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

ABSTRACT: 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.

SUBMITTER: Zhu B

PROVIDER: S-EPMC8762247 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Tenecteplase is a fibrinolytic drug with higher fibrin specificity and longer half-life than the standard stroke thrombolytic, alteplase, permitting the convenience of single bolus administration. Tenecteplase, at 0.5 mg/kg, has regulatory approval to treat ST-segment-elevation myocardial infarction, for which it has equivalent 30-day mortality and fewer systemic hemorrhages. Investigated as a thrombolytic for ischemic stroke over the past 15 years, tenecteplase is currently being studied in several phase 3 trials. Based on a systematic literature search, we provide a qualitative synthesis of published stroke clinical trials of tenecteplase that (1) performed randomized comparisons with alteplase, (2) compared different doses of tenecteplase, or (3) provided unique quantitative meta-analyses. Four phase 2 and one phase 3 study performed randomized comparisons with alteplase. These and other phase 2 studies compared different tenecteplase doses and effects on early outcomes of recanalization, reperfusion, and substantial neurological improvement, as well as symptomatic intracranial hemorrhage and 3-month disability on the modified Rankin Scale. Although no single trial prospectively demonstrated superiority or noninferiority of tenecteplase on clinical outcome, meta-analyses of these trials (1585 patients randomized) point to tenecteplase superiority in recanalization of large vessel occlusions and noninferiority in disability-free 3-month outcome, without increases in symptomatic intracranial hemorrhage or mortality. Doses of 0.25 and 0.4 mg/kg have been tested, but no advantage of the higher dose has been suggested by the results. Current clinical practice guidelines for stroke include intravenous tenecteplase at either dose as a second-tier option, with the 0.25 mg/kg dose recommended for large vessel occlusions, based on a phase 2 trial that demonstrated superior recanalization and improved 3-month outcome relative to alteplase. Ongoing randomized phase 3 trials may better define the comparative risks and benefits of tenecteplase and alteplase for stroke thrombolysis and answer questions of tenecteplase efficacy in the >4.5-hour time window, in wake-up stroke, and in combination with endovascular thrombectomy.

Project description:Background: Endovascular treatment (EVT) is effective for stroke patients with a large vessel occlusion (LVO) of the anterior circulation. To further improve personalized stroke care, it is essential to accurately predict outcome after EVT. Machine learning might outperform classical prediction methods as it is capable of addressing complex interactions and non-linear relations between variables. Methods: We included patients from the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands (MR CLEAN) Registry, an observational cohort of LVO patients treated with EVT. We applied the following machine learning algorithms: Random Forests, Support Vector Machine, Neural Network, and Super Learner and compared their predictive value with classic logistic regression models using various variable selection methodologies. Outcome variables were good reperfusion (post-mTICI ? 2b) and functional independence (modified Rankin Scale ?2) at 3 months using (1) only baseline variables and (2) baseline and treatment variables. Area under the ROC-curves (AUC) and difference of mean AUC between the models were assessed. Results: We included 1,383 EVT patients, with good reperfusion in 531 (38%) and functional independence in 525 (38%) patients. Machine learning and logistic regression models all performed poorly in predicting good reperfusion (range mean AUC: 0.53-0.57), and moderately in predicting 3-months functional independence (range mean AUC: 0.77-0.79) using only baseline variables. All models performed well in predicting 3-months functional independence using both baseline and treatment variables (range mean AUC: 0.88-0.91) with a negligible difference of mean AUC (0.01; 95%CI: 0.00-0.01) between best performing machine learning algorithm (Random Forests) and best performing logistic regression model (based on prior knowledge). Conclusion: In patients with LVO machine learning algorithms did not outperform logistic regression models in predicting reperfusion and 3-months functional independence after endovascular treatment. For all models at time of admission radiological outcome was more difficult to predict than clinical outcome.

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

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