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A Thrombotic Stroke Model Based On Transient Cerebral Hypoxia-ischemia
Published on: August 18, 2015
Prediction Model to Optimize Long-Term Antithrombotic Therapy Using Covert Vascular Brain Injury and Clinical
Kaori Miwa1, Kenta Tanaka2, Masatoshi Koga1
1Department of Cerebrovascular Medicine (K.M., M.K., Kanta Tanaka, M. Shiozawa, Sohei Yoshimura, K. Toyoda), National Cerebral and Cardiovascular Center, Suita, Japan.
Insights
New BAT2 scores predict major bleeding, intracranial hemorrhage (ICH), and ischemic stroke (IS) in patients on antithrombotic therapy. These scores incorporate covert vascular brain injury for personalized risk assessment, improving treatment decisions.
Area of Science:
- Neurology
- Cardiology
- Vascular Medicine
Background:
- Accurate risk assessment for major bleeding, intracranial hemorrhage (ICH), and ischemic stroke (IS) is vital for patients on antithrombotic therapy.
- Existing risk prediction tools are insufficient for evaluating the net clinical benefit of antithrombotic treatments.
- Covert vascular brain injury assessment is needed to personalize risk stratification.
Purpose of the Study:
- To develop novel risk scores, the BAT2 scores, for predicting major bleeding, ICH, and IS.
- To incorporate multimodal brain imaging findings, including cerebral small vessel disease, into risk prediction.
- To personalize risk assessment for patients receiving antithrombotic therapy.
Main Methods:
- Prospective, multicenter observational study (BAT2) of patients on oral antiplatelets or anticoagulants.
- Multimodal brain MRI at baseline to assess cerebral small vessel disease, infarcts, and intracranial artery disease.
- Development of risk scores using Cox proportional hazards models and validated with Harrell C-index and calibration slope.
Main Results:
- The study analyzed 5250 patients with a median follow-up of 2.0 years.
- Identified predictors for major bleeding, ICH, and IS, including age, renal impairment, and various brain imaging markers.
- Achieved optimism-adjusted C-indices ranging from 0.64 to 0.75 for the prediction models.
Conclusions:
- The developed BAT2 scores can aid in optimizing the risk-benefit balance of antithrombotic therapy.
- Personalized risk assessment incorporating vascular brain injury may improve patient outcomes.
- These scores offer a valuable tool for clinicians managing patients on antithrombotic agents.
Background:
Defining the risk of developing major bleeding, especially intracranial hemorrhage (ICH), or ischemic stroke (IS) in patients receiving antithrombotic therapy is crucial. Existing risk prediction tools would inadequately assess the net clinical benefit of antithrombotic therapy. We aimed to develop novel risk scores incorporating covert vascular brain injury to personalize the risk assessment of major bleeding, ICH, and IS in patients receiving antithrombotic therapy.
Methods:
The prospective, multicenter, observational study (BAT2 [Bleeding With Antithrombotic Therapy Study-2]) enrolled patients receiving oral antiplatelets or anticoagulants from 52 hospitals across Japan between 2016 and 2019. Multimodal brain magnetic resonance imaging was performed at baseline under prespecified conditions to determine cerebral small vessel disease (white matter hyperintensity, cerebral microbleed, lacune, enlarged perivascular space, and cortical superficial siderosis), nonlacunar infarct, and intracranial artery disease with central reading. Risk scores, collectively termed the BAT2 scores, were developed separately to evaluate the comparative risks of (1) major bleeding, (2) ICH, and (3) IS based on covariates from Cox proportional hazards models and clinical relevance. Model performance was assessed with the Harrell C-index and calibration slope adjusted for optimism via bootstrapping.
Results:
Of 5378 patients enrolled, 5250 were analyzed (mean age, 71±11 years, 33% women); 93 experienced major bleeding, including 55 had ICH, and 197 had IS during a median follow-up of 2.0 years. Predictors for bleeding included age, underweight, renal impairment, hypertension, cerebral microbleed, lacune, and antithrombotic treatment type. Predictors for ICH further included deep white matter hyperintensity but not renal impairment. For IS, predictors included age, renal impairment, diabetes, atrial fibrillation, lacune, cerebral microbleed, nonlacunar infarct, and intracranial artery disease. Prediction performance showed optimism-adjusted C-index and calibration slope of 0.69 (95% CI, 0.64-0.74) and 0.82 (95% CI, 0.62-1.06) for bleeding, 0.75 (95% CI, 0.67-0.80) and 0.80 (95% CI, 0.56-1.02) for ICH, and 0.64 (95% CI, 0.60-0.68) and 0.92 (95% CI, 0.73-1.18) for IS.
Conclusions:
The BAT2 scores may help optimize the balance between risks and benefits of antithrombotic therapy.
Registration:
URL: https://www.clinicaltrials.gov; Unique identifier: NCT02889653. URL: https://www.umin.ac.jp/ctr; Unique identifier: UMIN000023669.
