TY - JOUR

T1 - A web-based algorithm to rapidly classify seizures for the purpose of drug selection

AU - Beniczky, Sándor

AU - Asadi-Pooya, Ali A.

AU - Perucca, Emilio

AU - Rubboli, Guido

AU - Tartara, Elena

AU - Meritam Larsen, Pirgit

AU - Ebrahimi, Saqar

AU - Farzinmehr, Somayeh

AU - Rampp, Stefan

AU - Sperling, Michael R.

N1 - Funding Information: The study was supported by Filadelfia Research Foundation (Denmark). We would like to express our gratitude to Mette Nielsen (Danish Epilepsy Center, Dianalund, Denmark) and Carly Morcom, PA (Thomas Jefferson University) for applying the algorithm; to Carlo Andrea Galimberti (University of Pavia, Pavia, Italy) for providing data for the reference standard; and to Bo Martin Bibby (Aarhus University) for assistance with the statistical analyses. We gratefully acknowledge the contribution of the health care professionals who evaluated the feasibility and usefulness of the algorithm in underserved areas: Murtala Olusola Bankole (Nigeria), Daniela Bezerra (Brazil), Patricia Braga (Uruguay), Marcia Cavagnollo (Brazil), Lamin Dampha (the Gambia), Edson Pillotto Duarte (Brazil), Virginia George (Sierra Leone), Ayesha Haidery (Afghanistan), Joan Kagema (Kenya), Chernoh Foday Kamara (Sierra Leone), Saltanat Kamenova (Kazakhstan), Aida Kondybayeva (Kazakhstan), Giorgi Japaridze (Georgia), Marabishi Jasmin (Haiti), Ashok Kumar (India), Sofia Kasradze (Georgia), Milly Kumwenda (Malawi), Karlygash Kuzhybayeva (Kazakhstan), Jaime Lin (Brazil), Katia Lin (Brazil), Mariana Lunardi (Brazil), Hamid Nemati (Iran), Yahya Njie (the Gambia), Adaucto Nóbrega Jr (Brazil), Richmonda Pearce (Sierra Leone), Edward Shabangu (Swaziland), Praveen Sharma (India), Thea Shengelia (Georgia), Roger Walz (Brazil), Bhagyashri Wankhade (India), Elza Márcia Yacubian (Brazil), and Murat Zhanuzakov (Kazakhstan). Funding Information: The study was supported by Filadelfia Research Foundation (Denmark). We would like to express our gratitude to Mette Nielsen (Danish Epilepsy Center, Dianalund, Denmark) and Carly Morcom, PA (Thomas Jefferson University) for applying the algorithm; to Carlo Andrea Galimberti (University of Pavia, Pavia, Italy) for providing data for the reference standard; and to Bo Martin Bibby (Aarhus University) for assistance with the statistical analyses. We gratefully acknowledge the contribution of the health care professionals who evaluated the feasibility and usefulness of the algorithm in underserved areas: Murtala Olusola Bankole (Nigeria), Daniela Bezerra (Brazil), Patricia Braga (Uruguay), Marcia Cavagnollo (Brazil), Lamin Dampha (the Gambia), Edson Pillotto Duarte (Brazil), Virginia George (Sierra Leone), Ayesha Haidery (Afghanistan), Joan Kagema (Kenya), Chernoh Foday Kamara (Sierra Leone), Saltanat Kamenova (Kazakhstan), Aida Kondybayeva (Kazakhstan), Giorgi Japaridze (Georgia), Marabishi Jasmin (Haiti), Ashok Kumar (India), Sofia Kasradze (Georgia), Milly Kumwenda (Malawi), Karlygash Kuzhybayeva (Kazakhstan), Jaime Lin (Brazil), Katia Lin (Brazil), Mariana Lunardi (Brazil), Hamid Nemati (Iran), Yahya Njie (the Gambia), Adaucto N?brega Jr (Brazil), Richmonda Pearce (Sierra Leone), Edward Shabangu (Swaziland), Praveen Sharma (India), Thea Shengelia (Georgia), Roger Walz (Brazil), Bhagyashri Wankhade (India), Elza M?rcia Yacubian (Brazil), and Murat Zhanuzakov (Kazakhstan).

PY - 2021

Y1 - 2021

N2 - Objective: To develop and validate a pragmatic algorithm that classifies seizure types, to facilitate therapeutic decision-making. Methods: Using a modified Delphi method, five experts developed a pragmatic classification of nine types of epileptic seizures or combinations of seizures that influence choice of medication, and constructed a simple algorithm, freely available on the internet. The algorithm consists of seven questions applicable to patients with seizure onset at the age of 10 years or older. Questions to screen for nonepileptic attacks were added. Junior physicians, nurses, and physician assistants applied the algorithm to consecutive patients in a multicenter prospective validation study (ClinicalTrials.gov identifier: NCT03796520). The reference standard was the seizure classification by expert epileptologists, based on all available data, including electroencephalogram (EEG), video-EEG monitoring, and neuroimaging. In addition, physicians working in underserved areas assessed the feasibility of using the web-based algorithm in their clinical setting. Results: A total of 262 patients were assessed, of whom 157 had focal, 51 had generalized, and 10 had unknown onset epileptic seizures, and 44 had nonepileptic paroxysmal events. Agreement between the algorithm and the expert classification was 83.2% (95% confidence interval = 78.6%–87.8%), with an agreement coefficient (AC1) of.82 (95% confidence interval =.77–.87), indicating almost perfect agreement. Thirty-two health care professionals from 14 countries evaluated the feasibility of the web-based algorithm in their clinical setting, and found it applicable and useful for their practice (median = 6.5 on 7-point Likert scale). Significance: The web-based algorithm provides an accurate classification of seizure types, which can be used for selecting antiseizure medications in adolescents and adults.

AB - Objective: To develop and validate a pragmatic algorithm that classifies seizure types, to facilitate therapeutic decision-making. Methods: Using a modified Delphi method, five experts developed a pragmatic classification of nine types of epileptic seizures or combinations of seizures that influence choice of medication, and constructed a simple algorithm, freely available on the internet. The algorithm consists of seven questions applicable to patients with seizure onset at the age of 10 years or older. Questions to screen for nonepileptic attacks were added. Junior physicians, nurses, and physician assistants applied the algorithm to consecutive patients in a multicenter prospective validation study (ClinicalTrials.gov identifier: NCT03796520). The reference standard was the seizure classification by expert epileptologists, based on all available data, including electroencephalogram (EEG), video-EEG monitoring, and neuroimaging. In addition, physicians working in underserved areas assessed the feasibility of using the web-based algorithm in their clinical setting. Results: A total of 262 patients were assessed, of whom 157 had focal, 51 had generalized, and 10 had unknown onset epileptic seizures, and 44 had nonepileptic paroxysmal events. Agreement between the algorithm and the expert classification was 83.2% (95% confidence interval = 78.6%–87.8%), with an agreement coefficient (AC1) of.82 (95% confidence interval =.77–.87), indicating almost perfect agreement. Thirty-two health care professionals from 14 countries evaluated the feasibility of the web-based algorithm in their clinical setting, and found it applicable and useful for their practice (median = 6.5 on 7-point Likert scale). Significance: The web-based algorithm provides an accurate classification of seizure types, which can be used for selecting antiseizure medications in adolescents and adults.

KW - algorithm

KW - classification

KW - epilepsy

KW - seizure

KW - web-based application

U2 - 10.1111/epi.17039

DO - 10.1111/epi.17039

M3 - Journal article

C2 - 34420206

AN - SCOPUS:85113150854

VL - 62

SP - 2474

EP - 2484

JO - Epilepsia

JF - Epilepsia

SN - 0013-9580

IS - 10

ER -