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Predictors of follow-up care for critically-ill patients with seizures and epileptiform abnormalities on EEG monitoring.
Rice, Hunter J; Fernandes, Marta Bento; Punia, Vineet; Rubinos, Clio; Sivaraju, Adithya; Zafar, Sahar F.
Afiliação
  • Rice HJ; Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, United States; Center for Value-based Health Care and Sciences, Massachusetts General Hospital, Boston, MA, United States.
  • Fernandes MB; Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, United States; Center for Value-based Health Care and Sciences, Massachusetts General Hospital, Boston, MA, United States.
  • Punia V; Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH, United States.
  • Rubinos C; University of North Carolina, Chapel Hill, NC, United States.
  • Sivaraju A; Department of Neurology, Yale New Haven Hospital, Yale University, New Haven, CT, United States.
  • Zafar SF; Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, United States; Center for Value-based Health Care and Sciences, Massachusetts General Hospital, Boston, MA, United States. Electronic address: sfzafar@mgh.harvard.edu.
Clin Neurol Neurosurg ; 241: 108275, 2024 06.
Article em En | MEDLINE | ID: mdl-38640778
ABSTRACT

OBJECTIVE:

Post-hospitalization follow-up visits are crucial for preventing long-term complications. Patients with electrographic epileptiform abnormalities (EA) including seizures and periodic and rhythmic patterns are especially in need of follow-up for long-term seizure risk stratification and medication management. We sought to identify predictors of follow-up.

METHODS:

This is a retrospective cohort study of all patients (age ≥ 18 years) admitted to intensive care units that underwent continuous EEG (cEEG) monitoring at a single center between 01/2016-12/2019. Patients with EAs were included. Clinical and demographic variables were recorded. Follow-up status was determined using visit records 6-month post discharge, and visits were stratified as outpatient follow-up, neurology follow-up, and inpatient readmission. Lasso feature selection analysis was performed.

RESULTS:

723 patients (53 % female, mean (std) age of 62.3 (16.4) years) were identified from cEEG records with 575 (79 %) surviving to discharge. Of those discharged, 450 (78 %) had outpatient follow-up, 316 (55 %) had a neurology follow-up, and 288 (50 %) were readmitted during the 6-month period. Discharge on antiseizure medications (ASM), younger age, admission to neurosurgery, and proximity to the hospital were predictors of neurology follow-up visits. Discharge on ASMs, along with longer length of stay, younger age, emergency admissions, and higher illness severity were predictors of readmission.

SIGNIFICANCE:

ASMs at discharge, demographics (age, address), hospital care teams, and illness severity determine probability of follow-up. Parameters identified in this study may help healthcare systems develop interventions to improve care transitions for critically-ill patients with seizures and other EA.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Convulsões / Estado Terminal / Eletroencefalografia Limite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Clin Neurol Neurosurg Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Holanda

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Convulsões / Estado Terminal / Eletroencefalografia Limite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Clin Neurol Neurosurg Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Holanda