Subarachnoid hemorrhage admissions retrospectively identified using a prediction model.

ABSTRACT:

Objective

To create an accurate prediction model using variables collected in widely available health administrative data records to identify hospitalizations for primary subarachnoid hemorrhage (SAH).

Methods

A previously established complete cohort of consecutive primary SAH patients was combined with a random sample of control hospitalizations. Chi-square recursive partitioning was used to derive and internally validate a model to predict the probability that a patient had primary SAH (due to aneurysm or arteriovenous malformation) using health administrative data.

Results

A total of 10,322 hospitalizations with 631 having primary SAH (6.1%) were included in the study (5,122 derivation, 5,200 validation). In the validation patients, our recursive partitioning algor

SUBMITTER: English SW 

PROVIDER: S-EPMC5067543 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature