Discussion
IST-3, ICH, sICH, stroke
BLEEDING, STROKE
We conducted a systematic search to provide an overview of published models to predict sICH or ICH after reperfusion therapy and externally validated their ability to predict sICH in patients treated with EVT in daily clinical practice. Investigated models to predict sICH or ICH discriminated poorly between patients with a low and high risk of sICH after EVT.The IST-3, SITS-SICH, and STARTING-SICH showed overall the best predictive performance in terms of discrimination and calibration in stroke patients treated with EVT in daily clinical practice. The models had reasonable calibration characteristics, but the discriminative performance was poor. Even if a model would have perfect calibration characteristics (ie, predicted risk of the outcome for patients is equal to the observed risk), it is useless if it does not discriminate between patients with a low and high risk of the outcome.The poor discriminative performance of all models can be explained by several reasons. We included models that were developed to predict sICH or ICH according to different definitions, and this may affect strength and nature of the predictors. Another explanation is that most models were developed in patients treated with IVT alone. Although the incidence of sICH in patients treated with IVT alone is similar to patients treated with EVT,The predictive performance of models developed in patients treated with EVT in terms of both discrimination (n=7) and calibration (n=5) was poor. This could be explained because the population in which the models were developed was different from our external validation cohort. For example, TAG was developed in patients treated with EVT within 24 hours and ASIAN was developed in Chinese patients, who are at increased risk of sICH.External validation studies provide the best insight into the performance of a model, indicating how useful it might be in other participants, centers, regions, or settings.Our study has several limitations. First, we evaluated the external validity of models to predict sICH in patients treated with EVT within 6.5 hours of symptom onset. Therefore, we cannot draw any conclusions about the usefulness of these models to predict sICH in patients treated after 6.5 hours of symptom onset or treated with IVT alone. Secondly, some variables were not available in our dataset. Therefore, we were not able to evaluate the external validity of all models: for example, models that included magnetic resonance imaging parameters. For some models, we imputed a missing variable with a value of 0, which might underestimate the performance of a model. For example, GRASPS included Asian ethnicity as predictor, which was not available in our dataset. We assigned all patients a score of 0 (ie, non-Asian ethnicity), because the number of people with Asian ethnicity in the Netherlands is relatively low and we believe this variable could not be imputed based on other variables. However, this might have influenced the predictive performance of GRASPS. Lastly, the predicted outcome in this study was sICH according to the Heidelberg Bleeding Classification. Therefore, the performance of models developed for other outcome measures or other sICH definitions, might be underestimated. However, being able to predict sICH accurately is more relevant than all ICH, because it is more strongly associated with poor functional outcome.To conclude, the investigated models to predict sICH or ICH discriminate poorly between patients with a low and high risk of sICH after EVT in daily clinical practice. Therefore, these models are not clinically useful for this patient population.