u0026lt;0.001), correlating with higher anticoagulation rates (87.3%) prior to stroke. Among older patients with known AF, NOACs were the predominant anticoagulant choice (91.2%), while VKAs were limited to patients with specific comorbidities.\u003c/p\u003e\n\u003cp\u003eStrikingly, none of the patients with newly detected AF were anticoagulated at stroke onset (0.0% vs. 87.3%; OR, \u0026infin;; 95% CI, 49.9\u0026ndash;\u0026infin;; P\u0026lt;0.001), yet this group exhibited lower stroke severity (median NIHSS 4 [IQR, 2\u0026ndash;7]) compared to those with known AF (median NIHSS 7 [IQR, 4\u0026ndash;12]; P=0.002). This contrast suggests divergent pathophysiologic mechanisms and risk profiles between incident and chronic AF, with potential implications for post-stroke rhythm monitoring and treatment thresholds.\u003c/p\u003e\n\u003cp\u003eAmong patients with prior cerebrovascular events (n = 181), 97.8% were receiving secondary prevention at baseline\u0026mdash;primarily ASA (80.7%) or NOACs (17.1%). Despite this, recurrent ischemic events occurred, particularly in those with underlying AF (35.4%), including 39 with SDAF. This subgroup had elevated CHA₂DS₂-VASc scores and moderate stroke severity, underscoring the limitations of current antithrombotic strategies and the need for refined post-stroke risk stratification, especially in patients with occult or subclinical AF.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExploratory multivariable regression analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo explore factors independently associated with short-duration atrial fibrillation (SDAF), we conducted a multivariable logistic regression including age \u0026gt;65 years, female sex, hypertension, CHA₂DS₂-VASc score, and stroke subtype (with ESUS as reference category). SDAF was used as the dependent variable (1 = SDAF present, 0 = no AF or only longer-duration AF).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the final model, age \u0026gt;65 years (OR 3.7; 95% CI 2.4\u0026ndash;5.9; p \u0026lt; 0.001), female sex (OR 1.9; 95% CI 1.4\u0026ndash;2.7; p \u0026lt; 0.001), and hypertension (OR 1.8; 95% CI 1.2\u0026ndash;2.7; p = 0.004) emerged as independent predictors of SDAF. A higher CHA₂DS₂-VASc score was also significantly associated with the presence of SDAF (per point increase: OR 1.4; 95% CI 1.2\u0026ndash;1.6; p \u0026lt; 0.001). Importantly, the ESUS subtype remained significantly associated with SDAF compared to other stroke categories (OR 1.5; 95% CI 1.0\u0026ndash;2.3; p = 0.049). Full model results are presented in Supplementary Table 1. These findings support the descriptive results and suggest that traditional vascular risk factors and embolic stroke subtype are independently associated with the detection of short-duration atrial fibrillation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEchocardiographic characteristics by SDAF status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTransthoracic echocardiography was available for all patients. Patients with short-duration atrial fibrillation (SDAF) exhibited a distinct atrial profile compared to those without SDAF. Notably, left atrial volume index (LAVI) was significantly higher in the SDAF group (mean 39.5 \u0026plusmn; 10.6 mL/m\u0026sup2; vs. 33.4 \u0026plusmn; 9.1 mL/m\u0026sup2;; p = 0.002), and atrial reservoir strain was markedly reduced (\u0026ndash;17.1 \u0026plusmn; 4.3% vs. \u0026ndash;21.5 \u0026plusmn; 5.2%; p = 0.001). Measures of diastolic function, including E/e\u0026prime; ratio and LAVI/a\u0026prime; ratio, also differed significantly between groups. While left ventricular ejection fraction (LVEF) was numerically lower in the SDAF group, this difference did not reach statistical significance. Similarly, mitral regurgitation \u0026ge; mild was more common among SDAF patients, though the association was not statistically significant (p = 0.083). These findings suggest a subclinical atrial cardiomyopathy pattern in patients with SDAF. (\u003cem\u003eCorresponding data are summarized in Table 2)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Echocardiographic Characteristics by Presence of Short-Duration Atrial Fibrillation\u0026nbsp;\u003c/strong\u003eThis table summarizes echocardiographic differences between patients with and without SDAF. Those with SDAF had significantly larger left atria, higher LAVI, impaired atrial strain, and more advanced diastolic dysfunction, while LVEF and mitral regurgitation rates were similar between groups. Values are presented as mean \u0026plusmn; SD or number (percentage).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eEchocardiographic parameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eSDAF present (n=154)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eSDAF absent (n=375)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eLeft ventricular ejection fraction, % (mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e58.3 \u0026plusmn; 7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e59.8 \u0026plusmn; 6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eLeft atrial enlargement \u0026ge;42 mm, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e105 (68.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e159 (42.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eLeft atrial volume index (LAVI), mL/m\u0026sup2; (mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e39.5 \u0026plusmn; 10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e33.4 \u0026plusmn; 9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eSeptal PA-TDI, ms (mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e131.2 \u0026plusmn; 12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e102.3 \u0026plusmn; 10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eLAVI/a\u0026prime; ratio (mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e4.3 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e3.1 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eDiastolic dysfunction \u0026ge; grade II, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e64 (41.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e96 (25.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eE/e\u0026prime; ratio (mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e12.8 \u0026plusmn; 4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e10.4 \u0026plusmn; 3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eMitral regurgitation \u0026ge; mild, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e41 (26.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e67 (17.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eLeft atrial strain, % (mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026ndash;17.1 \u0026plusmn; 4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026ndash;21.5 \u0026plusmn; 5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are presented as mean \u0026plusmn; standard deviation or number (percentage), as appropriate. LAVI = left atrial volume index; LA strain = left atrial reservoir strain; E/e\u0026prime; = ratio of early mitral inflow velocity to mitral annular early diastolic velocity. Bold p-values indicate statistically significant differences (p \u0026lt; 0.05).\u003c/p\u003e"},{"header":"DISCUSSION ","content":"\u003cp\u003eThis study identifies a clinically important subgroup of stroke and TIA patients with short-duration atrial fibrillation (SDAF \u0026lt;30 seconds) who remain undetected or untreated under current diagnostic thresholds. Although these brief episodes fall below the guideline-defined cut-off for atrial fibrillation, \u003csup\u003e9\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e \u003csup\u003e17\u003c/sup\u003e\u003csup\u003e,\u0026nbsp;\u003c/sup\u003e\u003csup\u003e18\u003c/sup\u003e they were observed in nearly one-third of our cohort\u0026mdash;particularly among patients with embolic stroke of undetermined source and cryptogenic stroke. Most affected individuals had elevated CHA₂DS₂-VASc scores, mild to moderate stroke severity, and a disproportionate representation of older adults and women. These findings suggest that SDAF may represent an overlooked marker of embolic potential, highlighting a diagnostic blind spot in current stroke evaluation.\u003c/p\u003e\n\u003cp\u003eIn this prospective cohort of patients with ischemic stroke or TIA, short-duration AF (\u0026lt;30 seconds) was detected in 29% of all cases and 35.7% of ESUS. Over 80% of these patients had CHA₂DS₂-VASc scores \u0026ge;2, and nearly half had scores \u0026ge;5, indicating substantial thromboembolic risk despite not meeting the diagnostic AF threshold of \u0026ge;30 seconds \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003eFigures 1 \u0026amp; 2)\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eNIHSS scores were significantly higher among patients with elevated CHA₂DS₂-VASc scores, and a modest but significant correlation between stroke severity and thromboembolic risk was observed (\u0026rho; = 0.23, p = 0.001). Higher CHA₂DS₂-VASc scores are independently associated with increased embolic risk even in patients without atrial fibrillation, with a consistent stepwise increase in risk across large population-based studies\u0026nbsp;\u003csup\u003e19,20\u003c/sup\u003e. Nonetheless, its moderate discriminatory power limits clinical utility, and current guidelines do not support its use for anticoagulation decisions in non-AF populations. Our findings reinforce this association, demonstrating that elevated CHA₂DS₂-VASc scores may reflect relevant embolic risk even in patients without guideline-defined AF\u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn light of this, the adequacy of the 30-second threshold for defining clinically relevant AF may require re-evaluation in high-risk patients with brief arrhythmic episodes. While not all short AF episodes require treatment, the 30-second threshold remains a long-established diagnostic convention endorsed by major societies including the ESC, AHA/ASA, and HRS/EHRA.\u0026nbsp;\u003csup\u003e2\u003c/sup\u003e \u003csup\u003e12\u003c/sup\u003e \u003csup\u003e22\u003c/sup\u003e \u003csup\u003e23\u003c/sup\u003e \u003csup\u003e12,24\u003c/sup\u003e\u0026nbsp; This cutoff has been consistently applied in key stroke trials such as CRYSTAL-AF, EMBRACE, and STROKE-AF\u0026nbsp;\u003csup\u003e25\u003c/sup\u003e \u003csup\u003e26\u003c/sup\u003e \u003csup\u003e27\u003c/sup\u003e and is used to guide post-stroke rhythm monitoring and classification in international and national guidelines. In our study, the 30-second threshold was used solely for detection and classification\u0026mdash;not to imply a treatment indication. We also acknowledge that in real-world device-based monitoring, episode durations \u0026ge;6 minutes are commonly used to guide oral anticoagulation, as recently demonstrated in the ARTESiA trial\u0026nbsp;\u003csup\u003e28\u003c/sup\u003e. Our findings aim to inform early risk stratification and generate hypotheses, not to redefine treatment thresholds. Emerging evidence suggests that even AF episodes \u0026lt;30 seconds may predict future clinically manifest AF and should not be dismissed as benign\u0026mdash;particularly in high-risk stroke populations.\u003csup\u003e29\u003c/sup\u003e \u003csup\u003e30\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe ESUS classification seeks to identify embolic strokes without an identifiable cause, yet our findings demonstrate that short-duration AF is common in both ESUS and cryptogenic strokes. While the overall difference across stroke subtypes was statistically significant (p \u0026lt; 0.001), the higher SDAF prevalence in ESUS compared to large-artery stroke did not reach statistical significance (p = 0.08), suggesting a possible trend. Only 23% of our cohort met the guideline-defined AF threshold (\u0026ge;30 seconds), while a larger subset had shorter AF episodes. Among ESUS patients with short-duration AF, 80% had CHA₂DS₂-VASc \u0026ge;2 and nearly 50% had scores \u0026ge;5, suggesting a considerable embolic risk. Prior work indicates that CHA₂DS₂-VASc scores \u0026gt;2 are predictive of incident AF,\u0026nbsp;\u003csup\u003e31\u003c/sup\u003e \u003csup\u003e32\u003c/sup\u003e supporting the idea that these brief arrhythmias may reflect a clinically relevant underlying substrate.\u003csup\u003e33\u003c/sup\u003e Short-duration AF disproportionately affected older adults and women\u0026mdash;groups known to carry a higher baseline stroke risk. While these findings offer meaningful insights into the prevalence and clinical profile of short-duration AF in ischemic stroke, they should be interpreted in the context of the study\u0026rsquo;s observational design. Although causality cannot be firmly established, the associations observed are clinically relevant and underscore the need for prospective studies to further explore the potential pathophysiological and therapeutic implications of these brief arrhythmias. In our study, 89.4% of patients with short-duration AF were over 65 years, and over two-thirds were 75 or older. Women were more frequently affected than men (37.0% vs. 22.8%; OR 2.0; 95% CI, 1.4\u0026ndash;2.9; p \u0026lt; 0.001) and had more severe strokes, with a higher median NIHSS on admission (5 [IQR 3\u0026ndash;10] vs. 3 [IQR 2\u0026ndash;7]; p = 0.002), in line with epidemiologic data showing rising AF prevalence with age and greater stroke vulnerability in women.\u003csup\u003e14,31\u003c/sup\u003e These findings raise concerns about the recent removal of female sex as a standalone CHA₂DS₂-VASc component.\u0026nbsp;\u003csup\u003e34,35\u003c/sup\u003e Additionally CHA₂DS₂-VASc scores correlated significantly with stroke severity (\u0026rho; = 0.23; p = 0.001), indicating a non-negligible risk of recurrence even in patients without guideline-defined AF. Stroke severity also varied by AF status and stroke subtype. Cardioembolic strokes were associated with the highest NIHSS scores and most severe deficits. Patients with AF\u0026mdash;regardless of episode duration\u0026mdash;had significantly more severe strokes than those without AF (Figure 6).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNotably, the severity patterns observed in patients with short-duration AF did not clearly mirror those of classic cardioembolic strokes. Instead, the median NIHSS scores in this group fell within a moderate range and more closely resembled the distribution seen in atherosclerotic strokes (Figure 4). This suggests that stroke severity alone may not reliably distinguish embolic mechanisms in the presence of SDAF and supports the view that ESUS encompasses heterogeneous pathophysiologies\u0026mdash;including occult AF and non-stenotic atherosclerosis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe RE-SPECT ESUS and NAVIGATE ESUS trials failed to demonstrate a benefit of DOAC therapy in unselected ESUS populations\u003csup\u003e36\u003c/sup\u003e \u003csup\u003e37\u003c/sup\u003e but subgroup analyses by AF duration were not performed. Given that 30\u0026ndash;33% of cryptogenic and ESUS strokes in our study exhibited short-duration AF, this subgroup may merit separate consideration in future anticoagulation trials. Prolonged ECG monitoring, cardiac imaging, and biomarkers may improve detection of AF-related stroke risk. The SAFAS study showed that multimodal diagnostic approaches enhance post-stroke AF detection.\u003csup\u003e38\u003c/sup\u003e According to current studies, AF predictors in ESUS include older age, high CHA₂DS₂-VASc scores, rhythm irregularity burden (HR 3.12), elevated NT-proBNP, left atrial enlargement, NSAT, prolonged PR interval, and specific imaging patterns (e.g. non-lacunar, bihemispheric, multifocal) . \u003csup\u003e17\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e \u003csup\u003e39,40\u003c/sup\u003e and elevated CHA₂DS₂-VASc and CHA₂DS₂ scores have been linked to delayed post-stroke AF onset.\u003csup\u003e31,41\u003c/sup\u003e Recent data from the ARTESIA trial demonstrated that anticoagulation with apixaban reduces stroke risk in patients with subclinical AF lasting 6 minutes to 24 hours, albeit with an increased bleeding risk.\u003csup\u003e28\u003c/sup\u003e A subgroup analysis found particular benefit in patients with prior stroke or TIA and subclinical AF, demonstrating potential benefits of individualized anticoagulation decisions based on risk profile rather than fixed duration thresholds.\u003csup\u003e42,43\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eOur findings identified a clinically relevant subgroup of stroke patients with short-duration AF and elevated risk profiles. Future studies should explore the prognostic relevance and long-term outcomes of SDAF in high-risk stroke populations. Our results underscore the need for burden-adapted follow-up strategies that account for arrhythmia duration, patient risk profile, and stroke subtype.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLIMITATIONS AND STRENGTHS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis single-center design may limit generalizability. Data on prior anticoagulant or antiplatelet therapy were not available, and long-term follow-up was not performed, precluding assessment of stroke recurrence or AF progression. Strengths include the prospective design, standardized data collection, and structured neurological assessments during hospitalization. Daily clinical evaluation minimized missing data, and detailed ECG analysis enhanced detection and classification of atrial fibrillation. As with all descriptive observational studies, causality cannot be inferred; however, the consistent patterns observed in high-risk subgroups underscore the need for prospective validation, to see whether or not short-duration AF is a predictor for developing longer episodes of AF or recurrent strokes. Furthermore, the use of a 30-second threshold for AF detection, while standardized in trials and guidelines, may not align with real-world treatment practices, which often apply longer duration cutoffs (e.g., \u0026ge;6 minutes) when considering anticoagulation initiation\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eShort-duration atrial fibrillation occurred across all ischemic stroke subtypes, with the highest prevalence in cardioembolic, cryptogenic, and ESUS cases. It was more frequent in older adults and women and associated with elevated CHA₂DS₂-VASc scores, indicating substantial thromboembolic risk despite falling below current AF diagnostic thresholds. These findings suggest that SDAF may signal a clinically relevant embolic source and define a high-risk, undertreated subgroup.\u003c/p\u003e\n\u003cp\u003eThis diagnostic blind spot underscores the need to reconsider fixed duration thresholds in post-stroke AF detection. Risk-based approaches incorporating age, sex, and CHA₂DS₂-VASc may better guide monitoring and secondary prevention. Prospective trials are needed to determine whether targeted treatment of SDAF can improve outcomes in this overlooked population.\u003c/p\u003e"},{"header":"DECLARATIONS","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Medical Association of Westphalia-Lippe (file number 2015-091-fS). Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication:\u0026nbsp;\u003c/em\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to patient confidentiality and institutional data protection policies but are available from the corresponding author upon reasonable request and with appropriate ethical approval.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eClinical trial number:\u003c/em\u003e not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDisclosures\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLanguage editing assistance was supported by AI-based tools (e.g., Microsoft Copilot, and DeepL Write) for grammar and style refinement. No AI tools were used for data analysis, interpretation, or manuscript drafting beyond language support. All content and conclusions were developed by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors' contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePriyanka Boettger and Karolis Macius contributed to the conception and design of the study, data interpretation, and drafting of the manuscript. Jamschid Sedighi and Omar Alhaj Omar were involved in patient recruitment, data acquisition, and clinical assessments. Martin Juenemann and Bernhard Unsoeld contributed to statistical analysis, data interpretation, and manuscript revision. Henning Lemm and Kerstin Piayda supported the analysis and clinical validation of arrhythmia findings in the intensive care cohort. Michael Buerke and Samuel Sossalla supervised the project, reviewed all data critically, and provided senior oversight throughout the manuscript development. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank our colleagues at the University Hospital of Gießen for their valuable contributions to this study. We also acknowledge the support of the technical and administrative staff involved in data collection and patient care. Additionally, we are grateful for the resources provided by our respective institutions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNaser N, Kulic M, Dilic M, Dzubur A, Durak A, Pepic E, Smajic E, Kusljugic Z. 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Usefulness of Short-Lasting Episodes of Supraventricular Arrhythmia (Micro-Atrial Fibrillation) as a Risk Factor for Atrial Fibrillation. \u003cem\u003eAmerican Journal of Cardiology\u003c/em\u003e. 2018;122:1179\u0026ndash;1184. doi: 10.1016/j.amjcard.2018.06.030\u003c/li\u003e\n\u003cli\u003eNoseworthy PA, Kaufman ES, Chen LY, Chung MK, Elkind MSV, Joglar JA, Leal MA, McCabe PJ, Pokorney SD, Yao X, et al. Subclinical and Device-Detected Atrial Fibrillation: Pondering the Knowledge Gap: A Scientific Statement From the American Heart Association. \u003cem\u003eCirculation\u003c/em\u003e. 2019;140:e944\u0026ndash;e963. doi: 10.1161/CIR.0000000000000740\u003c/li\u003e\n\u003cli\u003eSaliba W, Gronich N, Barnett-Griness O, Rennert G. 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Multimodal Approach for the Prediction of Atrial Fibrillation Detected After Stroke: SAFAS Study. \u003cem\u003eFront Cardiovasc Med\u003c/em\u003e. 2022;9:949213. doi: 10.3389/fcvm.2022.949213\u003c/li\u003e\n\u003cli\u003evon Falkenhausen AS, Feil K, Sinner MF, Sch\u0026ouml;necker S, M\u0026uuml;ller J, Wischmann J, Eiffener E, Clauss S, Poli S, Poli K, et al. Atrial Fibrillation Risk Assessment after Embolic Stroke of Undetermined Source. \u003cem\u003eAnn Neurol\u003c/em\u003e. 2023;93:479\u0026ndash;488. doi: 10.1002/ana.26545\u003c/li\u003e\n\u003cli\u003eSposato LA, Sur NB, Katan M, Johansen MC, De Marchis GM, Caso V, Fischer U, Chaturvedi S. Embolic Stroke of Undetermined Source: New Data and New Controversies on Cardiac Monitoring and Anticoagulation. \u003cem\u003eNeurology\u003c/em\u003e. 2024;103:e209535. doi: 10.1212/wnl.0000000000209535\u003c/li\u003e\n\u003cli\u003eBae JH, Ryu JC, Ha SH, Cho MS, Cha MJ, Chang JY, Kang DW, Kwon SU, Kim JS, Kim BJ. Factors associated with the detection of atrial fibrillation in patients with embolic stroke of undetermined source. \u003cem\u003eBMC Neurol\u003c/em\u003e. 2025;25:15. doi: 10.1186/s12883-024-04008-0\u003c/li\u003e\n\u003cli\u003ePatel SM, Ruff CT. Subclinical Atrial Fibrillation and Anticoagulation: Weighing the Absolute Risks and Benefits. \u003cem\u003eCirculation\u003c/em\u003e. 2024;149:989\u0026ndash;992. doi: 10.1161/CIRCULATIONAHA.123.067919\u003c/li\u003e\n\u003cli\u003eShoamanesh A, Field TS, Coutts SB, Sharma M, Gladstone D, Hart RG, Boriani G, Wright DJ, Sticherling C, Birnie DH, et al. Apixaban versus aspirin for stroke prevention in people with subclinical atrial fibrillation and a history of stroke or transient ischaemic attack: subgroup analysis of the ARTESiA randomised controlled trial. \u003cem\u003eThe Lancet Neurology\u003c/em\u003e. 2025;24:140\u0026ndash;151. doi: 10.1016/S1474-4422(24)00475-7\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Atrial fibrillation, Electrocardiography, Stroke/etiology, Risk assessment, Ischemic stroke, Prospective studies","lastPublishedDoi":"10.21203/rs.3.rs-6813617/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6813617/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and Purpose \u003c/strong\u003eAtrial fibrillation (AF) episodes ≥30 seconds are currently considered clinically relevant in stroke diagnostics. However, shorter AF episodes may signal a significant embolic risk, especially in patients with embolic stroke of undetermined source (ESUS). This study investigates the prevalence, risk profile, and stroke severity associated with short-duration AF (SDAF \u0026lt;30 seconds) across ischemic stroke subtypes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eWe prospectively enrolled 714 consecutive patients with ischemic stroke or Transient ischemic attacks who underwent ≥48-hour ECG monitoring. AF episodes were classified as 0–14 s, 15–29 s, or ≥30 s. Stroke subtypes were defined using TOAST and ESUS criteria. Risk profiles, NIH Stroke Scale scores, and CHA₂DS₂-VASc scores were analyzed by AF duration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eAF of any duration was detected in 53.8% of patients; 22.8% had episodes ≥30 seconds and 29.9% had SDAF. Among ESUS patients, 35.7% exhibited SDAF, and 80.2% of these had CHA₂DS₂-VASc scores ≥2. Stroke severity and risk scores were significantly higher in patients with SDAF than those without AF. SDAF was more prevalent in women (37.0%) and in individuals aged \u0026gt;65 years (89.4%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eSDAF is common across stroke subtypes—particularly ESUS—and is associated with elevated thromboembolic risk despite falling below current diagnostic thresholds. These findings highlight a diagnostic blind spot in stroke workup and support reevaluation of duration-based criteria for post-stroke AF detection and risk profiling.\u003c/p\u003e","manuscriptTitle":"Short-Duration Atrial Fibrillation in Ischemic Stroke: High Risk Despite Subclinical Burden-A prospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-23 07:05:43","doi":"10.21203/rs.3.rs-6813617/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-07-23T07:34:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-22T23:31:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-20T14:56:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242179029097158954772628513115648753728","date":"2025-07-14T17:48:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205843985333046059053645588257035872241","date":"2025-07-14T12:28:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-14T11:18:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-14T04:37:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-07-13T15:35:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f6e68dd5-18c6-45fc-a293-1e88f904a036","owner":[],"postedDate":"July 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-25T16:35:46+00:00","versionOfRecord":{"articleIdentity":"rs-6813617","link":"https://doi.org/10.1186/s12872-025-05080-1","journal":{"identity":"bmc-cardiovascular-disorders","isVorOnly":false,"title":"BMC Cardiovascular Disorders"},"publishedOn":"2025-08-20 16:29:29","publishedOnDateReadable":"August 20th, 2025"},"versionCreatedAt":"2025-07-23 07:05:43","video":"","vorDoi":"10.1186/s12872-025-05080-1","vorDoiUrl":"https://doi.org/10.1186/s12872-025-05080-1","workflowStages":[]},"version":"v1","identity":"rs-6813617","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6813617","identity":"rs-6813617","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}