Subarachnoid haemorrhage (SAH) is a type of stroke that mainly affects younger people with a mean age of 58 years. [1] Although the outcome has improved within the last decades, SAH still has an in-hospital mortality of 26%. [2] Roughly 25% of the variation in outcome for patients with SAH is explained by known risk factors including older age, worse neurological state on admission, severity of SAH on the initial computed tomography (CT)-scan, a history of hypertension and the size and position of the aneurysm in the posterior circulation. [3,4]

Outcome prediction in aneurysmal subarachnoid haemorrhage (SAH) has been attempted utilizing a variety of scoring systems. One-month mortality rate for aneurysmal SAH is around 40%, with the 6-month rates approaching 50–60%. The intention through past attempts was to reliably predict, by way of clinical status and neuroimaging, the risk of developing the serious complications and adverse outcomes associated with SAH. Complications such as vasospasm, shunt-dependent hydrocephalus, subendocardial ischemia, and neurogenic pulmonary edema are of primary interest. In addition, length of intensive care unit (ICU) stay, ventilator dependency, tracheostomy and gastrostomy status, discharge rates, and 1- and 6-month outcome prediction are desired. [5]

Clinical prediction models statistically combine a set of characteristics of the patient and disease to estimate the probability of an outcome. They can be useful decision support tools, assisting clinicians in the complex choices they make about patient management and, in turn, facilitating evidence informed discussions with the patient and family members around outcome expectations for shared decision making. According to some studies, outcome prediction tools can outperform clinical intuition in patients with stroke. Several prediction models and risk scores have been developed for patients with SAH, but their uptake into clinical practice is limited. The reasons might be related to the use of suboptimal study design and analysis in the development of these tools, the lack of representativeness, the ease of use in clinical setting, or the absence of evidence of their generalisability in different settings, as shown in a systematic review. Therefore, validated easy to use prediction tools are urgently needed to support the management of SAH. [7]

So, in this study we aim to identify the predictors of outcome according to Modified Rankin Scale (MRS) in patients of aneurysmal SAH. We will evaluate the preoperative, intraoperative and postoperative parameters in patients of aneurysmal SAH to develop and validate a model to assess the level of disability and mortality in these patients.