Predicting 1-Hour Thrombolysis Effect of r-tPA in Patients With Acute Ischemic Stroke Using Machine Learning Algorithm

• Ngā kaituhi matua: Bin Zhu (Author), Jianlei Zhao (Author), Mingnan Cao (Author), Wanliang Du (Author), Liuqing Yang (Author), Mingliang Su (Author), Yue Tian (Author), Mingfen Wu (Author), Tingxi Wu (Author), Manxia Wang (Author), Xingquan Zhao (Author), Zhigang Zhao (Author)
• Hōputu: Pukapuka
• I whakaputaina: Frontiers Media S.A., 2022-01-01T00:00:00Z.
• Ngā marau: article
• Urunga tuihono: Connect to this object online.
• Notes: 1663-9812; 10.3389/fphar.2021.759782
• Keywords: thrombolysis; acute ischemic stroke; machine learning algorithms; r-tPA; models; Therapeutics. Pharmacology; RM1-950
• Source: Frontiers in Pharmacology, Vol 12 (2022)

Background: Thrombolysis with r-tPA is recommended for patients after acute ischemic stroke (AIS) within 4.5 h of symptom onset. However, only a few patients benefit from this therapeutic regimen. Thus, we aimed to develop an interpretable machine learning (ML)-based model to predict the thrombolysis effect of r-tPA at the super-early stage.Methods: A total of 353 patients with AIS were divided into training and test data sets. We then used six ML algorithms and a recursive feature elimination (RFE) method to explore the relationship among the clinical variables along with the NIH stroke scale score 1 h after thrombolysis treatment. Shapley additive explanations and local interpretable model-agnostic explanation algorithms were applied to interpret the ML models and determine the importance of the selected features.Results: Altogether, 353 patients with an average age of 63.0 (56.0-71.0) years were enrolled in the study. Of these patients, 156 showed a favorable thrombolysis effect and 197 showed an unfavorable effect. A total of 14 variables were enrolled in the modeling, and 6 ML algorithms were used to predict the thrombolysis effect. After RFE screening, seven variables under the gradient boosting decision tree (GBDT) model (area under the curve = 0.81, specificity = 0.61, sensitivity = 0.9, and F1 score = 0.79) demonstrated the best performance. Of the seven variables, activated partial thromboplastin clotting time (time), B-type natriuretic peptide, and fibrin degradation products were the three most important clinical characteristics that might influence r-tPA efficiency.Conclusion: This study demonstrated that the GBDT model with the seven variables could better predict the early thrombolysis effect of r-tPA.