Predicting Delayed Cerebral Ischemia with Quantified Aneurysmal Subarachnoid Blood Volume

Area of Science:

• Neurosurgery
• Radiology
• Neurology

Background:

• Delayed cerebral ischemia (DCI) prediction in aneurysmal subarachnoid hemorrhage (aSAH) often relies on computed tomography (CT) scans.
• Current prediction models using coarse grading scales have limited predictive accuracy.

Purpose of the Study:

• To develop a predictive model for DCI incorporating automatically quantified total blood volume (TBV).
• To compare the predictive performance of TBV with existing clinical grading scales.

Main Methods:

• Prospective aSAH registry data was analyzed.
• Total blood volume (TBV) was quantified using an automated hemorrhage algorithm.
• Logistic regression models were built and internally validated.

Main Results:

• 369 patients were included; only TBV independently predicted DCI.
• The TBV model achieved an R² of 6% and a c-statistic of 0.64.
• TBV demonstrated superior discrimination compared to Fisher (0.56) and modified Fisher (0.58) scales.

Conclusions:

• Automatically quantified TBV is an independent predictor of DCI in aSAH.
• TBV offers improved, yet moderate, predictive value over traditional scales.
• Further research is needed to identify additional factors for enhanced DCI prediction accuracy.