Risk Score for Intracranial Hemorrhage in Patients With Acute Ischemic Stroke Treated With Intravenous Tissue-Type Plasminogen Activator

Calgary Stroke Program, Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Ontario, Canada.
Stroke (Impact Factor: 5.72). 07/2012; 43(9):2293-9. DOI: 10.1161/STROKEAHA.112.660415
Source: PubMed


There are few validated models for prediction of risk of symptomatic intracranial hemorrhage (sICH) after intravenous tissue-type plasminogen activator treatment for ischemic stroke. We used data from Get With The Guidelines-Stroke (GWTG-Stroke) to derive and validate a prediction tool for determining sICH risk.
The population consisted of 10 242 patients from 988 hospitals who received intravenous tissue-type plasminogen activator within 3 hours of symptom onset from January 2009 to June 2010. This sample was randomly divided into derivation (70%) and validation (30%) cohorts. Multivariable logistic regression identified predictors of intravenous tissue-type plasminogen activator-related sICH in the derivation sample; model β coefficients were used to assign point scores for prediction.
sICH within 36 hours was noted in 496 patients (4.8%). Multivariable adjusted independent predictors of sICH were increasing age (17 points), higher baseline National Institutes of Health Stroke Scale (42 points), higher systolic blood pressure (21 points), higher blood glucose (8 points), Asian race (9 points), and male sex (4 points). The C-statistic was 0.71 in the derivation sample and 0.70 in the independent internal validation sample. Plots of observed versus predicted sICH showed good model calibration in the derivation and validation cohorts. The model was externally validated in National Institute of Neurological Disorders and Stroke trial patients with a C-statistic of 0.68.
The GWTG-Stroke sICH risk "GRASPS" score provides clinicians with a validated method to determine the risk of sICH in patients treated with intravenous tissue-type plasminogen activator within 3 hours of stroke symptom onset.

Download full-text


Available from: Bijoy K Menon,
  • Source
    • "can be used to estimate the risk of postthrombolysis hemorrhage [8–13,15] and poor functional outcome [6] [7] [12] [14] (Supplemental Table S1). Apart from the s-TPI, these predictive instruments, however, have been developed and validated in patients treated with intravenous recombinant tissue plasminogen activator (IV rtPA) according to restrictive criteria. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Objective: We evaluated the reliability of eight clinical prediction models for symptomatic intracerebral hemorrhage (sICH) and long-term functional outcome in stroke patients treated with thrombolytics according to clinical practice. Methods: In a cohort of 169 patients, 60 patients (35.5%) received IV rtPA according to the European license criteria. The remaining patients received off-label IV rtPA and/or were treated with intra-arterial thrombolysis. We used receiver operator characteristic curves to analyze the discriminative capacity of the MSS score, the HAT score, the SITS SICH score, the SEDAN score and the GRASPS score for sICH according to the NINDS and the ECASSII criteria. Similarly, the discriminative capacity of the s-TPI, the iScore and the DRAGON score were assessed for the modified Rankin Scale (mRS) score at 3 months poststroke. An area under the curve (c-statistic) >0.8 was considered to reflect good discriminative capacity. The reliability of the best performing prediction model was further examined with calibration curves. Separate analyses were performed for patients meeting the European license criteria for IV rtPA and patients outside these criteria. Results: For prediction of sICH c-statistics were 0.66-0.86 and the MMS yielded the best results. For functional outcome c-statistics ranged from 0.72 to 0.86 with s-TPI as best performer. The s-TPI had the lowest absolute error on the calibration curve for predicting excellent outcome (mRS 0-1) and catastrophic outcome (mRS 5-6). Conclusions: All eight clinical models for outcome prediction after thrombolysis for acute ischemic stroke showed fair predictive value in patients treated according daily practice. The s-TPI had the best discriminatory ability and was well calibrated in our study population.
    Clinical Neurology and Neurosurgery 08/2014; 125C:189-193. DOI:10.1016/j.clineuro.2014.08.011 · 1.13 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Recombinant tissue plasminogen activator (rtPA) is an effective treatment for acute ischemic stroke but is associated with an increased risk of intracranial hemorrhage (ICH). We sought to identify the risk factors for ICH with a systematic review of the published literature. We searched for studies of rtPA-treated stroke patients that reported an association between a variable measured before rtPA infusion and clinically important ICH (parenchymal ICH or ICH associated with clinical deterioration). We calculated associations between baseline variables and ICH with random-effect meta-analyses. We identified 55 studies that measured 43 baseline variables in 65 264 acute ischemic stroke patients. Post-rtPA ICH was associated with higher age (odds ratio, 1.03 per year; 95% confidence interval, 1.01-1.04), higher stroke severity (odds ratio, 1.08 per National Institutes of Health Stroke Scale point; 95% confidence interval, 1.06-1.11), and higher glucose (odds ratio, 1.10 per mmol/L; 95% confidence interval, 1.05-1.14). There was approximately a doubling of the odds of ICH with the presence of atrial fibrillation, congestive heart failure, renal impairment, previous antiplatelet agents, leukoaraiosis, and a visible acute cerebral ischemic lesion on pretreatment brain imaging. Little of the variation in the sizes of the associations among different studies was explained by the source of the cohort, definition of ICH, or degree of adjustment for confounding variables. Individual baseline variables were modestly associated with post-rtPA ICH. Prediction of post-rtPA ICH therefore is likely to be difficult if based on single clinical or imaging factors alone. These observational data do not provide a reliable method for the individualization of treatment according to predicted ICH risk.
    Stroke 09/2012; 43(11):2904-9. DOI:10.1161/STROKEAHA.112.665331 · 5.72 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Background: The Oxfordshire Community Stroke Project (OCSP) classification is a simple tool to categorize clinical stroke syndromes. We compared the outcomes of stroke patients after intravenous thrombolysis stratified by the baseline National Institutes of Health Stroke Scale (NIHSS) score or by the OCSP classification. Methods: We assessed the safety of thrombolysis in consecutive stroke patients who received intravenous thrombolysis within 3h after onset. The patients were grouped by the NIHSS score into mild to moderate stroke (≤ 20) and severe stroke (>20), and also by the OCSP classification as having total anterior circulation infarcts (TACI), partial anterior circulation infarcts (PACI), posterior circulation infarcts (POCI), or lacunar infarcts (LACI). Symptomatic intracerebral hemorrhage (SICH) was used as the primary outcome. Results: Of the 145 patients included in the study, 45 had a baseline NIHSS score>20. Their stroke syndromes were as follows: 78 with TACI, 29 with PACI, 16 with POCI, and 22 with LACI. The proportion of SICH was comparable between patients with high or low NIHSS score (11.1% vs. 9.0%, P=0.690). The chance of SICH was highest in patients with TACI (15.4%), followed by LACI (4.5%), PACI (3.4%), and POCI (0%). After adjustment for age, baseline glucose, and use of antiplatelet agents before admission, SICH was significantly increased in patients with TACI relative to those with non-TACI (odds ratio 5.92; 95% confidence interval 1.24-28.33, P=0.026). Conclusions: The OCSP clinical classification may help clinicians evaluate the risk of SICH following intravenous thrombolysis.
    Journal of the neurological sciences 10/2012; 324(1-2). DOI:10.1016/j.jns.2012.10.003 · 2.47 Impact Factor
Show more