Computed tomography interobserver agreement in the assessment of aneurysmal subarachnoid hemorrhage and predictors for clinical outcome

The value of modified hijdra score in patients with aneurysmal subarachnoid hemorrhage

Background

The complexity of calculating the Hijdra score has limited its clinical utility in aiding the diagnosis of intracranial ruptured aneurysms.

Objective

This study aimed to investigate the diagnostic and prognostic value of the modified Hijdra score in patients with aneurysmal subarachnoid hemorrhage (aSAH).

Methods

Data from 773 patients with subarachnoid hemorrhage (SAH) at the First People's Hospital of Lianyungang from January 2018 to June 2023 were collected. The modified Hijdra scoring method simplifies the assessment of 10 basal cisterns/cisterns fissures compared to the traditional scoring method, with scores ranging from 0 to 2 for each item, and assigns specific scores to hematomas larger than 1 cm in diameter. The data were divided into an evaluation group (n = 641) and a validation group (n = 132). In the evaluation group, the performance of the modified Hijdra score in diagnosis and prognostic prediction was assessed, while the diagnostic and prognostic prediction efficacy of the modified Hijdra method was evaluated using the validation set.

Results

Among the 641 patients in the evaluation group,550 (85. 8%) were diagnosed with intracranial aneurysms. The modified Hijdra score demonstrated an AUC of 0. 894 for aneurysm diagnosis, with a sensitivity of 98. 0% and a specificity of 64. 8% at a CutOff value of 7. 5. The diagnostic efficacy of the modified Hijdra score was 93. 24%, with a negative predictive value of 84. 29%, while the Hijdra score 's diagnostic efficacy was 85. 34% with a negative predictive value of 48. 89%. The AUC of the modified Hijdra score for predicting prognosis in patients with aneurysms was 0. 824, with a sensitivity of 84. 3% and a specificity of 70. 0% at a CutOff value of 16. 5. In CTA-negative patients, the modified Hijdra score was significantly higher (P < 0. 0001) in patients with aneurysmal SAH (15. 48 ± 3. 93) compared to those with non-aneurysmal SAH (6. 31 ± 4. 52).

Conclusions

The modified Hijdra score is a valuable tool for assisting in the diagnosis and prognosis prediction of aneurysmal subarachnoid hemorrhage.

Red Blood Cells in the Cerebrospinal Fluid Compartment After Subarachnoid Haemorrhage: Significance and Emerging Therapeutic Strategies

Subarachnoid haemorrhage (SAH) is a subtype of stroke that predominantly impacts younger individuals. It is associated with high mortality rates and can cause long-term disabilities. This review examines the contribution of the initial blood load and the dynamics of clot clearance to the pathophysiology of SAH and the risk of adverse outcomes. These outcomes include hydrocephalus and delayed cerebral ischaemia (DCI), with a particular focus on the impact of blood located in the cisternal spaces, as opposed to ventricular blood, in the development of DCI. The literature described underscores the prognostic value of haematoma characteristics, such as volume, density, and anatomical location. The limitations of traditional radiographic grading systems are discussed, compared with the more accurate volumetric quantification techniques for predicting patient prognosis. Further, the significance of red blood cells (RBCs) and their breakdown products in secondary brain injury after SAH is explored. The review presents novel interventions designed to accelerate clot clearance or mitigate the effects of toxic byproducts released from erythrolysis in the cerebrospinal fluid following SAH. In conclusion, this review offers deeper insights into the complex dynamics of SAH and discusses the potential pathways available for advancing its management.

Effects of a comprehensive brain computed tomography deep learning model on radiologist detection accuracy

OBJECTIVES

Non-contrast computed tomography of the brain (NCCTB) is commonly used to detect intracranial pathology but is subject to interpretation errors. Machine learning can augment clinical decision-making and improve NCCTB scan interpretation. This retrospective detection accuracy study assessed the performance of radiologists assisted by a deep learning model and compared the standalone performance of the model with that of unassisted radiologists.

METHODS

A deep learning model was trained on 212,484 NCCTB scans drawn from a private radiology group in Australia. Scans from inpatient, outpatient, and emergency settings were included. Scan inclusion criteria were age ≥ 18 years and series slice thickness ≤ 1.5 mm. Thirty-two radiologists reviewed 2848 scans with and without the assistance of the deep learning system and rated their confidence in the presence of each finding using a 7-point scale. Differences in AUC and Matthews correlation coefficient (MCC) were calculated using a ground-truth gold standard.

RESULTS

The model demonstrated an average area under the receiver operating characteristic curve (AUC) of 0.93 across 144 NCCTB findings and significantly improved radiologist interpretation performance. Assisted and unassisted radiologists demonstrated an average AUC of 0.79 and 0.73 across 22 grouped parent findings and 0.72 and 0.68 across 189 child findings, respectively. When assisted by the model, radiologist AUC was significantly improved for 91 findings (158 findings were non-inferior), and reading time was significantly reduced.

CONCLUSIONS

The assistance of a comprehensive deep learning model significantly improved radiologist detection accuracy across a wide range of clinical findings and demonstrated the potential to improve NCCTB interpretation.

CLINICAL RELEVANCE STATEMENT

This study evaluated a comprehensive CT brain deep learning model, which performed strongly, improved the performance of radiologists, and reduced interpretation time. The model may reduce errors, improve efficiency, facilitate triage, and better enable the delivery of timely patient care.

KEY POINTS

• This study demonstrated that the use of a comprehensive deep learning system assisted radiologists in the detection of a wide range of abnormalities on non-contrast brain computed tomography scans. • The deep learning model demonstrated an average area under the receiver operating characteristic curve of 0.93 across 144 findings and significantly improved radiologist interpretation performance. • The assistance of the comprehensive deep learning model significantly reduced the time required for radiologists to interpret computed tomography scans of the brain.

Predicting the formation of mixed pattern hemorrhages in ruptured middle cerebral artery aneurysms based on a decision tree model: A multicenter study

OBJECTIVE

Mixed-pattern hemorrhages (MPH) commonly occur in ruptured middle cerebral artery (MCA) aneurysms and are associated with poor clinical outcomes. This study aimed to predict the formation of MPH in a multicenter database of MCA aneurysms using a decision tree model.

METHODS

We retrospectively reviewed patients with ruptured MCA aneurysms between January 2009 and June 2020. The MPH was defined as subarachnoid hemorrhages with intracranial hematomas and/or intraventricular hemorrhages and/or subdural hematomas. Univariate and multivariate logistic regression analyses were used to explore the prediction factors of the formation of MPH. Based on these prediction factors, a decision tree model was developed to predict the formation of MPH. Additional independent datasets were used for external validation.

RESULTS

We enrolled 436 patients with ruptured MCA aneurysms detected by computed tomography angiography; 285 patients had MPH (65.4%). A multivariate logistic regression analysis showed that age, aneurysm size, multiple aneurysms, and the presence of a daughter dome were the independent prediction factors of the formation of MPH. The areas under receiver operating characteristic curves of the decision tree model in the training, internal, and external validation cohorts were 0.951, 0.927, and 0.901, respectively.

CONCLUSION

Age, aneurysm size, the presence of a daughter dome, and multiple aneurysms were the independent prediction factors of the formation of MPH. The decision tree model is a useful visual triage tool to predict the formation of MPH that could facilitate the management of unruptured aneurysms in routine clinical work.

Comparing radiographic scores for prediction of complications and outcome of aneurysmal subarachnoid hemorrhage: Which performs best?

BACKGROUND AND PURPOSE

Aneurysmal subarachnoid hemorrhage (aSAH) is characterized by high morbidity and mortality proceeding from the initial severity and following complications of aSAH. Various scores have been developed to predict these risks. We aimed to analyze the clinical value of different radiographic scores for prognostication of aSAH outcome.

METHODS

Initial computed tomography scans (≤48 h after ictus) of 745 aSAH cases treated between January 2003 and June 2016 were reviewed with regard to Subarachnoid Hemorrhage Early Brain Edema Score (SEBES), and Claassen, Barrow Neurological Institute (BNI), Hijdra, original Graeb and Fisher scale scores. The primary endpoints were development of delayed cerebral ischemia (DCI), in-hospital mortality and unfavorable outcome (modified Rankin Scale score >3) at 6 months after subarachnoid hemorrhage. Secondary endpoints included the different complications that can occur during aSAH. Clinically relevant cutoffs were defined using receiver-operating characteristic curves. The radiographic scores with the highest values for area under the curve (AUC) were included in the final multivariate analysis.

RESULTS

The Hijdra sum score had the most accurate predictive value and independent associations with all primary endpoints: DCI (AUC 0.678, adjusted odds ratio [aOR] 2.83; p < 0.0001); in-hospital mortality (AUC 0.704, aOR 2.83; p < 0.0001) and unfavorable outcome (AUC 0.726, aOR 2.91; p < 0.0001). Multivariate analyses confirmed the independent predictive value of the radiographic scales for risk of decompressive craniectomy (SEBES and Fisher score), cerebral vasospasm (SEBES, BNI score and Fisher score) and shunt dependency (Hijdra ventricle score and Fisher score) after aSAH.

CONCLUSIONS

Initial radiographic severity of aSAH was independently associated with occurrence of different complications during aSAH and the final outcome. The Hijdra sum score showed the highest diagnostic accuracy and robust predictive value for early detection of risk of DCI, in-hospital mortality and unfavorable outcome after aSAH.

Automated Quantification of Compartmental Blood Volumes Enables Prediction of Delayed Cerebral Ischemia and Outcomes After Aneurysmal Subarachnoid Hemorrhage

OBJECTIVE

The role of hemorrhage volume in risk of vasospasm, delayed cerebral ischemia (DCI), and poor outcomes after aneurysmal subarachnoid hemorrhage (SAH) is well established. However, the relative contribution of blood within individual compartments is unclear. We present an automated technique for measuring not only total but also volumes of blood in each major compartment after SAH.

METHODS

We trained convolutional neural networks to identify compartmental blood (cisterns, sulci, and ventricles) from baseline computed tomography scans of patients with SAH. We compared automated blood volumes against traditional markers of bleeding (modified Fisher score [mFS], Hijdra sum score [HSS]) in 190 SAH patients for prediction of vasospasm, DCI, and functional status (modified Rankin Scale) at hospital discharge.

RESULTS

Combined cisternal and sulcal volume was better correlated with mFS and HSS than cisternal volume alone (ρ = 0.63 vs. 0.58 and 0.75 vs. 0.70, P < 0.001). Only blood volume in combined cisternal plus sulcal compartments was independently associated with DCI (OR 1.023 per mL, 95% CI 1.002-1.048), after adjusting for clinical factors while ventricular blood volume was not. Total and specifically sulcal blood volume was strongly associated with poor outcome (OR 1.03 per mL, 1.01-1.06, P = 0.006 and OR 1.04, 1.00-1.08 for sulcal) as was HSS (OR 1.06 per point, 1.00-1.12, P = 0.04), while mFS was not (P = 0.24).

CONCLUSIONS

An automated imaging algorithm can measure the volume of bleeding after SAH within individual compartments, demonstrating cisternal plus sulcal (and not ventricular) blood contributes to risk of DCI/vasospasm. Automated blood volume was independently associated with outcome, while qualitative grading was not.

External Validation of the HATCH (Hemorrhage, Age, Treatment, Clinical State, Hydrocephalus) Score for Prediction of Functional Outcome After Subarachnoid Hemorrhage

BACKGROUND

The Hemorrhage, Age, Treatment, Clinical State, Hydrocephalus (HATCH) Score has previously shown to predict functional outcome in aneurysmal subarachnoid hemorrhage (aSAH).

OBJECTIVE

To validate the HATCH score.

METHODS

This is a pooled cohort study including prospective collected data on 761 patients with aSAH from 4 different hospitals. The HATCH score for prediction of functional outcome was validated using calibration and discrimination analysis (area under the curve). HATCH score model performance was compared with the World Federation of Neurosurgical Societies and Barrow Neurological Institute score.

RESULTS

At the follow-up of at least 6 months, favorable (Glasgow Outcome Score 4-5) and unfavorable functional outcomes (Glasgow Outcome Score 1-3) were observed in 512 (73%) and 189 (27%) patients, respectively. A higher HATCH score was associated with an increased risk of unfavorable outcome with a score of 1 showing a risk of 1.3% and a score of 12 yielding a risk of 67%. External validation showed a calibration intercept of -0.07 and slope of 0.60 with a Brier score of 0.157 indicating good model calibration and accuracy. With an area under the curve of 0.81 (95% CI 0.77-0.84), the HATCH score demonstrated superior discriminative ability to detect favorable outcome at follow-up compared with the World Federation of Neurosurgical Societies and Barrow Neurological Institute score with 0.72 (95% CI 0.67-0.75) and 0.63 (95% CI 0.59-0.68), respectively.

CONCLUSION

This multicenter external validation analysis confirms the HATCH score to be a strong independent predictor for functional outcome. Its incorporation into daily practice may be of benefit for goal-directed patient care in aSAH.

A Quantitative Subarachnoid Hemorrhage Grading System, Including Supratentorial and Infratentorial Cisterns, With Multiplanar Computed Tomography Reformations

Background

Subarachnoid hemorrhage (SAH) grading scales typically evaluate a limited number of cisterns on the axial plane. The goal of our study is to apply a simple quantitative yet comprehensive SAH grading scale to all major intracranial cisterns, including the infratentorial cisterns, with multiplanar computed tomography (CT) reformations.

Methodology

We performed a retrospective review of 94 consecutive cases of spontaneous SAH presenting within 72 hours of onset. SAH was categorized into five grades based on the short-axis thickness of SAH in 20 intracranial cisterns measured on the axial, coronal, and sagittal planes. Statistical analysis was performed for inter-rater agreement with kappa statistics, for inter-plane agreement by Spearman correlation statistics, and for inter-rater and inter-plane agreement by Pearson correlation statistics.

Results

The extended kappa coefficient for the three reviewers across all 20 cisterns varied from 0.38 (0.27, 0.50) to 0.59 (0.52, 0.65) on the axial plane. The kappa coefficient for two reviewers varied from 0.46 (0.33, 0.59) to 0.70 (0.60, 0.80) on the coronal plane and from 0.35 (0.20, 0.49) to 0.87 (0.77, 0.96) on the sagittal plane. The average grade of cisterns per case demonstrated mostly excellent correlation between the imaging planes with Spearman correlation statistics (≥0.70). Pairwise concordance correlation coefficient of the total SAH score revealed agreement ranging from 0.81 to 0.90 in all three planes. Pearson correlation statistics of the average total SAH scores revealed excellent correlation among the three planes (≥0.91).

Conclusion

A simple quantitative SAH grading scale can be successfully applied to the supratentorial and infratentorial cisterns in three standard CT imaging planes.

Comparison of Conventional Logistic Regression and Machine Learning Methods for Predicting Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage: A Multicentric Observational Cohort Study

Background

Timely and accurate prediction of delayed cerebral ischemia is critical for improving the prognosis of patients with aneurysmal subarachnoid hemorrhage. Machine learning (ML) algorithms are increasingly regarded as having a higher prediction power than conventional logistic regression (LR). This study aims to construct LR and ML models and compare their prediction power on delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH).

Methods

This was a multicenter, retrospective, observational cohort study that enrolled patients with aneurysmal subarachnoid hemorrhage from five hospitals in China. A total of 404 aSAH patients were prospectively enrolled. We randomly divided the patients into training (N = 303) and validation cohorts (N = 101) according to a ratio of 75–25%. One LR and six popular ML algorithms were used to construct models. The area under the receiver operating characteristic curve (AUC), accuracy, balanced accuracy, confusion matrix, sensitivity, specificity, calibration curve, and Hosmer–Lemeshow test were used to assess and compare the model performance. Finally, we calculated each feature of importance.

Results

A total of 112 (27.7%) patients developed DCI. Our results showed that conventional LR with an AUC value of 0.824 (95%CI: 0.73–0.91) in the validation cohort outperformed k-nearest neighbor, decision tree, support vector machine, and extreme gradient boosting model with the AUCs of 0.792 (95%CI: 0.68–0.9, P = 0.46), 0.675 (95%CI: 0.56–0.79, P < 0.01), 0.677 (95%CI: 0.57–0.77, P < 0.01), and 0.78 (95%CI: 0.68–0.87, P = 0.50). However, random forest (RF) and artificial neural network model with the same AUC (0.858, 95%CI: 0.78–0.93, P = 0.26) were better than the LR. The accuracy and the balanced accuracy of the RF were 20.8% and 11% higher than the latter, and the RF also showed good calibration in the validation cohort (Hosmer-Lemeshow: P = 0.203). We found that the CT value of subarachnoid hemorrhage, WBC count, neutrophil count, CT value of cerebral edema, and monocyte count were the five most important features for DCI prediction in the RF model. We then developed an online prediction tool (https://dynamic-nomogram.shinyapps.io/DynNomapp-DCI/) based on important features to calculate DCI risk precisely.

Conclusions

In this multicenter study, we found that several ML methods, particularly RF, outperformed conventional LR. Furthermore, an online prediction tool based on the RF model was developed to identify patients at high risk for DCI after SAH and facilitate timely interventions.

Clinical Trial Registration

http://www.chictr.org.cn, Unique identifier: ChiCTR2100044448.

Development and external validation of a dynamic nomogram for delayed cerebral ischaemia after aneurysmal subarachnoid hemorrhage: a study protocol for a multicentre retrospective cohort study

INTRODUCTION

Delayed cerebral ischaemia (DCI) caused by aneurysmal subarachnoid haemorrhage (aSAH) is the most frequent complication and typically contributes to poor neurological outcome or deterioration of patients' condition. Therefore, early accurate and effective prediction of DCI is urgently needed. This study aims to construct a dynamic nomogram for precisely calculating the risk of DCI in patients with aSAH. Internal validation of this tool is conducted using the training cohort, and independent external validation is completed by using other medical centre datasets.

METHODS AND ANALYSIS

This study is a multicentre, retrospective, observational cohort study using data from patients with aSAH. The participants include all adult patients who received surgical treatment in neurosurgery of multiple medical centres from 1 September 2019 to 1 April 2021, including Renmin Hospital of Wuhan University, Huzhou Central Hospital, First Affiliated Hospital of Harbin Medical University, General Hospital of Northern Theatre Command and Affiliated Hospital of Panzhihua University. Clinical information is collected via the electronic medical record system, including demographic data, clinical state on admission and serum laboratory tests. Modified Fisher grade at admission, admission subarachnoid clot and cerebral oedema density, and residual postoperative subarachnoid clot density are determined using the electronic imagine record software. The primary outcome is DCI.

ETHICS AND DISSEMINATION

This study protocol was reviewed and approved by the Medical Ethics Committee of Renmin Hospital of Wuhan University, which is the principal affiliation of this study (approval number: WDRM2021-K022). The other Ethics Committees, including Huzhou Central Hospital (approval number: 202108005-01), First Affiliated Hospital of Harbin Medical University (approval number: H202156), General Hospital of Northern Theater Command (approval number: Y2021060) and Affiliated Hospital of Panzhihua University (approval number: 202105002), also approved the protocol. The results of this research will be published in a peer-reviewed medical journal.

TRIAL REGISTRATION NUMBER

ChiCTR2100044448.

An Externally Validated Dynamic Nomogram for Predicting Unfavorable Prognosis in Patients With Aneurysmal Subarachnoid Hemorrhage

Background: Aneurysmal subarachnoid hemorrhage (aSAH) leads to severe disability and functional dependence. However, no reliable method exists to predict the clinical prognosis after aSAH. Thus, this study aimed to develop a web-based dynamic nomogram to precisely evaluate the risk of poor outcomes in patients with aSAH.

Methods: Clinical patient data were retrospectively analyzed at two medical centers. One center with 126 patients was used to develop the model. Least absolute shrinkage and selection operator (LASSO) analysis was used to select the optimal variables. Multivariable logistic regression was applied to identify independent prognostic factors and construct a nomogram based on the selected variables. The C-index and Hosmer–Lemeshow p-value and Brier score was used to reflect the discrimination and calibration capacities of the model. Receiver operating characteristic curve and calibration curve (1,000 bootstrap resamples) were generated for internal validation, while another center with 84 patients was used to validate the model externally. Decision curve analysis (DCA) and clinical impact curves (CICs) were used to evaluate the clinical usefulness of the nomogram.

Results: Unfavorable prognosis was observed in 46 (37%) patients in the training cohort and 24 (29%) patients in the external validation cohort. The independent prognostic factors of the nomogram, including neutrophil-to-lymphocyte ratio (NLR) (p = 0.005), World Federation of Neurosurgical Societies (WFNS) grade (p = 0.002), and delayed cerebral ischemia (DCI) (p = 0.0003), were identified using LASSO and multivariable logistic regression. A dynamic nomogram (https://hu-ping.shinyapps.io/DynNomapp/) was developed. The nomogram model demonstrated excellent discrimination, with a bias-corrected C-index of 0.85, and calibration capacities (Hosmer–Lemeshow p-value, 0.412; Brier score, 0.12) in the training cohort. Application of the model to the external validation cohort yielded a C-index of 0.84 and a Brier score of 0.13. Both DCA and CIC showed a superior overall net benefit over the entire range of threshold probabilities.

Conclusion: This study identified that NLR on admission, WFNS grade, and DCI independently predicted unfavorable prognosis in patients with aSAH. These factors were used to develop a web-based dynamic nomogram application to calculate the precise probability of a poor patient outcome. This tool will benefit personalized treatment and patient management and help neurosurgeons make better clinical decisions.

Radiomics in Stroke Neuroimaging: Techniques, Applications, and Challenges

Stroke is a leading cause of disability and mortality worldwide, resulting in substantial economic costs for post-stroke care each year. Neuroimaging, such as cranial computed tomography or magnetic resonance imaging, is the backbone of stroke management strategies, which can guide treatment decision-making (thrombolysis or hemostasis) at an early stage. With advances in computational technologies, particularly in machine learning, visual image information can now be converted into numerous quantitative features in an objective, repeatable, and high-throughput manner, in a process known as radiomics. Radiomics is mainly used in the field of oncology, which remains an area of active research. Over the past few years, investigators have attempted to apply radiomics to stroke in the hope of gaining benefits similar to those obtained in cancer management, i.e., in promoting the development of personalized precision medicine. Currently, radiomic analysis has shown promise for a variety of applications in stroke, including the diagnosis of stroke lesions, early prediction of outcomes, and evaluation for long-term prognosis. In this article, we elaborate the contributions of radiomics to stroke, as well as the subprocesses and techniques involved in radiomics studies. We also discuss the potential challenges facing its widespread implementation in routine practice and the directions for future research.

The Use of Acute Normovolemic Hemodilution in Clipping Surgery for Aneurysmal Subarachnoid Hemorrhage

BACKGROUND

The occurrence of coronavirus disease 2019 (COVID-19) has overwhelmed the blood supply chain worldwide and severely influenced clinical procedures with potential massive blood loss, such as clipping surgery for aneurysmal subarachnoid hemorrhage (aSAH). Whether acute normovolemic hemodilution (ANH) is safe and effective in aneurysm clipping remains largely unknown.

METHODS

Patients with aSAH who underwent clipping surgery within 72 hours from bleeding were included. The patients in the ANH group received 400 mL autologous blood collection, and the blood was returned as needed during surgery. The relationships between ANH and perioperative allogeneic blood transfusion, postoperative outcome, and complications were analyzed.

RESULTS

Sixty-two patients with aSAH were included between December 2019 and June 2020 (20 in the ANH group and 42 in the non-ANH group). ANH did not reduce the need of perioperative blood transfusion (3 [15%] vs. 5 [11.9%]; P = 0.734). However, ANH significantly increased serum hemoglobin levels on postoperative day 1 (11.5 ± 2.5 g/dL vs. 10.3 ± 2.0 g/dL; P = 0.045) and day 3 (12.1 ± 2.0 g/dL vs. 10.7 ± 1.3 g/dL; P = 0.002). Multivariable analysis indicated that serum hemoglobin level on postoperative day 1 (odds ratio, 0.895; 95% confidence interval, 0.822-0.973; P = 0.010) was an independent risk factor for unfavorable outcome, and receiver operating characteristic curve analysis showed that it had a comparable predictive power to World Federation of Neurosurgical Societies grade (Z = 0.275; P > 0.05).

CONCLUSIONS

ANH significantly increased postoperative hemoglobin levels, and it may hold the potential to improve patients' outcomes. Routine use of ANH should be considered in aneurysm clipping surgery.

The Modified Fisher Scale Lacks Interrater Reliability

BACKGROUND

The modified Fisher scale (mFS) is a critical clinical and research tool for risk stratification of cerebral vasospasm. As such, the mFS is included as a common data element by the National Institute of Neurological Disorders and Stroke SAH Working Group. There are few studies assessing the interrater reliability of the mFS.

METHODS

We distributed a survey to a convenience sample with snowball sampling of practicing neurointensivists and through the research survey portion of the Neurocritical Care Society Web site. The survey consisted of 15 scrollable CT scans of patients with SAH for mFS grading, two questions regarding the definitions of the scale criteria and demographics of the responding physician. Kendall's coefficient of concordance was used to determine the interrater reliability of mFS grading.

RESULTS

Forty-six participants (97.8% neurocritical care fellowship trained, 78% UCNS-certified in neurocritical care, median 5 years (IQR 3-6.3) in practice, treating median of 80 patients (IQR 50-100) with SAH annually from 32 institutions) completed the survey. By mFS criteria, 30% correctly identified that there is no clear measurement of thin versus thick blood, and 42% correctly identified that blood in any ventricle is scored as "intraventricular blood." The overall interrater reliability by Kendall's coefficient of concordance for the mFS was moderate (W = 0.586, p < 0.0005).

CONCLUSIONS

Agreement among raters in grading the mFS is only moderate. Online training tools could be developed to improve mFS reliability and standardize research in SAH.

Randomized, placebo-controlled, double-blind, pilot trial to investigate safety and efficacy of Cerebrolysin in patients with aneurysmal subarachnoid hemorrhage

Background

There are limited neuroprotective treatment options for patients with aneurysmal subarachnoid hemorrhage (SAH). Cerebrolysin, a brain-specific proposed pleiotropic neuroprotective agent, has been suggested to improve global functional outcomes in ischemic stroke. We investigated the efficacy, safety and feasibility of administering Cerebrolysin for SAH patients.

Methods

This was a prospective, randomized, double-blind, placebo-controlled, single-center, parallel-group pilot study. Fifty patients received either daily Cerebrolysin (30 ml/day) or a placebo (saline) for 14 days (25 patients per study group). The primary endpoint was a favorable Extended Glasgow Outcome Scale (GOSE) of 5 to 8 (moderate disability to good recovery) at six-months. Secondary endpoints included the modified Ranking Scale (mRS), the Montreal Cognitive Assessment (MOCA) score, occurrence of adverse effects and the occurrence of delayed cerebral ischemia (DCI).

Results

No severe adverse effects or mortality attributable to Cerebrolysin were observed. No significant difference was detected in the proportion of patients with favorable six-month GOSE in either study group (odds ratio (OR): 1.49; 95% confidence interval (CI): 0.43–5.17). Secondary functional outcome measures for favorable six-month recovery i.e. a mRS of 0 to 3 (OR: 3.45; 95% CI 0.79–15.01) were comparable for both groups. Similarly, there was no difference in MOCA neurocognitive performance (p-value: 0.75) and in the incidence of DCI (OR: 0.85 95% CI: 0.28–2.59).

Conclusions

Use of Cerebrolysin in addition to standard-of-care management of aneurysmal SAH is safe, well tolerated and feasible. However, the neutral results of this trial suggest that it does not improve the six-month global functional performance of patients.

Clinical trial registration

Name of Registry: ClinicalTrials.gov

Trial Registration Number: NCT01787123.

Date of Registration: 8th February 2013.

Prediction of Outcome Using Quantified Blood Volume in Aneurysmal SAH

BACKGROUND AND PURPOSE

In patients with SAH, the amount of blood is strongly associated with clinical outcome. However, it is commonly estimated with a coarse grading scale, potentially limiting its predictive value. Therefore, we aimed to develop and externally validate prediction models for clinical outcome, including quantified blood volumes, as candidate predictors.

MATERIALS AND METHODS

Clinical and radiologic candidate predictors were included in a logistic regression model. Unfavorable outcome was defined as a modified Rankin Scale score of 4-6. An automatic hemorrhage-quantification algorithm calculated the total blood volume. Blood was manually classified as cisternal, intraventricular, or intraparenchymal. The model was selected with bootstrapped backward selection and validated with the R ², C-statistic, and calibration plots. If total blood volume remained in the final model, its performance was compared with models including location-specific blood volumes or the modified Fisher scale.

RESULTS

The total blood volume, neurologic condition, age, aneurysm size, and history of cardiovascular disease remained in the final models after selection. The externally validated predictive accuracy and discriminative power were high (R ² = 56% ± 1.8%; mean C-statistic = 0.89 ± 0.01). The location-specific volume models showed a similar performance (R ² = 56% ± 1%, P = .8; mean C-statistic = 0.89 ± 0.00, P = .4). The modified Fisher models were significantly less accurate (R ² = 45% ± 3%, P < .001; mean C-statistic = 0.85 ± 0.01, P = .03).

CONCLUSIONS

The total blood volume-based prediction model for clinical outcome in patients with SAH showed a high predictive accuracy, higher than a prediction model including the commonly used modified Fisher scale.

Functional Outcome Following Ultra-Early Treatment for Ruptured Aneurysms in Patients with Poor-Grade Subarachnoid Hemorrhage

BACKGROUND

Little is known regarding functional outcome following poor-grade (World Federation of Neurosurgical Societies grades IV and V) aneurysmal subarachnoid hemorrhage (aSAH), especially in individuals treated aggressively in the early phase after ictus.

METHODS

We provided patients with aSAH with ultra-early definitive treatment, coiling or clipping, within 6 hours from arrival as per protocol. We classified the patients into 3 groups according to their computed tomography findings: Group 1, intraventricular hemorrhage with obstructive hydrocephalus; Group 2, massive intracerebral hemorrhage with brain herniation; and Group 3, neither Group 1 nor Group 2. We retrospectively evaluated patients with poor-grade aSAH who were admitted to our department between January 2013 and December 2016. We evaluated functional outcome at 6 months, defining modified Rankin Scale (mRS) scores of 0-2 as good and those of 3-6 as poor outcomes.

RESULTS

A good functional outcome was observed in 39.4% (28/71) of all cases. All-cause mortality at 6 months was 15.5% (11/71). A good outcome in Group 3 was significantly higher than that in the other two groups (Group 1 and 2 vs. Group 3, 20.8% vs. 48.9%, p = 0.02), even after adjustment with a multiple logistic regression analysis (odds ratio 6.1, 95% confidence interval 1.1 to 34.8).

CONCLUSIONS

Approximately 40% of patients with poor-grade aSAH became functionally independent, and approximately half of the patients with poor-grade aSAH who had neither intraventricular hemorrhage with obstructive hydrocephalus nor with brain herniation had good functional outcomes. Although further trials are required to confirm our results, ultra-early surgery may be considered for patients with poor-grade aSAH.

Early prediction of clinical outcomes in patients with aneurysmal subarachnoid hemorrhage using computed tomography texture analysis

Radiological evaluation of subarachnoid hemorrhage (SAH) is often subject to interobserver variability. The aim of this study was to retrospectively detect computed tomography (CT) texture parameters in the early postictal state to predict cerebral vasospasm, delayed cerebral ischemia (DCI), and functional outcome in aneurysmal SAH using quantitative CT texture analysis (CTTA) via a commercially available software program and routine CT images. 40 patients with aneurysmal SAH surgically treated at the Keio University Hospital during a four-year period were analyzed. CT texture analyses were performed using a commercially available software program (Synapse Vincent). The following texture parameters of blood clots in the subarachnoid space and cerebral edema were assessed: mean CT value, entropy, skewness, and kurtosis. The mean CT value of blood clots in the subarachnoid space was significantly associated with cerebral vasospasm, DCI, and functional outcome. The mean CT value ≥ 49.64 Hounsfield units (HU) predicted cerebral vasospasm with a sensitivity and specificity of 85.7% and 61.5%, respectively (area under the curve [AUC] = 0.758). The mean CT value ≥ 49.95 HU predicted DCI with a sensitivity and specificity of 100% and 60.6%, respectively (AUC = 0.810). The mean CT value ≥ 53.00 HU predicted poor functional outcome with a sensitivity and specificity of 56.3% and 91.7%, respectively (AUC = 0.747). CTTA using a commercially available software program demonstrated that the mean CT value of clots in the subarachnoid space in the early postictal state could predict vasospasm, DCI, and clinical outcome with a high sensitivity and specificity.

Is Admission Lipoprotein-Associated Phospholipase A2 a Novel Predictor of Vasospasm and Outcome in Patients With Aneurysmal Subarachnoid Hemorrhage?

BACKGROUND

The relationships between lipoprotein-associated phospholipase A2 (Lp-PLA2) level, vasospasm, and clinical outcome of patients with aneurysmal subarachnoid hemorrhage (aSAH) are still unclear.

OBJECTIVE

To identify the associations between admission Lp-PLA2 and vasospasm following subarachnoid hemorrhage and the clinical outcome of aSAH.

METHODS

A total of 103 aSAH patients who had Lp-PLA2 level obtained within 24 h postbleeding were included. The relationships between Lp-PLA2 level, vasospasm, and clinical outcome were analyzed.

RESULTS

Vasospasm was observed in 52 patients (50.49%). Patients with vasospasm had significantly higher Lp-PLA2 level than those without (P &lt; .001). Both modified Fisher grade (P = .014) and Lp-PLA2 level (P &lt; .001) were significant predictors associated with vasospasm. The Z test revealed that power of Lp-PLA2 was significantly higher than that of modified Fisher grade in predicting vasospasm (Z = 2.499, P = .012). At 6-mo follow-up, 44 patients (42.72%) had unfavorable outcome and 36 patients (34.95%) died. The World Federation of Neurosurgical Societies (WFNS) grade and Lp-PLA2 level were both significant predictors associated with 6-mo unfavorable outcome and mortality (all P &lt; .001). The predictive values of Lp-PLA2 for unfavorable outcome and mortality at 6-mo tended to be lower than those of the WFNS grade, but the differences were not statistically significant (P = .366 and 0.115, respectively). Poor-grade patients having Lp-PLA2 &gt; 200 μg/L had significantly worse 6-mo survival rate than poor-grade patients having Lp-PLA2 ≤ 200 μg/L (P = .001).

CONCLUSION

The Lp-PLA2 might be useful as a novel predictor in aSAH patients. A total of 30 poor-grade patients; those with elevated Lp-PLA2 level have higher risk of 6-mo mortality compared to those without.