Age dependency and modification of the Subarachnoid Hemorrhage Early Brain Edema Score

Gender-specific prognosis models reveal differences in subarachnoid hemorrhage patients between sexes

BACKGROUND

Subarachnoid hemorrhage (SAH) represents a severe stroke subtype. Our study aims to develop gender-specific prognostic prediction models derived from distinct prognostic factors observed among different-gender patients.

METHODS

Inclusion comprised SAH-diagnosed patients from January 2014 to March 2016 in our institution. Collected data encompassed patients' demographics, admission severity, treatments, imaging findings, and complications. Three-month post-discharge prognoses were obtained via follow-ups. Analyses assessed gender-based differences in patient information. Key factors underwent subgroup analysis, followed by univariate and multivariate analyses to identify gender-specific prognostic factors and establish/validate gender-specific prognostic models.

RESULTS

A total of 929 patients, with a median age of 57 (16) years, were analyzed; 372 (40%) were male, and 557 (60%) were female. Differences in age, smoking history, hypertension, aneurysm presence, and treatment interventions existed between genders (p < 0.01), yet no disparity in prognosis was noted. Subgroup analysis explored hypertension history, aneurysm presence, and treatment impact, revealing gender-specific variations in these factors' influence on the disease. Screening identified independent prognostic factors: age, SEBES score, admission GCS score, and complications for males; and age, admission GCS score, intraventricular hemorrhage, treatment interventions, symptomatic vasospasm, hydrocephalus, delayed cerebral ischemia, and seizures for females. Evaluation and validation of gender-specific models yielded an AUC of 0.916 (95% CI: 0.878-0.954) for males and 0.914 (95% CI: 0.885-0.944) for females in the ROC curve. Gender-specific prognostic models didn't significantly differ from the overall population-based model (model 3) but exhibited robust discriminative ability and clinical utility.

CONCLUSION

Variations in baseline and treatment-related factors among genders contribute partly to gender-based prognosis differences. Independent prognostic factors vary by gender. Gender-specific prognostic models exhibit favorable prognostic performance.

Predicting the Outcome of Patients with Aneurysmal Subarachnoid Hemorrhage: A Machine-Learning-Guided Scorecard

Aneurysmal subarachnoid hemorrhage (aSAH) frequently causes long-term disability, but predicting outcomes remains challenging. Routine parameters such as demographics, admission status, CT findings, and blood tests can be used to predict aSAH outcomes. The aim of this study was to compare the performance of traditional logistic regression with several machine learning algorithms using readily available indicators and to generate a practical prognostic scorecard based on machine learning. Eighteen routinely available indicators were collected as outcome predictors for individuals with aSAH. Logistic regression (LR), random forest (RF), support vector machines (SVMs), and fully connected neural networks (FCNNs) were compared. A scorecard system was established based on predictor weights. The results show that machine learning models and a scorecard achieved 0.75~0.8 area under the curve (AUC) predicting aSAH outcomes (LR 0.739, RF 0.749, SVM 0.762~0.793, scorecard 0.794). FCNNs performed best (~0.95) but lacked interpretability. The scorecard model used only five factors, generating a clinically useful tool with a total cutoff score of ≥5, indicating poor prognosis. We developed and validated machine learning models proven to predict outcomes more accurately in individuals with aSAH. The parameters found to be the most strongly predictive of outcomes were NLR, lymphocyte count, monocyte count, hypertension status, and SEBES. The scorecard system provides a simplified means of applying predictive analytics at the bedside using a few key indicators.

Early Brain Injury After Subarachnoid Hemorrhage: Incidence and Mechanisms

Aneurysmal subarachnoid hemorrhage is a devastating condition causing significant morbidity and mortality. While outcomes from subarachnoid hemorrhage have improved in recent years, there continues to be significant interest in identifying therapeutic targets for this disease. In particular, there has been a shift in emphasis toward secondary brain injury that develops in the first 72 hours after subarachnoid hemorrhage. This time period of interest is referred to as the early brain injury period and comprises processes including microcirculatory dysfunction, blood-brain-barrier breakdown, neuroinflammation, cerebral edema, oxidative cascades, and neuronal death. Advances in our understanding of the mechanisms defining the early brain injury period have been accompanied by improved imaging and nonimaging biomarkers for identifying early brain injury, leading to the recognition of an elevated clinical incidence of early brain injury compared with prior estimates. With the frequency, impact, and mechanisms of early brain injury better defined, there is a need to review the literature in this area to guide preclinical and clinical study.

Systemic inflammatory markers of persistent cerebral edema after aneurysmal subarachnoid hemorrhage

BACKGROUND

Cerebral edema (CE) at admission is a surrogate marker of 'early brain injury' (EBI) after subarachnoid hemorrhage (SAH). Only recently has the focus on the changes in CE after SAH such as delayed resolution or newly developed CE been examined. Among several factors, an early systemic inflammatory response has been shown to be associated with CE. We investigate inflammatory markers in subjects with early CE which does not resolve, i.e., persistent CE after SAH.

METHODS

Computed tomography scans of SAH patients were graded at admission and at 7 days after SAH for CE using the 0-4 'subarachnoid hemorrhage early brain edema score' (SEBES). SEBES ≤ 2 and SEBES ≥ 3 were considered good and poor grade, respectively. Serum samples from the same subject cohort were collected at 4 time periods (at < 24 h [T1], at 24 to 48 h [T2]. 3-5 days [T3] and 6-8 days [T4] post-admission) and concentration levels of 17 cytokines (implicated in peripheral inflammatory processes) were measured by multiplex immunoassay. Multivariable logistic regression analyses were step-wisely performed to identify cytokines independently associated with persistent CE adjusting for covariables including age, sex and past medical history (model 1), and additional inclusion of clinical and radiographic severity of SAH and treatment modality (model 2).

RESULTS

Of the 135 patients enrolled in the study, 21 of 135 subjects (15.6%) showed a persistently poor SEBES grade. In multivariate model 1, higher Eotaxin (at T1 and T4), sCD40L (at T4), IL-6 (at T1 and T3) and TNF-α (at T4) were independently associated with persistent CE. In multivariate model 2, Eotaxin (at T4: odds ratio [OR] = 1.019, 95% confidence interval [CI] = 1.002-1.035) and possibly PDGF-AA (at T4), sCD40L (at T4), and TNF-α (at T4) was associated with persistent CE.

CONCLUSIONS

We identified serum cytokines at different time points that were independently associated with persistent CE. Specifically, persistent elevations of Eotaxin is associated with persistent CE after SAH.

Development of a nomogram for predicting clinical outcome in patients with angiogram-negative subarachnoid hemorrhage

To the best of our knowledge, this is the largest clinical retrospective study in AN‐SAH patients, and is the first time to establish accurate predictive models paired with bleeding pattern.

Subarachnoid Hemorrhage Early Brain Edema Score (SEBES) as a radiographic marker of clinically relevant intracranial hypertension and unfavorable outcome after subarachnoid hemorrhage

BACKGROUND AND PURPOSE

The severity of early brain edema (EBE) after aneurysm rupture was reported to be strongly associated with the risk of poor outcome after aneurysmal subarachnoid hemorrhage (SAH). Using the recently developed Subarachnoid Hemorrhage Early Brain Edema Score (SEBES), we analyzed the predictors of EBE and its impact on complications related to intracranial pressure (ICP) increase after SAH and on poor outcome.

METHODS

All consecutive SAH cases treated between January 2003 and June 2016 with assessable SEBES were included (n = 745). Data on demographic characteristics, medical history, initial severity of SAH, need for conservative ICP treatment and decompressive craniectomy, occurrence of cerebral infarctions and unfavorable outcome at 6 months (modified Rankin scale score > 2) were collected. Univariable and multivariable analyses were performed.

RESULTS

Younger age (<55 years; adjusted odds ratio [aOR] 3.16, 95% confidence interval [CI] 2.28-4.38), female sex (aOR 1.64, 95% CI 1.16-2.31), poor initial clinical condition (World Federation of Neurosurgical Societies score 4-5; aOR 1.74, 95% CI 1.23-2.46), presence of intracerebral hemorrhage (aOR 1.63, 95% CI 1.12-2.36), hypothyroidism (aOR 0.60, 95% CI 0.37-0.98) and renal comorbidity (aOR 0.29, 95% CI 0.11-0.78) were independently associated with SEBES (scores 3-4). There was an independent association between SEBES 3-4 and the need for conservative ICP treatment (aOR 2.43, 95% CI 1.73-3.42), decompressive craniectomy (aOR 2.68, 95% CI 1.84-3.89), development of cerebral infarcts (aOR 2.24, 95% CI 1.53-3.29) and unfavorable outcome (aOR 1.48, 95% CI 1.0-2.17).

CONCLUSIONS

SEBES is a reliable predictor of ICP-related complications and poor outcome of SAH. Our findings highlight the need for further research of the impact of patients' demographic characteristics and comorbidities on the severity of EBE after SAH.

Automated Quantification of Reduced Sulcal Volume Identifies Early Brain Injury After Aneurysmal Subarachnoid Hemorrhage

BACKGROUND AND PURPOSE

Early brain injury may be a more significant contributor to poor outcome after aneurysmal subarachnoid hemorrhage (aSAH) than vasospasm and delayed cerebral ischemia. However, studying this process has been hampered by lack of a means of quantifying the spectrum of injury. Global cerebral edema (GCE) is the most widely accepted manifestation of early brain injury but is currently assessed only through subjective, qualitative or semi-quantitative means. Selective sulcal volume (SSV), the CSF volume above the lateral ventricles, has been proposed as a quantitative biomarker of GCE, but is time-consuming to measure manually. Here we implement an automated algorithm to extract SSV and evaluate the age-dependent relationship of reduced SSV on early outcomes after aSAH.

METHODS

We selected all adults with aSAH admitted to a single institution with imaging within 72 hours of ictus. Scans were assessed for qualitative presence of GCE. SSV was automatically segmented from serial CTs using a deep learning-based approach. Early SSV was the lowest SSV from all early scans. Modified Rankin Scale score of 4 to 6 at hospital discharge was classified as a poor outcome.

RESULTS

Two hundred forty-four patients with aSAH were included. Sixty-five (27%) had GCE on admission while 24 developed it subsequently within 72 hours. Median SSV on admission was 10.7 mL but frequently decreased, with minimum early SSV being 3.0 mL (interquartile range, 0.3-11.9). Early SSV below 5 mL was highly predictive of qualitative GCE (area under receiver-operating-characteristic curve, 0.90). Reduced early SSV was an independent predictor of poor outcome, with a stronger effect in younger patients.

CONCLUSIONS

Automated assessment of SSV provides an objective biomarker of GCE that can be leveraged to quantify early brain injury and dissect its impact on outcomes after aSAH. Such quantitative analysis suggests that GCE may be more impactful to younger patients with SAH.