Evaluating the safety and efficacy of intravenous thrombolysis for acute ischemic stroke patients with a history of intracerebral hemorrhage: a systematic review and meta-analysis

Goh, Sherill; Tan, Nicholas Y.; Tan, Choon Han; Leow, Aloysius Sheng‐Ting; Sia, Ching‐Hui; Ho, Andrew Fu Wah; Lim, Mervyn Jun Rui; Yeo, Leonard L.L.; Tan, Benjamin Yq

doi:10.1007/s11239-021-02531-2

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“…Extensive efforts have been made to stratify the long-term outcomes of AIS after r-tPA ( Di Lorenzo et al, 2021 ; Goh et al, 2021 ). However, early intervention for AIS is particularly important as a disease with a high mortality and disability rate.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Discussionmentioning

confidence: 99%