Classification Model for Epileptic Seizure Using Simple Postictal Laboratory Indices.
Sun Jin JinTae Sic LeeHyun Eui MoonEun Seok ParkSue Hyun LeeYoung Il RohDong Min SeoWon-Joo KimHeewon HwangPublished in: Journal of clinical medicine (2023)
Distinguishing syncope from epileptic seizures in patients with sudden loss of consciousness is important. Various blood tests have been used to indicate epileptic seizures in patients with impaired consciousness. This retrospective study aimed to predict the diagnosis of epilepsy in patients with transient loss of consciousness using the initial blood test results. A seizure classification model was constructed using logistic regression, and predictors were selected from a cohort of 260 patients using domain knowledge and statistical methods. The study defined the diagnosis of seizures and syncope based on the consistency of the diagnosis made by an emergency medicine specialist at the first visit to the emergency room and the diagnosis made by an epileptologist or cardiologist at the first outpatient visit using the International Classification of Diseases 10th revision (ICD-10) code. Univariate analysis showed higher levels of white blood cells, red blood cells, hemoglobin, hematocrit, delta neutrophil index, creatinine kinase, and ammonia levels in the seizure group. The ammonia level had the highest correlation with the diagnosis of epileptic seizures in the prediction model. Therefore, it is recommended to be included in the first examination at the emergency room.
Keyphrases
- temporal lobe epilepsy
- machine learning
- deep learning
- healthcare
- emergency department
- public health
- red blood cell
- end stage renal disease
- emergency medicine
- pulmonary embolism
- chronic kidney disease
- ejection fraction
- newly diagnosed
- prognostic factors
- signaling pathway
- oxidative stress
- cell death
- metabolic syndrome
- cell proliferation
- anaerobic digestion
- endoplasmic reticulum stress
- uric acid
- cerebral ischemia
- brain injury
- protein kinase