Electronic Health Data Predict Outcomes After Aneurysmal Subarachnoid Hemorrhage.

ABSTRACT:

Backgroud

Using electronic health data, we sought to identify clinical and physiological parameters that in combination predict neurologic outcomes after aneurysmal subarachnoid hemorrhage (aSAH).

Methods

We conducted a single-center retrospective cohort study of patients admitted with aSAH between 2011 and 2016. A set of 473 predictor variables was evaluated. Our outcome measure was discharge Glasgow Outcome Scale (GOS). For laboratory and physiological data, we computed the minimum, maximum, median, and variance for the first three admission days. We created a penalized logistic regression model to determine predictors of outcome and a multivariate multilevel prediction model to predict poor (GOS 1-2), intermediate (GOS 3), or good (GOS 4-5) outcomes.

Results

One hu

SUBMITTER: Zafar SF 

PROVIDER: S-EPMC5886829 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature