Trick or Treat(ment): Should We Still Fear Reperfusion Therapy in Anticoagulated Stroke Patients?

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Jessica Seetge, Balázs Cséke, Zsófia Nozomi Karádi, Edit Bosnyák, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6335019/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The management of acute ischemic stroke (AIS) in anticoagulated patients presents a clinical challenge, as concerns about safety and efficacy often limit access to recanalization therapies. Despite the widespread use of direct oral anticoagulants (DOACs) and vitamin K antagonists (VKAs), their impact on functional recovery and mortality following intravenous thrombolysis (IVT) and mechanical thrombectomy (MT) remains uncertain. Therefore, this study investigates the association between prior anticoagulation and 90-day outcomes in AIS patients undergoing reperfusion therapy. Methods: We conducted a retrospective cohort analysis using our institutional stroke registry, including AIS patients admitted to the Department of Neurology at our university between February 2023 and 2025. Anticoagulated patients were 1:1 propensity score-matched with non-anticoagulated controls ( n =126 per group) using Mahalanobis distance matching with a caliper, adjusting for age, sex, hypertension, diabetes, stroke severity (National Institutes of Health Stroke Scale [NIHSS] at admission and 72 hours), and pre-stroke functional status (pre-morbid modified Rankin Scale [pre-mRS]). Primary endpoints at 90 days were functional independence (modified Rankin Scale [mRS] ≤2), mRS-shift, and mortality (mRS=6). Predictors of outcome were assessed using multivariable logistic regression and generalized additive models (GAM). Subgroup analyses evaluated the effects of anticoagulation type and treatment modality. Results: Among 866 AIS patients (DOAC n =100, VKA n =48, non-anticoagulated n =718), 426 (49.2%) underwent reperfusion therapy (IVT n =195, MT n =163, IVT+MT n =68). Before matching, anticoagulated patients were less likely to achieve functional independence (34.5% vs. 52.1%, odds ratio [OR]=0.48, 95% confidence interval [CI] [0.33–0.70], p<0.001), had a greater mRS-shift (2.53 vs. 1.79, p<0.001), and higher mortality (30.4% vs. 14.5%, OR=2.58, 95% CI [1.72–3.88], p<0.001). However, after matching, these differences were no longer statistically significant. NIHSS, 72hNIHSS, and pre-mRS were the strongest independent predictors of outcome (p<0.001), while anticoagulation status had no significant effect. Conclusion: Recanalization therapy appears to be a safe and effective strategy for anticoagulated AIS patients, regardless of anticoagulant type or treatment modality. These findings reinforce that prior anticoagulation alone should not preclude reperfusion therapy and underscore the importance of individualized, evidence-based decision-making in acute stroke care. acute ischemic stroke anticoagulated patients functional outcomes reperfusion therapy propensity score matching Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Acute ischemic stroke (AIS) remains one of the leading causes of morbidity and mortality worldwide [ 1 ]. Time-sensitive reperfusion therapies, such as intravenous thrombolysis (IVT) and mechanical thrombectomy (MT), are the cornerstone of acute stroke management, offering significantly improved outcomes when administered promptly [ 2 , 3 ]. Oral anticoagulants (OACs), including vitamin K antagonists (VKAs) and direct oral anticoagulants (DOACs), effectively reduce the risk of cardioembolic stroke, particularly in patients with atrial fibrillation [ 4 ]. However, their presence during the acute phase introduces clinical challenges, as concerns over an increased risk of intracranial hemorrhage (ICH) complicate decisions regarding reperfusion therapy [ 5 , 6 ]. Consequently, clinicians are often faced with considerable uncertainty when determining the safest and most effective treatment strategy for anticoagulated stroke patients. Current guidelines, including those from the American Heart Association/American Stroke Association (AHA/ASA) and the European Stroke Organisation (ESO), outline clear eligibility criteria for administering IVT in anticoagulated patients. Specifically, IVT is considered safe in VKA users with an international normalized ratio (INR) ≤ 1.7, and in DOAC-treated patients if at least 48 hours have passed since the last dose (assuming normal renal function) or if specific coagulation assays (e.g., anti-Xa activity, thrombin time) indicate adequate anticoagulant clearance [ 7 , 8 ]. MT, by contrast, has no formal contraindications related to anticoagulation status [ 3 , 7 ]. Despite these permissive recommendations, real-world practice continues to reflect substantial hesitancy in offering reperfusion therapy to anticoagulated patients, even when they meet eligibility criteria. Among AIS patients on DOACs, an estimated 28% are eligible for IVT (presenting within 4.5 hours and with a National Institutes of Health Stroke Scale [NIHSS] score ≥ 4) [ 9 , 10 ]. However, registry data from Germany [ 11 ] and Switzerland [ 12 ] show that only 6–15% of eligible patients actually receive IVT, with anticoagulation frequently cited as the main reason for withholding treatment. While much of the debate has focused on expanding access to reperfusion beyond current guideline-defined thresholds, a more immediate and underexplored question remains: Do anticoagulated patients who meet all existing eligibility criteria for reperfusion therapy actually have worse outcomes than their non-anticoagulated counterparts? If the answer is no, then withholding treatment solely based on anticoagulation status, even in otherwise eligible individuals, may reflect an overly cautious approach not justified by current evidence. To address this critical question, we conducted a retrospective analysis using a large, single-center stroke registry. Employing rigorous propensity score matching, we compared 90-day outcomes, including functional independence, mRS-shift, and mortality, between anticoagulated and non-anticoagulated AIS patients who received reperfusion therapy in accordance with current guideline recommendations. Our aim was to determine whether anticoagulation status independently affects clinical outcomes and to evaluate whether therapeutic hesitation in this population is truly supported by data. 2 Materials and Methods 2.1 Study Design A retrospective cohort analysis was conducted using the TINL (Transzlációs Idegtudományi Nemzeti Laboratórium) STROKE-registry, which included patients admitted with AIS to the Department of Neurology at the University of Pécs between February 2023 and 2025. 2.2 Data Collection and Measurements Baseline characteristics included demographic data (age, sex), clinical variables such as pre-stroke functional status (pre-morbidity modified Rankin Scale [pre-mRS] scores), and stroke severity (assessed by the NIHSS at both admission and 72 hours post-stroke) [ 13 ], ICH status, stroke etiology, onset-to-door-time, and plasma glucose levels. Comorbidities, including hypertension and diabetes mellitus, were documented. In anticoagulated patients, additional information was collected regarding the type of anticoagulant (DOAC vs. VKA), presence of atrial fibrillation, and history of prior stroke. Treatment modalities (IVT, MT or combined therapy [IVT + MT]) were also recorded. 2.3 Inclusion and Exclusion Criteria A total of 1,102 patients with AIS were initially assessed. Of these, 236 were excluded due to incomplete data: 233 had missing 90-day modified Rankin Scale (mRS) scores, 2 lacked admission NIHSS scores, and 1 had no recorded 72-hour NIHSS score. The final study population consisted of 866 patients with complete baseline and outcome data. 2.4 Caliper-Matched Propensity Score Matching To minimize baseline differences and confounding between anticoagulated and non-anticoagulated patients, 1:1 propensity score matching was performed using Mahalanobis distance within a caliper. Matching variables included age, sex, hypertension, diabetes, pre-mRS, and NIHSS scores at admission and at 72 hours. An initial 148 matched pairs (296 patients) were identified, of which 126 high-quality pairs (252 patients) were retained after caliper restriction. Balance between matched groups was assessed using standardized mean differences (SMDs), variance ratios (VRs), and the Kolmogorov-Smirnov test. 2.5 Outcome Assessment The primary outcomes were functional independence at 90 days, defined as an mRS score of ≤ 2, overall functional trajectory, assessed as mRS-shift (i.e., the change from pre-mRS to 90-day mRS); and 90-day mortality, defined as an mRS score of 6 [ 14 ]. Secondary analyses explored whether outcomes varied by type of anticoagulation (DOAC vs. VKA) or by reperfusion strategy (IVT, MT, or IVT + MT). 2.6 Statistical Analysis All statistical analyses were performed using Python (version 3.14.0). Continuous variables were assessed for normality using the Shapiro-Wilk test and reported as either mean ± standard deviation (SD) for normally distributed data or median with interquartile range (IQR) for non-normally distributed data. Categorical variables were summarized as absolute frequencies and percentages. Baseline characteristics between anticoagulated and non-anticoagulated patients were compared using Fisher’s exact test or Chi-square test for categorical variables. For continuous variables, independent samples t-tests were used when normally distributed, and the Kruskal-Wallis test was applied for non-normally distributed variables. Clinical outcomes were analyzed both before and after matching. For binary outcomes, including functional independence at 90 days and mortality, group comparisons were conducted using Chi-square or Fisher’s exact tests, as appropriate. Corresponding odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to assess effect sizes. Ordinal analysis of mRS-shift was performed using the Mann–Whitney U test, and treatment group comparisons were evaluated using the Kruskal-Wallis test. To identify predictors of outcomes, multivariable logistic regression was applied to the matched cohort for both functional independence and mortality. Functional trajectory, assessed as mRS-shift across the full range of the scale, was analyzed using generalized additive models (GAMs) in the matched cohort. Within the anticoagulated subgroup, separate analyses were performed to identify predictors of favorable outcome, mRS-shift, and mortality using the same modeling approaches. Sensitivity analyses included variance inflation factor (VIF) assessment to detect multicollinearity, and E-value calculations to estimate the potential influence of unmeasured confounders on observed associations. 2.7 Ethics approval This study was conducted in accordance with the Declaration of Helsinki and reviewed and approved by the Scientific and Research Ethics Committee of the Medical Research Council of the University of Pécs (RRF-2.3.1-21-2022-00011, 01/09/22) and the Scientific and Research Ethics Committee of the Medical Research Council of Hungary (BM/22444-1/2024, 01/09/24) to ensure that it met all regulatory requirements and ethical guidelines, including participant privacy and data protection standards. All study procedures were carried out in compliance with applicable ethical guidelines, and ongoing monitoring by the ethics committees ensured adherence to approved protocols. 3 Results The final cohort included 866 patients with acute ischemic stroke, of whom 148 (17.1%) were receiving oral anticoagulation prior to admission, 100 (11.5%) DOACs and 48 (5.5%) VKAs. The remaining 718 patients (82.9%) had no history of anticoagulant use. Reperfusion therapy was administered to 426 patients (49.2%), including 195 who received IVT, 163 treated with MT, and 68 who underwent both IVT and MT. 3.1 Baseline and Clinical Characteristics Before matching, anticoagulated patients were significantly older than non-anticoagulated patients (mean age: 76.5 ± 11.1 vs. 69.7 ± 12.1 years, p<0.001). They also had worse pre-stroke functional status (median pre-mRS: 0 [0–2] vs. 0 [0–1], p=0.018) and presented with more severe strokes at 72 hours (median NIHSS: 4 [1–11] vs. 2 [0–7], p=0.030). Cardioembolic strokes were significantly more common among anticoagulated patients (73.7% vs. 26.9%, p<0.001), as were hypertension (93.9% vs. 80.8%, p<0.001) and diabetes mellitus (39.9% vs. 34.0%, p=0.020). Differences in recanalization therapy were also evident: anticoagulated patients were much less likely to receive IVT (3.4% vs. 26.5%, p<0.001), but more likely to undergo MT (26.4% vs. 17.3%, p=0.014). Rates of combined IVT and MT did not significantly differ between groups (p=0.086). A summary of these baseline differences is presented in Table 1. Table 1. Baseline and Clinical Characteristics Before and After Propensity Score Matching Non-anticoagulated ( n =718) Anticoagulated ( n =148) p-Value Matched non-anticoagulated ( n =126) Matched anticoagulated ( n =126) p-Value Demographics Age (years), mean ± SD 69.69 ± 12.14 76.46 ± 11.05 <0.001* 74.94 ± 10.37 75.66 ± 10.78 0.578 Sex, male, n (%) 356 (49.6%) 64 (43.2%) 0.189 51 (40.5%) 51 (40.5%) 1.000 Clinical Characteristics Pre-mRS score, median [IQR] 0 (0-1) 0 (0-2) 0.018* 0 (0-1) 0 (0-1) 0.889 NIHSS score at admission, median [IQR] 5 (3-8) 5 (3-11) 0.071 4 (3-7) 4 (2-9) 0.904 NIHSS score at 72 hours, median [IQR] 2 (0-7) 4 (1-11) 0.030* 3 (2-7) 3 (1-7) 0.926 ICH, n (%) 33 (4.6%) 8 (5.4%) 0.573 8 (6.4%) 5 (4.0%) 0.571 Etiology, cardioembolic, n (%) 193 (26.9%) 109 (73.7%) <0.001* 38 (30.2%) 95 (75.4%) <0.001* Onset-to-door time (min), median [IQR] 310 (102-826) 309 (158-821) 0.503 265 (96-847) 285 (101-731) 0.865 Plasma glucose (mmol/l), mean ± SD 7,69 ± 2.96 7,45 ± 2.86 0.147 7,81 ± 3.42 7,15 ± 1.86 0.350 Medical History, n (%) Hypertension 580 (80.8%) 139 (93.9%) <0.001* 120 (95.2%) 120 (95.2%) 1.000 Diabetes mellitus 244 (34.0%) 59 (39.9%) 0.020* 47 (37.3%) 47 (37.3%) 1.000 Recanalization Therapy, n (%) IVT 190 (26.5%) 5 (3.4%) <0.001* 39 (30.9%) 5 (4.0%) <0.001* MT 124 (17.3%) 39 (26.4%) 0.014* 16 (12.7%) 32 (25.4%) 0.016* IVT + MT 62 (8.6%) 6 (4.1%) 0.086 10 (7.9%) 5 (4.0%) 0.287 Abbreviations: SD = standard deviation, mRS = modified Rankin Scale, NIHSS = National Institutes of Health Stroke Scale, ICH = intracranial hemorrhage, IQR = interquartile range, IVT = intravenous thrombolysis, MT = mechanical thrombectomy After matching, most demographic and clinical characteristics were well balanced between groups. All key covariates demonstrated SMDs below 0.1, indicating minimal residual imbalance (Table 2). In addition, VRs were close to 1.0, and Kolmogorov–Smirnov test p-values showed no significant distributional differences between groups (Table 3). However, a few significant differences persisted after matching. Cardioembolic stroke etiology remained more prevalent in the anticoagulated group (75.4% vs. 30.2%, p<0.001), IVT was still administered less frequently (4.0% vs. 30.9%, p<0.001), and MT was more commonly performed (25.4% vs. 12.7%, p=0.016). Crucially, subsequent multivariable regression and GAM analyses demonstrated that neither cardioembolic stroke etiology nor treatment modality was independently associated with favorable outcomes, mRS-shift, or mortality, suggesting that these residual imbalances did not confound the study’s primary findings. Table 2. Standardized Mean Differences (SMDs) Before and After Matching Before matching After matching Age 0.583 0.067 Sex 0.127 0.000 Pre-mRS score 0.190 0.013 NIHSS score at admission 0.241 0.053 NIHSS score at 72 hours 0.174 0.029 Hypertension 0.403 0.000 Diabetes mellitus 0.122 0.000 Abbreviations: SMD = standardized mean difference, mRS = modified Rankin Scale, NIHSS = National Institutes of Health Stroke Scale Table 3. Variance Ratios (VRs) and Kolmogorov-Smirnov Test Before and After Matching Before matching After matching Age 0.829, p<0.001 1.080, p=0.999 Sex 0.987, p=0.680 1.000, p=1.000 Pre-mRS score 1.227, p=0.214 0.995, p=1.000 NIHSS score at admission 1.703, p=0.037 1.294, p=0.907 NIHSS score at 72 hours 1.209, p=0.084 1.052, p=0.963 Hypertension 0.369, p=0.026 1.000, p=1.000 Diabetes mellitus 1.074, p=0.764 1.000, p=1.000 Abbreviations: SMD = standardized mean difference, mRS = modified Rankin Scale, NIHSS = National Institutes of Health Stroke Scale 3.2 Favorable Outcome Before matching, anticoagulated patients were significantly less likely to achieve functional independence at 90 days, with only 34.5% (95% CI [27.3%-42.4%]) reaching an mRS ≤2, compared to 52.1% (95% CI [48.4%-55.7%]) in the non-anticoagulated group (p<0.001). The corresponding OR=0.48 (95% CI [0.33-0.70], p<0.001) indicated that anticoagulated patients had 52% lower odds of a favorable outcome. After matching, this difference was no longer statistically significant. In the matched cohort, 39.7% (95% CI [31.6%-48.4%]) of anticoagulated patients achieved an mRS ≤2, compared to 45.2% (95% CI [36.8%-53.9%]) in non-anticoagulated patients (p=0.445). The OR=0.80 (95% CI [0.48-1.31]) suggested no independent association between anticoagulation and functional outcome. For the comparison between DOAC- and VKA-treated patients, no significant differences were observed. Before matching, the OR=0.82 (95% CI [0.40-1.68], p=0.586), and after matching, the OR remained similar (0.82, 95% CI [0.39-1.74], p=0.700), indicating that anticoagulant type did not influence 90-day outcomes. Regarding treatment modalities, prior to matching, IVT was associated with higher odds of functional independence compared to SC (OR=2.66, 95% CI [1.86-3.81], p<0.001), while MT was linked to lower odds (OR=0.47, 95% CI [0.32-0.69], p<0.001). Combination therapy showed no significant benefit (OR=1.06, 95% CI [0.64-1.77], p=0.896). After matching, these associations were attenuated: IVT (OR=1.48, 95% CI [0.75-2.91], p=0.302) and IVT+MT (OR=0.62, 95% CI [0.20-1.89], p=0.428). Although MT remained significant in univariate analysis (OR=0.46, 95% CI [0.22-0.94], p=0.041), the association did not persist in multivariable regression. When adjusting for covariates in the matched cohort, including variables not fully balanced by matching such as stroke etiology and treatment modality, the adjusted OR (aOR=0.64, 95% CI [0.26-1.61], p=0.346) confirmed the absence of a significant association between anticoagulation status and functional independence. 3.3 mRS-shift The Shapiro–Wilk test confirmed non-normal distribution of mRS-shift scores in both groups (p<0.001), supporting the use of non-parametric methods. Before matching, anticoagulated patients had a significantly greater mean mRS-shift (2.53 ± 2.23) compared to non-anticoagulated patients (1.79 ± 1.96, p<0.001), indicating more pronounced functional decline. After matching, this difference was attenuated and no longer statistically significant (2.31 ± 2.25 vs. 1.87 ± 1.91, p=0.227). In the anticoagulated subgroup, DOAC users had numerically higher mRS-shift than VKA users, though differences were not statistically significant either before (2.76 ± 2.22 vs. 2.04 ± 2.18, p=0.060) or after matching (2.54 ± 2.25 vs. 1.86 ± 2.19, p=0.086). Among treatment modalities, IVT was associated with a lower mRS-shift compared to SC before matching (1.21 ± 1.62 vs. 1.88 ± 2.02, p<0.001), but this difference was no longer significant after matching (p=0.630). MT was linked to significantly greater mRS-shift both before (2.74 ± 2.07 vs. 1.88 ± 2.02, p<0.001) and after matching (2.98 ± 2.06 vs. 1.88 ± 2.12, p=0.002). Combination therapy showed a significant increase only after matching (3.20 ± 2.37 vs. 1.88 ± 2.12, p=0.041), though not before (p=0.252). However, none of these associations remained significant in multivariable analyses. After adjusting for covariates in the matched sample, including variables not fully balanced through matching, such as cardioembolic etiology and recanalization therapy, no significant difference in mRS-shift was observed between anticoagulated and non-anticoagulated patients (adjusted coefficient = 0.29, 95% CI [-0.18-0.76], p=0.223). 3.4 Mortality Before matching, mortality was significantly higher among anticoagulated patients (30.4%) compared to non-anticoagulated patients (14.5%), with OR=2.58 (95% CI [1.72-3.88], p<0.001). After matching, mortality remained elevated (22.2% vs. 11.9%), with a still significant OR=2.11 (95% CI [1.07-4.19], p=0.044), though the effect size was reduced. In the anticoagulated subgroup, no significant differences in mortality were found between DOAC and VKA users. Pre-matching OR=1.27 (95% CI [0.59-2.72], p=0.573), and post-matching OR=1.33 (95% CI [0.53-3.33], p=0.652), indicating no meaningful variation by anticoagulant type. Patients receiving IVT had significantly lower mortality than those receiving SC before matching (7.2% vs. 19.1%, OR=0.33, 95% CI [0.18-0.59], p<0.001). While this trend persisted after matching (6.8% vs. 17.9%), it no longer reached statistical significance (OR=0.33, 95% CI [0.10-1.17], p=0.094). MT was associated with slightly higher mortality than SC both before (OR=1.33, 95% CI [0.87-2.05], p=0.211) and after matching (OR=1.20, 95% CI [0.53-2.72], p=0.672), but neither result was significant. Combination therapy also showed no mortality benefit, with OR=0.91 (95% CI [0.47-1.77], p=0.869) before and OR=1.66 (95% CI [0.49-5.64], p=0.485) after matching. When adjusting for covariates in the matched cohort, including stroke etiology and treatment modality, the association between anticoagulation and mortality was no longer statistically significant (aOR=2.35, 95% CI [0.79-7.02], p=0.125). 3.5 Predictors of Outcome in the Matched Cohort Favorable Outcome Multivariable logistic regression analysis (Table 4) identified NIHSS at admission (p=0.035), NIHSS at 72 hours (p<0.001), pre-mRS (p<0.001), and sex (p=0.043) as significant predictors of favorable functional outcome. Specifically, being male was associated with higher odds of favorable outcomes compared to females. Anticoagulation status was not a significant predictor (p=0.346). The model demonstrated a good fit (Pseudo R²=0.4949) and a highly significant overall model effect (Log-Likelihood Ratio [LLR], p=4.691e-30), supporting the robustness of these predictors. Table 4. Multivariate Regression of Favorable Outcome in the Matched Cohort ( n =252) Variable Coefficient p-Value 95% CI Anticoagulation status 0.4417 0.346 -0.477 to 1.360 Age 0.0183 0.393 -0.024 to 0.060 Sex 0.8660 0.043* 0.026 to 1.706 Pre-stroke mRS score 1.4207 <0.001* 0.896 to 1.945 NIHSS score at admission 0.1072 0.035* 0.008 to 0.207 NIHSS score at 72 hours 0.5210 <0.001* 0.343 to 0.699 Etiology, cardioembolic -0.0044 0.992 -0.872 to 0.863 Hypertension 1.7742 0.069 -0.140 to 3.688 Diabetes mellitus 0.4666 0.256 -0.338 to 1.272 IVT 0.0903 0.865 -0.954 to 1.135 MT 0.0407 0.946 -1.128 to 1.209 IVT + MT -0.7414 0.425 -2.564 to 1.082 Abbreviations: CI = confidence interval, mRS = modified Rankin Scale, NIHSS = National Institutes of Health Stroke Scale, IVT = thrombolysis, MT = mechanical thrombectomy In terms of predictive performance (Figure 1), the model showed excellent discrimination with an area under the curve (AUC) of 0.93, an accuracy of 86.5%, sensitivity of 85.5%, and specificity of 87.9%. Precision was high at 90.5%, with an F1 score of 87.9%, indicating strong reliability in identifying patients at risk of poor functional recovery. Calibration was also good, reflected by a Brier Score of 0.1055. The optimal classification threshold, determined via Youden’s Index, was 0.50. mRS-shift Higher NIHSS scores at admission (p=0.026), NIHSS at 72 hours (p<0.001), and pre-stroke mRS (p<0.001) were significant predictors of increased mRS-shift, indicating worse functional outcomes. Anticoagulation status was not significantly associated with functional outcomes (p=1.00). The GAM demonstrated strong explanatory power, as indicated by a high Pseudo R² of 0.612, effective degrees of freedom (DoF) of 33.61, and a log-likelihood of -456.25. An Akaike Information Criterion (AIC) of 981.71 further supported the model’s optimal balance between complexity and fit. GAM-derived plots (Figure 2) highlighted non-linear associations between continuous predictors and mRS-shifts. Elevated NIHSS scores at admission and at 72 hours consistently predicted greater mRS-shift. Mortality Logistic regression analysis (Table 5) identified NIHSS scores at admission and at 72 hours (both p<0.001) as significant independent predictors of mortality. In contrast, anticoagulation status was not significantly associated with mortality risk (p=0.125). The model exhibited strong overall performance, with a Pseudo R² of 0.3860, a log-likelihood of -70.69 (compared to LL-Null of -115.14), and a highly significant likelihood ratio test (LLR p=8.083e-14), indicating robust model fit. Table 5. Multivariate Regression of Mortality in the Matched Cohort ( n =252) Variable Coefficient p-Value 95% CI Anticoagulation status 0.8562 0.125 -0.237 to 1.949 Age 0.0445 0.131 -0.013 to 0.102 Sex -0.4154 0.409 -1.401 to 0.570 Pre-stroke mRS score -0.0785 0.673 -0.443 to 0.286 NIHSS score at admission 0.1772 <0.001* 0.092 to 0.262 NIHSS score at 72 hours 0.2075 <0.001* 0.120 to 0.295 Etiology, cardioembolic 0.3235 0.548 -0.732 to 1.379 Hypertension -0.1735 0.879 -2.414 to 2.067 Diabetes mellitus 0.9030 0.065 -0.056 to 1.862 IVT 0.1266 0.876 -1.469 to 1.722 MT -0.5796 0.313 -1.706 to 0.546 IVT + MT -0.8911 0.351 -2.764 to 0.982 Abbreviations: CI = confidence interval, mRS = modified Rankin Scale, NIHSS = National Institutes of Health Stroke Scale, IVT = thrombolysis, MT = mechanical thrombectomy Regarding discrimination (Figure 3), the model achieved an excellent AUC of 0.92, with an accuracy of 80%, sensitivity of 98%, and specificity of 76%. Precision was 0.46, and the F1 score was 0.62, highlighting the model's strong capability in predicting mortality while managing false positives effectively. Calibration was also robust, as demonstrated by a low Brier Score of 0.0905. The optimal classification threshold, based on Youden’s Index, was determined to be 0.11. 3.6 Predictors of Outcome in Anticoagulated Patients Favorable Outcome Within the anticoagulated cohort, multivariable logistic regression analysis (Table 6) identified NIHSS scores at 72 hours (p<0.001) and pre-stroke mRS (p=0.001) as the strongest predictors of favorable functional outcomes. Neither treatment modality nor the type of anticoagulation significantly influenced the risk of favorable recovery. Atrial fibrillation and previous stroke were included due to their established clinical relevance in anticoagulated patients, although neither was a significant predictor in this model. The model demonstrated excellent fit and robustness, with a high Pseudo R² of 0.5815, a log-likelihood of -35.42 (compared to LL-Null of -84.64), and a highly significant likelihood ratio test (LLR p=9.461e-15). Table 6. Multivariate Regression of Favorable Outcome in Anticoagulated Patients ( n =126) Variable Coefficient p-Value 95% CI Atrial fibrillation 0.3466 0.718 -1.532 to 2.225 Previous stroke 0.5961 0.472 -1.029 to 2.222 Age 0.0026 0.946 -0.072 to 0.077 Sex -1.0821 0.154 -2.570 to 0.406 Pre-stroke mRS score -2.0189 0.001* -3.188 to -0.849 NIHSS score at admission -0.1298 0.107 -0.288 to 0.028 NIHSS score at 72 hours -0.5987 <0.001* -0.915 to -0.282 Etiology, cardioembolic -0.2166 0.784 -1.768 to 1.335 Hypertension -2.0567 0.241 -5.491 to 1.378 Diabetes mellitus -0.7042 0.356 -2.198 to 0.790 IVT 1.3349 0.421 -1.915 to 4.585 MT -0.8451 0.424 -2.919 to 1.228 IVT + MT -2.2298 0.180 -5.487 to 1.027 Type of anticoagulant 1.2983 0.140 -0.427 to 3.023 Abbreviations: CI = confidence interval, mRS = modified Rankin Scale, NIHSS = National Institutes of Health Stroke Scale, IVT = thrombolysis, MT = mechanical thrombectomy In terms of discriminative performance, the model exhibited strong predictive capabilities, achieving an AUC of 0.95, accuracy of 90%, sensitivity of 92%, specificity of 88%, precision of 84%, and an F1 score of 0.88. These metrics underscore the model's reliability in distinguishing patients who experienced unfavorable outcomes from those who did not. Calibration was also excellent, as indicated by a Brier Score of 0.0846. mRS-shift To further explore predictors of disability progression within the anticoagulated cohort, GAMs were applied to assess mRS-shift. NIHSS at 72 hours (p<0.001) and pre-stroke mRS (p=0.001) emerged as significant predictors of worsening functional status. In contrast, treatment modality and type of anticoagulation were not significantly associated with mRS-shift. The model demonstrated strong flexibility and predictive performance, reflected by an effective DoF of 38.35, a log-likelihood of -252.61, and an AIC of 583.93. GAM-derived plots (Figure 4) illustrate non-linear relationships between continuou s predictors and mRS-shift. 72-hour NIHSS scores and pre-mRS score were consistently associated with greater mRS-shift, aligning with trends observed in the broader cohort. Mortality Within the anticoagulated cohort, logistic regression analysis (Table 7) identified NIHSS scores at admission (p=0.028) and at 72 hours (p=0.001) as the strongest independent predictors of mortality. Neither the treatment modality nor the type of anticoagulation showed a significant association with increased mortality risk. The model demonstrated a moderate fit, with a Pseudo R² of 0.3106, an improved log-likelihood of -46.014 (compared to the LL-Null of -66.743), and a highly significant overall model effect (LLR p=1.5e-4). Table 7. Multivariate Regression of Mortality in Anticoagulated Patients ( n =126) Variable Coefficient p-Value 95% CI Atrial fibrillation 0.1055 0.890 -1.386 to 1.597 Previous stroke -0.1026 0.892 -1.586 to 1.381 Age 0.0690 0.076 -0.007 to 0.145 Sex -1.0762 0.086 -2.306 to 0.154 Pre-stroke mRS score -0.1025 0.690 -0.607 to 0.402 NIHSS score at admission 0.1209 0.028* 0.013 to 0.228 NIHSS score at 72 hours 0.1689 0.001* 0.065 to 0.272 Etiology, cardioembolic 0.0117 0.987 -1.365 to 1.388 Hypertension -0.1276 0.920 -2.603 to 2.348 Diabetes mellitus 0.8259 0.215 -0.479 to 2.130 IVT 0.5256 0.750 -2.703 to 3.754 MT 0.3485 0.623 -1.041 to 1.738 IVT + MT 1.1625 0.471 -1.998 to 4.323 Type of anticoagulant -0.5735 0.426 -1.987 to 0.840 Abbreviations: CI = confidence interval, mRS = modified Rankin Scale, NIHSS = National Institutes of Health Stroke Scale, IVT = thrombolysis, MT = mechanical thrombectomy Regarding discriminatory performance, the model achieved an AUC of 0.87 and an overall accuracy of 84%. Specificity was high at 95%, indicating strong capability in identifying patients at lower risk of mortality; however, sensitivity was lower at 46%, reflecting a limitation in detecting all high-risk individuals. The model’s precision (0.72) and F1 score (0.57) further highlight this trade-off. Calibration was robust, as evidenced by a Brier Score of 0.1176. 4 Discussion 4.1 Summary of Findings In this propensity score-matched cohort of patients with AIS, we found no significant differences in 90-day functional independence, mRS-shift, or mortality between anticoagulated and non-anticoagulated patients who underwent reperfusion therapy. After adjusting for confounders, including stroke severity and pre-stroke functional status, anticoagulation status was not independently associated with adverse outcomes. Furthermore, neither the type of anticoagulant nor the modality of reperfusion therapy significantly influenced clinical outcomes. 4.2 Interpretation and Clinical Implications These findings challenge the widely held assumption that anticoagulated patients are inherently at greater risk for poor outcomes following reperfusion therapy. While unadjusted analyses suggested increased mortality and disability in the anticoagulated group, these differences disappeared after accounting for baseline differences. Despite clear recommendations from major guidelines (AHA/ASA, ESO), many eligible anticoagulated patients remain untreated with reperfusion therapy in routine clinical practice. One of the reasons for this ongoing uncertainty is the limited quality of evidence supporting the safety and efficacy of reperfusion therapy in this population. Landmark reperfusion trials, such as NINDS [15] and ECASS III [2] for IVT and MR CLEAN[16], SWIFT PRIME[17], ESCAPE[18], DAWN[19], DEFUSE 3[20] for MT either excluded anticoagulated patients or were conducted before the widespread adoption of DOACs. As a result, current guideline recommendations rely more on expert consensus than on high-quality randomized trial data [7, 8, 21]. 4.3 Comparison with Existing Literature Our results are consistent with several retrospective studies reporting comparable outcomes and ICH rates between anticoagulated and non-anticoagulated patients, including those treated outside strict guideline thresholds [22–26]. Interestingly, a recent meta-analysis [27] even suggested that DOAC-treated patients may experience lower rates of ICH than their non-anticoagulated counterparts, further challenging the notion that anticoagulation alone confers elevated bleeding risk. Similarly, observational studies have suggested that MT can be safely performed in anticoagulated patients [28–30]. Although these data are encouraging, the evidence base remains largely observational and insufficient to define definitive clinical standards. Several randomized controlled trials (RCTs) are currently underway to address this gap, including the DOAC Intravenous Thrombolysis (DO-IT) study (NCT06571149| 2024-08-22), the Safe IVT FXa (SIFT) study (NCT06878066|2025-03-10), and the ACT-GLOBAL Adaptive Platform Trial (NCT06352632|2024-04-02). While their findings are awaited, results are not anticipated in the near term. Until then, real-world guideline-conforming studies such as ours provide critical insight to support evidence-based decision-making in anticoagulated stroke patients. 4.4 Limitations This study has several limitations. First, although rigorous propensity score matching was used, the retrospective observational design inherently limits causal inference and may be subject to residual confounding from unmeasured variables. Second, anticoagulation status was primarily determined through medication records and patient self-report, as point-of-care coagulation testing was not consistently available. While some patients underwent coagulation assays, DOAC plasma concentrations were not measured, and no standardized laboratory confirmation of anticoagulant activity was performed. This reliance on clinical documentation and patient recall may have introduced misclassification or recall bias, particularly in assessing treatment eligibility. 5 Conclusion In summary, anticoagulation status was not independently associated with worse functional outcomes in AIS patients treated with reperfusion therapy in accordance with current guidelines. Clinical outcomes were primarily influenced by stroke severity and pre-stroke functional status. These findings support the inclusion of eligible anticoagulated patients in reperfusion strategies and underscore the importance of individualized, evidence-based decision-making in acute stroke care. Abbreviations AIS DOAC VKA IVT MT NIHSS pre-mRS mRS GAM OR CI OAC ICH AHA/ASA ESO INR TINL SMD VR SD IQR VIF SC aOR LLR AUC DoF AIC RCT DO-IT SIFT acute ischemic stroke direct oral anticoagulant vitamin K antagonist intravenous thrombolysis mechanical thrombectomy National Institutes of Health Stroke Scale pre-morbid modified Rankin Scale modified Rankin Scale generalized additive model odds ratio confidence interval oral anticoagulant intracranial hemorrhage American Heart Association/American Stroke Association European Stroke Organisation international normalized ratio Transzlációs Idegtudományi Nemzeti Laboratórium standardized mean difference variance ratio standard deviation interquartile range variance inflation factor standard care adjusted odds ratio log-likelihood ratio area under the curve degrees of freedom Akaike information criterion randomized control trial DOAC Intravenous Thrombolysis Safe IVT FXa Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki and reviewed and approved by the Scientific and Research Ethics Committee of the Medical Research Council of the University of Pécs (RRF-2.3.1-21-2022-00011, 01/09/22) and the Scientific and Research Ethics Committee of the Medical Research Council of Hungary (BM/22444-1/2024, 01/09/24). Informed consent was waived for this study as the data were collected as part of routine clinical documentation, in accordance with the institutional ethics approval. Consent for publication Not applicable. Availability of data and materials The data supporting the findings of this study are provided within the manuscript and its supplementary information files. For any additional requests regarding raw data, please contact the corresponding authors. Competing interests The authors declare that they have no competing interests. Funding Not applicable. Author’s contributions J.S. led all major aspects of the study, including conceptualization, methodology, formal analysis, data curation, visualization, and original draft preparation. B.C. contributed significantly to the study design, data interpretation, and manuscript writing. Z.K. and E.B. assisted with validation, literature review, and critical revisions. E.J. supported data collection and helped with administrative and technical tasks during the study. L.S. supervised the project, provided methodological guidance, and oversaw overall administration. All authors made substantial intellectual contributions, reviewed the manuscript, and approved the final version for publication. References Feigin VL, Abate MD, Abate YH, Abd ElHafeez S, Abd-Allah F, Abdelalim A, et al. Global, regional, and national burden of stroke and its risk factors, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Neurol. 2024;23:973–1003. Hacke W, Kaste M, Bluhmki E, Brozman M, Dávalos A, Guidetti D, et al. Thrombolysis with Alteplase 3 to 4.5 Hours after Acute Ischemic Stroke. N Engl J Med. 2008;359:1317–29. Turc G, Bhogal P, Fischer U, Khatri P, Lobotesis K, Mazighi M, et al. European Stroke Organisation (ESO) - European Society for Minimally Invasive Neurological Therapy (ESMINT) Guidelines on Mechanical Thrombectomy in Acute Ischemic Stroke. J Neurointerv Surg. 2023;15:e8–8. Joglar JA, Chung MK, Armbruster AL, Benjamin EJ, Chyou JY, Cronin EM et al. 2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2024;149. Emberson J, Lees KR, Lyden P, Blackwell L, Albers G, Bluhmki E, et al. Effect of treatment delay, age, and stroke severity on the effects of intravenous thrombolysis with alteplase for acute ischaemic stroke: a meta-analysis of individual patient data from randomised trials. Lancet. 2014;384:1929–35. Yaghi S, Willey JZ, Cucchiara B, Goldstein JN, Gonzales NR, Khatri P et al. Treatment and Outcome of Hemorrhagic Transformation After Intravenous Alteplase in Acute Ischemic Stroke: A Scientific Statement for Healthcare Professionals From the American Heart Association/American Stroke Association. Stroke. 2017;48. Powers WJ, Rabinstein AA, Ackerson T, Adeoye OM, Bambakidis NC, Becker K et al. 2018 Guidelines for the Early Management of Patients With Acute Ischemic Stroke: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association. Stroke. 2018;49. Berge E, Whiteley W, Audebert H, De Marchis G, Fonseca AC, Padiglioni C, et al. European Stroke Organisation (ESO) guidelines on intravenous thrombolysis for acute ischaemic stroke. Eur Stroke J. 2021;6:I–LXII. Seiffge DJ, De Marchis GM, Koga M, Paciaroni M, Wilson D, Cappellari M, et al. Ischemic Stroke despite Oral Anticoagulant Therapy in Patients with Atrial Fibrillation. Ann Neurol. 2020;87:677–87. Seiffge DJ, Wilson D, Wu TY-H. Administering Thrombolysis for Acute Ischemic Stroke in Patients Taking Direct Oral Anticoagulants. JAMA Neurol. 2021;78:515. Purrucker JC, Hölscher K, Kollmer J, Ringleb PA. Etiology of Ischemic Strokes of Patients with Atrial Fibrillation and Therapy with Anticoagulants. J Clin Med. 2020;9:2938. Meinel TR, Branca M, De Marchis GM, Nedeltchev K, Kahles T, Bonati L, et al. Prior Anticoagulation in Patients with Ischemic Stroke and Atrial Fibrillation. Ann Neurol. 2021;89:42–53. Brott T, Adams HP, Olinger CP, Marler JR, Barsan WG, Biller J, et al. Measurements of acute cerebral infarction: a clinical examination scale. Stroke. 1989;20:864–70. van Swieten JC, Koudstaal PJ, Visser MC, Schouten HJ, van Gijn J. Interobserver agreement for the assessment of handicap in stroke patients. Stroke. 1988;19:604–7. The National Institute of Neurological Disorders and Stroke rt-PA Stroke Study Group. Tissue Plasminogen Activator for Acute Ischemic Stroke. N Engl J Med. 1995;333:1581–8. Berkhemer OA, Fransen PSS, Beumer D, van den Berg LA, Lingsma HF, Yoo AJ, et al. A Randomized Trial of Intraarterial Treatment for Acute Ischemic Stroke. N Engl J Med. 2015;372:11–20. Saver JL, Goyal M, Bonafe A, Diener H-C, Levy EI, Pereira VM, et al. Stent-Retriever Thrombectomy after Intravenous t-PA vs. t-PA Alone in Stroke. N Engl J Med. 2015;372:2285–95. Goyal M, Demchuk AM, Menon BK, Eesa M, Rempel JL, Thornton J, et al. Randomized Assessment of Rapid Endovascular Treatment of Ischemic Stroke. N Engl J Med. 2015;372:1019–30. Nogueira RG, Jadhav AP, Haussen DC, Bonafe A, Budzik RF, Bhuva P, et al. Thrombectomy 6 to 24 Hours after Stroke with a Mismatch between Deficit and Infarct. N Engl J Med. 2018;378:11–21. Albers GW, Marks MP, Kemp S, Christensen S, Tsai JP, Ortega-Gutierrez S, et al. Thrombectomy for Stroke at 6 to 16 Hours with Selection by Perfusion Imaging. N Engl J Med. 2018;378:708–18. Ahmed N, Audebert H, Turc G, Cordonnier C, Christensen H, Sacco S et al. Consensus statements and recommendations from the ESO-Karolinska Stroke Update Conference, Stockholm 11–13 November 2018. Eur Stroke J. 2019;4:307–17. Bücke P, Jung S, Kaesmacher J, Goeldlin MB, Horvath T, Prange U, et al. Intravenous thrombolysis in patients with recent intake of direct oral anticoagulants: A target trial analysis after the liberalization of institutional guidelines. Eur Stroke J. 2024;9:959–67. Kam W, Holmes DN, Hernandez AF, Saver JL, Fonarow GC, Smith EE, et al. Association of Recent Use of Non–Vitamin K Antagonist Oral Anticoagulants With Intracranial Hemorrhage Among Patients With Acute Ischemic Stroke Treated With Alteplase. JAMA. 2022;327:760. Meinel TR, Wilson D, Gensicke H, Scheitz JF, Ringleb P, Goganau I, et al. Intravenous Thrombolysis in Patients With Ischemic Stroke and Recent Ingestion of Direct Oral Anticoagulants. JAMA Neurol. 2023;80:233. Boehme C, Mayer-Suess L, Mikšová D, Lang W, Knoflach M, Kiechl S. Prime Time for a Trial Assessing Safety of Intravenous Thrombolysis in Patients Treated With Direct Oral Anticoagulants. Stroke. 2024;55. Matusevicius M, Säflund M, Balestrino M, Cappellari M, Ferrandi D, Ghoreishi A et al. Intravenous Thrombolysis in Patients Taking Direct Oral Anticoagulation Treatment Before Stroke Onset: Results from the Safe Implementations of Treatments in Stroke International Stroke Registry. Ann Neurol. 2025. https://doi.org/10.1002/ana.27189 Liang H, Wang X, Quan X, Qin B, Zhang J, Liang S, et al. Safety and efficacy of intravenous thrombolysis in patients with acute ischemic stroke taking direct oral anticoagulants prior to stroke: a meta-analysis. J Neurol. 2023;270:4192–200. L’Allinec V, Sibon I, Mazighi M, Labreuche J, Kyheng M, Boissier E et al. MT in anticoagulated patients. Neurology. 2020;94. Küpper C, Feil K, Wollenweber FA, Tiedt S, Herzberg M, Dorn F, et al. Endovascular stroke treatment in orally anticoagulated patients: an analysis from the German Stroke Registry-Endovascular Treatment. J Neurol. 2021;268:1762–9. Goldhoorn R-JB, van de Graaf RA, van Rees JM, Lingsma HF, Dippel DWJ, Hinsenveld WH, et al. Endovascular Treatment for Acute Ischemic Stroke in Patients on Oral Anticoagulants. Stroke. 2020;51:1781–9. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-6335019","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":451582558,"identity":"100d578f-03da-4932-8e71-f2379a1f010c","order_by":0,"name":"Jessica Seetge","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBACxgYogx9MshGtJYGBQbKBWC0QANRicIBYLcwN7A8ffPxRK2d8fnWaBEOZDTEO4zE2nJFw3NjsxtvNBgzn0ojSwibNk3AscduNsxsfMLYdJkYL+/PffxKO1W+ecXbDAca2/8RoYTBjZkioSTDg7wXZcoAILc08xpI9aQcMZ9zg3WyQcC6ZsBbD9vaHH37Y1Mnz95/dJvGhzI4ILc1gCuhriQRw/BAG8hCqDphiDhCjfhSMglEwCkYiAABUGzxhrXZ6UQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Pécs","correspondingAuthor":true,"prefix":"","firstName":"Jessica","middleName":"","lastName":"Seetge","suffix":""},{"id":451582559,"identity":"6bce3dc5-5618-424a-a427-245dffe13104","order_by":1,"name":"Balázs Cséke","email":"","orcid":"","institution":"University of Pécs","correspondingAuthor":false,"prefix":"","firstName":"Balázs","middleName":"","lastName":"Cséke","suffix":""},{"id":451582560,"identity":"56d9cb73-1185-4170-8f71-8d4f695e4405","order_by":2,"name":"Zsófia Nozomi Karádi","email":"","orcid":"","institution":"University of Pécs","correspondingAuthor":false,"prefix":"","firstName":"Zsófia","middleName":"Nozomi","lastName":"Karádi","suffix":""},{"id":451582562,"identity":"72040d4d-55e4-4a47-b0e2-179991bf4ca3","order_by":3,"name":"Edit Bosnyák","email":"","orcid":"","institution":"University of Pécs","correspondingAuthor":false,"prefix":"","firstName":"Edit","middleName":"","lastName":"Bosnyák","suffix":""},{"id":451582563,"identity":"a6967040-9bc1-4b74-aa6f-2916314751d4","order_by":4,"name":"Eszter Johanna Jozifek","email":"","orcid":"","institution":"University of Pécs","correspondingAuthor":false,"prefix":"","firstName":"Eszter","middleName":"Johanna","lastName":"Jozifek","suffix":""},{"id":451582566,"identity":"662bbcc4-05f1-4e97-9d6e-1e89fa9b7dd9","order_by":5,"name":"László Szapáry","email":"","orcid":"","institution":"University of Pécs","correspondingAuthor":false,"prefix":"","firstName":"László","middleName":"","lastName":"Szapáry","suffix":""}],"badges":[],"createdAt":"2025-03-29 15:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6335019/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6335019/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82150701,"identity":"81d012c8-163d-4e1e-af82-6148cd051162","added_by":"auto","created_at":"2025-05-07 07:17:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":234872,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver Operating Characteristic Curve of Favorable Outcome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations: ROC = receiver operating characteristic, mRS = modified Rankin Scale, AUC = area under the curve\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6335019/v1/36de81b3f361e40e69b5771c.png"},{"id":82150705,"identity":"55282e82-918e-4509-a5a8-55f25febdeac","added_by":"auto","created_at":"2025-05-07 07:17:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":148756,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeneralized Additive Modelof mRS-shift in the Matched Cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations: NIHSS = National Institutes of Health Stroke Scale, mRS = modified Rankin Scale, GAM = generalized additive model. Note: Predicted mRS-shift values were clipped at 6 to reflect the maximum possible shift on the mRS scale.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6335019/v1/aa30bc8ce5de68b5399eaaf0.png"},{"id":82150704,"identity":"3a1a6cbf-8488-4376-be71-10640a494601","added_by":"auto","created_at":"2025-05-07 07:17:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61729,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver Operating Characteristic Curve of Mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations: ROC = receiver operating characteristic, AUC = area under the curve\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6335019/v1/c697573cd9a875c5f587b9e3.png"},{"id":82154752,"identity":"efebaca8-35c9-4a5e-b358-7c0492cf8239","added_by":"auto","created_at":"2025-05-07 07:33:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":114892,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeneralized Additive Modelof mRS-shift in Anticoagulated Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations: NIHSS = National Institutes of Health Stroke Scale, mRS = modified Rankin Scale, GAM = generalized additive model. Note: Predicted mRS-shift values were clipped at 6 to reflect the maximum possible shift on the mRS scale.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6335019/v1/3cd848714a7aa2496da2fece.png"},{"id":95800785,"identity":"2b59bf65-a708-41cb-b6af-51709885d69f","added_by":"auto","created_at":"2025-11-13 08:23:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1875491,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6335019/v1/49f2c76e-1ff7-4e1f-b05e-9e6cf76bd25e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trick or Treat(ment): Should We Still Fear Reperfusion Therapy in Anticoagulated Stroke Patients?","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAcute ischemic stroke (AIS) remains one of the leading causes of morbidity and mortality worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Time-sensitive reperfusion therapies, such as intravenous thrombolysis (IVT) and mechanical thrombectomy (MT), are the cornerstone of acute stroke management, offering significantly improved outcomes when administered promptly [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Oral anticoagulants (OACs), including vitamin K antagonists (VKAs) and direct oral anticoagulants (DOACs), effectively reduce the risk of cardioembolic stroke, particularly in patients with atrial fibrillation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, their presence during the acute phase introduces clinical challenges, as concerns over an increased risk of intracranial hemorrhage (ICH) complicate decisions regarding reperfusion therapy [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Consequently, clinicians are often faced with considerable uncertainty when determining the safest and most effective treatment strategy for anticoagulated stroke patients.\u003c/p\u003e \u003cp\u003e Current guidelines, including those from the American Heart Association/American Stroke Association (AHA/ASA) and the European Stroke Organisation (ESO), outline clear eligibility criteria for administering IVT in anticoagulated patients. Specifically, IVT is considered safe in VKA users with an international normalized ratio (INR)\u0026thinsp;\u0026le;\u0026thinsp;1.7, and in DOAC-treated patients if at least 48 hours have passed since the last dose (assuming normal renal function) or if specific coagulation assays (e.g., anti-Xa activity, thrombin time) indicate adequate anticoagulant clearance [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. MT, by contrast, has no formal contraindications related to anticoagulation status [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite these permissive recommendations, real-world practice continues to reflect substantial hesitancy in offering reperfusion therapy to anticoagulated patients, even when they meet eligibility criteria. Among AIS patients on DOACs, an estimated 28% are eligible for IVT (presenting within 4.5 hours and with a National Institutes of Health Stroke Scale [NIHSS] score\u0026thinsp;\u0026ge;\u0026thinsp;4) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, registry data from Germany [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and Switzerland [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] show that only 6\u0026ndash;15% of eligible patients actually receive IVT, with anticoagulation frequently cited as the main reason for withholding treatment.\u003c/p\u003e \u003cp\u003e While much of the debate has focused on expanding access to reperfusion beyond current guideline-defined thresholds, a more immediate and underexplored question remains: Do anticoagulated patients who meet all existing eligibility criteria for reperfusion therapy actually have worse outcomes than their non-anticoagulated counterparts? If the answer is no, then withholding treatment solely based on anticoagulation status, even in otherwise eligible individuals, may reflect an overly cautious approach not justified by current evidence.\u003c/p\u003e \u003cp\u003eTo address this critical question, we conducted a retrospective analysis using a large, single-center stroke registry. Employing rigorous propensity score matching, we compared 90-day outcomes, including functional independence, mRS-shift, and mortality, between anticoagulated and non-anticoagulated AIS patients who received reperfusion therapy in accordance with current guideline recommendations. Our aim was to determine whether anticoagulation status independently affects clinical outcomes and to evaluate whether therapeutic hesitation in this population is truly supported by data.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design\u003c/h2\u003e \u003cp\u003eA retrospective cohort analysis was conducted using the TINL (Transzl\u0026aacute;ci\u0026oacute;s Idegtudom\u0026aacute;nyi Nemzeti Laborat\u0026oacute;rium) STROKE-registry, which included patients admitted with AIS to the Department of Neurology at the University of P\u0026eacute;cs between February 2023 and 2025.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Collection and Measurements\u003c/h2\u003e \u003cp\u003eBaseline characteristics included demographic data (age, sex), clinical variables such as pre-stroke functional status (pre-morbidity modified Rankin Scale [pre-mRS] scores), and stroke severity (assessed by the NIHSS at both admission and 72 hours post-stroke) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], ICH status, stroke etiology, onset-to-door-time, and plasma glucose levels. Comorbidities, including hypertension and diabetes mellitus, were documented. In anticoagulated patients, additional information was collected regarding the type of anticoagulant (DOAC vs. VKA), presence of atrial fibrillation, and history of prior stroke. Treatment modalities (IVT, MT or combined therapy [IVT\u0026thinsp;+\u0026thinsp;MT]) were also recorded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Inclusion and Exclusion Criteria\u003c/h2\u003e \u003cp\u003eA total of 1,102 patients with AIS were initially assessed. Of these, 236 were excluded due to incomplete data: 233 had missing 90-day modified Rankin Scale (mRS) scores, 2 lacked admission NIHSS scores, and 1 had no recorded 72-hour NIHSS score. The final study population consisted of 866 patients with complete baseline and outcome data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Caliper-Matched Propensity Score Matching\u003c/h2\u003e \u003cp\u003eTo minimize baseline differences and confounding between anticoagulated and non-anticoagulated patients, 1:1 propensity score matching was performed using Mahalanobis distance within a caliper. Matching variables included age, sex, hypertension, diabetes, pre-mRS, and NIHSS scores at admission and at 72 hours. An initial 148 matched pairs (296 patients) were identified, of which 126 high-quality pairs (252 patients) were retained after caliper restriction. Balance between matched groups was assessed using standardized mean differences (SMDs), variance ratios (VRs), and the Kolmogorov-Smirnov test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Outcome Assessment\u003c/h2\u003e \u003cp\u003eThe primary outcomes were functional independence at 90 days, defined as an mRS score of \u0026le;\u0026thinsp;2, overall functional trajectory, assessed as mRS-shift (i.e., the change from pre-mRS to 90-day mRS); and 90-day mortality, defined as an mRS score of 6 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Secondary analyses explored whether outcomes varied by type of anticoagulation (DOAC vs. VKA) or by reperfusion strategy (IVT, MT, or IVT\u0026thinsp;+\u0026thinsp;MT).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using Python (version 3.14.0). Continuous variables were assessed for normality using the Shapiro-Wilk test and reported as either mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for normally distributed data or median with interquartile range (IQR) for non-normally distributed data. Categorical variables were summarized as absolute frequencies and percentages. Baseline characteristics between anticoagulated and non-anticoagulated patients were compared using Fisher\u0026rsquo;s exact test or Chi-square test for categorical variables. For continuous variables, independent samples t-tests were used when normally distributed, and the Kruskal-Wallis test was applied for non-normally distributed variables.\u003c/p\u003e \u003cp\u003eClinical outcomes were analyzed both before and after matching. For binary outcomes, including functional independence at 90 days and mortality, group comparisons were conducted using Chi-square or Fisher\u0026rsquo;s exact tests, as appropriate. Corresponding odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to assess effect sizes. Ordinal analysis of mRS-shift was performed using the Mann\u0026ndash;Whitney U test, and treatment group comparisons were evaluated using the Kruskal-Wallis test.\u003c/p\u003e \u003cp\u003eTo identify predictors of outcomes, multivariable logistic regression was applied to the matched cohort for both functional independence and mortality. Functional trajectory, assessed as mRS-shift across the full range of the scale, was analyzed using generalized additive models (GAMs) in the matched cohort.\u003c/p\u003e \u003cp\u003eWithin the anticoagulated subgroup, separate analyses were performed to identify predictors of favorable outcome, mRS-shift, and mortality using the same modeling approaches. Sensitivity analyses included variance inflation factor (VIF) assessment to detect multicollinearity, and E-value calculations to estimate the potential influence of unmeasured confounders on observed associations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Ethics approval\u003c/h2\u003e \u003cp\u003e This study was conducted in accordance with the Declaration of Helsinki and reviewed and approved by the Scientific and Research Ethics Committee of the Medical Research Council of the University of P\u0026eacute;cs (RRF-2.3.1-21-2022-00011, 01/09/22) and the Scientific and Research Ethics Committee of the Medical Research Council of Hungary (BM/22444-1/2024, 01/09/24) to ensure that it met all regulatory requirements and ethical guidelines, including participant privacy and data protection standards. All study procedures were carried out in compliance with applicable ethical guidelines, and ongoing monitoring by the ethics committees ensured adherence to approved protocols.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003eThe final cohort included 866 patients with acute ischemic stroke, of whom 148 (17.1%) were receiving oral anticoagulation prior to admission, 100 (11.5%) DOACs \u0026nbsp;and 48 (5.5%) VKAs. The remaining 718 patients (82.9%) had no history of anticoagulant use. Reperfusion therapy was administered to 426 patients (49.2%), including 195 who received IVT, 163 treated with MT, and 68 who underwent both IVT and MT.\u003c/p\u003e\n\u003ch2\u003e3.1 Baseline and Clinical Characteristics\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eBefore matching, anticoagulated patients were significantly older than non-anticoagulated patients (mean age: 76.5 \u0026plusmn; 11.1 vs. 69.7 \u0026plusmn; 12.1 years, p\u0026lt;0.001). They also had worse pre-stroke functional status (median pre-mRS: 0 [0\u0026ndash;2] vs. 0 [0\u0026ndash;1], p=0.018) and presented with more severe strokes at 72 hours (median NIHSS: 4 [1\u0026ndash;11] vs. 2 [0\u0026ndash;7], p=0.030). Cardioembolic strokes were significantly more common among anticoagulated patients (73.7% vs. 26.9%, p\u0026lt;0.001), as were hypertension (93.9% vs. 80.8%, p\u0026lt;0.001) and diabetes mellitus (39.9% vs. 34.0%, p=0.020).\u003c/p\u003e\n\u003cp\u003eDifferences in recanalization therapy were also evident: anticoagulated patients were much less likely to receive IVT (3.4% vs. 26.5%, p\u0026lt;0.001), but more likely to undergo MT (26.4% vs. 17.3%, p=0.014). Rates of combined IVT and MT did not significantly differ between groups (p=0.086). A summary of these baseline differences is presented in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Baseline and Clinical Characteristics Before and After Propensity Score Matching\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"756\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-anticoagulated\u0026nbsp;\u003cbr\u003e(\u003cem\u003en\u003c/em\u003e=718)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAnticoagulated\u0026nbsp;\u003cbr\u003e(\u003cem\u003en\u003c/em\u003e=148)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMatched non-anticoagulated (\u003cem\u003en\u003c/em\u003e=126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMatched anticoagulated (\u003cem\u003en\u003c/em\u003e=126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge (years), mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e69.69 \u0026plusmn; 12.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76.46 \u0026plusmn; 11.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e74.94 \u0026plusmn; 10.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75.66 \u0026plusmn; 10.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSex, male, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e356 (49.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64 (43.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51 (40.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51 (40.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical Characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePre-mRS score, median [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0-2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.018*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.889\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNIHSS score at admission, median [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (3-8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (3-11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (3-7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (2-9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNIHSS score at 72 hours, median [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (0-7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (1-11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.030*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (2-7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (1-7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.926\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eICH, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (6.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (4.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEtiology, cardioembolic, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e193 (26.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e109 (73.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38 (30.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95 (75.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\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\"\u003e\n \u003cp\u003eOnset-to-door time (min), median [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e310 (102-826)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e309 (158-821)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e265 (96-847)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e285 (101-731)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePlasma glucose (mmol/l), mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7,69 \u0026plusmn; 2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7,45 \u0026plusmn; 2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7,81 \u0026plusmn; 3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7,15 \u0026plusmn; 1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedical History, \u003cem\u003en\u003c/em\u003e (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e580 (80.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e139 (93.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e120 (95.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e120 (95.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e244 (34.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59 (39.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.020*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47 (37.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47 (37.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecanalization Therapy, \u003cem\u003en\u003c/em\u003e (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIVT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e190 (26.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39 (30.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (4.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\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\"\u003e\n \u003cp\u003eMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e124 (17.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39 (26.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.014*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (12.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32 (25.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.016*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIVT + MT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62 (8.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (7.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (4.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: SD = standard deviation, mRS = modified Rankin Scale, NIHSS = National Institutes of Health Stroke Scale, ICH = intracranial hemorrhage, IQR = interquartile range, IVT = intravenous thrombolysis, MT = mechanical thrombectomy\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter matching, most demographic and clinical characteristics were well balanced between groups. All key covariates demonstrated SMDs below 0.1, indicating minimal residual imbalance (Table 2). In addition, VRs were close to 1.0, and Kolmogorov\u0026ndash;Smirnov test p-values showed no significant distributional differences between groups (Table 3).\u003c/p\u003e\n\u003cp\u003eHowever, a few significant differences persisted after matching. Cardioembolic stroke etiology remained more prevalent in the anticoagulated group (75.4% vs. 30.2%, p\u0026lt;0.001), IVT was still administered less frequently (4.0% vs. 30.9%, p\u0026lt;0.001), and MT was more commonly performed (25.4% vs. 12.7%, p=0.016). Crucially, subsequent multivariable regression and GAM analyses demonstrated that neither cardioembolic stroke etiology nor treatment modality was independently associated with favorable outcomes, mRS-shift, or mortality, suggesting that these residual imbalances did not confound the study\u0026rsquo;s primary findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Standardized Mean Differences (SMDs) Before and After Matching\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBefore matching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAfter matching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePre-mRS score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNIHSS score at admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNIHSS score at 72 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: SMD = standardized mean difference, mRS = modified Rankin Scale, NIHSS = National Institutes of Health Stroke Scale\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Variance Ratios (VRs) and Kolmogorov-Smirnov Test Before and After Matching\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBefore matching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAfter matching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.829, p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.080, p=0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.987, p=0.680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000, p=1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePre-mRS score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.227, p=0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.995, p=1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNIHSS score at admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.703, p=0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.294, p=0.907\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNIHSS score at 72 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.209, p=0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.052, p=0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.369, p=0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000, p=1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.074, p=0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000, p=1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: SMD = standardized mean difference, mRS = modified Rankin Scale, NIHSS = National Institutes of Health Stroke Scale\u003c/p\u003e\n\u003ch2\u003e3.2 Favorable Outcome\u003c/h2\u003e\n\u003cp\u003eBefore matching, anticoagulated patients were significantly less likely to achieve functional independence at 90 days, with only 34.5% (95% CI [27.3%-42.4%]) reaching an mRS \u0026le;2, compared to 52.1% (95% CI [48.4%-55.7%]) in the non-anticoagulated group (p\u0026lt;0.001). The corresponding OR=0.48 (95% CI [0.33-0.70], p\u0026lt;0.001) indicated that anticoagulated patients had 52% lower odds of a favorable outcome.\u003c/p\u003e\n\u003cp\u003eAfter matching, this difference was no longer statistically significant. In the matched cohort, 39.7% (95% CI [31.6%-48.4%]) of anticoagulated patients achieved an mRS \u0026le;2, compared to 45.2% (95% CI [36.8%-53.9%]) in non-anticoagulated patients (p=0.445). The OR=0.80 (95% CI [0.48-1.31]) suggested no independent association between anticoagulation and functional outcome.\u003c/p\u003e\n\u003cp\u003eFor the comparison between DOAC- and VKA-treated patients, no significant differences were observed. Before matching, the OR=0.82 (95% CI [0.40-1.68], p=0.586), and after matching, the OR remained similar (0.82, 95% CI [0.39-1.74], p=0.700), indicating that anticoagulant type did not influence 90-day outcomes.\u003c/p\u003e\n\u003cp\u003eRegarding treatment modalities, prior to matching, IVT was associated with higher odds of functional independence compared to SC (OR=2.66, 95% CI [1.86-3.81], p\u0026lt;0.001), while MT was linked to lower odds (OR=0.47, 95% CI [0.32-0.69], p\u0026lt;0.001). Combination therapy showed no significant benefit (OR=1.06, 95% CI [0.64-1.77], p=0.896). After matching, these associations were attenuated: IVT (OR=1.48, 95% CI [0.75-2.91], p=0.302) and IVT+MT (OR=0.62, 95% CI [0.20-1.89], p=0.428). Although MT remained significant in univariate analysis (OR=0.46, 95% CI [0.22-0.94], p=0.041), the association did not persist in multivariable regression.\u003c/p\u003e\n\u003cp\u003eWhen adjusting for covariates in the matched cohort, including variables not fully balanced by matching such as stroke etiology and treatment modality, the adjusted OR (aOR=0.64, 95% CI [0.26-1.61], p=0.346) confirmed the absence of a significant association between anticoagulation status and functional independence.\u003c/p\u003e\n\u003ch2\u003e3.3 mRS-shift\u003c/h2\u003e\n\u003cp\u003eThe Shapiro\u0026ndash;Wilk test confirmed non-normal distribution of mRS-shift scores in both groups (p\u0026lt;0.001), supporting the use of non-parametric methods.\u003c/p\u003e\n\u003cp\u003eBefore matching, anticoagulated patients had a significantly greater mean mRS-shift (2.53 \u0026plusmn; 2.23) compared to non-anticoagulated patients (1.79 \u0026plusmn; 1.96, p\u0026lt;0.001), indicating more pronounced functional decline. After matching, this difference was attenuated and no longer statistically significant (2.31 \u0026plusmn; 2.25 vs. 1.87 \u0026plusmn; 1.91, p=0.227).\u003c/p\u003e\n\u003cp\u003eIn the anticoagulated subgroup, DOAC users had numerically higher mRS-shift than VKA users, though differences were not statistically significant either before (2.76 \u0026plusmn; 2.22 vs. 2.04 \u0026plusmn; 2.18, p=0.060) or after matching (2.54 \u0026plusmn; 2.25 vs. 1.86 \u0026plusmn; 2.19, p=0.086).\u003c/p\u003e\n\u003cp\u003eAmong treatment modalities, IVT was associated with a lower mRS-shift compared to SC before matching (1.21 \u0026plusmn; 1.62 vs. 1.88 \u0026plusmn; 2.02, p\u0026lt;0.001), but this difference was no longer significant after matching (p=0.630). MT was linked to significantly greater mRS-shift both before (2.74 \u0026plusmn; 2.07 vs. 1.88 \u0026plusmn; 2.02, p\u0026lt;0.001) and after matching (2.98 \u0026plusmn; 2.06 vs. 1.88 \u0026plusmn; 2.12, p=0.002). Combination therapy showed a significant increase only after matching (3.20 \u0026plusmn; 2.37 vs. 1.88 \u0026plusmn; 2.12, p=0.041), though not before (p=0.252). However, none of these associations remained significant in multivariable analyses.\u003c/p\u003e\n\u003cp\u003eAfter adjusting for covariates in the matched sample, including variables not fully balanced through matching, such as cardioembolic etiology and recanalization therapy, no significant difference in mRS-shift was observed between anticoagulated and non-anticoagulated patients (adjusted coefficient = 0.29, 95% CI [-0.18-0.76], p=0.223).\u003c/p\u003e\n\u003ch2\u003e3.4 Mortality\u003c/h2\u003e\n\u003cp\u003eBefore matching, mortality was significantly higher among anticoagulated patients (30.4%) compared to non-anticoagulated patients (14.5%), with OR=2.58 (95% CI [1.72-3.88], p\u0026lt;0.001). After matching, mortality remained elevated (22.2% vs. 11.9%), with a still significant OR=2.11 (95% CI [1.07-4.19], p=0.044), though the effect size was reduced.\u003c/p\u003e\n\u003cp\u003eIn the anticoagulated subgroup, no significant differences in mortality were found between DOAC and VKA users. Pre-matching OR=1.27 (95% CI [0.59-2.72], p=0.573), and post-matching OR=1.33 (95% CI [0.53-3.33], p=0.652), indicating no meaningful variation by anticoagulant type.\u003c/p\u003e\n\u003cp\u003ePatients receiving IVT had significantly lower mortality than those receiving SC before matching (7.2% vs. 19.1%, OR=0.33, 95% CI [0.18-0.59], p\u0026lt;0.001). While this trend persisted after matching (6.8% vs. 17.9%), it no longer reached statistical significance (OR=0.33, 95% CI [0.10-1.17], p=0.094). MT was associated with slightly higher mortality than SC both before (OR=1.33, 95% CI [0.87-2.05], p=0.211) and after matching (OR=1.20, 95% CI [0.53-2.72], p=0.672), but neither result was significant. Combination therapy also showed no mortality benefit, with OR=0.91 (95% CI [0.47-1.77], p=0.869) before and OR=1.66 (95% CI [0.49-5.64], p=0.485) after matching.\u003c/p\u003e\n\u003cp\u003eWhen adjusting for covariates in the matched cohort, including stroke etiology and treatment modality, the association between anticoagulation and mortality was no longer \u0026nbsp;statistically significant (aOR=2.35, 95% CI [0.79-7.02], p=0.125).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.5 Predictors of Outcome in the Matched Cohort\u003c/h2\u003e\n\u003ch3\u003eFavorable Outcome\u003c/h3\u003e\n\u003cp\u003eMultivariable logistic regression analysis (Table 4) identified NIHSS at admission (p=0.035), NIHSS at 72 hours (p\u0026lt;0.001), pre-mRS (p\u0026lt;0.001), and sex (p=0.043) as significant predictors of favorable functional outcome. Specifically, being male was associated with higher odds of favorable outcomes compared to females. Anticoagulation status was not a significant predictor (p=0.346). The model demonstrated a good fit (Pseudo R\u0026sup2;=0.4949) and a highly significant overall model effect (Log-Likelihood Ratio [LLR], p=4.691e-30), supporting the robustness of these predictors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Multivariate Regression of Favorable Outcome in the Matched Cohort (\u003cem\u003en\u003c/em\u003e=252)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAnticoagulation status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.4417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.477 to 1.360\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.024 to 0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.043*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.026 to 1.706\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePre-stroke mRS score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.4207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.896 to 1.945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNIHSS score at admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.035*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008 to 0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNIHSS score at 72 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.343 to 0.699\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEtiology, cardioembolic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.0044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.872 to 0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.7742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.140 to 3.688\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.4666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.338 to 1.272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIVT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.954 to 1.135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.128 to 1.209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIVT + MT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.7414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.564 to 1.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: CI = confidence interval, mRS = modified Rankin Scale, NIHSS = National Institutes of Health Stroke Scale, IVT = thrombolysis, MT = mechanical thrombectomy\u003c/p\u003e\n\u003cp\u003eIn terms of predictive performance (Figure 1), the model showed excellent discrimination with an area under the curve (AUC) of 0.93, an accuracy of 86.5%, sensitivity of 85.5%, and specificity of 87.9%. Precision was high at 90.5%, with an F1 score of 87.9%, indicating strong reliability in identifying patients at risk of poor functional recovery. Calibration was also good, reflected by a Brier Score of 0.1055. The optimal classification threshold, determined via Youden\u0026rsquo;s Index, was 0.50. \u0026nbsp;\u003c/p\u003e\n\u003ch3\u003emRS-shift\u003c/h3\u003e\n\u003cp\u003eHigher NIHSS scores at admission (p=0.026), NIHSS at 72 hours (p\u0026lt;0.001), and pre-stroke mRS (p\u0026lt;0.001) were significant predictors of increased mRS-shift, indicating worse functional outcomes. Anticoagulation status was not significantly associated with functional outcomes (p=1.00).\u003c/p\u003e\n\u003cp\u003eThe GAM demonstrated strong explanatory power, as indicated by a high Pseudo R\u0026sup2; of 0.612, effective degrees of freedom (DoF) of 33.61, and a log-likelihood of -456.25. An Akaike Information Criterion (AIC) of 981.71 further supported the model\u0026rsquo;s optimal balance between complexity and fit.\u003c/p\u003e\n\u003cp\u003eGAM-derived plots (Figure 2) highlighted non-linear associations between continuous predictors and mRS-shifts. Elevated NIHSS scores at admission and at 72 hours consistently predicted greater mRS-shift.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eMortality\u003c/h3\u003e\n\u003cp\u003eLogistic regression analysis (Table 5) identified NIHSS scores at admission and at 72 hours (both p\u0026lt;0.001) as significant independent predictors of mortality. In contrast, anticoagulation status was not significantly associated with mortality risk (p=0.125). The model exhibited strong overall performance, with a Pseudo R\u0026sup2; of 0.3860, a log-likelihood of -70.69 (compared to LL-Null of -115.14), and a highly significant likelihood ratio test (LLR p=8.083e-14), indicating robust model fit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Multivariate Regression of Mortality in the Matched Cohort (\u003cem\u003en\u003c/em\u003e=252)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAnticoagulation status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.237 to 1.949\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.013 to 0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.4154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.401 to 0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePre-stroke mRS score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.0785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.443 to 0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNIHSS score at admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.092 to 0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNIHSS score at 72 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.120 to 0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEtiology, cardioembolic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.732 to 1.379\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.1735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.414 to 2.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.056 to 1.862\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIVT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.469 to 1.722\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.5796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.706 to 0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIVT + MT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.8911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.764 to 0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: CI = confidence interval, mRS = modified Rankin Scale, NIHSS = National Institutes of Health Stroke Scale, IVT = thrombolysis, MT = mechanical thrombectomy\u003c/p\u003e\n\u003cp\u003eRegarding discrimination (Figure 3), the model achieved an excellent AUC of 0.92, with an accuracy of 80%, sensitivity of 98%, and specificity of 76%. Precision was 0.46, and the F1 score was 0.62, highlighting the model\u0026apos;s strong capability in predicting mortality while managing false positives effectively. Calibration was also robust, as demonstrated by a low Brier Score of 0.0905. The optimal classification threshold, based on Youden\u0026rsquo;s Index, was determined to be 0.11.\u003c/p\u003e\n\u003ch2\u003e3.6 Predictors of Outcome in Anticoagulated Patients\u003c/h2\u003e\n\u003ch3\u003eFavorable Outcome\u003c/h3\u003e\n\u003cp\u003eWithin the anticoagulated cohort, multivariable logistic regression analysis (Table 6) identified NIHSS scores at 72 hours (p\u0026lt;0.001) and pre-stroke mRS (p=0.001) as the strongest predictors of favorable functional outcomes. Neither treatment modality nor the type of anticoagulation significantly influenced the risk of favorable recovery. Atrial fibrillation and previous stroke were included due to their established clinical relevance in anticoagulated patients, although neither was a significant predictor in this model. The model demonstrated excellent fit and robustness, with a high Pseudo R\u0026sup2; of 0.5815, a log-likelihood of -35.42 (compared to LL-Null of -84.64), and a highly significant likelihood ratio test (LLR p=9.461e-15).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. Multivariate Regression of Favorable Outcome in Anticoagulated Patients (\u003cem\u003en\u003c/em\u003e=126)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAtrial fibrillation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.532 to 2.225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrevious stroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.029 to 2.222\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.072 to 0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.0821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.570 to 0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePre-stroke mRS score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.0189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.188 to -0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNIHSS score at admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.1298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.288 to 0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNIHSS score at 72 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.5987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.915 to -0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEtiology, cardioembolic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.2166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.768 to 1.335\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.0567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-5.491 to 1.378\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.7042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.198 to 0.790\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIVT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.3349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.915 to 4.585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.8451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.919 to 1.228\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIVT + MT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.2298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-5.487 to 1.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eType of anticoagulant\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.2983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.427 to 3.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: CI = confidence interval, mRS = modified Rankin Scale, NIHSS = National Institutes of Health Stroke Scale, IVT = thrombolysis, MT = mechanical thrombectomy\u003c/p\u003e\n\u003cp\u003eIn terms of discriminative performance, the model exhibited strong predictive capabilities, achieving an AUC of 0.95, accuracy of 90%, sensitivity of 92%, specificity of 88%, precision of 84%, and an F1 score of 0.88. These metrics underscore the model\u0026apos;s reliability in distinguishing patients who experienced unfavorable outcomes from those who did not. Calibration was also excellent, as indicated by a Brier Score of 0.0846.\u003c/p\u003e\n\u003ch3\u003emRS-shift\u003c/h3\u003e\n\u003cp\u003eTo further explore predictors of disability progression within the anticoagulated cohort, GAMs were applied to assess mRS-shift. NIHSS at 72 hours (p\u0026lt;0.001) and pre-stroke mRS (p=0.001) emerged as significant predictors of worsening functional status. In contrast, treatment modality and type of anticoagulation were not significantly associated with mRS-shift. The model demonstrated strong flexibility and predictive performance, reflected by an effective DoF of 38.35, a log-likelihood of -252.61, and an AIC of 583.93.\u003c/p\u003e\n\u003cp\u003eGAM-derived plots (Figure 4) illustrate non-linear relationships between continuou \u0026nbsp;s predictors and mRS-shift. 72-hour NIHSS scores and pre-mRS score were consistently associated with greater mRS-shift, aligning with trends observed in the broader cohort.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eMortality\u003c/h3\u003e\n\u003cp\u003eWithin the anticoagulated cohort, logistic regression analysis (Table 7) identified NIHSS scores at admission (p=0.028) and at 72 hours (p=0.001) as the strongest independent predictors of mortality. Neither the treatment modality nor the type of anticoagulation showed a significant association with increased mortality risk. The model demonstrated a moderate fit, with a Pseudo R\u0026sup2; of 0.3106, an improved log-likelihood of -46.014 (compared to the LL-Null of -66.743), and a highly significant overall model effect (LLR p=1.5e-4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7. Multivariate Regression of Mortality in Anticoagulated Patients (\u003cem\u003en\u003c/em\u003e=126)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAtrial fibrillation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.386 to 1.597\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrevious stroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.1026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.586 to 1.381\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.007 to 0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.0762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.306 to 0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePre-stroke mRS score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.1025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.607 to 0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNIHSS score at admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.028*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.013 to 0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNIHSS score at 72 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.065 to 0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEtiology, cardioembolic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.365 to 1.388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.1276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.603 to 2.348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.479 to 2.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIVT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.703 to 3.754\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.041 to 1.738\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIVT + MT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.1625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.998 to 4.323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eType of anticoagulant\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.5735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.987 to 0.840\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: CI = confidence interval, mRS = modified Rankin Scale, NIHSS = National Institutes of Health Stroke Scale, IVT = thrombolysis, MT = mechanical thrombectomy\u003c/p\u003e\n\u003cp\u003eRegarding discriminatory performance, the model achieved an AUC of 0.87 and an overall accuracy of 84%. Specificity was high at 95%, indicating strong capability in identifying patients at lower risk of mortality; however, sensitivity was lower at 46%, reflecting a limitation in detecting all high-risk individuals. The model\u0026rsquo;s precision (0.72) and F1 score (0.57) further highlight this trade-off. Calibration was robust, as evidenced by a Brier Score of 0.1176.\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003ch2\u003e4.1 Summary of Findings\u003c/h2\u003e\n\u003cp\u003eIn this propensity score-matched cohort of patients with AIS, we found no significant differences in 90-day functional independence, mRS-shift, or mortality between anticoagulated and non-anticoagulated patients who underwent reperfusion therapy. After adjusting for confounders, including stroke severity and pre-stroke functional status, anticoagulation status was not independently associated with adverse outcomes. Furthermore, neither the type of anticoagulant nor the modality of reperfusion therapy significantly influenced clinical outcomes.\u003c/p\u003e\n\u003ch2\u003e4.2 Interpretation and Clinical Implications\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThese findings challenge the widely held assumption that anticoagulated patients are inherently at greater risk for poor outcomes following reperfusion therapy. While unadjusted analyses suggested increased mortality and disability in the anticoagulated group, these differences disappeared after accounting for baseline differences.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite clear recommendations from major guidelines (AHA/ASA, ESO), many eligible anticoagulated patients remain untreated with reperfusion therapy in routine clinical practice. One of the reasons for this ongoing uncertainty is the limited quality of evidence supporting the safety and efficacy of reperfusion therapy in this population. Landmark reperfusion trials, such as NINDS\u0026nbsp;[15]\u0026nbsp;and ECASS III\u0026nbsp;[2]\u0026nbsp;for IVT and MR CLEAN[16], SWIFT PRIME[17], ESCAPE[18], DAWN[19], DEFUSE 3[20]\u0026nbsp;for MT either excluded anticoagulated patients or were conducted before the widespread adoption of DOACs. As a result, current guideline recommendations rely more on expert consensus than on high-quality randomized trial data\u0026nbsp;[7, 8, 21].\u003c/p\u003e\n\u003ch2\u003e4.3 Comparison with Existing Literature\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eOur results are consistent with several retrospective studies reporting comparable outcomes and ICH rates between anticoagulated and non-anticoagulated patients, including those treated outside strict guideline thresholds [22–26]. Interestingly, a recent meta-analysis [27] even suggested that DOAC-treated patients may experience lower rates of ICH than their non-anticoagulated counterparts, further challenging the notion that anticoagulation alone confers elevated bleeding risk. Similarly, observational studies have suggested that MT can be safely performed in anticoagulated patients [28–30].\u003c/p\u003e\n\u003cp\u003eAlthough these data are encouraging, the evidence base remains largely observational and insufficient to define definitive clinical standards. Several randomized controlled trials (RCTs) are currently underway to address this gap, including the DOAC Intravenous Thrombolysis (DO-IT) study (NCT06571149|\u0026nbsp;2024-08-22), the Safe IVT FXa (SIFT) study (NCT06878066|2025-03-10), and the ACT-GLOBAL Adaptive Platform Trial (NCT06352632|2024-04-02). While their findings are awaited, results are not anticipated in the near term. Until then, real-world guideline-conforming studies such as ours provide critical insight to support evidence-based decision-making in anticoagulated stroke patients.\u003c/p\u003e\n\u003ch2\u003e4.4 Limitations\u003c/h2\u003e\n\u003cp\u003eThis study has several limitations. First, although rigorous propensity score matching was used, the retrospective observational design inherently limits causal inference and may be subject to residual confounding from unmeasured variables. Second, anticoagulation status was primarily determined through medication records and patient self-report, as point-of-care coagulation testing was not consistently available. While some patients underwent coagulation assays, DOAC plasma concentrations were not measured, and no standardized laboratory confirmation of anticoagulant activity was performed. This reliance on clinical documentation and patient recall may have introduced misclassification or recall bias, particularly in assessing treatment eligibility.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn summary, anticoagulation status was not independently associated with worse functional outcomes in AIS patients treated with reperfusion therapy in accordance with current guidelines. Clinical outcomes were primarily influenced by stroke severity and pre-stroke functional status. These findings support the inclusion of eligible anticoagulated patients in reperfusion strategies and underscore the importance of individualized, evidence-based decision-making in acute stroke care.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"662\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eAIS\u003c/p\u003e\n \u003cp\u003eDOAC\u003c/p\u003e\n \u003cp\u003eVKA\u003c/p\u003e\n \u003cp\u003eIVT\u003c/p\u003e\n \u003cp\u003eMT\u003c/p\u003e\n \u003cp\u003eNIHSS\u003c/p\u003e\n \u003cp\u003epre-mRS\u003c/p\u003e\n \u003cp\u003emRS\u003c/p\u003e\n \u003cp\u003eGAM\u003c/p\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003cp\u003eOAC\u003c/p\u003e\n \u003cp\u003eICH\u003c/p\u003e\n \u003cp\u003eAHA/ASA\u003c/p\u003e\n \u003cp\u003eESO\u003c/p\u003e\n \u003cp\u003eINR\u003c/p\u003e\n \u003cp\u003eTINL\u003c/p\u003e\n \u003cp\u003eSMD\u003c/p\u003e\n \u003cp\u003eVR\u003c/p\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003cp\u003eIQR\u003c/p\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003cp\u003eSC\u003c/p\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003cp\u003eLLR\u003c/p\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003cp\u003eDoF\u003c/p\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003cp\u003eRCT\u003c/p\u003e\n \u003cp\u003eDO-IT\u003c/p\u003e\n \u003cp\u003eSIFT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 548px;\"\u003e\n \u003cp\u003eacute ischemic stroke\u003c/p\u003e\n \u003cp\u003edirect oral anticoagulant\u0026nbsp;\u003c/p\u003e\n \u003cp\u003evitamin K antagonist\u003c/p\u003e\n \u003cp\u003eintravenous thrombolysis\u003c/p\u003e\n \u003cp\u003emechanical thrombectomy\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNational Institutes of Health Stroke Scale\u0026nbsp;\u003c/p\u003e\n \u003cp\u003epre-morbid modified Rankin Scale\u003c/p\u003e\n \u003cp\u003emodified Rankin Scale\u0026nbsp;\u003c/p\u003e\n \u003cp\u003egeneralized additive model\u003c/p\u003e\n \u003cp\u003eodds ratio\u003c/p\u003e\n \u003cp\u003econfidence interval\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eoral anticoagulant\u003c/p\u003e\n \u003cp\u003eintracranial hemorrhage\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAmerican Heart Association/American Stroke Association\u003c/p\u003e\n \u003cp\u003eEuropean Stroke Organisation\u003c/p\u003e\n \u003cp\u003einternational normalized ratio\u003c/p\u003e\n \u003cp\u003eTranszl\u0026aacute;ci\u0026oacute;s Idegtudom\u0026aacute;nyi Nemzeti Laborat\u0026oacute;rium\u0026nbsp;\u003c/p\u003e\n \u003cp\u003estandardized mean difference\u003c/p\u003e\n \u003cp\u003evariance ratio\u003c/p\u003e\n \u003cp\u003estandard deviation\u003c/p\u003e\n \u003cp\u003einterquartile range\u003c/p\u003e\n \u003cp\u003evariance inflation factor\u003c/p\u003e\n \u003cp\u003estandard care\u003c/p\u003e\n \u003cp\u003eadjusted odds ratio\u003c/p\u003e\n \u003cp\u003elog-likelihood ratio\u003c/p\u003e\n \u003cp\u003earea under the curve\u003c/p\u003e\n \u003cp\u003edegrees of freedom\u003c/p\u003e\n \u003cp\u003eAkaike information criterion\u003c/p\u003e\n \u003cp\u003erandomized control trial\u003c/p\u003e\n \u003cp\u003eDOAC Intravenous Thrombolysis\u003c/p\u003e\n \u003cp\u003eSafe IVT FXa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and reviewed and approved by the Scientific and Research Ethics Committee of the Medical Research Council of the University of Pécs (RRF-2.3.1-21-2022-00011, 01/09/22) and the Scientific and Research Ethics Committee of the Medical Research Council of Hungary (BM/22444-1/2024, 01/09/24). Informed consent was waived for this study as the data were collected as part of routine clinical documentation, in accordance with the institutional ethics approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are provided within the manuscript and its supplementary information files. For any additional requests regarding raw data, please contact the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor’s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.S. led all major aspects of the study, including conceptualization, methodology, formal analysis, data curation, visualization, and original draft preparation. B.C. contributed significantly to the study design, data interpretation, and manuscript writing. Z.K. and E.B. assisted with validation, literature review, and critical revisions. E.J. supported data collection and helped with administrative and technical tasks during the study. L.S. supervised the project, provided methodological guidance, and oversaw overall administration. All authors made substantial intellectual contributions, reviewed the manuscript, and approved the final version for publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFeigin VL, Abate MD, Abate YH, Abd ElHafeez S, Abd-Allah F, Abdelalim A, et al. Global, regional, and national burden of stroke and its risk factors, 1990\u0026ndash;2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Neurol. 2024;23:973\u0026ndash;1003.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHacke W, Kaste M, Bluhmki E, Brozman M, D\u0026aacute;valos A, Guidetti D, et al. Thrombolysis with Alteplase 3 to 4.5 Hours after Acute Ischemic Stroke. N Engl J Med. 2008;359:1317\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurc G, Bhogal P, Fischer U, Khatri P, Lobotesis K, Mazighi M, et al. European Stroke Organisation (ESO) - European Society for Minimally Invasive Neurological Therapy (ESMINT) Guidelines on Mechanical Thrombectomy in Acute Ischemic Stroke. J Neurointerv Surg. 2023;15:e8\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoglar JA, Chung MK, Armbruster AL, Benjamin EJ, Chyou JY, Cronin EM et al. 2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2024;149.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEmberson J, Lees KR, Lyden P, Blackwell L, Albers G, Bluhmki E, et al. Effect of treatment delay, age, and stroke severity on the effects of intravenous thrombolysis with alteplase for acute ischaemic stroke: a meta-analysis of individual patient data from randomised trials. Lancet. 2014;384:1929\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYaghi S, Willey JZ, Cucchiara B, Goldstein JN, Gonzales NR, Khatri P et al. Treatment and Outcome of Hemorrhagic Transformation After Intravenous Alteplase in Acute Ischemic Stroke: A Scientific Statement for Healthcare Professionals From the American Heart Association/American Stroke Association. Stroke. 2017;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePowers WJ, Rabinstein AA, Ackerson T, Adeoye OM, Bambakidis NC, Becker K et al. 2018 Guidelines for the Early Management of Patients With Acute Ischemic Stroke: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association. Stroke. 2018;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerge E, Whiteley W, Audebert H, De Marchis G, Fonseca AC, Padiglioni C, et al. European Stroke Organisation (ESO) guidelines on intravenous thrombolysis for acute ischaemic stroke. Eur Stroke J. 2021;6:I\u0026ndash;LXII.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeiffge DJ, De Marchis GM, Koga M, Paciaroni M, Wilson D, Cappellari M, et al. Ischemic Stroke despite Oral Anticoagulant Therapy in Patients with Atrial Fibrillation. Ann Neurol. 2020;87:677\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeiffge DJ, Wilson D, Wu TY-H. Administering Thrombolysis for Acute Ischemic Stroke in Patients Taking Direct Oral Anticoagulants. JAMA Neurol. 2021;78:515.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePurrucker JC, H\u0026ouml;lscher K, Kollmer J, Ringleb PA. Etiology of Ischemic Strokes of Patients with Atrial Fibrillation and Therapy with Anticoagulants. J Clin Med. 2020;9:2938.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeinel TR, Branca M, De Marchis GM, Nedeltchev K, Kahles T, Bonati L, et al. Prior Anticoagulation in Patients with Ischemic Stroke and Atrial Fibrillation. Ann Neurol. 2021;89:42\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrott T, Adams HP, Olinger CP, Marler JR, Barsan WG, Biller J, et al. Measurements of acute cerebral infarction: a clinical examination scale. Stroke. 1989;20:864\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Swieten JC, Koudstaal PJ, Visser MC, Schouten HJ, van Gijn J. Interobserver agreement for the assessment of handicap in stroke patients. Stroke. 1988;19:604\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe National Institute of Neurological Disorders and Stroke rt-PA Stroke Study Group. Tissue Plasminogen Activator for Acute Ischemic Stroke. N Engl J Med. 1995;333:1581\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerkhemer OA, Fransen PSS, Beumer D, van den Berg LA, Lingsma HF, Yoo AJ, et al. A Randomized Trial of Intraarterial Treatment for Acute Ischemic Stroke. N Engl J Med. 2015;372:11\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaver JL, Goyal M, Bonafe A, Diener H-C, Levy EI, Pereira VM, et al. Stent-Retriever Thrombectomy after Intravenous t-PA vs. t-PA Alone in Stroke. N Engl J Med. 2015;372:2285\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoyal M, Demchuk AM, Menon BK, Eesa M, Rempel JL, Thornton J, et al. Randomized Assessment of Rapid Endovascular Treatment of Ischemic Stroke. N Engl J Med. 2015;372:1019\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNogueira RG, Jadhav AP, Haussen DC, Bonafe A, Budzik RF, Bhuva P, et al. Thrombectomy 6 to 24 Hours after Stroke with a Mismatch between Deficit and Infarct. N Engl J Med. 2018;378:11\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlbers GW, Marks MP, Kemp S, Christensen S, Tsai JP, Ortega-Gutierrez S, et al. Thrombectomy for Stroke at 6 to 16 Hours with Selection by Perfusion Imaging. N Engl J Med. 2018;378:708\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed N, Audebert H, Turc G, Cordonnier C, Christensen H, Sacco S et al. Consensus statements and recommendations from the ESO-Karolinska Stroke Update Conference, Stockholm 11\u0026ndash;13 November 2018. Eur Stroke J. 2019;4:307\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB\u0026uuml;cke P, Jung S, Kaesmacher J, Goeldlin MB, Horvath T, Prange U, et al. Intravenous thrombolysis in patients with recent intake of direct oral anticoagulants: A target trial analysis after the liberalization of institutional guidelines. Eur Stroke J. 2024;9:959\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKam W, Holmes DN, Hernandez AF, Saver JL, Fonarow GC, Smith EE, et al. Association of Recent Use of Non\u0026ndash;Vitamin K Antagonist Oral Anticoagulants With Intracranial Hemorrhage Among Patients With Acute Ischemic Stroke Treated With Alteplase. JAMA. 2022;327:760.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeinel TR, Wilson D, Gensicke H, Scheitz JF, Ringleb P, Goganau I, et al. Intravenous Thrombolysis in Patients With Ischemic Stroke and Recent Ingestion of Direct Oral Anticoagulants. JAMA Neurol. 2023;80:233.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoehme C, Mayer-Suess L, Mikšov\u0026aacute; D, Lang W, Knoflach M, Kiechl S. Prime Time for a Trial Assessing Safety of Intravenous Thrombolysis in Patients Treated With Direct Oral Anticoagulants. Stroke. 2024;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatusevicius M, S\u0026auml;flund M, Balestrino M, Cappellari M, Ferrandi D, Ghoreishi A et al. Intravenous Thrombolysis in Patients Taking Direct Oral Anticoagulation Treatment Before Stroke Onset: Results from the Safe Implementations of Treatments in Stroke International Stroke Registry. Ann Neurol. 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ana.27189\u003c/span\u003e\u003cspan address=\"10.1002/ana.27189\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang H, Wang X, Quan X, Qin B, Zhang J, Liang S, et al. Safety and efficacy of intravenous thrombolysis in patients with acute ischemic stroke taking direct oral anticoagulants prior to stroke: a meta-analysis. J Neurol. 2023;270:4192\u0026ndash;200.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026rsquo;Allinec V, Sibon I, Mazighi M, Labreuche J, Kyheng M, Boissier E et al. MT in anticoagulated patients. Neurology. 2020;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK\u0026uuml;pper C, Feil K, Wollenweber FA, Tiedt S, Herzberg M, Dorn F, et al. Endovascular stroke treatment in orally anticoagulated patients: an analysis from the German Stroke Registry-Endovascular Treatment. J Neurol. 2021;268:1762\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoldhoorn R-JB, van de Graaf RA, van Rees JM, Lingsma HF, Dippel DWJ, Hinsenveld WH, et al. Endovascular Treatment for Acute Ischemic Stroke in Patients on Oral Anticoagulants. Stroke. 2020;51:1781\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"acute ischemic stroke, anticoagulated patients, functional outcomes, reperfusion therapy, propensity score matching","lastPublishedDoi":"10.21203/rs.3.rs-6335019/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6335019/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e The management of acute ischemic stroke (AIS) in anticoagulated patients presents a clinical challenge, as concerns about safety and efficacy often limit access to recanalization therapies. Despite the widespread use of direct oral anticoagulants (DOACs) and vitamin K antagonists (VKAs), their impact on functional recovery and mortality following intravenous thrombolysis (IVT) and mechanical thrombectomy (MT) remains uncertain. Therefore, this study investigates the association between prior anticoagulation and 90-day outcomes in AIS patients undergoing reperfusion therapy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We conducted a retrospective cohort analysis using our institutional stroke registry, including AIS patients admitted to the Department of Neurology at our university between February 2023 and 2025. Anticoagulated patients were 1:1 propensity score-matched with non-anticoagulated controls (\u003cem\u003en\u003c/em\u003e=126 per group) using Mahalanobis distance matching with a caliper, adjusting for age, sex, hypertension, diabetes, stroke severity (National Institutes of Health Stroke Scale [NIHSS] at admission and 72 hours), and pre-stroke functional status (pre-morbid modified Rankin Scale [pre-mRS]). Primary endpoints at 90 days were functional independence (modified Rankin Scale [mRS] ≤2), mRS-shift, and mortality (mRS=6). Predictors of outcome were assessed using multivariable logistic regression and generalized additive models (GAM). Subgroup analyses evaluated the effects of anticoagulation type and treatment modality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAmong 866 AIS patients (DOAC \u003cem\u003en\u003c/em\u003e=100, VKA \u003cem\u003en\u003c/em\u003e=48, non-anticoagulated \u003cem\u003en\u003c/em\u003e=718), 426 (49.2%) underwent reperfusion therapy (IVT \u003cem\u003en\u003c/em\u003e=195, MT \u003cem\u003en\u003c/em\u003e=163, IVT+MT \u003cem\u003en\u003c/em\u003e=68). Before matching, anticoagulated patients were less likely to achieve functional independence (34.5% vs. 52.1%, odds ratio [OR]=0.48, 95% confidence interval [CI] [0.33–0.70], p\u0026lt;0.001), had a greater mRS-shift (2.53 vs. 1.79, p\u0026lt;0.001), and higher mortality (30.4% vs. 14.5%, OR=2.58, 95% CI [1.72–3.88], p\u0026lt;0.001). However, after matching, these differences were no longer statistically significant. NIHSS, 72hNIHSS, and pre-mRS were the strongest independent predictors of outcome (p\u0026lt;0.001), while anticoagulation status had no significant effect.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eRecanalization therapy appears to be a safe and effective strategy for anticoagulated AIS patients, regardless of anticoagulant type or treatment modality. These findings reinforce that prior anticoagulation alone should not preclude reperfusion therapy and underscore the importance of individualized, evidence-based decision-making in acute stroke care.\u003c/p\u003e","manuscriptTitle":"Trick or Treat(ment): Should We Still Fear Reperfusion Therapy in Anticoagulated Stroke Patients?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 07:17:48","doi":"10.21203/rs.3.rs-6335019/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5de987d5-b0f0-4267-9c18-c5f4849ee2b2","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-12T09:09:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 07:17:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6335019","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6335019","identity":"rs-6335019","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}