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Predicting the progression to super-refractory status epilepticus: A machine-learning study

  • Author Footnotes
    1 These authors contributed equally.
    Francesco Brigo
    Correspondence
    Corresponding author at: Hospital of Merano, Department of Neurology, Merano, Italy.
    Footnotes
    1 These authors contributed equally.
    Affiliations
    Hospital of Merano-Meran (SABES-ASDAA), Department of Neurology, Merano-Meran, Italy
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  • Author Footnotes
    1 These authors contributed equally.
    Gianni Turcato
    Footnotes
    1 These authors contributed equally.
    Affiliations
    Hospital of Santorso (AULSS-7), Department of Internal Medicine, Santorso, Italy
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  • Simona Lattanzi
    Affiliations
    Marche Polytechnic University, Neurological Clinic, Department of Experimental and Clinical Medicine, Ancona, Italy
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  • Niccolò Orlandi
    Affiliations
    Azienda Ospedaliera-Universitaria di Modena, Neurology Department, Modena, Italy

    University of Modena and Reggio-Emilia, Department of Biomedical, Metabolic, and Neural Sciences, Modena and Reggio-Emilia, Italy
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  • Giulia Turchi
    Affiliations
    Azienda Ospedaliera-Universitaria di Modena, Neurology Department, Modena, Italy
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  • Arian Zaboli
    Affiliations
    Hospital of Merano-Meran (SABES-ASDAA), Department of Emergency Medicine, Merano-Meran, Italy
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  • Author Footnotes
    1 These authors contributed equally.
    Giada Giovannini
    Footnotes
    1 These authors contributed equally.
    Affiliations
    Azienda Ospedaliera-Universitaria di Modena, Neurology Department, Modena, Italy

    University of Modena and Reggio-Emilia, PhD Programm in Clinical and Experimental Medicine,Modena, Italy
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  • Author Footnotes
    1 These authors contributed equally.
    Stefano Meletti
    Footnotes
    1 These authors contributed equally.
    Affiliations
    Azienda Ospedaliera-Universitaria di Modena, Neurology Department, Modena, Italy

    University of Modena and Reggio-Emilia, Department of Biomedical, Metabolic, and Neural Sciences, Modena and Reggio-Emilia, Italy
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  • Author Footnotes
    1 These authors contributed equally.
Published:October 27, 2022DOI:https://doi.org/10.1016/j.jns.2022.120481

      Highlights

      • SRSE is a SE that continues or recurs ≥24 h after the onset of anesthesia.
      • Machine learning prediction of progression to SRSE and 30-day mortality.
      • Acute hypoxic etiology and younger age are major predictors of progression to SRSE.
      • In patients SRSE, older age is associated with increased risk of 30-day mortality.

      Abstract

      Aim

      Super-refractory status epilepticus (SRSE) is a status epilepticus (SE) that continues or recurs ≥24 h after the onset of anesthesia. We aimed to identify the predictors of progression to SRSE and the risk of 30-day mortality in patients with SRSE by using a machine learning technique.

      Methods

      We reviewed consecutive SE episodes in patients aged ≥14 years at Baggiovara Civil Hospital (Modena, Italy) from 2013 to 2021. A classification and regression tree analysis was performed to develop a predictive model of progression to SRSE in SE patients. In SRSE patients, a multivariate analysis was conducted to identify predictors of 30-day mortality.

      Results

      We included 705 patients, 16% of whom (113/705) progressed to SRSE. Acute symptomatic hypoxic etiology and age ≤ 68.5 years predicted the highest risk (87.1%) of progression to SRSE. Etiology other than acute symptomatic hypoxic and absence of NCSE predicted the lowest risk (3.6%) of progression to SRSE. The predictive model was accurate in 96.1% of patients not evolving to SRSE and in 48.7% of those evolving to SRSE. Among patients with SRSE, 46.9% (53/113) died within 30 days compared to 25.2% (149/592) of patients without SRSE (p < 0.001). Among patients with SRSE, older age was associated with increased 30-day mortality (odds ratio 1.075; 95% confidence interval: 1.031–1.112; p = 0.001).

      Conclusions

      Acute symptomatic hypoxic etiology and younger age are major predictors of progression to SRSE. In patients with SRSE, older age is associated with increased risk of short-term mortality.

      Keywords

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