Ventricular Volume Change as a Predictor of Shunt-Dependent Hydrocephalus in Aneurysmal Subarachnoid Hemorrhage

Automated segmentation of ventricular volumes and subarachnoid hemorrhage from computed tomography images: Evaluation of a rule-based pipeline approach

Changes in ventricular size, related to brain edema and hydrocephalus, as well as the extent of hemorrhage are associated with adverse outcomes in patients with subarachnoid hemorrhage (SAH). Frequently, these are measured manually using consecutive non-contrast computed tomography scans. Here, we developed a rule-based approach which incorporates both intensity and spatial normalization and utilizes user-defined thresholds and anatomical templates to segment both lateral ventricle (LV) and SAH blood volumes automatically from CT images. The algorithmic segmentations were evaluated against two expert neuroradiologists on representative slices from 20 admission scans from aneurysmal SAH patients. Previous methods have been developed to automate this time-consuming task, but they lack user feedback and are hard to implement due to large-scale data and complex design processes. Our results using automatic ventricular segmentation aligned well with expert reviewers with a median Dice coefficient of 0.81, AUC of 0.91, sensitivity of 81%, and precision of 84%. Automatic segmentation of SAH blood was most reliable near the base of the brain with a median Dice coefficient of 0.51, an AUC of 0.75, precision of 68%, and sensitivity of 50%. Ultimately, we developed a rule-based method that is easily adaptable through user feedback, generates spatially normalized segmentations that are comparable regardless of brain morphology or acquisition conditions, and automatically segments LV with good overall reliability and basal SAH blood with good precision. Our approach could benefit longitudinal studies in patients with SAH by streamlining assessment of edema and hydrocephalus progression, as well as blood resorption.

A comparison of ventricular volume and linear indices in predicting shunt dependence in aneurysmal subarachnoid hemorrhage

Background

Guidelines for determining shunt dependence after aneurysmal subarachnoid hemorrhage (aSAH) remain unclear. We previously demonstrated change in ventricular volume (VV) between head CT scans taken pre- and post-EVD clamping was predictive of shunt dependence in aSAH. We sought to compare the predictive value of this measure to more commonly used linear indices.

Methods

We retrospectively analyzed images of 68 patients treated for aSAH who required EVD placement and underwent one EVD weaning trial, 34 of whom underwent shunt placement. We utilized an in-house MATLAB program to analyze VV and supratentorial VV (sVV) in head CT scans obtained before and after EVD clamping. Evans' index (EI), frontal and occipital horn ratio (FOHR), Huckman's measurement, minimum lateral ventricular width (LV-Min.), and lateral ventricle body span (LV-Body) were measured using digital calipers in PACS. Receiver operating curves (ROC) were generated.

Results

Area under the ROC curves (AUC) for the change in VV, sVV, EI, FOHR, Huckman's, LV-Min., and LV-Body with clamping were 0.84, 0.84, 0.65, 0.71.0.69, 0.67, and 0.66, respectively. AUC for post-clamp scan measurements were 0.75, 0.75, 0.74, 0.72, 0.72, 0.70, and 0.75, respectively.

Conclusion

VV change with EVD clamping was more predictive of shunt dependence in aSAH than change in linear measurements with clamping and all post-clamp measurements. Measurement of ventricular size on serial imaging with volumetrics or linear indices utilizing multidimensional data points may therefore be a more robust metric than unidimensional linear indices in predicting shunt dependence in this cohort. Prospective studies are needed for validation.

Pilot Study of Neurologic Pupil Index as A Predictor of External Ventricular Drain Clamp Trial Failure After Subarachnoid Hemorrhage

BACKGROUND

External ventricular drains (EVDs) provide a temporary egress for cerebrospinal fluid (CSF) in patients with symptomatic hydrocephalus following aneurysmal subarachnoid hemorrhage. Before EVD removal, a wean trial, which involves clamping the EVD, is typically attempted to ensure that CSF self-regulation is achieved. Automated infrared pupillometry (AIP) has been shown to detect early neurologic decline. We sought to explore the use of AIP to detect early EVD clamping trial failure.

METHODS

This prospective observational pilot study enrolled aneurysmal subarachnoid hemorrhage patients before an EVD clamp trial. On initiating the clamp trial, nurses included hourly AIP assessment in documentation. Clamp trial outcome was based on neurologic examination and neuroimaging. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) models were constructed to explore computed tomography (CT) versus AIP as predictors of clamp trial outcome.

RESULTS

Among the 30 subjects enrolled, there were 38 clamping trials and 22 successful EVD removals. CT scan as a predictor of clamp trial was found to have a sensitivity of 68.8% and specificity of 89.5% (PPV = 84.6%, NPV = 77.3%). AIP assessment as a predictor of wean trial outcome was found to have a sensitivity of 58.3% and specificity of 100% (PPV = 100%, NPV = 63.2%).

CONCLUSIONS

The pilot study data support that Neurological Pupil index <3 is a potential indicator of early clamp trial failure, but a CT scan has a higher sensitivity and NPV for predicting successful EVD removal. This finding suggests the benefits of including AIP assessments during clamping trials.