Validation and comparison of multiple risk scores for prediction of symptomatic intracerebral hemorrhage after intravenous thrombolysis in VISTA

Background and aims: Prediction models/scores may help to identify patients at high risk of symptomatic intracerebral hemorrhage (sICH) after intravenous thrombolysis. We aimed to validate and compare the performance of different prediction models for sICH after thrombolysis using direct model estimation in the Virtual International Stroke Trials Archive (VISTA). Methods: We searched PubMed for potentially eligible prediction models from inception to 1 June 2019. Simple and practical models/scores were validated in VISTA. The primary outcome was sICH based on two criteria (National Institute of Neurological Diseases and Stroke, NINDS; Safe Implementation of Thrombolysis in Stroke-Monitoring Study, SITS-MOST) and the secondary outcome was parenchymal hematoma (PH). The discrimination performance of each model was evaluated using area under the curve (AUC) and calibration was evaluated by Hosmer–Lemeshow goodness-of-fit tests. Results: We found 13 prediction models and five models (HAT, MSS, SPAN-100, GRASPS and THRIVE) were finally validated in VISTA. A total of 1884 participants were eligible for our study, of whom the proportion with sICH was 4.6% (87/1884) per NINDS and 3.9% (73/1884) per SITS-MOST, and with PH was 11.3% (213/1884). MSS and GRASPS had the greatest predictive ability for sICH (NINDS criteria: MSS AUC 0.7, 95% CI 0.63–0.77, p < 0.001; GRASPS AUC 0.69, 95% CI 0.63–0.76, p < 0.001; SITS-MOST criteria: MSS, AUC 0.76, 95% CI 0.68–0.85, p < 0.001; GRASPS, AUC 0.79, 95% CI 0.71–0.87, p < 0.001). Similar results were found for PH (MSS AUC 0.68, 95% CI 0.64–0.73, p = 0.017; GRASPS AUC 0.68, 95% CI 0.63–0.72, p = 0.017). The calibration of each model was almost good. Conclusion: MSS and GRASPS had good discrimination and calibration for sICH and PH after thrombolysis as assessed in VISTA. These two models could be used in clinical practice and clinical trials to identity individuals with high risk of sICH.