Validation of an acute ischemic stroke model: does diffusion-weighted imaging lesion volume offer a clinically significant improvement in prediction of outcome?

BACKGROUND AND
PURPOSE: Prediction models for ischemic stroke outcome have the potential to contribute prognostic information in the clinical and/or research setting. The importance of diffusion-weighted magnetic resonance imaging (DWI) in the prediction of clinical outcome, however, is unclear. The purpose of this study was to combine acute clinical data and DWI lesion volume for ischemic stroke patients to determine whether DWI improves the prediction of clinical outcome.
METHODS: Patients (N=382) with baseline DWI data from the Glycine Antagonist In Neuroprotection and citicoline (010 and 018) trials were used to develop the prediction models by multivariable logistic regression. Data from prospectively collected patients (N=266) from the Acute Stroke Accurate Prediction Study were used to externally validate the model equations. The models predicted either full recovery or nursing home-level disability/death, as defined by the National Institutes of Health Stroke Scale, Barthel Index, or modified Rankin Scale.
RESULTS: The full-recovery models with DWI lesion volume had areas under the receiver operating characteristic curves (AUCs) of 0.799 to 0.821, and those without DWI lesion volume had AUCs of 0.758 to 0.798. The nursing home-level disability/death models with DWI had AUCs of 0.832 to 0.882, and those without DWI had AUCs of 0.827 to 0.867. All models had mean absolute errors < or =0.4 for calibration.
CONCLUSIONS: All 12 models had excellent discrimination and calibration, with 8 of 12 meeting prespecified performance criteria (AUC > or =0.8, mean absolute error < or =0.4). Although DWI lesion volume significantly increased model explanatory power, the magnitude of increase was not large enough to be clinically important.