Early Warning of Delayed Cerebral Ischemia

The goal of this observational study is to learn about the possibility to predict clinical course of subarachnoid hemorrhage (SAH) patients by performing the retrospective analysis of clinical data available in early pre-vasospasm phase. The main questions it aims to answer are: * What biomarkers retrieved from Computed Tomography (CT) and Computed Tomography Angiography (SAH location, leaked blood volume, cerebrospinal fluid volume, etc.) can be used to predict development of cerebral vasospasms, delayed cerebral ischemia and patients' outcome. * What biomarkers retrieved from transcranial Doppler examinations in early pre vasospasm can be used to predict development of cerebral vasospasms, delayed cerebral ischemia and patients' outcome. * What biomarkers retrieved from multimodal physiological monitoring in early pre vasospasm can be used to predict development of cerebral vasospasms, delayed cerebral ischemia and patients' outcome. * What is impact of other clinical data (blood test results, age, gender, etc.) on development of cerebral vasospasms and delayed cerebral ischemia.

An adequate timely prognosis of cerebral vasospasms and delayed cerebral ischemia (DCI) occuring after spontaneous aneurysmal subarachnoid haemorrhage (aSAH), is vitally essential to selecting an individualized and timely treatment strategy. Cerebral vasospasms (CV) occur in up to 70% of aSAH cases, and in two thirds of those cases DCI develops, thus increasing the mortality rate up to 50%. Every year up to 770 000 people suffer from aSAH and receive neurosurgical treatment. Because of a large amount of factors (blood leak volume and its distribution in the subarachnoid space, reactivity of cerebral blood vessels, biochemical blood composition and demographical factors) and their complex interactions to contribute the development of CV/DCI, currently there are no reliable methods and technologies allowing reliably prediction of the consequences of SAH. For a more effective treatment of aSAH patients an innovative method for early warning of CV and DCI phenomena is offered. The method is based on identifying of the associations of different physiological modalities and prognostic factors with the SAH patients' outcome (factors the obtained from CT images analysis, numerical modelling of SAH evolution and multimodal cerebral hemodynamics monitoring). The goal of this observational study is to learn about the possibility to predict clinical course of subarachnoid hemorrhage (SAH) patients by performing the retrospective analysis of clinical data available in early pre-vasospasm phase. The main questions it aims to answer are: * What biomarkers retrieved from Computed Tomography (CT) and Computed Tomography Angiography (SAH location, leaked blood volume, cerebrospinal fluid volume, etc.) can be used to predict development of cerebral vasospasms, delayed cerebral ischemia and patients' outcome. * What biomarkers retrieved from transcranial Doppler examinations in early pre-vasospasm can be used to predict development of cerebral vasospasms, delayed cerebral ischemia and patients' outcome. * What biomarkers retrieved from multimodal physiological monitoring in early pre vasospasm can be used to predict development of cerebral vasospasms, delayed cerebral ischemia and patients' outcome. * What is impact of other clinical data (blood test results, age, gender, etc.) into development of cerebral vasospasms, delayed cerebral ischemia. Study objectives: 1. To perform retrospective analysis neuroradiological examination data (CT/CTA images) of patients who experienced spontaneous aSAH, by identifying factors that allow early prediction of CV and DCI 2. To perform retrospective analysis of the collected data of multimodal brain physiological monitoring (intracranial pressure, arterial blood pressure, cerebral blood flow velocity, heart rate) by determining the factors that allow to predict CV and DCI. 3. To perform retrospective analysis of other clinical data (blood test results), demographic data (gender, age) and their impact on development of CV and DCI. 4. To develop the algorithm of predicting of CV, DCI and patients' outcome based on the accumulated neuroradiological examination and multimodal monitoring data.