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Prognostication in Epilepsy with Integrated Analysis of Blood Parameters and Clinical Data.
Park, Kyung-Il; Hwang, Sungeun; Son, Hyoshin; Moon, Jangsup; Lee, Soon-Tae; Jung, Keun-Hwa; Jung, Ki-Young; Chu, Kon; Lee, Sang Kun.
Afiliación
  • Park KI; Department of Neurology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.
  • Hwang S; Department of Neurology, Seoul National University Healthcare System Gangnam Center, Seoul 06236, Republic of Korea.
  • Son H; Department of Neurology, Ewha Womans University Mokdong Hospital, Seoul 07985, Republic of Korea.
  • Moon J; Department of Neurology, Catholic University of Korea Eunpyeong St Mary's Hospital, Seoul 03312, Republic of Korea.
  • Lee ST; Department of Neurology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.
  • Jung KH; Department of Genomic Medicine, Seoul National University Hospital, Seoul 03080, Republic of Korea.
  • Jung KY; Department of Neurology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.
  • Chu K; Department of Neurology, Seoul National University Hospital, Seoul 03080, Republic of Korea.
  • Lee SK; Department of Neurology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.
J Clin Med ; 13(18)2024 Sep 18.
Article en En | MEDLINE | ID: mdl-39337003
ABSTRACT
Background/

Objectives:

Determining the outcome of epilepsy is crucial for making proactive and timely treatment decisions and for counseling patients. Recent research efforts have focused on using various imaging techniques and EEG for prognostication; however, there is insufficient evidence regarding the role of blood parameters. Our study aimed to investigate the additional prognostic value of routine blood parameters in predicting epilepsy outcomes.

Methods:

We analyzed data from 1782 patients who underwent routine blood tests within 90 days of their first visit and had a minimum follow-up duration of three years. The etiological types were structural (35.1%), genetic (14.2%), immune (4.7%), infectious (2.9%), and unknown (42.6%). The outcome was defined as the presence of seizures in the last year.

Results:

Initially, a multivariate analysis was conducted based on clinical variables, MRI data, and EEG data. This analysis revealed that sex, age of onset, referred cases, epileptiform discharge, structural etiology, and the number of antiseizure medications were related to the outcome, with an area under the curve (AUC) of 0.705. Among the blood parameters, fibrinogen, bilirubin, uric acid, and aPTT were significant, with AUCs of 0.602, 0.597, 0.455, and 0.549, respectively. Including these blood parameters in the analysis slightly improved the AUC to 0.710.

Conclusions:

Some blood parameters were found to be related to the final outcome, potentially paving the way to understanding the mechanisms of epileptogenesis and drug resistance.
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Clin Med Año: 2024 Tipo del documento: Article Pais de publicación: Suiza

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Clin Med Año: 2024 Tipo del documento: Article Pais de publicación: Suiza