Prognostication in Acute Neurological Emergencies

Machine learning predictors of risk of death within 7 days in patients with non-traumatic subarachnoid hemorrhage in the intensive care unit: A multicenter retrospective study

“…Firstly, retrospective studies are prone to selection bias. Secondly, although we collected variables related to patients with spider blood as comprehensively as possible, some variables were not available in time, such as Hunt-Hess classification [ 52 ], which determines the severity of SAH patients, mFisher score [ 53 ], which predicts vasospasm, and mRs score at admission [ 54 ], which may limit the generalizability of this study, and we regret this absence and may consider adding these important variables in subsequent studies. Lastly, important ancillary tests, such as computerized tomography (CT) information, were not entirely collected, which may have resulted in missing important variables of interest.…”