Improved ischemic stroke outcome prediction using model estimation of outcome probability: the THRIVE-c calculation.

ABSTRACT

BACKGROUND AND PURPOSE: The Totaled Health Risks in Vascular Events (THRIVE) score is a previously validated ischemic stroke outcome prediction tool. Although simplified scoring systems like the THRIVE score facilitate ease-of-use, when computers or devices are available at the point of care, a more accurate and patient-specific estimation of outcome probability should be possible by computing the logistic equation with patient-specific continuous variables. METHODS: We used data from 12 207 subjects from the Virtual International Stroke Trials Archive and the Safe Implementation of Thrombolysis in Stroke - Monitoring Study to develop and validate the performance of a model-derived estimation of outcome probability, the THRIVE-c calculation. Models were built with logistic regression using the underlying predictors from the THRIVE score age, National Institutes of Health Stroke Scale score, and the Chronic Disease Scale (presence of hypertension, diabetes mellitus, or atrial fibrillation). Receiver operator characteristics analysis was used to assess model performance and compare the THRIVE-c model to the traditional THRIVE score, using a two-tailed Chi-squared test. RESULTS: The THRIVE-c model performed similarly in the randomly chosen development cohort (n = 6194, area under the curve = 0·786, 95% confidence interval 0·774-0·798) and validation cohort (n = 6013, area under the curve = 0·784, 95% confidence interval 0·772-0·796) (P = 0·79). Similar performance was also seen in two separate external validation cohorts. The THRIVE-c model (area under the curve = 0·785, 95% confidence interval 0·777-0·793) had superior performance when compared with the traditional THRIVE score (area under the curve = 0·746, 95% confidence interval 0·737-0·755) (P < 0·001). CONCLUSION: By computing the logistic equation with patient-specific continuous variables in the THRIVE-c calculation, outcomes at the individual patient level are more accurately estimated. Given the widespread availability of computers and devices at the point of care, such calculations can be easily performed with a simple user interface.