Scholarlink

Background <p>Status epilepticus (SE) is a critical neurological emergency with mortality rates reaching 15–25%. While several clinical scores exist for risk stratification of SE, they tend to perform poorly in a real-world scenario, where the prognostic role of electroencephalography (EEG) remains secondary.</p> Objectives <p>This study aimed to evaluate the clinical utility of existing prognostic scales and to determine if a simplified, structured EEG reporting framework provides incremental value in predicting mortality and length of stay (LOS).</p> Methods <p>We retrospectively analyzed 182 adult SE patients (mean age 67 ± 15.9&#xa0;years) between 2018 and 2024. We collected clinical scores and structured EEG features (based on Salzburg criteria and nomenclature from the American Clinical Neurophysiology Society position paper on critical care EEG). Outcomes included 30-day in-hospital mortality and LOS, analyzed using logistic regression, ROC curves, and Cox proportional-hazards models.</p> Results <p>In-hospital mortality was 23.1%, and mean LOS was 26.1 ± 29.8&#xa0;days. Clinical scores alone demonstrated acceptable predictive value for mortality (AUC 0.60–0.76), but suffered from low specificity. Adding structured EEG features significantly improved mortality prediction (AUC = 0.88). Notably, plasma markers of systemic involvement primarily drove mortality&#xa0;prediction, whereas EEG patterns—specifically Salzburg criteria and spatio-temporal evolution—were strong predictors of LOS.</p> Conclusions <p>Current clinical scores demonstrate limited accuracy in the risk stratification of SE due to high etiological variability. In contrast, structured EEG interpretation significantly enhances prognostic specificity and provides distinct predictive value for LOS. Future efforts should focus on integrating EEG parameters with existing clinical scores to harmonize and improve risk stratification models.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Structured EEG report complements the prognostic stratification of status epilepticus

  • 期刊论文

  • 发表日期：

    2026年4月16日

查看全文
  • Jacopo Lanzone,
  • Anna Bellini,
  • Marco Cursi,
  • Federico Pedroni,
  • Davide G. Curti,
  • Giovanna Franca Fanelli,
  • Federica Agosta,
  • Massimo Filippi

摘要

Background

Status epilepticus (SE) is a critical neurological emergency with mortality rates reaching 15–25%. While several clinical scores exist for risk stratification of SE, they tend to perform poorly in a real-world scenario, where the prognostic role of electroencephalography (EEG) remains secondary.

Objectives

This study aimed to evaluate the clinical utility of existing prognostic scales and to determine if a simplified, structured EEG reporting framework provides incremental value in predicting mortality and length of stay (LOS).

Methods

We retrospectively analyzed 182 adult SE patients (mean age 67 ± 15.9 years) between 2018 and 2024. We collected clinical scores and structured EEG features (based on Salzburg criteria and nomenclature from the American Clinical Neurophysiology Society position paper on critical care EEG). Outcomes included 30-day in-hospital mortality and LOS, analyzed using logistic regression, ROC curves, and Cox proportional-hazards models.

Results

In-hospital mortality was 23.1%, and mean LOS was 26.1 ± 29.8 days. Clinical scores alone demonstrated acceptable predictive value for mortality (AUC 0.60–0.76), but suffered from low specificity. Adding structured EEG features significantly improved mortality prediction (AUC = 0.88). Notably, plasma markers of systemic involvement primarily drove mortality prediction, whereas EEG patterns—specifically Salzburg criteria and spatio-temporal evolution—were strong predictors of LOS.

Conclusions

Current clinical scores demonstrate limited accuracy in the risk stratification of SE due to high etiological variability. In contrast, structured EEG interpretation significantly enhances prognostic specificity and provides distinct predictive value for LOS. Future efforts should focus on integrating EEG parameters with existing clinical scores to harmonize and improve risk stratification models.