Clinical outcome prediction in aneurysmal subarachnoid hemorrhage – Alterations in brain–body interface

BACKGROUND Brain-body associations are essential in influencing outcome in patients with ruptured brain aneurysms. Thus far, there is scarce literature on such important relationships. METHODS The multicenter Tirilazad database (3551 patients) was used to create this clinical outcome prediction model in order to elucidate significant brain-body associations. Traditional binary logistic regression models were used. RESULTS Binary logistic regression main effects model included four statistically significant single prognostic variables, namely, neurological grade, age, stroke, and time to surgery. Logistic regression models demonstrated the significance of hypertension and liver disease in development of brain swelling, as well as the negative consequences of seizures in patients with a history of myocardial infarction and post-admission fever worsening neurological outcome. CONCLUSIONS Using the aforementioned results generated from binary logistic regression models, we can identify potential patients who are in the high risk group of neurological deterioration. Specific therapies can be tailored to prevent these detriments, including treatment of hypertension, seizures, early detection and treatment of myocardial infarction, and prevention of hepatic encephalopathy.