Predicting outcome after acute basilar artery occlusion based on admission characteristics

Neurology. 2012 Apr 3;78(14):1058-63. doi: 10.1212/WNL.0b013e31824e8f40. Epub 2012 Mar 21.

Abstract

Objective: To develop a simple prognostic model to predict outcome at 1 month after acute basilar artery occlusion (BAO) with readily available predictors.

Methods: The Basilar Artery International Cooperation Study (BASICS) is a prospective, observational, international registry of consecutive patients who presented with an acute symptomatic and radiologically confirmed BAO. We considered predictors available at hospital admission in multivariable logistic regression models to predict poor outcome (modified Rankin Scale [mRS] score 4-5 or death) at 1 month. We used receiver operator characteristic curves to assess the discriminatory performance of the models.

Results: Of the 619 patients, 429 (69%) had a poor outcome at 1 month: 74 (12%) had a mRS score of 4, 115 (19%) had a mRS score of 5, and 240 (39%) had died. The main predictors of poor outcome were older age, absence of hyperlipidemia, presence of prodromal minor stroke, higher NIH Stroke Scale (NIHSS) score, and longer time to treatment. A prognostic model that combined demographic data and stroke risk factors had an area under the receiver operating characteristic curve (AUC) of 0.64. This performance improved by including findings from the neurologic examination (AUC 0.79) and CT imaging (AUC 0.80). A risk chart showed predictions of poor outcome at 1 month varying from 25 to 96%.

Conclusion: Poor outcome after BAO can be reliably predicted by a simple model that includes older age, absence of hyperlipidemia, presence of prodromal minor stroke, higher NIHSS score, and longer time to treatment.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Arterial Occlusive Diseases / diagnosis*
  • Arterial Occlusive Diseases / mortality*
  • Basilar Artery / pathology*
  • Female
  • Humans
  • Logistic Models*
  • Male
  • Middle Aged
  • Patient Admission / trends*
  • Predictive Value of Tests
  • Prospective Studies
  • Registries
  • Risk Factors
  • Treatment Outcome
  • Young Adult