{
  "abstract": "Introduction Ischemic stroke remains a leading cause of morbidity and mortality, despite substantial progress in acute interventions. Understanding prognostic factors is essential for individualized treatment strategies and optimizing clinical decision-making.Aim of Study This study aims to identify and compare clinical parameters available immediately after endovascular treatment and at 24 hours that predict good functional outcome, defined as a modified-Rankin-Scale (mRS) score of 0–2 at 90 days. Analyses are based on a large, multicenter stroke registry and employ machine learning-backed model testing and validation to ensure robustness and generalizability of results.Method All patients from the German Stroke Registry were screened (n=18,069; 2015–2023). Patients with anterior circulation stroke and documented 90-day mRS scores were included (n=9,449). Missing data were addressed using Multiple Imputation by Chained Equations (five imputations, 20 iterations). Predictor selection was performed using logistic regression with nested cross-validation (outer: 5-fold, 3 repeats; inner: 5-fold).Results Key predictors of good outcome immediately post-procedure included pre-stroke mRS (normalized adjusted odds ratio [naOR] 0.42; 95%CI:0.38–0.46), age (naOR 0.48; 95%CI:0.44–0.52), and successful recanalization (mTICI≥2b; naOR 1.78; 95%CI:1.57–1.83), with AUC of 0.83 (95%CI:0.82–0.85). At 24 hours, NIHSS was the strongest predictor (naOR 0.18; 95%CI:0.16–0.20), improving the AUC to 0.90 (95%CI:0.89–0.91).Abstract A51 Figure 1Forest plots of normalized adjusted odds ratios for good functional outcome defined as mRS 0–2 at day 90 and multivariable logistic regression model receiver operating characteristic area under the curve (ROC AUCs). Left: clinical data post-procedure; right: clinical data at 24h post-procedureConclusion Clinical variables available post-procedure, particularly age, pre-stroke disability, and recanalization success, provide strong early prognostic value. Incorporating 24-hour NIHSS, which reflects both immediate treatment effect and early neurological evolution, significantly enhances prediction of functional outcomes.Conflict of Interest Yes Helge Kniep and Fabian Flottmann are consultants for Eppdata GmbH. Helge Kniep is shareholder of Eppdata GmbH. Götz Thomalla received fees as consultant from Acandis, Boehringer Ingelheim, Bayer, and Portola, and fees as lecturer from Acandis, Alexion, Amarin, Bayer, Boehringer-Ingelheim, BMS/Pfizer, Daiichii Sankyo and Portola. He serves in the board of the TEA Stroke Study and of ESO. Jens Fiehler is consultant for Cerenovus, Medtronic, Microvention, Penumbra, Phenox, Roche and Tonbridge. He serves in the advisory board of Stryker and Phenox. He is stock holder of Tegus Medical, Eppdata and Vastrax.",
  "authors": [
    {
      "affiliations": [
        "University Medical Center Hamburg-Eppendorf, Department of Diagnostic and Interventional Neuroradiology, Hamburg, Germany"
      ],
      "name": "Helge Kniep"
    },
    {
      "affiliations": [
        "University Medical Center Hamburg-Eppendorf, Department of Diagnostic and Interventional Neuroradiology, Hamburg, Germany"
      ],
      "name": "Lukas Meyer"
    },
    {
      "affiliations": [
        "HELIOS Medical Center, Campus of MSH Medical School Hamburg, Department of Neuroradiology, Schwerin, Germany"
      ],
      "name": "Gabriel Broocks"
    },
    {
      "affiliations": [
        "University Medical Center Hamburg-Eppendorf, Department of Diagnostic and Interventional Neuroradiology, Hamburg, Germany"
      ],
      "name": "Matthias Bechstein"
    },
    {
      "affiliations": [
        "University Medical Center Hamburg-Eppendorf, Department of Diagnostic and Interventional Neuroradiology, Hamburg, Germany"
      ],
      "name": "Christian Heitkamp"
    },
    {
      "affiliations": [
        "University Medical Center Hamburg-Eppendorf, Department of Diagnostic and Interventional Neuroradiology, Hamburg, Germany"
      ],
      "name": "Laurens Winkelmeier"
    },
    {
      "affiliations": [
        "University Medical Center Hamburg-Eppendorf, Department of Diagnostic and Interventional Neuroradiology, Hamburg, Germany"
      ],
      "name": "Vincent Geest"
    },
    {
      "affiliations": [
        "University Medical Center Hamburg-Eppendorf, Department of Diagnostic and Interventional Neuroradiology, Hamburg, Germany"
      ],
      "name": "Christian Thaler"
    },
    {
      "affiliations": [
        "University Medical Center Hamburg-Eppendorf, Department of Diagnostic and Interventional Neuroradiology, Hamburg, Germany"
      ],
      "name": "Fabian Flottmann"
    },
    {
      "affiliations": [
        "University Medical Center Hamburg-Eppendorf, Department of Diagnostic and Interventional Neuroradiology, Hamburg, Germany"
      ],
      "name": "Caspar Brekenfeld"
    },
    {
      "affiliations": [
        "University Medical Center Hamburg-Eppendorf, Department of Neurology, Hamburg, Germany"
      ],
      "name": "Maximilian Schell"
    },
    {
      "affiliations": [
        "University Medical Center Hamburg-Eppendorf, Department of Diagnostic and Interventional Neuroradiology, Hamburg, Germany"
      ],
      "name": "Uta Hanning"
    },
    {
      "affiliations": [
        "University Medical Center Hamburg-Eppendorf, Department of Neurology, Hamburg, Germany"
      ],
      "name": "Götz Thomalla"
    },
    {
      "affiliations": [
        "University Medical Center Hamburg-Eppendorf, Department of Diagnostic and Interventional Neuroradiology, Hamburg, Germany"
      ],
      "name": "Jens Fiehler"
    },
    {
      "affiliations": [
        "University Medical Center Hamburg-Eppendorf, Department of Diagnostic and Interventional Neuroradiology, Hamburg, Germany"
      ],
      "name": "Susanne Gellißen"
    }
  ],
  "title": "A51 Machine learning-based prediction of stroke outcome: value of immediate vs. 24-hour clinical parameters – insights from large-scale multicenter registry data",
  "uid": "830f3560-540d-579f-8770-e77f2f3af2e1"
}