{
  "abstract": "Background Predicting futile recanalisation following endovascular treatment (EVT) in patients with large core infarctions is crucial for guiding clinical decisions, optimising perioperative management and improving healthcare resource allocation. This study aimed to compare four machine learning (ML) algorithms and identify the most effective model for preinterventional prediction of futile recanalisation.Methods Patients achieving successful reperfusion (expanded Thrombolysis in Cerebral Infarction Score≥2b) from the EVT in Acute Anterior Circulation Large Vessel Occlusive Patients With a Large Infarct Core trial were stratified into two groups: no-futile recanalisation (90-day modified Rankin Scale (mRS) 0–3) and futile recanalisation (mRS 4–6). The least absolute shrinkage and selection operator regression method was used for feature selection, and four ML algorithms, including logistic regression, support vector machine (SVM), decision tree and random forest, were applied. Model performance was evaluated using receiver operating characteristic curves, calibration plots and decision curve analysis. Feature importance was ranked using SHapley Additive exPlanation (SHAP) values.Results Among 146 patients, 74 experienced futile recanalisation. Eight predictors were identified and ranked by SHAP analysis from highest to lowest importance: sex, age, National Institutes of Health Stroke Scale, glucose, systolic blood pressure, neutrophil-to-lymphocyte ratio, fibrinogen and occlusion site. Among the four models, the SVM model achieved the highest area under the curve of 0.891 (95% CI 0.837 to 0.945), along with good calibration (Hosmer-Lemeshow test, p=0.103) and clinical utility.Conclusion The SVM model emerges as the optimal predictive tool for futile recanalisation following EVT in patients with large core infarction. Nevertheless, external validation is required to confirm its performance before clinical application.Trial registration number NCT04551664.",
  "authors": [
    {
      "affiliations": [
        "Interventional Neuroradiology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China"
      ],
      "name": "Yawen Gan"
    },
    {
      "affiliations": [
        "Interventional Neuroradiology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China",
        "Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, China"
      ],
      "name": "Shuang Song"
    },
    {
      "affiliations": [
        "China National Clinical Research Center for Neurological Diseases, Capital Medical University, Beijing, China"
      ],
      "name": "Dingwen Zhang"
    },
    {
      "affiliations": [
        "Interventional Neuroradiology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China"
      ],
      "name": "Fangguang Chen"
    },
    {
      "affiliations": [
        "Interventional Neuroradiology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China"
      ],
      "name": "Jie He"
    },
    {
      "affiliations": [
        "Interventional Neuroradiology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China"
      ],
      "name": "Zhongao Guan"
    },
    {
      "affiliations": [
        "Interventional Neuroradiology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China"
      ],
      "name": "Ketao Tu"
    },
    {
      "affiliations": [
        "Interventional Neuroradiology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China"
      ],
      "name": "Zhenfei Yu"
    },
    {
      "affiliations": [
        "Interventional Neuroradiology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China",
        "China National Clinical Research Center for Neurological Diseases, Capital Medical University, Beijing, China"
      ],
      "name": "Yuesong Pan"
    },
    {
      "affiliations": [
        "Interventional Neuroradiology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China"
      ],
      "name": "Zhongrong Miao"
    },
    {
      "affiliations": [
        "Interventional Neuroradiology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China"
      ],
      "name": "Dapeng Mo"
    },
    {
      "affiliations": [
        "Interventional Neuroradiology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China"
      ],
      "name": "Xu Tong"
    }
  ],
  "title": "Machine learning models for predicting futile recanalisation after endovascular treatment in patients with large core infarction",
  "uid": "a4d9bca3-6b00-54aa-8fc4-d53404612910"
}