Acute ischemic stroke (AIS) is a leading cause of morbidity and mortality worldwide, with anterior circulation strokes accounting for the majority of cases (1). Early and accurate identification of large vessel occlusion (LVO) in patients with AIS is critical for timely intervention, particularly for determining the appropriateness of advanced therapies such as mechanical thrombectomy (2). Among the tools developed to streamline this process, the Rapid Arterial Occlusion Evaluation (RACE) scale has gained recognition as a rapid, reliable, and effective prehospital assessment for predicting LVO (3). The endovascular method is an emerging therapeutic option alongside pharmaceutical therapy. The endovascular method has a greater percentage of full revascularization and a longer time frame. Data indicate that reducing the interval between symptom onset and endovascular therapy in stroke centres is essential to validate the efficacy of this treatment. Large vessel occlusions (LVOs) account for 24–46% of acute ischemic strokes (AIS) and are critical due to high morbidity/mortality without timely intervention (4). Consequently, a straightforward and precise scale for paramedics could help identify patients with LVO (5). Focused diagnostic techniques are necessary because large-artery occlusions in the anterior circulatory system, if left untreated, can cause significant neurological impairments and poor functional outcomes (6). This highlights the need for rapid and accurate identification of LVO, as timely intervention can significantly improve outcomes. LVO prevalence in Latin America was reported at 92% in meta-analyses, but with very low certainty due to heterogeneous access to diagnostics/treatment (7). Endovascular therapy has emerged as an effective treatment modality for LVO, offering a longer therapeutic window and higher rates of successful revascularization compared to pharmacological approaches alone (1). Reducing delays from symptom onset to endovascular therapy is crucial to maximize the benefits of this intervention. Consequently, the development of simple, reliable, and rapid prehospital screening tools for identifying LVO is essential. Several stroke scales have been developed to detect LVO, including the RACE scale, the Vision, Aphasia, Neglect (VAN) score, the CPSS, the LAMS, the APSS, and the FAST scale, among others. the National Institutes of Health Stroke Scale (NIHSS) is commonly used in emergency departments to measure stroke severity, although its complexity and time-consuming nature limit its usefulness in prehospital settings. The RACE scale, based on the NIHSS, facilitates identification of patients at high risk for LVO, aiding prehospital triage and reducing time-to-treatment (3). However, the prevalence of LVO in anterior circulation strokes, as assessed by the RACE scale across different clinical settings, has not been extensively studied, particularly in diverse populations. The VAN score is another simple tool for detecting LVO, focusing on vision, aphasia, and neglect, three critical symptoms of LVO strokes (8). It evaluates whether patients can see in all directions, comprehend spoken words, and respond to stimuli on both sides of the body. The purpose of this research is to evaluate the RACE scale's predictive performance relative to the VAN and NIHSS scores, assessing its effectiveness in identifying LVO in a prehospital setting and its potential to improve triage for endovascular treatment.

Study Design and Setting This was a cross-sectional, observational, analytical study conducted at a tertiary care teaching hospital equipped with advanced radiological and neurological services. The study was conducted from 2023 to 2025. The study was approved by the Institutional Ethics Committees, and written informed consent was obtained from all the patients prior to the commencement of the study. Study Population and Sampling A total of 400 patients presenting to the Emergency Department with suspected acute stroke were enrolled consecutively based on eligibility criteria. All participants underwent a structured clinical and radiological evaluation for the presence of large vessel occlusion (LVO). Inclusion and Exclusion Criteria Patients were included in the study if they presented to the Emergency Department with clinical features suggestive of acute stroke and were suitable candidates for neuroimaging. Exclusion criteria comprised any contraindication to CT angiography, such as an estimated creatinine clearance of less than 60 mL/min, known hypersensitivity to contrast agents, or any other condition that precluded the use of contrast imaging. Additionally, patients were excluded if initial neuroimaging revealed a hemorrhagic stroke or if the ischemic event involved the posterior circulation. Clinical and Radiological Assessment On presentation, all patients underwent an initial clinical assessment using the Rapid Arterial Occlusion Evaluation (RACE) scale. This was followed by CT or MR angiography of the brain to confirm or exclude LVO. The RACE scale was compared with CT/MRI findings and other scales to exclude LVO. In addition to the RACE scale, the VAN (Vision, Aphasia, Neglect) assessment and the National Institutes of Health Stroke Scale (NIHSS) were also applied to evaluate neurological deficits. Data Collection and Variables Demographic details, clinical features, and radiological findings were systematically recorded. Imaging data were reviewed using CT and MR angiographic plates. The primary variable of interest was the presence or absence of large-vessel occlusion, as confirmed by imaging. Clinical scores (RACE, VAN, and NIHSS) were documented and compared. Sample Size The sample size was calculated assuming a prevalence of large-vessel occlusion of 21%, based on prior studies, at a 95% confidence level with a margin of error of ±4%. This resulted in a required sample size of 400 patients. However, due to practical limitations during the study period, only 105 patients were ultimately enrolled and included in the final analysis. Statistical Analysis The diagnostic performance of the RACE scale in identifying LVO was evaluated by calculating sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall diagnostic accuracy. The correlation between the RACE score and other clinical scales (VAN and NIHSS) was assessed using appropriate statistical tests. All analyses were performed using SPSS. A p-value <0.05 was considered statistically significant.

A total of 105 patients were included in the study. The mean age of the study population was 59.00 ± 15.66 years, with 68 (64.8%) males and 37 (35.2%) females. NIHSS score at presentation was 12.50 ± 6.28, indicating moderate to severe neurological impairment. The mean RACE score was 4.97 ± 1.88, indicating a moderate probability of large-vessel occlusion in the study population. Among the participants, 45 (42.9%) had an observed history of diabetes mellitus. In addition, 42 (40.0%) patients had hypertension. Eighty-nine (84.8%) patients had a positive VAN score, whereas 16 (15.2%) had a negative score. Among patients with the RACE categorical variable, 65 (61.9%) had a higher suspicion of large-vessel occlusion, and radiological evaluation confirmed LVO in 89 (84.8%) (Table 1). A statistically significant correlation was observed between LVO and both a positive VAN score and a "Yes" classification under the RACE_CAT variable. These findings underscore the utility of the VAN and RACE prehospital stroke scales in predicting LVO (Table 2.). No statistically significant association was noted between LVO and sex, diabetes mellitus, or hypertension. These results suggest that while certain clinical scales are reliable predictors of LVO, baseline demographic characteristics and common comorbidities may not independently influence LVO detection. The area under the curve (AUC) of 0.905 ± 0.029, the RACE Score proved to be very accurate in predicting LVO (Figure 1.). A 95% confidence interval of 0.848 to 0.962 was used to determine the optimal cut-off value, which was 5 (Table 3.). The statistical significance of this finding suggests that the RACE Score may be used to identify individuals at risk of LVO. The diagnostic performance metrics of the RACE and VAN scores in predicting LVO among the study population (Table 4.). The RACE score demonstrated perfect specificity (100%) and positive predictive value (100%), indicating a high ability to correctly identify patients without LVO and a strong likelihood of LVO when the score is positive. However, it showed moderate sensitivity (73.03%) and low negative predictive value (39.93%), indicating limited performance in excluding LVO in negative cases. The overall accuracy of the RACE score was 77.13%. Table 1: Baseline Demographic and Clinical Characteristics of the Study Population (n = 105) Variable Value Age, mean ± SD (years) 59.00 ± 15.66 Sex, n = 105 (%) Male 68 (64.8%) Female 37 (35.2%) NIHSS score, mean ± SD 12.50 ± 6.28 RACE score, mean ± SD 4.97 ± 1.88 Diabetes Mellitus, n = 105 (%) Present 45 (42.9%) Hypertension, n = 105 (%) Present 42 (40.0%) VAN score, n = 105 (%) Positive 89 (84.8%) Negative 16 (15.2%) RACE Yes 65 (61.9%) Large vessel occlusion (LVO), n = 105 (%) Present 89 (84.8%) Table 2: Association Between Clinical Variables and Large Vessel Occlusion (LVO) Variable LVO Present n (%) LVO Absent n (%) Total P-value VAN Score <0.0001 Positive 81 (91.0%) 8 (9.0%) 89 Negative 8 (50.0%) 8 (50.0%) 16 RACE_CAT <0.0001 Yes 65 (100.0%) 0 (0.0%) 65 No 24 (60.0%) 16 (40.0%) 40 Sex 0.717 Male 57 (83.8%) 11 (16.2%) 68 Female 32 (86.5%) 5 (13.5%) 37 Diabetes Mellitus 0.531 Yes 37 (82.2%) 8 (17.8%) 45 No 52 (86.7%) 8 (13.3%) 60 Hypertension 0.183 Yes 38 (90.5%) 4 (9.5%) 42 No 51 (81.0%) 12 (19.0%) 63 Table 3. The area under the ROC curve for RACE Score for the prediction of Large Vessel Occlusion. Parameter Optimal Cut-Off Based on ROC AUC ± SE 95% CI of AUC p-value RACE Score 5 0.905 ± 0.029 0.848-0.962 <0.001 Reference value = 0.500. SE – Standard Error, ***P-value<0.001. Table 4: Diagnostic Performance of RACE and VAN Scores for Predicting Large Vessel Occlusion (LVO) Scoring Tool Sensitivity (%) Specificity (%) Positive Predictive Value (PPV, %) Negative Predictive Value (NPV, %) Accuracy (%) RACE Score 73.03 100.00 100.00 39.93 77.13 VAN Score 91.01 50.00 91.01 50.00 84.76 STROBE Statement—Checklist of items that should be included in reports of cross-sectional studies Item No Recommendation Title and abstract 1 (a) Indicate the study’s design with a commonly used term in the title or the abstract (b) Provide in the abstract an informative and balanced summary of what was done and what was found Introduction Background/rationale 2 Explain the scientific background and rationale for the investigation being reported Objectives 3 State specific objectives, including any prespecified hypotheses Methods Study design 4 Present key elements of study design early in the paper Setting 5 Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection Participants 6 (a) Give the eligibility criteria, and the sources and methods of selection of participants Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if applicable Data sources/ measurement 8* For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe the comparability of assessment methods if there is more than one group Bias 9 Describe any efforts to address potential sources of bias Study size 10 Explain how the study size was arrived at Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and why Statistical methods 12 (a) Describe all statistical methods, including those used to control for confounding (b) Describe any methods used to examine subgroups and interactions (c) Explain how missing data were addressed (d) If applicable, describe analytical methods taking account of sampling strategy (e) Describe any sensitivity analyses Results Participants 13* (a) Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analysed (b) Give reasons for non-participation at each stage (c) Consider use of a flow diagram Descriptive data 14* (a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential confounders (b) Indicate number of participants with missing data for each variable of interest Outcome data 15* Report numbers of outcome events or summary measures Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95% confidence interval). Make clear which confounders were adjusted for and why they were included (b) Report category boundaries when continuous variables were categorized (c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analyses Discussion Key results 18 Summarise key results with reference to study objectives Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence Generalisability 21 Discuss the generalisability (external validity) of the study results Other information Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based

This study evaluated the predictive utility of the RACE and VAN scores for identifying large vessel occlusion (LVO) in patients with anterior circulation acute ischemic stroke (AIS). Our findings support the use of these scales, particularly the RACE scale, in prehospital triage and early stroke recognition. The mean age of our cohort (59.00 ± 15.66 years) was notably younger than in other AIS cohorts, such as those reported by Rafiemanesh et al. (66.9 ± 13.9 years), Navalkele et al. (66 ± 13 years), and Duvekot et al. (median 72 years, IQR 61–81), possibly reflecting population or regional differences (9–11). Similarly, our mean NIHSS score of 12.50 ± 6.28 is consistent with moderate-to-severe stroke severity and is comparable to studies focusing on LVO, though slightly lower than cohorts that included exclusively confirmed LVO cases (10,11). The mean RACE score of 4.97 ± 1.88 aligns with prior LVO-focused studies, including Duvekot et al. (median 6 for aLVO) and Carrera et al. (median 7 [LVO] vs 3 [non-LVO]) (11,12). Regarding sex distribution, males accounted for 64.8% of our cohort, higher than the proportions reported in several prior studies, which reported a more balanced distribution or female predominance (3,10,11,13,14). Regarding comorbidities, the prevalence of diabetes mellitus (42.9%) in our study exceeds that reported by Rafiemanesh et al. (30.5%) and Duvekot et al. (19%), potentially indicating regional variation or population-level risk factors (9,11). Conversely, hypertension was present in 40.0% of our patients—markedly lower than rates reported in other suspected stroke cohorts, which commonly exceed 60% (9,11,13). Among our cohort, 84.8% had LVO, which is significantly higher than in comparable studies, in which reported LVO prevalence ranged from 12% to 31% (3,10–12,14,15). This elevated rate likely reflects our focus on anterior circulation AIS and patient selection criteria. The VAN score demonstrated high sensitivity (91.01%) and accuracy (84.76%), though specificity was modest (50%). These findings are consistent with those of Navalkele et al., who reported VAN sensitivity and specificity of 79% and 69%, respectively, and exceed those reported by Hosny, who found 56% sensitivity and 77% specificity. The significant association between VAN and LVO in our study (p < 0.0001) is supported by prior evidence demonstrating fair predictive power (AUC 0.74–0.76) (10,15). Similarly, the RACE score showed high specificity (100%) and PPV (100%) in predicting LVO, with an accuracy of 77.13% and a statistically significant association (p < 0.0001). Our findings align with prior studies reporting high AUC values for RACE in LVO detection: Pérez de la Ossa et al. (AUC 0.82), Carrera et al. (AUC 0.77), and Duvekot et al. (AUC 0.83), all of which support its diagnostic utility with cut-off scores ≥5 (3,11,12). This study observed no significant associations between LVO and sex (p = 0.717), diabetes (p = 0.531), or hypertension (p = 0.183). These findings are consistent with the absence of statistically significant associations reported by Navalkele et al., Duvekot et al., and Rafiemanesh et al., suggesting that although these comorbidities may contribute to overall stroke risk, they are not independent predictors of LVO (9–11). Limitations: The cross-sectional study on LVO prevalence using the RACE scale reports high specificity (100%) and a strong AUC (0.905), with robust assessment protocols. However, its single-centre design, small sample size, and high prevalence of LVO raise concerns about generalizability and selection bias. The absence of blinding, interrater reliability, power calculations, and periodic evaluation further limits the validity. Future studies should be multicentre, adequately powered, and incorporate blinding and inter-rater reliability for broader and more reliable results.

This study demonstrates that the RACE scale is a reliable and clinically practical tool for the early detection of large-vessel occlusion in acute ischemic stroke of the anterior circulation. Its high specificity and predictive value support its integration into prehospital stroke assessment protocols. Routine use of the RACE scale may facilitate more accurate triage, expedite referral for endovascular therapy, and ultimately improve patient outcomes in stroke care systems.

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