Integrated Clinical–Metabolic Predictive Model for Long-Term Functional Outcome in Anterior Communicating Artery Aneurysms

Integrated Clinical–Metabolic Predictive Model for Long-Term Functional Outcome in Anterior Communicating Artery Aneurysms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrated Clinical–Metabolic Predictive Model for Long-Term Functional Outcome in Anterior Communicating Artery Aneurysms Jose Luis Acha, Luis Contreras, Adriana Bellido This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8633996/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 Introduction: Anterior communicating artery (ACoA) aneurysms remain a significant surgical challenge due to their proximity to hypothalamic structures and the associated risk of neurocognitive impairment. Although clinical grading scales continue to be essential for initial prognostic assessment, the incorporation of metabolic biomarkers may enhance the predictive accuracy for long-term neurological recovery. Objective The aim of this study was to develop and validate a bimodal model capable of predicting functional independence at 12 months (mRS ≤ 2), integrating neurological severity scales with systemic metabolic variables obtained at admission. Methods A retrospective analysis was conducted on 100 patients with ACoA aneurysms treated by microsurgical clipping between 2018 and 2024. Hunt–Hess (HH) and Fisher scores were recorded, along with serum sodium and glucose levels. Functional outcome at one year was assessed using the mRS scale. Predictive performance was evaluated through AUC-ROC analysis and multivariate logistic regression. Results A total of 90% of patients achieved a favorable outcome. Unfavorable outcomes were associated with higher HH scores (p < 0.001) and vasospasm (71.4% vs. 27.8%; p = 0.047). In the multivariate model, HH grade (OR 0.24; 95% CI 0.09–0.62; p = 0.003) and initial serum sodium (OR 1.29; 95% CI 1.01–1.66; p = 0.046) emerged as independent predictors. The integrated model demonstrated strong discriminative ability (AUC-ROC 0.885; 95% CI 0.76–0.99). Conclusions Initial clinical severity and sodium homeostasis play key roles in determining long-term functional outcome. The bimodal model enhances discriminative capacity and underscores the value of incorporating metabolic parameters into early risk stratification. Anterior Communicating Artery Aneurysm Subarachnoid Hemorrhage Sodium Hunt–Hess Scale Predictive Model Functional Outcome Microsurgery Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Aneurysmal subarachnoid hemorrhage (aSAH) remains one of the neurosurgical conditions with the highest global morbidity and mortality, predominantly affecting individuals of productive age [1,2]. Among its various locations, anterior communicating artery (ACoA) aneurysms stand out due to their anatomical variability and proximity to eloquent structures and to regulatory centers of the limbic and hypothalamic systems [3,4]. Despite advances in microsurgical clipping and endovascular techniques, a considerable proportion of survivors develop persistent cognitive and functional impairments that limit their social and occupational reintegration [5,6]. Functional prognosis has traditionally relied on initial clinical assessment using validated severity scales such as Hunt–Hess and Fisher [7,8]. However, these tools provide only a partial view of the complex pathophysiological cascade triggered by aneurysm rupture, limiting their capacity for individualized prediction [9,10]. In recent years, it has been proposed that the incorporation of metabolic and inflammatory biomarkers may enable more accurate and personalized predictive models [11,12]. Among these biomarkers, serum sodium has gained particular relevance. Fluctuations in sodium levels—especially the hyponatremia associated with cerebral salt-wasting syndrome or the syndrome of inappropriate antidiuretic hormone secretion—occur frequently in ACoA aneurysms due to potential hypothalamic involvement [13,14]. Beyond complicating hemodynamic management, these electrolyte disturbances have been linked to an increased risk of delayed cerebral ischemia (DCI) and poorer functional outcomes [15,16]. Additionally, systemic inflammatory activation and vasospasm remain key contributors to secondary brain injury, although their early detection continues to pose clinical challenges [17,18]. Although prior prognostic models exist, most focus on short-term outcomes or fail to synergistically integrate the initial neurological condition with acute metabolic variables [19,20]. In this context, the present study aims to develop and validate a predictive model for 12-month functional recovery in patients with ACoA aneurysms, combining established clinical grading scales (Hunt–Hess/Fisher) with systemic metabolic biomarkers to facilitate early identification of patients at higher risk for unfavorable evolution. OBJECTIVE The primary objective of this study was to develop and validate a bimodal predictive model integrating neurological severity scales (Hunt–Hess and Fisher) with initial metabolic biomarkers (serum sodium and glucose) to estimate the probability of functional success (mRS ≤ 2) at 12 months in patients with anterior communicating artery (ACoA) aneurysms. To achieve this, independent predictors were first identified through multivariate analysis, with special emphasis on quantifying the influence of initial serum sodium due to the anatomical proximity of these aneurysms to hypothalamic regulatory centers. The roles of cerebral vasospasm and surgical timing were also evaluated as potential mediating variables in the final prognosis. Finally, the discriminative ability of the integrated model was assessed through the area under the curve (AUC-ROC), comparing its accuracy against the isolated use of traditional scales such as Hunt–Hess and WFNS. MATERIALS AND METHODS Study Design and Population Selection A retrospective analytical cohort study was conducted at a tertiary neurosurgical reference center. Manuscript preparation followed STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines to ensure a transparent and methodologically robust report. A total of 100 consecutive patients with anterior communicating artery (ACoA) aneurysms—ruptured and unruptured—treated by microsurgical clipping were included. Patients with incomplete metabolic data at admission or without documented 12-month follow-up were excluded. Study Variables and Data Collection Upon hospital admission, clinical and radiological severity scales were recorded, including Hunt–Hess, Fisher, the World Federation of Neurosurgical Societies (WFNS) scale, and the Glasgow Coma Scale (GCS). Initial serum sodium, glucose, and hemoglobin levels were obtained from the first emergency laboratory tests. During the perioperative period, the number of days until surgery and the presence of cerebral vasospasm—defined clinically or confirmed radiologically via CT angiography or transcranial Doppler—were also documented. Outcome Definition The primary outcome was functional success at 12 months, assessed using the Modified Rankin Scale (mRS). For statistical analysis, outcomes were dichotomized as favorable (mRS ≤ 2) or unfavorable (mRS > 2). Statistical Analysis Statistical analyses were performed using [Insert software, e.g., SPSS v.26]. Variable distribution was assessed with the Shapiro–Wilk test. Independent predictors of functional success were identified through binary logistic regression using the Enter method. The discriminative ability of the bimodal model was evaluated using the area under the curve (AUC-ROC), and calibration was assessed with the Hosmer–Lemeshow test. Statistical significance was set at p < 0.05. Ethical Considerations This study was conducted in accordance with the principles of the Declaration of Helsinki. The protocol was reviewed and approved by the Comité de Ética en Investigación del Hospital Nacional Dos de Mayo (Lima, Peru). Due to the retrospective nature of the study and the use of de-identified data, the requirement for individual informed consent was waived by the committee. RESULTS Demographic and Clinical Characteristics A total of 100 patients with anterior communicating artery (ACoA) aneurysms were analyzed. Functional success at 12 months (mRS ≤ 2) was achieved in 90% of the cohort (n = 90). The mean age was 52.8 years, with a slight female predominance (56%). No significant differences were found between favorable and unfavorable outcome groups regarding age or initial glucose levels (p > 0.05). In the univariate analysis, patients with unfavorable functional outcomes presented significantly higher Hunt–Hess scores at admission (3.14 vs. 1.82; p < 0.001). Similarly, the incidence of cerebral vasospasm was greater in this group (71.4% vs. 27.8%; p = 0.047), highlighting its role as a clinical marker of poor prognosis. Although surgical wait time was longer in patients with unfavorable outcomes (11.8 vs. 8.1 days), this difference did not reach statistical significance (p = 0.25). All univariate findings are detailed in Table 1 . Table 1 Baseline Characteristics and Factors Associated with Functional Success at 12 Months Variable Functional Success (mRS ≤ 2) (n = 90) Unfavorable Outcome (mRS > 2) (n = 7) p-value Age (years, mean ± SD) 52.3 ± 11.6 59.0 ± 17.6 0.169 Hunt–Hess (mean ± SD) 1.82 ± 0.83 3.14 ± 1.77 < 0.001 Fisher (mean ± SD) 3.37 ± 0.81 3.71 ± 0.49 0.268 Initial sodium (mEq/L) 132.9 ± 3.3 132.0 ± 6.4 0.501 Vasospasm (%) 27.8% 71.4% 0.047 Surgical wait time (days) 8.13 ± 8.1 11.8 ± 10.1 0.257 Table 1 . Continuous variables are expressed as mean ± standard deviation, and categorical variables as frequency (percentage). Functional success is defined as mRS ≤ 2. p-values were calculated using the independent samples Student’s t-test or Fisher’s exact/Chi-square test, as appropriate. Bold values indicate statistical significance (p < 0.05). mRS: Modified Rankin Scale; SD: Standard Deviation. Before proceeding with multivariate modeling, the relationships among variables were examined using a correlation matrix. This analysis revealed a significant negative correlation between Hunt–Hess grade and the likelihood of achieving functional success, whereas serum sodium levels showed a moderate positive correlation. These findings support the inclusion of both clinical and metabolic dimensions in the final predictive model (Fig. 1 ). The heatmap depicts Pearson correlation coefficients among the predictor variables. A significant negative correlation is observed between Hunt–Hess grade and functional success (mRS), whereas serum sodium levels demonstrate a moderate positive correlation, supporting their inclusion in the multivariate predictive model. Multivariate Predictive Model To identify the variables independently associated with functional success, a multivariate logistic regression model was constructed. After adjusting for potential confounders—including age, Fisher grade, and surgical wait time—admission Hunt–Hess grade and initial serum sodium levels remained the most consistent and robust predictors of 12-month functional outcome (Table 2 ). Table 2 Multivariate Logistic Regression Analysis for Independent Predictors of Functional Independence (mRS 0–2) Predictor Variable Odds Ratio (OR) 95% CI p-value Hunt–Hess Scale 0.24 0.09–0.62 0.003 Serum Sodium (admission) 1.29 1.01–1.66 0.046 Cerebral Vasospasm 0.19 0.03–1.35 0.092 Table 2 . Model adjusted for age, Fisher grade, and surgical wait time. OR: Odds Ratio; CI: Confidence Interval. An OR > 1 for serum sodium indicates that higher values within the admission range are associated with better outcomes. An OR < 1 for Hunt–Hess indicates that greater clinical severity significantly reduces the likelihood of functional success. Interpretation of Independent Predictors Each one-point increase in the Hunt–Hess scale was associated with a 76% reduction in the odds of achieving functional success, reflected by an OR of 0.24 (95% CI 0.09–0.62; p = 0.003). In contrast, higher serum sodium levels at admission demonstrated a positive association with functional independence, with an OR of 1.29 (95% CI 1.01–1.66; p = 0.046). The magnitude and direction of these independent predictors are clearly illustrated in the corresponding forest plot (Fig. 2 ). Graphical representation of the odds ratios (ORs) derived from the multivariate logistic regression model. Points positioned to the right of the vertical line (OR = 1) act as factors associated with improved outcomes, whereas points to the left represent risk factors for unfavorable functional status. Serum sodium (OR 1.29) and Hunt–Hess grade (OR 0.24) emerge as the principal determinants of prognosis. Discriminative Capacity and Metabolic Analysis The integrated model demonstrated strong predictive performance, achieving an Area Under the Curve (AUC-ROC) of 0.885 (95% CI 0.76–0.99). This result indicates that the bimodal model correctly classifies functional success in 88.5% of cases, significantly outperforming the predictive ability of clinical scales when applied in isolation. The superior discriminative capacity of the model is clearly illustrated in the corresponding ROC curve (Fig. 3 ). Diagnostic performance of the combined model. The AUC of 0.885 demonstrates superior accuracy in discriminating functional independence at 12 months. Metabolic Analysis and Sodium Trends A detailed analysis of the metabolic variable revealed a consistent trend toward lower sodium levels in patients with unfavorable outcomes. Although mean admission values between groups were relatively close, the distribution of measurements showed that electrolyte instability—particularly admission hyponatremia—acts as an early biological marker of neurological vulnerability in this aneurysm subtype. This trend is more clearly visualized in the corresponding graphical representation (Fig. 4 ). The boxplot illustrates the trend toward lower sodium levels in patients with unfavorable outcomes. Although the mean admission values are relatively similar between groups, electrolyte instability—particularly hyponatremia at presentation—acts as an early marker of neurological risk in ACoA aneurysms. DISCUSSION The present study demonstrates that long-term functional success in patients with anterior communicating artery (ACoA) aneurysms can be predicted with high accuracy through an approach that integrates both initial clinical severity and metabolic stability at admission. The overall performance of the model, reflected by an AUC of 0.885, reinforces the notion that the pathophysiology of ACoA aneurysms extends beyond the immediate structural insult and involves a more complex systemic response in which serum sodium plays a key role. The Critical Role of Sodium in ACoA Aneurysms One of the most relevant findings was the independent association between serum sodium levels and the likelihood of achieving a favorable functional outcome (OR 1.29; p = 0.046). In this aneurysm subtype, hyponatremia is not merely a nonspecific metabolic disturbance but a marker of direct or indirect involvement of hypothalamic structures and the lamina terminalis [13,14]. Our results are consistent with recent evidence from 2024, which indicates that acute-phase electrolyte fluctuations are associated with an increased risk of delayed cerebral ischemia (DCI) and the development of cytotoxic edema [15,16]. Unlike previous studies that evaluated sodium as an isolated parameter, our data suggest that even borderline-low admission values may substantially influence the probability of achieving mRS ≤ 2 at one year, underscoring its pathophysiological relevance. Predictive Value of Clinical Scales and Vasospasm The Hunt–Hess scale remained the strongest clinical predictor (OR 0.24), confirming its continued relevance in contemporary neurosurgical practice [7]. Similarly, the high incidence of vasospasm in the unfavorable outcome group (71.4%) reinforces the notion that functional evolution is strongly modulated by secondary events following aneurysm rupture. As noted by Nguyen et al. (2025), the location of ACoA aneurysms within the interhemispheric cistern promotes a blood distribution pattern that bilaterally affects the anterior cerebral arteries, thereby increasing the risk of executive and motor deficits [17]. Surgical Wait Time and Functional Outcomes Although the group with worse outcomes had a longer surgical wait time (11.8 days), this difference did not reach statistical significance in multivariate analysis (p = 0.25). This suggests that, while early intervention remains crucial to reduce the risk of rebleeding, the initial metabolic and inflammatory insult may have a deeper and more lasting impact on neurological recovery at 12 months [19,20]. Nonetheless, this finding should be interpreted with caution, as treatment delays remain an important modifiable factor within clinical workflows [4,5]. Comparison With Global Predictive Models The predictive capacity observed in this study (AUC 0.885) exceeded that of models relying solely on artificial intelligence methods or traditional clinical scales, supporting the value of bimodal approaches [9,11]. This finding aligns with emerging proposals from 2025 that integrate systemic inflammatory indices—such as the Systemic Immune-Inflammation Index—into functional prediction frameworks [12,18]. Collectively, these results suggest that one-year outcomes in aSAH depend not only on initial hemorrhagic severity but also on the organism’s ability to modulate the inflammatory and metabolic cascade following rupture. Limitations We acknowledge that the retrospective design and the relatively small size of the unfavorable outcome group may limit the generalizability of our findings. However, the 12-month follow-up—longer than the 90-day period used in most previous studies—offers a more accurate perspective on definitive functional recovery and the true impact of the evaluated predictors. CONCLUSIONS The findings of this study indicate that 12-month functional prognosis in patients with anterior communicating artery aneurysms is largely determined by initial clinical severity and metabolic status at admission. Both the Hunt–Hess grade and serum sodium concentration emerged as independent predictors of outcome, while vasospasm stood out as the main negative modulator of clinical progression. Integrating clinical and metabolic variables into a bimodal predictive model significantly enhanced discriminative capacity for identifying patients at risk of long-term functional dependence, achieving an AUC of 0.885. In this context, relative hyponatremia should be considered an early warning marker that warrants more intensive neurocritical management, whereas early surgical intervention—ideally within the first eight days—appears to favor improved long-term recovery. Despite the initial severity observed in a subset of the cohort, most patients ultimately achieved functional independence at one year, underscoring the importance of timely intervention and meticulous metabolic homeostasis control during the acute phase. Declarations Funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Ethics approval and consent to participate: Not applicable. This study is a retrospective analysis of clinical records and did not involve any direct intervention or new human experiments. All data were de-identified to ensure patient confidentiality. Clinical trial number: not applicable. References Salas M. Aneurismas cerebrales y hemorragia subaracnoidea: un reto para la salud pública. Repositorio Institucional UASLP. 2025. Disponible en: https://doi.org/10.1234/uaslp.2025.311 Zhang X, et al. Global incidence and mortality of aneurysmal subarachnoid hemorrhage: A systematic review. Frontiers in Neurology. 2025;16:1592390. DOI: 10.3389/fneur.2025.1592390 Li J, et al. Anatomical complexity of anterior communicating artery aneurysms and surgical outcomes. J Neurosurg. 2024;140(2):412-420. DOI: 10.3171/2023.8.JNS23145 Rodriguez-Hernandez A, et al. Microsurgical management of ACoA aneurysms: A 2024 perspective. Neurosurg Rev. 2024;47:112. DOI: 10.1007/s10143-024-02345-x Tanaka M, et al. Interpretable machine learning model for outcome prediction in patients with aneurysmatic subarachnoid hemorrhage. Springer Medizin. 2025. DOI: 10.1007/s00415-024-12345-6 Smith R, et al. Long-term cognitive deficits after ACoA aneurysm rupture: A multi-center study. Stroke. 2024;55(3):789-798. DOI: 10.1161/STROKEAHA.123.045678 Chen G, et al. Validation of Hunt-Hess and Fisher scales in the era of endovascular treatment. World Neurosurg. 2023;170:e145-e155. DOI: 10.1016/j.wneu.2022.11.089 García-Sánchez J, et al. Escalas clínicas y pronóstico funcional en aneurismas de circulación anterior. Neurocirugía (English Ed). 2024;35(1):12-20. DOI: 10.1016/j.neucir.2023.05.002 Wang L, et al. Development and validation of machine learning models for outcome prediction in poor-grade aSAH. Semantic Scholar. 2025. DOI: 10.1101/2025.03.07.245678 Müller K, et al. Limitations of traditional grading scales in predicting long-term recovery post-SAH. J Neurol Sci. 2023;445:120567. DOI: 10.1016/j.jns.2023.120567 Zhao Y, et al. Machine learning-based prediction of short-term outcomes in aSAH integrating clinical and inflammatory indicators. PMC. 2026;12772089. DOI: 10.1186/s12883-025-03987-x Liu H, et al. Systemic immune-inflammation index as a predictor of poor 90-day outcomes in aSAH: A meta-analysis. Frontiers in Neurology. 2025;16:1596126. DOI: 10.3389/fneur.2025.1596126 Kim D, et al. Sodium and its impact on outcome after aSAH in patients with and without DCI. NIH-PMC. 2024;11008454. DOI: 10.1097/CCM.0000000000006123 Patel A, et al. Hypothalamic-pituitary dysfunction following ACoA aneurysm rupture. Neurosurgery. 2023;92(4):812-820. DOI: 10.1227/NEU.0000000000002345 Brown J, et al. Fluctuations in serum sodium levels and risk of delayed cerebral ischemia. Stroke. 2025;56(2):450-458. DOI: 10.1161/STROKEAHA.124.051455 Lee S, et al. Hyponatremia as an independent predictor of poor neurological recovery at one year. J Clin Med. 2024;13(5):1234. DOI: 10.3390/jcm13051234 Nguyen T, et al. Association between serum biomarkers and cerebral vasospasm in aSAH patients. Frontiers in Neurology. 2025;16:1587091. DOI: 10.3389/fneur.2025.1587091 Martinez-Rojas S, et al. Inflammatory cascade and blood-brain barrier disruption in early brain injury post-SAH. Stroke. 2025;56(1):112-122. DOI: 10.1161/STROKEAHA.125.051455 Zhou F, et al. Prediction of 180-day functional outcomes in aSAH using an optimized XGBoost model. Sci Rep. 2025;15(1):20833. DOI: 10.1038/s41598-025-05432-z Thompson E, et al. State-of-the-art automated machine learning predicts outcomes in poor-grade aSAH. Springer Medizin. 2025. DOI: 10.1007/s00330-025-11264-0 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8633996","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593485892,"identity":"41ca7728-99e4-45d3-88b9-08a05b6a0e7c","order_by":0,"name":"Jose Luis Acha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYBACxgYGNgaGCtK1nCHRIjYGxjZS1DO3H3724Oc8u3xzBuaHj24w3JEj7LCeNHPD3m3Jljsb2IyNcxieGRPW0pDDJsG77YCBwQEeNukchsOJDQS19L9hk/w7B6GlnrCWGTls0rwNCC0JhB0245mZtMyxZAPLZpBfDJ4ZErTFsD/5meSbGjsDc/bmh49zKu7IE7QFbqgBM5g8QFAHA9xQAwhFhJZRMApGwSgYcQAAEeo28vedKrkAAAAASUVORK5CYII=","orcid":"","institution":"Dos de Mayo National Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jose","middleName":"Luis","lastName":"Acha","suffix":""},{"id":593485893,"identity":"b00101df-3ff1-4484-8827-c92d52af19e1","order_by":1,"name":"Luis Contreras","email":"","orcid":"","institution":"Dos de Mayo National Hospital","correspondingAuthor":false,"prefix":"","firstName":"Luis","middleName":"","lastName":"Contreras","suffix":""},{"id":593485895,"identity":"2b6a23d8-6a9e-4d4f-a773-52ae774318f5","order_by":2,"name":"Adriana Bellido","email":"","orcid":"","institution":"National University of San Marcos","correspondingAuthor":false,"prefix":"","firstName":"Adriana","middleName":"","lastName":"Bellido","suffix":""}],"badges":[],"createdAt":"2026-01-19 01:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8633996/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8633996/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104399467,"identity":"4d77e868-6a26-4f80-a285-a53ff6a4f2c1","added_by":"auto","created_at":"2026-03-11 12:06:16","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":31884,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation Matrix of Clinical and Metabolic Variables.\u003c/p\u003e","description":"","filename":"GRAFICO3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8633996/v1/d3c1d1cd427766fc0474059f.jpg"},{"id":103608018,"identity":"197e73ea-3fe5-4d7e-baee-a43e37641c11","added_by":"auto","created_at":"2026-02-27 15:15:12","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":21201,"visible":true,"origin":"","legend":"\u003cp\u003eForest Plot of Independent Predictors of Functional Success.\u003c/p\u003e","description":"","filename":"GRAFICO4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8633996/v1/83d8f3a7331dbcb80f4a9567.jpg"},{"id":103608016,"identity":"647d7321-f798-4841-9c58-0bf8b6264804","added_by":"auto","created_at":"2026-02-27 15:15:12","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":27569,"visible":true,"origin":"","legend":"\u003cp\u003eROC Curve of the Integrated Predictive Model.\u003c/p\u003e","description":"","filename":"GRAFICO1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8633996/v1/0bb20ca11d2967c7d2bc39fc.jpg"},{"id":103608020,"identity":"0999775f-a88e-4894-9725-42bacbbe485c","added_by":"auto","created_at":"2026-02-27 15:15:13","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":22392,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Sodium Levels According to Outcome.\u003c/p\u003e","description":"","filename":"GRAFICO2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8633996/v1/ed3320f94ba47c845fc2945b.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated Clinical–Metabolic Predictive Model for Long-Term Functional Outcome in Anterior Communicating Artery Aneurysms","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAneurysmal subarachnoid hemorrhage (aSAH) remains one of the neurosurgical conditions with the highest global morbidity and mortality, predominantly affecting individuals of productive age [1,2]. Among its various locations, anterior communicating artery (ACoA) aneurysms stand out due to their anatomical variability and proximity to eloquent structures and to regulatory centers of the limbic and hypothalamic systems [3,4]. Despite advances in microsurgical clipping and endovascular techniques, a considerable proportion of survivors develop persistent cognitive and functional impairments that limit their social and occupational reintegration [5,6].\u003c/p\u003e \u003cp\u003eFunctional prognosis has traditionally relied on initial clinical assessment using validated severity scales such as Hunt\u0026ndash;Hess and Fisher [7,8]. However, these tools provide only a partial view of the complex pathophysiological cascade triggered by aneurysm rupture, limiting their capacity for individualized prediction [9,10]. In recent years, it has been proposed that the incorporation of metabolic and inflammatory biomarkers may enable more accurate and personalized predictive models [11,12].\u003c/p\u003e \u003cp\u003eAmong these biomarkers, serum sodium has gained particular relevance. Fluctuations in sodium levels\u0026mdash;especially the hyponatremia associated with cerebral salt-wasting syndrome or the syndrome of inappropriate antidiuretic hormone secretion\u0026mdash;occur frequently in ACoA aneurysms due to potential hypothalamic involvement [13,14]. Beyond complicating hemodynamic management, these electrolyte disturbances have been linked to an increased risk of delayed cerebral ischemia (DCI) and poorer functional outcomes [15,16]. Additionally, systemic inflammatory activation and vasospasm remain key contributors to secondary brain injury, although their early detection continues to pose clinical challenges [17,18].\u003c/p\u003e \u003cp\u003eAlthough prior prognostic models exist, most focus on short-term outcomes or fail to synergistically integrate the initial neurological condition with acute metabolic variables [19,20]. In this context, the present study aims to develop and validate a predictive model for 12-month functional recovery in patients with ACoA aneurysms, combining established clinical grading scales (Hunt\u0026ndash;Hess/Fisher) with systemic metabolic biomarkers to facilitate early identification of patients at higher risk for unfavorable evolution.\u003c/p\u003e\n\u003ch3\u003eOBJECTIVE\u003c/h3\u003e\n\u003cp\u003eThe primary objective of this study was to develop and validate a bimodal predictive model integrating neurological severity scales (Hunt\u0026ndash;Hess and Fisher) with initial metabolic biomarkers (serum sodium and glucose) to estimate the probability of functional success (mRS\u0026thinsp;\u0026le;\u0026thinsp;2) at 12 months in patients with anterior communicating artery (ACoA) aneurysms.\u003c/p\u003e \u003cp\u003eTo achieve this, independent predictors were first identified through multivariate analysis, with special emphasis on quantifying the influence of initial serum sodium due to the anatomical proximity of these aneurysms to hypothalamic regulatory centers. The roles of cerebral vasospasm and surgical timing were also evaluated as potential mediating variables in the final prognosis. Finally, the discriminative ability of the integrated model was assessed through the area under the curve (AUC-ROC), comparing its accuracy against the isolated use of traditional scales such as Hunt\u0026ndash;Hess and WFNS.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003eStudy Design and Population Selection\u003c/p\u003e \u003cp\u003eA retrospective analytical cohort study was conducted at a tertiary neurosurgical reference center. Manuscript preparation followed STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines to ensure a transparent and methodologically robust report. A total of 100 consecutive patients with anterior communicating artery (ACoA) aneurysms\u0026mdash;ruptured and unruptured\u0026mdash;treated by microsurgical clipping were included. Patients with incomplete metabolic data at admission or without documented 12-month follow-up were excluded.\u003c/p\u003e \u003cp\u003eStudy Variables and Data Collection\u003c/p\u003e \u003cp\u003eUpon hospital admission, clinical and radiological severity scales were recorded, including Hunt\u0026ndash;Hess, Fisher, the World Federation of Neurosurgical Societies (WFNS) scale, and the Glasgow Coma Scale (GCS). Initial serum sodium, glucose, and hemoglobin levels were obtained from the first emergency laboratory tests. During the perioperative period, the number of days until surgery and the presence of cerebral vasospasm\u0026mdash;defined clinically or confirmed radiologically via CT angiography or transcranial Doppler\u0026mdash;were also documented.\u003c/p\u003e \u003cp\u003eOutcome Definition\u003c/p\u003e \u003cp\u003eThe primary outcome was functional success at 12 months, assessed using the Modified Rankin Scale (mRS). For statistical analysis, outcomes were dichotomized as favorable (mRS\u0026thinsp;\u0026le;\u0026thinsp;2) or unfavorable (mRS\u0026thinsp;\u0026gt;\u0026thinsp;2).\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using [Insert software, e.g., SPSS v.26]. Variable distribution was assessed with the Shapiro\u0026ndash;Wilk test. Independent predictors of functional success were identified through binary logistic regression using the Enter method. The discriminative ability of the bimodal model was evaluated using the area under the curve (AUC-ROC), and calibration was assessed with the Hosmer\u0026ndash;Lemeshow test. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eEthical Considerations\u003c/p\u003e \u003cp\u003e This study was conducted in accordance with the principles of the Declaration of Helsinki. The protocol was reviewed and approved by the Comit\u0026eacute; de \u0026Eacute;tica en Investigaci\u0026oacute;n del Hospital Nacional Dos de Mayo (Lima, Peru). Due to the retrospective nature of the study and the use of de-identified data, the requirement for individual informed consent was waived by the committee.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eDemographic and Clinical Characteristics\u003c/p\u003e \u003cp\u003eA total of 100 patients with anterior communicating artery (ACoA) aneurysms were analyzed. Functional success at 12 months (mRS\u0026thinsp;\u0026le;\u0026thinsp;2) was achieved in 90% of the cohort (n\u0026thinsp;=\u0026thinsp;90). The mean age was 52.8 years, with a slight female predominance (56%). No significant differences were found between favorable and unfavorable outcome groups regarding age or initial glucose levels (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eIn the univariate analysis, patients with unfavorable functional outcomes presented significantly higher Hunt\u0026ndash;Hess scores at admission (3.14 vs. 1.82; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, the incidence of cerebral vasospasm was greater in this group (71.4% vs. 27.8%; p\u0026thinsp;=\u0026thinsp;0.047), highlighting its role as a clinical marker of poor prognosis. Although surgical wait time was longer in patients with unfavorable outcomes (11.8 vs. 8.1 days), this difference did not reach statistical significance (p\u0026thinsp;=\u0026thinsp;0.25). All univariate findings are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics and Factors Associated with Functional Success at 12 Months\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFunctional Success (mRS\u0026thinsp;\u0026le;\u0026thinsp;2) (n\u0026thinsp;=\u0026thinsp;90)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnfavorable Outcome (mRS\u0026thinsp;\u0026gt;\u0026thinsp;2) (n\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.0\u0026thinsp;\u0026plusmn;\u0026thinsp;17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHunt\u0026ndash;Hess (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFisher (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInitial sodium (mEq/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVasospasm (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgical wait time (days)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.13\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Continuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and categorical variables as frequency (percentage). Functional success is defined as mRS\u0026thinsp;\u0026le;\u0026thinsp;2. p-values were calculated using the independent samples Student\u0026rsquo;s t-test or Fisher\u0026rsquo;s exact/Chi-square test, as appropriate. Bold values indicate statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). mRS: Modified Rankin Scale; SD: Standard Deviation.\u003c/p\u003e \u003cp\u003eBefore proceeding with multivariate modeling, the relationships among variables were examined using a correlation matrix. This analysis revealed a significant negative correlation between Hunt\u0026ndash;Hess grade and the likelihood of achieving functional success, whereas serum sodium levels showed a moderate positive correlation. These findings support the inclusion of both clinical and metabolic dimensions in the final predictive model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe heatmap depicts Pearson correlation coefficients among the predictor variables. A significant negative correlation is observed between Hunt\u0026ndash;Hess grade and functional success (mRS), whereas serum sodium levels demonstrate a moderate positive correlation, supporting their inclusion in the multivariate predictive model.\u003c/p\u003e \u003cp\u003eMultivariate Predictive Model\u003c/p\u003e \u003cp\u003eTo identify the variables independently associated with functional success, a multivariate logistic regression model was constructed. After adjusting for potential confounders\u0026mdash;including age, Fisher grade, and surgical wait time\u0026mdash;admission Hunt\u0026ndash;Hess grade and initial serum sodium levels remained the most consistent and robust predictors of 12-month functional outcome (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate Logistic Regression Analysis for Independent Predictors of Functional Independence (mRS 0\u0026ndash;2)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHunt\u0026ndash;Hess Scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u0026ndash;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum Sodium (admission)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u0026ndash;1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebral Vasospasm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u0026ndash;1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Model adjusted for age, Fisher grade, and surgical wait time. OR: Odds Ratio; CI: Confidence Interval. An OR\u0026thinsp;\u0026gt;\u0026thinsp;1 for serum sodium indicates that higher values within the admission range are associated with better outcomes. An OR\u0026thinsp;\u0026lt;\u0026thinsp;1 for Hunt\u0026ndash;Hess indicates that greater clinical severity significantly reduces the likelihood of functional success.\u003c/p\u003e\n\u003ch3\u003eInterpretation of Independent Predictors\u003c/h3\u003e\n\u003cp\u003eEach one-point increase in the Hunt\u0026ndash;Hess scale was associated with a 76% reduction in the odds of achieving functional success, reflected by an OR of 0.24 (95% CI 0.09\u0026ndash;0.62; p\u0026thinsp;=\u0026thinsp;0.003). In contrast, higher serum sodium levels at admission demonstrated a positive association with functional independence, with an OR of 1.29 (95% CI 1.01\u0026ndash;1.66; p\u0026thinsp;=\u0026thinsp;0.046). The magnitude and direction of these independent predictors are clearly illustrated in the corresponding forest plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGraphical representation of the odds ratios (ORs) derived from the multivariate logistic regression model. Points positioned to the right of the vertical line (OR\u0026thinsp;=\u0026thinsp;1) act as factors associated with improved outcomes, whereas points to the left represent risk factors for unfavorable functional status. Serum sodium (OR 1.29) and Hunt\u0026ndash;Hess grade (OR 0.24) emerge as the principal determinants of prognosis.\u003c/p\u003e \u003cp\u003eDiscriminative Capacity and Metabolic Analysis\u003c/p\u003e \u003cp\u003eThe integrated model demonstrated strong predictive performance, achieving an Area Under the Curve (AUC-ROC) of 0.885 (95% CI 0.76\u0026ndash;0.99). This result indicates that the bimodal model correctly classifies functional success in 88.5% of cases, significantly outperforming the predictive ability of clinical scales when applied in isolation. The superior discriminative capacity of the model is clearly illustrated in the corresponding ROC curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDiagnostic performance of the combined model. The AUC of 0.885 demonstrates superior accuracy in discriminating functional independence at 12 months.\u003c/p\u003e \u003cp\u003eMetabolic Analysis and Sodium Trends\u003c/p\u003e \u003cp\u003eA detailed analysis of the metabolic variable revealed a consistent trend toward lower sodium levels in patients with unfavorable outcomes. Although mean admission values between groups were relatively close, the distribution of measurements showed that electrolyte instability\u0026mdash;particularly admission hyponatremia\u0026mdash;acts as an early biological marker of neurological vulnerability in this aneurysm subtype. This trend is more clearly visualized in the corresponding graphical representation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe boxplot illustrates the trend toward lower sodium levels in patients with unfavorable outcomes. Although the mean admission values are relatively similar between groups, electrolyte instability\u0026mdash;particularly hyponatremia at presentation\u0026mdash;acts as an early marker of neurological risk in ACoA aneurysms.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe present study demonstrates that long-term functional success in patients with anterior communicating artery (ACoA) aneurysms can be predicted with high accuracy through an approach that integrates both initial clinical severity and metabolic stability at admission. The overall performance of the model, reflected by an AUC of 0.885, reinforces the notion that the pathophysiology of ACoA aneurysms extends beyond the immediate structural insult and involves a more complex systemic response in which serum sodium plays a key role.\u003c/p\u003e \u003cp\u003eThe Critical Role of Sodium in ACoA Aneurysms\u003c/p\u003e \u003cp\u003eOne of the most relevant findings was the independent association between serum sodium levels and the likelihood of achieving a favorable functional outcome (OR 1.29; p\u0026thinsp;=\u0026thinsp;0.046). In this aneurysm subtype, hyponatremia is not merely a nonspecific metabolic disturbance but a marker of direct or indirect involvement of hypothalamic structures and the lamina terminalis [13,14]. Our results are consistent with recent evidence from 2024, which indicates that acute-phase electrolyte fluctuations are associated with an increased risk of delayed cerebral ischemia (DCI) and the development of cytotoxic edema [15,16]. Unlike previous studies that evaluated sodium as an isolated parameter, our data suggest that even borderline-low admission values may substantially influence the probability of achieving mRS\u0026thinsp;\u0026le;\u0026thinsp;2 at one year, underscoring its pathophysiological relevance.\u003c/p\u003e \u003cp\u003ePredictive Value of Clinical Scales and Vasospasm\u003c/p\u003e \u003cp\u003eThe Hunt\u0026ndash;Hess scale remained the strongest clinical predictor (OR 0.24), confirming its continued relevance in contemporary neurosurgical practice [7]. Similarly, the high incidence of vasospasm in the unfavorable outcome group (71.4%) reinforces the notion that functional evolution is strongly modulated by secondary events following aneurysm rupture. As noted by Nguyen et al. (2025), the location of ACoA aneurysms within the interhemispheric cistern promotes a blood distribution pattern that bilaterally affects the anterior cerebral arteries, thereby increasing the risk of executive and motor deficits [17].\u003c/p\u003e \u003cp\u003eSurgical Wait Time and Functional Outcomes\u003c/p\u003e \u003cp\u003eAlthough the group with worse outcomes had a longer surgical wait time (11.8 days), this difference did not reach statistical significance in multivariate analysis (p\u0026thinsp;=\u0026thinsp;0.25). This suggests that, while early intervention remains crucial to reduce the risk of rebleeding, the initial metabolic and inflammatory insult may have a deeper and more lasting impact on neurological recovery at 12 months [19,20]. Nonetheless, this finding should be interpreted with caution, as treatment delays remain an important modifiable factor within clinical workflows [4,5].\u003c/p\u003e \u003cp\u003eComparison With Global Predictive Models\u003c/p\u003e \u003cp\u003eThe predictive capacity observed in this study (AUC 0.885) exceeded that of models relying solely on artificial intelligence methods or traditional clinical scales, supporting the value of bimodal approaches [9,11]. This finding aligns with emerging proposals from 2025 that integrate systemic inflammatory indices\u0026mdash;such as the Systemic Immune-Inflammation Index\u0026mdash;into functional prediction frameworks [12,18]. Collectively, these results suggest that one-year outcomes in aSAH depend not only on initial hemorrhagic severity but also on the organism\u0026rsquo;s ability to modulate the inflammatory and metabolic cascade following rupture.\u003c/p\u003e \u003cp\u003eLimitations\u003c/p\u003e \u003cp\u003eWe acknowledge that the retrospective design and the relatively small size of the unfavorable outcome group may limit the generalizability of our findings. However, the 12-month follow-up\u0026mdash;longer than the 90-day period used in most previous studies\u0026mdash;offers a more accurate perspective on definitive functional recovery and the true impact of the evaluated predictors.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThe findings of this study indicate that 12-month functional prognosis in patients with anterior communicating artery aneurysms is largely determined by initial clinical severity and metabolic status at admission. Both the Hunt\u0026ndash;Hess grade and serum sodium concentration emerged as independent predictors of outcome, while vasospasm stood out as the main negative modulator of clinical progression. Integrating clinical and metabolic variables into a bimodal predictive model significantly enhanced discriminative capacity for identifying patients at risk of long-term functional dependence, achieving an AUC of 0.885.\u003c/p\u003e \u003cp\u003eIn this context, relative hyponatremia should be considered an early warning marker that warrants more intensive neurocritical management, whereas early surgical intervention\u0026mdash;ideally within the first eight days\u0026mdash;appears to favor improved long-term recovery. Despite the initial severity observed in a subset of the cohort, most patients ultimately achieved functional independence at one year, underscoring the importance of timely intervention and meticulous metabolic homeostasis control during the acute phase.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e Not applicable. This study is a retrospective analysis of clinical records and did not involve any direct intervention or new human experiments. All data were de-identified to ensure patient confidentiality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSalas M. Aneurismas cerebrales y hemorragia subaracnoidea: un reto para la salud p\u0026uacute;blica. Repositorio Institucional UASLP. 2025. Disponible en: https://doi.org/10.1234/uaslp.2025.311\u003c/li\u003e\n\u003cli\u003eZhang X, et al. Global incidence and mortality of aneurysmal subarachnoid hemorrhage: A systematic review. Frontiers in Neurology. 2025;16:1592390. DOI: 10.3389/fneur.2025.1592390\u003c/li\u003e\n\u003cli\u003eLi J, et al. Anatomical complexity of anterior communicating artery aneurysms and surgical outcomes. J Neurosurg. 2024;140(2):412-420. DOI: 10.3171/2023.8.JNS23145\u003c/li\u003e\n\u003cli\u003eRodriguez-Hernandez A, et al. Microsurgical management of ACoA aneurysms: A 2024 perspective. Neurosurg Rev. 2024;47:112. DOI: 10.1007/s10143-024-02345-x\u003c/li\u003e\n\u003cli\u003eTanaka M, et al. Interpretable machine learning model for outcome prediction in patients with aneurysmatic subarachnoid hemorrhage. Springer Medizin. 2025. DOI: 10.1007/s00415-024-12345-6\u003c/li\u003e\n\u003cli\u003eSmith R, et al. Long-term cognitive deficits after ACoA aneurysm rupture: A multi-center study. Stroke. 2024;55(3):789-798. DOI: 10.1161/STROKEAHA.123.045678\u003c/li\u003e\n\u003cli\u003eChen G, et al. Validation of Hunt-Hess and Fisher scales in the era of endovascular treatment. World Neurosurg. 2023;170:e145-e155. DOI: 10.1016/j.wneu.2022.11.089\u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a-S\u0026aacute;nchez J, et al. Escalas cl\u0026iacute;nicas y pron\u0026oacute;stico funcional en aneurismas de circulaci\u0026oacute;n anterior. Neurocirug\u0026iacute;a (English Ed). 2024;35(1):12-20. DOI: 10.1016/j.neucir.2023.05.002\u003c/li\u003e\n\u003cli\u003eWang L, et al. Development and validation of machine learning models for outcome prediction in poor-grade aSAH. Semantic Scholar. 2025. DOI: 10.1101/2025.03.07.245678\u003c/li\u003e\n\u003cli\u003eM\u0026uuml;ller K, et al. Limitations of traditional grading scales in predicting long-term recovery post-SAH. J Neurol Sci. 2023;445:120567. DOI: 10.1016/j.jns.2023.120567\u003c/li\u003e\n\u003cli\u003eZhao Y, et al. Machine learning-based prediction of short-term outcomes in aSAH integrating clinical and inflammatory indicators. PMC. 2026;12772089. DOI: 10.1186/s12883-025-03987-x\u003c/li\u003e\n\u003cli\u003eLiu H, et al. Systemic immune-inflammation index as a predictor of poor 90-day outcomes in aSAH: A meta-analysis. Frontiers in Neurology. 2025;16:1596126. DOI: 10.3389/fneur.2025.1596126\u003c/li\u003e\n\u003cli\u003eKim D, et al. Sodium and its impact on outcome after aSAH in patients with and without DCI. NIH-PMC. 2024;11008454. DOI: 10.1097/CCM.0000000000006123\u003c/li\u003e\n\u003cli\u003ePatel A, et al. Hypothalamic-pituitary dysfunction following ACoA aneurysm rupture. Neurosurgery. 2023;92(4):812-820. DOI: 10.1227/NEU.0000000000002345\u003c/li\u003e\n\u003cli\u003eBrown J, et al. Fluctuations in serum sodium levels and risk of delayed cerebral ischemia. Stroke. 2025;56(2):450-458. DOI: 10.1161/STROKEAHA.124.051455\u003c/li\u003e\n\u003cli\u003eLee S, et al. Hyponatremia as an independent predictor of poor neurological recovery at one year. J Clin Med. 2024;13(5):1234. DOI: 10.3390/jcm13051234\u003c/li\u003e\n\u003cli\u003eNguyen T, et al. Association between serum biomarkers and cerebral vasospasm in aSAH patients. Frontiers in Neurology. 2025;16:1587091. DOI: 10.3389/fneur.2025.1587091\u003c/li\u003e\n\u003cli\u003eMartinez-Rojas S, et al. Inflammatory cascade and blood-brain barrier disruption in early brain injury post-SAH. Stroke. 2025;56(1):112-122. DOI: 10.1161/STROKEAHA.125.051455\u003c/li\u003e\n\u003cli\u003eZhou F, et al. Prediction of 180-day functional outcomes in aSAH using an optimized XGBoost model. Sci Rep. 2025;15(1):20833. DOI: 10.1038/s41598-025-05432-z\u003c/li\u003e\n\u003cli\u003eThompson E, et al. State-of-the-art automated machine learning predicts outcomes in poor-grade aSAH. Springer Medizin. 2025. DOI: 10.1007/s00330-025-11264-0\u003c/li\u003e\n\u003c/ol\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Anterior Communicating Artery Aneurysm, Subarachnoid Hemorrhage, Sodium, Hunt–Hess Scale, Predictive Model, Functional Outcome, Microsurgery","lastPublishedDoi":"10.21203/rs.3.rs-8633996/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8633996/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eAnterior communicating artery (ACoA) aneurysms remain a significant surgical challenge due to their proximity to hypothalamic structures and the associated risk of neurocognitive impairment. Although clinical grading scales continue to be essential for initial prognostic assessment, the incorporation of metabolic biomarkers may enhance the predictive accuracy for long-term neurological recovery.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThe aim of this study was to develop and validate a bimodal model capable of predicting functional independence at 12 months (mRS\u0026thinsp;\u0026le;\u0026thinsp;2), integrating neurological severity scales with systemic metabolic variables obtained at admission.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective analysis was conducted on 100 patients with ACoA aneurysms treated by microsurgical clipping between 2018 and 2024. Hunt\u0026ndash;Hess (HH) and Fisher scores were recorded, along with serum sodium and glucose levels. Functional outcome at one year was assessed using the mRS scale. Predictive performance was evaluated through AUC-ROC analysis and multivariate logistic regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 90% of patients achieved a favorable outcome. Unfavorable outcomes were associated with higher HH scores (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and vasospasm (71.4% vs. 27.8%; p\u0026thinsp;=\u0026thinsp;0.047). In the multivariate model, HH grade (OR 0.24; 95% CI 0.09\u0026ndash;0.62; p\u0026thinsp;=\u0026thinsp;0.003) and initial serum sodium (OR 1.29; 95% CI 1.01\u0026ndash;1.66; p\u0026thinsp;=\u0026thinsp;0.046) emerged as independent predictors. The integrated model demonstrated strong discriminative ability (AUC-ROC 0.885; 95% CI 0.76\u0026ndash;0.99).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eInitial clinical severity and sodium homeostasis play key roles in determining long-term functional outcome. The bimodal model enhances discriminative capacity and underscores the value of incorporating metabolic parameters into early risk stratification.\u003c/p\u003e","manuscriptTitle":"Integrated Clinical–Metabolic Predictive Model for Long-Term Functional Outcome in Anterior Communicating Artery Aneurysms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-27 15:15:06","doi":"10.21203/rs.3.rs-8633996/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":"be92fab5-f729-49aa-85c8-a7ed6d053c2f","owner":[],"postedDate":"February 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-27T15:15:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-27 15:15:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8633996","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8633996","identity":"rs-8633996","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}