A parsimonious score with a free web tool for predicting disability after an ischemic stroke: the Parsifal Score

BACKGROUND

Most of the models to predict prognosis after an ischemic stroke include complex mathematical equations or too many variables, making them difficult to use in the daily clinic. We want to predict disability 3 months after an ischemic stroke in an independent patient not receiving recanalization treatment within the first 24 h, using a minimum set of variables and an easy tool to facilitate its implementation. As a secondary aim, we calculated the capacity of the score to predict an excellent/devastating outcome and mortality.

METHODS

Eight hundred and forty-four patients were evaluated. A multivariable ordinal logistic regression was used to obtain the score. The Modified Rankin Scale (mRS) was used to estimate disability at the third month. The results were replicated in another independent cohort (378 patients). The "polr" function of R was used to perform the regression, stratifying the sample into seven groups with different cutoffs (from mRS 0 to 6).

RESULTS

The Parsifal score was generated with: age, previous mRS, initial NIHSS, glycemia on admission, and dyslipidemia. This score predicts disability with an accuracy of 80-76% (discovery-replication cohorts). It has an AUC of 0.86 in the discovery and replication cohort. The specificity was 90-80% (discovery-replication cohorts); while, the sensitivity was 64-74% (discovery-replication cohorts). The prediction of an excellent or devastating outcome, as well as mortality, obtained good discrimination with AUC > 0.80.

CONCLUSIONS

The Parsifal Score is a model that predicts disability at the third month, with only five variables, with good discrimination and calibration, and being replicated in an independent cohort.