Non-cerebral Vasospasm Factors and Cerebral Vasospasm Predict Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage

Non-cerebral Vasospasm Factors and Cerebral Vasospasm Predict Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage

Aneurysmal subarachnoid hemorrhage (aSAH) is a critical neurosurgical emergency with significant implications for patient prognosis. The severity of the initial illness and postoperative complications, particularly delayed cerebral ischemia (DCI), are crucial determinants of patient outcomes. DCI occurs in up to 30% of aSAH patients, typically manifesting two weeks post-hemorrhage, and is a leading cause of death and disability in this population. While cerebral vasospasm (CVS) is widely recognized as a primary factor in DCI development, a subset of patients still develop DCI despite aggressive anti-CVS therapy, suggesting that CVS is not the sole cause.

Emerging evidence points to other contributing factors such as microcirculatory spasm, microthrombosis, cortical diffusion depolarization, and cerebral autonomic dysregulation. However, these factors are challenging to measure and impractical for routine clinical use. Consequently, there is a need to identify and utilize more accessible clinical indicators to predict DCI and improve patient outcomes.

This study aimed to develop a predictive model for DCI by integrating non-CVS factors with CVS, thereby enhancing the diagnostic accuracy and clinical utility of DCI prediction. The research was conducted in accordance with the Declaration of Helsinki and approved by the local ethics committee of the First Affiliated Hospital of Fujian Medical University. Informed consent was obtained from all participants or their authorized legal representatives. Patient management adhered to the American Heart Association/American Stroke Association guidelines for aSAH treatment, with surgical interventions (craniotomy or endovascular treatment) tailored to individual patient conditions and preferences.

The study retrospectively analyzed clinical data from 711 aSAH patients who underwent surgical treatment at the Department of Neurosurgery, The First Affiliated Hospital of Fujian Medical University, between 2013 and 2018. Inclusion criteria were age between 18 and 60 years, confirmed SAH due to intracranial aneurysm via computed tomography angiography (CTA) or digital subtraction angiography (DSA), and completion of pre-operative laboratory testing at the institution. Exclusion criteria included age outside the specified range, pre-admission DCI diagnosis, history of neurological diseases, and systemic conditions such as uremia, cirrhosis, renal dysfunction, or malignancy.

DCI was defined as clinical deterioration or new infarct on CT scan not visible on admission or immediate postoperative imaging. CVS diagnosis required persistent arterial spasm on DSA or CTA, excluding other causes. General patient conditions on admission, including fever, hypertension, loss of consciousness (LOC), mechanical ventilation (MV) therapy, and significant past medical history, were recorded. Inflammatory response-related variables such as neutrophil-to-lymphocyte ratio (NLR), systemic inflammation response index (SIRI), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and glucose to potassium ratio (GPR) were calculated from routine laboratory tests.

Post-admission clinical assessments included Glasgow Coma Score (GCS), Hunt and Hess grade (HH), World Federation of Neurological Surgeons Scale (WFNS), modified Fisher score (mFisher), and VASOGRADE. Post-operative GCS was assessed on the first postoperative day, and all patients underwent DSA or CTA within two days of surgery to confirm aneurysm occlusion and assess vasospasm. Clinical intervention details, including surgical procedure, surgical time, number of arteries, and post-operative MV use, were recorded. Neurological conditions were documented immediately after surgery, with follow-up reviews conducted within one month post-discharge. The observation endpoint was 30 days post-surgery or DCI occurrence.

Statistical analyses were performed to investigate the relationship between DCI and collected parameters. Data normality was assessed using the Shapiro-Wilk test, with continuous variables compared using the Student’s t-test or Mann-Whitney U test, as appropriate. Categorical variables were analyzed using the chi-square or Fisher exact test. Multivariate logistic regression analysis identified significant predictors of DCI, with hazard ratios (HR) and 95% confidence intervals (CI) calculated. A non-CVS prediction model of DCI was developed using backward logistic regression, and its predictive performance was compared with CVS and a combined non-CVS and CVS model.

Among the 711 aSAH patients, DCI occurred in 57 cases. Univariate analysis revealed significant associations between DCI and several variables, including LOC, hypertension, VASOGRADE, number of aneurysms, surgical procedure, postMV use, and postGCS. Multivariate logistic regression identified these factors as meaningful indicators for DCI prediction. The non-CVS model, CVS model, and combined model were evaluated for discriminatory power using sensitivity, specificity, Youden index, and C-statistic. The combined model demonstrated superior predictive performance, with a C-statistic of 0.933, compared to 0.805 for the non-CVS model and 0.851 for the CVS model. Calibration plots and bootstrap validation confirmed the models’ accuracy and lack of overfitting.

The study highlighted the importance of integrating non-CVS factors with CVS to enhance DCI prediction. Variables such as LOC, hypertension, VASOGRADE, number of aneurysms, surgical procedure, postMV use, and postGCS were significant predictors of DCI. The combined model’s superior performance underscores the multifactorial nature of DCI and the need for comprehensive predictive tools in clinical practice.

Despite its contributions, the study has limitations, including its retrospective design, potential selection bias, and limited sample size. External validation is necessary to generalize the findings. Future prospective cohort studies are planned to address these limitations and further refine the predictive models.

In conclusion, the non-CVS model, based on readily available clinical indices, achieves comparable DCI prediction to the CVS model. When combined with CVS, it significantly improves DCI prediction, offering a practical and effective tool for clinical use. The integration of non-CVS factors with CVS enhances our understanding of DCI pathophysiology and supports the development of more accurate and comprehensive predictive models.

doi.org/10.1097/CM9.0000000000001844

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