The 
This study was able to demonstrate that hemorrhagic volume estimated by an easy‐to‐use and clinically feasible quantitative method was associated with functional outcome at discharge and delayed cerebral ischemia. 

Quantitatively measured hemorrhagic volume could act as a powerful and reliable predictor of outcome and other secondary complications in aneurysmal subarachnoid hemorrhage. Our simplified volumetric method could support clinical decision‐making and guide therapeutic interventions based on objective metric variables. 

Study Population We retrospectively reviewed the electronic health records of 277 patients with aneurysmal SAH seen at the Mayo Clinic in Jacksonville, Florida, between January 5, 2012, and February 24, 2022. Demographics, medical history, clinical assessment, computed tomography (CT), and hospital course were analyzed. Inclusion criteria for the study required noncontrast CT (NCCT) head imaging to be performed at admission within 24 hours of SAH ictus, and an aneurysmal cause of SAH to be diagnosed through vascular imaging such as CT or magnetic resonance angiography, or cerebral angiography. Furthermore, NCCT data had to have imaging slices of ≤5 mm or at least equivalent reformatting to adequately measure volumetric data quantitatively. If DCI was suspected, vascular neuroimaging modalities such as CT angiography, magnetic resonance imaging, magnetic resonance angiography, or digital subtraction angiography were employed to establish a diagnosis and to definitively rule out ischemic stroke. Criteria for exclusion included the absence of NCCT imaging, no diagnosis of SAH, or only associated diseases (only intracerebral hemorrhage in the medical record, IVH, or subdural hemorrhage without SAH), as well as traumatic SAH. This single‐center, retrospective study was approved by the Mayo Clinic's institutional review board, since it used preexisting deidentified patients within the Mayo Clinic firewall along with stored clinical, laboratory, and radiographic data. The institutional review board deemed this research exempt from formal consent because the head CT data were deidentified, reviewed retrospectively, performed for standard‐of‐care management reasons for the patient, and could not be practically performed since several patients were deceased. The data that support the findings of this study are available from the corresponding author upon reasonable request. Outcome and DCI Outcome was quantified via mRS score at discharge and dichotomized into good outcome (mRS 0–3) and poor outcome (mRS 4–6). DCI was defined in accordance with the consensus definition by Vergouwen et al as the occurrence of focal neurological impairment or a decrease of at least 2 points on GCS, persisting for more than an hour, not attributable to other clinically assessable conditions, as well as intraoperative or postoperative changes or complications. If patients died within 72 hours after admission, they were excluded from analysis of DCI. Imaging Analysis The extraction of patient data, as well as the analysis of imaging data, were conducted by the principal investigator. To reduce the risk of bias, each analytic segment of this study was performed separately from another. Two methods of SAHV estimation were conceptualized: the first was a simplified quantitative volumetric model (sQv or ABC/2‐derived SAHV, model 1 [M1]) derived by ABC/2 mathematical linear measurements in 3 dimensions. The second was a manual segmentation method (msQv or segment‐SAHV, model 2 [M2]). For matters of comparability, both methods were applied to the same NCCT images of the enrolled patients. The analyzed images were stored either in Digital Imaging and Communications in Medicine (DICOM) format or Neuroimaging Informatics Technology Initiative (NIfTI) format, as appropriate. Imaging analysis was performed on a personal computer using medical imaging software QReads for determination of metric extensions and open‐source segmentation software RIL Contour version 0.1.947 (Mayo Digital Innovation Lab, Mayo Clinic) for manual segmentation. M1 : ABC /2‐Derived SAHV Methodology SAHV was measured in the following 5 predefined anatomic regions: prepontine cistern, perimesencephalic cistern, suprasellar cistern, left and right Sylvian fissure, middle cerebral artery fissure, and anterior interhemispheric fissure (Figure ). Similar to the ABC/2‐volume measurement tool for intracerebral hemorrhage (ICH), cylindrical volumetrics can be estimated by calculating volume through application of ABC/2 formula in 3 dimensions (ie, A=width, thickest portion in cross‐section; B=length, a linear measurement in the long axis of the SAH blood; and C=vertical dimensions on the number of CT slices) (Figure ) and assumes that the basal cisterns “star‐shaped” morphology can be compartmentalized into subsegmental ellipsoid volumes (M1). ABC/2 is a simplified derivation of the ellipsoid formula and has previously been proven to yield accurate volumetric estimations in ICH and subdural hematoma (SDH). , , The first step in measuring SAH hemorrhage was obtaining each of the 3 dimensions for each of the 5 compartments; for example, the maximal thickness (A) and length (B) for each individual cistern. Then, the number of slices vertically was counted containing discernible SAH blood and subsequently multiplied by the slice thickness (millimeters) of the given CT image modality to account for the vertical extension (C). These 3 variables were then inputted into the ABC/2 (Figure ) equation to obtain the simplified volumetric estimate for each of the 5 cisternal compartments, and the sum of the 5 were then calculated for each NCCT. Because of the inherent morphologic limitations of the ventricular systems and complex hemorrhagic distribution patterns, we did not include IVH volumetrics in this study; however, we did obtain modified Graeb IVH volumes during NCCT measurements. M2 : Segmentation SAHV Methodology The same NCCT images were then separately analyzed by applying a manual segmentation approach using open‐source RIL‐Contour and Anaconda. For M2, a region of interest technique was applied, and SAHV‐M2 was manually contoured slice by slice. Voxels within these marked or painted areas were then translated into metric scales of millimeters, and SAHV was calculated under consideration of the vertical extension and slice thickness of NCCT imaging. The same 5 predefined neuroanatomic compartments were measured using this M2 method and summated to calculate the total blood for SAHV‐M2. Statistical Analysis Patient characteristics were dichotomized based on their outcome at discharge and descriptively analyzed. Differences in categorical variables were determined via Fisher exact test, and differences in continuous variables were analyzed using paired/unpaired t test or Mann–Whitney U test, as appropriate. Descriptive statistics were used to depict volume estimations. To assess our SAHV models’ predictiveness of poor functional outcome and DCI, multiple logistic regression analysis was performed with a classification cutoff of 0.5. Outcome in multiple logistic regression was dichotomized based on the patients' mRS score at discharge. Differences in discriminative accuracy of SAHV and mFS were examined via receiver operator characteristic (ROC) curves and subsequent area under the curve (AUC) analysis. We also proposed specific outcome and DCI cutoff values for SAHV‐M1 using Youden index (J = sensitivity + specificity – 1 or J = [true positives] / [true positives = false negatives] + [true negatives] / [true negative = false positives] − 1) by determining optimum cut points in ROC curve analysis. For statistical analysis, SPSS (IBM) and GraphPad software were used on a Windows‐operated personal computer, and a P value of 0.05 was considered statistically significant. 

We retrospectively reviewed the electronic health records of 277 patients with aneurysmal SAH seen at the Mayo Clinic in Jacksonville, Florida, between January 5, 2012, and February 24, 2022. Demographics, medical history, clinical assessment, computed tomography (CT), and hospital course were analyzed. Inclusion criteria for the study required noncontrast CT (NCCT) head imaging to be performed at admission within 24 hours of SAH ictus, and an aneurysmal cause of SAH to be diagnosed through vascular imaging such as CT or magnetic resonance angiography, or cerebral angiography. Furthermore, NCCT data had to have imaging slices of ≤5 mm or at least equivalent reformatting to adequately measure volumetric data quantitatively. If DCI was suspected, vascular neuroimaging modalities such as CT angiography, magnetic resonance imaging, magnetic resonance angiography, or digital subtraction angiography were employed to establish a diagnosis and to definitively rule out ischemic stroke. Criteria for exclusion included the absence of NCCT imaging, no diagnosis of SAH, or only associated diseases (only intracerebral hemorrhage in the medical record, IVH, or subdural hemorrhage without SAH), as well as traumatic SAH. This single‐center, retrospective study was approved by the Mayo Clinic's institutional review board, since it used preexisting deidentified patients within the Mayo Clinic firewall along with stored clinical, laboratory, and radiographic data. The institutional review board deemed this research exempt from formal consent because the head CT data were deidentified, reviewed retrospectively, performed for standard‐of‐care management reasons for the patient, and could not be practically performed since several patients were deceased. The data that support the findings of this study are available from the corresponding author upon reasonable request. 

DCI Outcome was quantified via mRS score at discharge and dichotomized into good outcome (mRS 0–3) and poor outcome (mRS 4–6). DCI was defined in accordance with the consensus definition by Vergouwen et al as the occurrence of focal neurological impairment or a decrease of at least 2 points on GCS, persisting for more than an hour, not attributable to other clinically assessable conditions, as well as intraoperative or postoperative changes or complications. If patients died within 72 hours after admission, they were excluded from analysis of DCI. 

The extraction of patient data, as well as the analysis of imaging data, were conducted by the principal investigator. To reduce the risk of bias, each analytic segment of this study was performed separately from another. Two methods of SAHV estimation were conceptualized: the first was a simplified quantitative volumetric model (sQv or ABC/2‐derived SAHV, model 1 [M1]) derived by ABC/2 mathematical linear measurements in 3 dimensions. The second was a manual segmentation method (msQv or segment‐SAHV, model 2 [M2]). For matters of comparability, both methods were applied to the same NCCT images of the enrolled patients. The analyzed images were stored either in Digital Imaging and Communications in Medicine (DICOM) format or Neuroimaging Informatics Technology Initiative (NIfTI) format, as appropriate. Imaging analysis was performed on a personal computer using medical imaging software QReads for determination of metric extensions and open‐source segmentation software RIL Contour version 0.1.947 (Mayo Digital Innovation Lab, Mayo Clinic) for manual segmentation. 

: ABC /2‐Derived SAHV Methodology SAHV was measured in the following 5 predefined anatomic regions: prepontine cistern, perimesencephalic cistern, suprasellar cistern, left and right Sylvian fissure, middle cerebral artery fissure, and anterior interhemispheric fissure (Figure ). Similar to the ABC/2‐volume measurement tool for intracerebral hemorrhage (ICH), cylindrical volumetrics can be estimated by calculating volume through application of ABC/2 formula in 3 dimensions (ie, A=width, thickest portion in cross‐section; B=length, a linear measurement in the long axis of the SAH blood; and C=vertical dimensions on the number of CT slices) (Figure ) and assumes that the basal cisterns “star‐shaped” morphology can be compartmentalized into subsegmental ellipsoid volumes (M1). ABC/2 is a simplified derivation of the ellipsoid formula and has previously been proven to yield accurate volumetric estimations in ICH and subdural hematoma (SDH). , , The first step in measuring SAH hemorrhage was obtaining each of the 3 dimensions for each of the 5 compartments; for example, the maximal thickness (A) and length (B) for each individual cistern. Then, the number of slices vertically was counted containing discernible SAH blood and subsequently multiplied by the slice thickness (millimeters) of the given CT image modality to account for the vertical extension (C). These 3 variables were then inputted into the ABC/2 (Figure ) equation to obtain the simplified volumetric estimate for each of the 5 cisternal compartments, and the sum of the 5 were then calculated for each NCCT. Because of the inherent morphologic limitations of the ventricular systems and complex hemorrhagic distribution patterns, we did not include IVH volumetrics in this study; however, we did obtain modified Graeb IVH volumes during NCCT measurements. 

: Segmentation SAHV Methodology The same NCCT images were then separately analyzed by applying a manual segmentation approach using open‐source RIL‐Contour and Anaconda. For M2, a region of interest technique was applied, and SAHV‐M2 was manually contoured slice by slice. Voxels within these marked or painted areas were then translated into metric scales of millimeters, and SAHV was calculated under consideration of the vertical extension and slice thickness of NCCT imaging. The same 5 predefined neuroanatomic compartments were measured using this M2 method and summated to calculate the total blood for SAHV‐M2. 

Patient characteristics were dichotomized based on their outcome at discharge and descriptively analyzed. Differences in categorical variables were determined via Fisher exact test, and differences in continuous variables were analyzed using paired/unpaired t test or Mann–Whitney U test, as appropriate. Descriptive statistics were used to depict volume estimations. To assess our SAHV models’ predictiveness of poor functional outcome and DCI, multiple logistic regression analysis was performed with a classification cutoff of 0.5. Outcome in multiple logistic regression was dichotomized based on the patients' mRS score at discharge. Differences in discriminative accuracy of SAHV and mFS were examined via receiver operator characteristic (ROC) curves and subsequent area under the curve (AUC) analysis. We also proposed specific outcome and DCI cutoff values for SAHV‐M1 using Youden index (J = sensitivity + specificity – 1 or J = [true positives] / [true positives = false negatives] + [true negatives] / [true negative = false positives] − 1) by determining optimum cut points in ROC curve analysis. For statistical analysis, SPSS (IBM) and GraphPad software were used on a Windows‐operated personal computer, and a P value of 0.05 was considered statistically significant. 

Patient Demographics Among the initial 277 patients with SAH in our data set, we excluded 72 because of traumatic SAH or nonaneurysmal SAH, lack of imaging quality, and transferal to other hospitals, for a total study population of 205 patients with SAH. Patient characteristics are shown in Table . Median Glasgow Coma Scale scores on admission were 15 (range, 13–15) in the good outcome group and 7 (range, 7–15) in the poor outcome group ( P <0.0001). Only 24 patients (11.7%) presented with concomitant IPH, with most being in the poor outcome group (20 [83.3%]). Forty‐one patients (20.0%) had symptomatic (clinical) vasospasm, and most vasospasm events occurred in the poor outcome group (27 [65.9%]). Higher modified Graeb score of IVH predicted worse outcome ( P <0.001). DCI was more frequently encountered than symptomatic vasospasm (51 [24.4%]), and most patients with DCI (43 [84.3%]) were discharged with poor outcomes. The predominant mFS grading observed in this study among all patients was mFS 4 (121 [59.0%]), followed by an mFS score of 3 (48 [23.4%]). Volumetric Analysis ABC/2‐derived SAHV in all 205 patients were estimated using our M1 methodology. Mean SAHV was 11.3 mL (95% CI, 9.58–12.85) and the respective range was 125 mL, with a minimum of 0 mL (when no blood was present at the level of the cistern) and a maximum of 125 mL. Volumetric comparison of M1 with manually segmented CT images (M2) is shown in Figure . The mean ABC/2‐derived SAHV measured by M1 was not significantly different than manually segmented SAHV‐M2 ( P =0.14) (Figure ). Association of Volume With Outcome and DCI The volumetric relationship between predictor variables and dichotomized target variables is shown in Figures and . For M1, the mean SAHV at discharge for the good outcome group was 7.0 mL (95% CI, 5.89–8.09), whereas the poor outcome group had significantly higher volumes, with a mean SAHV of 16.6 mL (95% CI, 13.49–19.77). These differences in SAH volume are statistically significant ( P <0.05). Volumetric analysis for patients with DCI showed significantly higher SAH volumes compared with patients without DCI for both SAHV‐M1 ( P <0.05) and SAHV‐M2 ( P <0.05) (Tables and ). ROC curve analysis (Figure through ) demonstrated that SAHV‐M1 exhibits a moderate to high discriminative and predictive strength for outcome, with an AUC of 0.78 (95% CI, 0.71–0.84) (Figure ). To account for the capabilities of these variables to predict DCI, logistic regression analysis and ROC curve analysis were performed separately. For this analytical segment, 10 patients had to be excluded because of death within 72 hours after admission. Therefore, 195 patients were included in this study analysis. ROC curve analysis of ABC/2‐derived SAHV for DCI revealed an AUC of 0.71 (95% CI, 0.63–0.79) (Table , Figure ). These AUC data suggest that SAHV‐M1 could predict outcomes and DCI more accurately than the mFS (Figure ). Determination of SAHV Cutoff Values With Outcomes Using the Youden Index The optimum cut point is defined as the highest Youden index within a given set of ROC curve data and represents the maximal sensitivity and specificity cut point. Cutoff values were determined based on the predictor variable SAHV‐M1 estimated for outcome and DCI. For both outcome and DCI, a cutoff value of 10 mL SAHV‐M1 (J ABC‐SAHV‐outcome =10.0855 mL and J ABC‐SAHC‐DCI =10.117 mL) was determined in this study population (Figure ). Results of the sensitivity, specificity, positive predictive, and negative predictive for outcome and DCI using a cutoff value of 10 mL ABC/2‐derived SAHV are shown in Table . 

Among the initial 277 patients with SAH in our data set, we excluded 72 because of traumatic SAH or nonaneurysmal SAH, lack of imaging quality, and transferal to other hospitals, for a total study population of 205 patients with SAH. Patient characteristics are shown in Table . Median Glasgow Coma Scale scores on admission were 15 (range, 13–15) in the good outcome group and 7 (range, 7–15) in the poor outcome group ( P <0.0001). Only 24 patients (11.7%) presented with concomitant IPH, with most being in the poor outcome group (20 [83.3%]). Forty‐one patients (20.0%) had symptomatic (clinical) vasospasm, and most vasospasm events occurred in the poor outcome group (27 [65.9%]). Higher modified Graeb score of IVH predicted worse outcome ( P <0.001). DCI was more frequently encountered than symptomatic vasospasm (51 [24.4%]), and most patients with DCI (43 [84.3%]) were discharged with poor outcomes. The predominant mFS grading observed in this study among all patients was mFS 4 (121 [59.0%]), followed by an mFS score of 3 (48 [23.4%]). 

ABC/2‐derived SAHV in all 205 patients were estimated using our M1 methodology. Mean SAHV was 11.3 mL (95% CI, 9.58–12.85) and the respective range was 125 mL, with a minimum of 0 mL (when no blood was present at the level of the cistern) and a maximum of 125 mL. Volumetric comparison of M1 with manually segmented CT images (M2) is shown in Figure . The mean ABC/2‐derived SAHV measured by M1 was not significantly different than manually segmented SAHV‐M2 ( P =0.14) (Figure ). 

DCI The volumetric relationship between predictor variables and dichotomized target variables is shown in Figures and . For M1, the mean SAHV at discharge for the good outcome group was 7.0 mL (95% CI, 5.89–8.09), whereas the poor outcome group had significantly higher volumes, with a mean SAHV of 16.6 mL (95% CI, 13.49–19.77). These differences in SAH volume are statistically significant ( P <0.05). Volumetric analysis for patients with DCI showed significantly higher SAH volumes compared with patients without DCI for both SAHV‐M1 ( P <0.05) and SAHV‐M2 ( P <0.05) (Tables and ). ROC curve analysis (Figure through ) demonstrated that SAHV‐M1 exhibits a moderate to high discriminative and predictive strength for outcome, with an AUC of 0.78 (95% CI, 0.71–0.84) (Figure ). To account for the capabilities of these variables to predict DCI, logistic regression analysis and ROC curve analysis were performed separately. For this analytical segment, 10 patients had to be excluded because of death within 72 hours after admission. Therefore, 195 patients were included in this study analysis. ROC curve analysis of ABC/2‐derived SAHV for DCI revealed an AUC of 0.71 (95% CI, 0.63–0.79) (Table , Figure ). These AUC data suggest that SAHV‐M1 could predict outcomes and DCI more accurately than the mFS (Figure ). 

SAHV Cutoff Values With Outcomes Using the Youden Index The optimum cut point is defined as the highest Youden index within a given set of ROC curve data and represents the maximal sensitivity and specificity cut point. Cutoff values were determined based on the predictor variable SAHV‐M1 estimated for outcome and DCI. For both outcome and DCI, a cutoff value of 10 mL SAHV‐M1 (J ABC‐SAHV‐outcome =10.0855 mL and J ABC‐SAHC‐DCI =10.117 mL) was determined in this study population (Figure ). Results of the sensitivity, specificity, positive predictive, and negative predictive for outcome and DCI using a cutoff value of 10 mL ABC/2‐derived SAHV are shown in Table . 

This study aimed to develop a simplified quantitative and more precise volumetric method to determine SAHV blood volume in aneurysmal SAH. We explored and compared 2 models and their association with outcomes and DCI. The SAH ABC/2‐derived volumetric methodology appears similar to the well‐established ABC/2 volumetric models for intracerebral hemorrhage, which is powerfully predictive on outcomes. , , Therefore, this SAH volumetric research has critical importance to advance future SAH translational research, and, to our knowledge, is the first attempt to establish a clinically applicable, easy‐to‐use quantitative SAHV methodology similar to ABC/2 estimates for intracerebral hemorrhage. Further, prior studies have quantified SAH blood primarily through the application of automated or manual imaging segmentation software, , , which is not available in many emergency departments or widely used in clinical practice. We demonstrated that a simple ABC/2‐derived SAHV method can predict poor outcomes by hospital discharge and a higher risk of future DCI during hospitalization. In a comparative AUC analysis, we found that the ABC/2‐derived SAHV‐M1 is at least equal or better in predicting outcome and DCI compared with the well‐known mFS, which is prone to underestimate the true volumetric relationship of SAHV on clinical outcomes. Furthermore, comparative volumetric descriptions showed that the ABC/2‐derived SAHV‐M1 can approximate complex and time‐consuming manual segmentation (SAHV‐M2) on average. Although some differences between the estimated volumetrics of SAHV‐M1 and other segmentation methods are evident, these discrepancies might be explained by difficult‐to‐define neuroanatomical SAH spaces on the gyri, sulci, and hemispheric convexities of the brain as well as a nonlinear‐shaped ventricular system. , Last, we were able to identify a potential SAHV cutoff value using Youden index of 10 mL by SAHV‐M1, which was associated with a higher risk of poor outcome and DCI. This preliminary SAHV cutoff value may be particularly useful for future larger, prospective, randomized controlled SAH research trials and requires more validation. The identification of SAH volume as a continuous variable is clinically significant since it can be used to risk‐stratify patients with SAH and to establish a robust “dose–response” relationship for future neuroprotective therapies and using an individualized dosing approach. Furthermore, precise SAHV measurement helps provide a more robust and reliable baseline of blood volume and location to potentially perform targeted therapy with alternative routes of intrathecal or intraventricular drug delivery, or even adjunctive drug therapy. The potential discovery of a minimal SAHV dose of 10 mL associated with poor outcomes is clinically relevant and could become a target for future interventions or used as predictive tool for a quantifiable risk assessment of DCI. The ABC/2‐derived method of measuring intracerebral hemorrhage was found to be a powerful predictor on patient outcomes and became incorporated at comprehensive stroke centers as a severity of illness in this disease. Similarly, we think that ABC/2 for measuring SAHV can be easily protocolized and standardized at both primary and comprehensive stroke centers. Our ABC/2‐derived SAHV‐M1 does not require any coding expertise, costly imaging software, or machine‐learning algorithms. A basic medical imaging view station of the NCCT with simple linear measurement tools is sufficient to determine the ABCs of SAH blood in the 5 main “star‐shaped” major cisterns. This simple method could be used by clinicians worldwide (eg, physicians, nurse practitioners, and radiologists) wherever the NCCT is available, and even in rural and underserved areas without comprehensive stroke centers. Measurement and calculation of ABC/2‐derived SAHV‐M1 is similar to the learning curve for measuring ICH. In contrast, a manual segmentation approach takes much longer depending on the complexity and extent of SAH distribution. While manual segmentation has been demonstrated to be a quantifiable and precise method in radiologic evaluation of SAH, it remains highly demanding in terms of the overall time investment to manually segment slice by slice. Manual segmentation is not practical in most emergency SAH scenarios. Manual segmentation is useful mostly for comparative analysis in SAH research to help compare, derive, and develop primarily automated segmentation models. Therefore, while not clinically applicable, manual segmentation approaches such as SAH‐M2 can still be used in prospective randomized trials to further validate our SAHV‐M1 or other future SAHV models. When comparing our ABC/2‐derived SAHV‐M1 with established radiological frameworks such as the mFS and FS (Table ), one frequently expressed aspect of critique pertains to the substantial interobserver variability observed in the mFS when grading NCCT. This inherent variability subsequently undermines the robustness of its predictive validity. Recent data suggest that mFS achieves only moderate interobserver agreement with overall IRR by Kendell coefficient varying between Kw=0.41 (95% CI, 0.33–0.49) and Kw=0.586 ( P <0.0005). , , It is hypothesized that the IRR is negatively influenced by the vague definitions of mFS grading criteria as the hemorrhagic extent was described as either thin or thick. No specific metric characterization was proposed, thereby naturally compromising the model's reproducibility. , , While not evidently based, again we think that a more quantitative approach, such as the ABC/2‐derived SAHV method, would allow for increased reproducibility. In addition, both the mFS and original FS arguably lack discriminative accuracy between individual groups, therefore they would not allow for reasonable quantifiable risk assessment, as it is our observation that most patients diagnosed with acute SAH are either graded as mFS 3 or mFS 4. This would notably restrict the potential of relevant therapeutic abstractions within the realm of clinical decision‐making. However, given the inherent characteristics of the “dose–response” relationship existing between SAHV and functional outcomes or secondary complications, , , , we think that our ABC/2‐derived SAHV‐M1 would represent a more comprehensive portrayal of this association. Consequently, it is plausible that our model would be better poised to support within the decision‐making process regarding management of patients with acute SAH. We acknowledge that our ABC/2‐derived SAHV‐M1 has limitations (Table ) because of the complex morphology of SAH blood patterns and cisternal anatomy, which do not fully represent an ellipsoidal shape. Therefore, this mathematical simplification and volumetric estimation of SAH should be considered purely a volumetric approximation compared with more time‐intensive segmentation methods that allow for almost pixel‐level measurement beyond these 5 major cisterns. However, we feel this finding is important since it provides clinicians and researchers a relatively simple‐to‐use tool for patients with SAH, similar to ICH and SDH, which also use ABC/2‐derived formulas. , , Prior literature has shown how 3 linear measurements (A, B, and C), even in crescent‐shaped SDH, works as an approximation attributable to subtraction of 2 ellipsoid shapes to make one simplified shape. There are certainly more complicated ways to measure these 5 major SAH neuroanatomic spaces and yet they likely still require splitting up each cisternal space into multiple (smaller) compartmentalized volumes and summation of these values. Last, volumetric estimation of SAH beyond the cisternal level was not performed using our ABC/2‐derived SAH‐M1. Mathematically, this observation stems from the fact that as SAH blood approaches the cerebral convexities, the actual volume becomes extremely small (ie, Lim x → 0 ) and contributes little to the main star‐shaped cisternal SAH blood volume pattern seen on NCCT scans which represents the majority of quantifiable SAH blood. Also, the measurement of tiny distal neuroanatomic spaces would add tremendous methodical complexity and considerable time costs, thereby decreasing potential clinical applicability and scalability. It was not possible during this initial study to determine IRRs for ABC/2‐derived SAHV nor evaluate the methodological reproducibility of all of the different mathematical systems of measurement. However, drawing on the findings of prior investigations, it is probable that future studies could develop a more quantitative methodology and compare IRRs among models, as well as older semiquantitative models such as FS and mFS. , , Last, we were not able to analyze IPH, SDH, and IVH‐associated SAH blood patterns separately but we plan to incorporate these hemorrhage types into a future larger study. Of note, the number of large visible IVH and SDH patterns were much less frequent in our data set and would not provide a statistically significant power or sample size for our primary SAH study. Therefore, this would limit our ability to abstract any clinically relevant or predictive capabilities on outcomes because of the small sample size of these cases. Also, we note that a recent study showed that IPH did not significantly impact a model's predictive value when analyzed independently. 

A simplified ABC/2‐derived SAHV model using admission head NCCT estimates blood volume similar to segmentation methods and is predictive of discharge mRS outcomes and future risk of DCI. An SAHV cutoff value of ≥10 mL appears to predict higher risk of poor clinical course and DCI. SAHV should be validated similar to the ICH score by Hemphill et al but for patients with SAH. Similarly, we believe an ABC/2‐derived SAHV model measurement could spark future translational SAH research and more robust SAH research in neuroprotective drugs and interventions and help more precisely quantify whether SAHV has a dose–response relationship on future clinical outcomes and DCI. 

None. 

None. 

Table S1. 

