Machine learning prediction of symptomatic intracerebral hemorrhage after stroke thrombolysis: a cross-cultural validation in Caucasian and Han Chinese cohort

Abstract

Background: Previous studies found that Asians seemed to have higher risk of HT after thrombolysis than Caucasians due to its race differences in genetic polymorphism. Whether the model developed by Caucasians could predict risk of symptomatic intracerebral hemorrhage (sICH) in Asians was unknown. Objectives: To develop a machine learning–based model for predicting sICH after stroke thrombolysis in Caucasians and externally validate it in an independent Han Chinese cohort. Design: The derivation Caucasian sample included 1738 ischemic stroke (IS) patients from the Virtual International Stroke Trials Archive (VISTA) data set, and the external validation Han Chinese cohort included 296 IS patients who were treated with intravenous thrombolysis. Methods: Twenty-eight variables were collected across both samples. According to their properties, we classified them into six distinct clusters (ie, demographic variables, medical history, previous medication, baseline blood biomarkers, neuroimaging markers on initial CT scan and clinical characteristics). A support vector machine (SVM) model, which consisted of data processing, model training, testing and a 10-fold cross-validation, was developed to predict the risk of sICH after stroke thrombolysis. The receiving operating characteristic (ROC) was used to assess the prediction performance of the SVM model. A domain contribution analysis was then performed to test which cluster had the highest influence on the performance of the model. Results: In total, 85 (4.9%) patients developed sICH in the Caucasians, and 29 (9.8%) patients developed sICH in the Han Chinese cohort. Eight features including age, NIHSS score, SBP (systolic blood pressure), DBP (diastolic blood pressure), ALP (alkaline phosphatase), ALT (alanine transaminase), glucose, and creatine level were included in the final model, all of which were from demographic, clinical characteristics, and blood biomarkers clusters, respectively. The SVM model showed a good predictive performance in both Caucasians (AUC = 0.87) and Han Chinese patients (AUC = 0.74). Domain contribution analysis showed that inclusion/exclusion of clinical characteristic cluster (NIHSS score, SBP, and DBP), had the highest influence on the performance of predicting sICH in both Caucasian and Han Chinese cohorts. Conclusion: The established SVM model is feasible for predicting the risk of sICH after thrombolysis quickly and efficiently in both Caucasian and Han Chinese cohort.

Artificial Intelligence and Machine Learning Applications in Critically Ill Brain Injured Patients

“…97 However, various supervised ML algorithms, using demographic, clinical, and CT imaging data, have shown a significant boost in predictive performance for sICH postthrombolysis, with AUCs ranging from 0.77 to 0.87, demonstrating adaptability across diverse ethnic groups. [98][99][100] Final infarct volume post-AIS is a critical radiographic measure linked to functional outcomes and complications like malignant cerebral edema and herniation. 101 Although imaging in the hyperacute/acute setting may not accurately reflect final infract volume, ML applications on acute MRI sequences have predicted it with high accuracy (AUC: 0.88).…”

Machine Learning Application: A Bibliometric Analysis From a Half-Century of Research on Stroke

“…Moreover, Safe Implementation of Treatments in Stroke (SITS) and the Virtual International Stroke Trials Archive (VISTA) stand as pivotal organizations that offer platforms and databases for the international analysis and advancement of stroke therapies [ 16 , 17 ]. Datasets from SITS and VISTA have been harnessed in a variety of studies to predict stroke outcomes through machine learning techniques [ 5 , 18 , 19 ]. Ultimately, all endeavors in advancing stroke research converge on the overarching goal of saving lives and enriching the quality of life for those affected by stroke.…”

CNS-LAND score: predicting early neurological deterioration after intravenous thrombolysis based on systemic responses and injury

“…At the same time, brainderived glutamate toxicity also promotes the synthesis of ALT, which metabolizes blood glutamate to provide protection. But more importantly, ALT has been found to be associated with symptomatic intracranial hemorrhage and mortality after AIS in recent studies (39,40). Liver dysfunction (including fibrosis and cirrhosis), significantly impaired platelet aggregation, and diminished antifibrinolytic activity are hypothesized to cause the syndrome.…”