A machine-learning guided method for predicting add-on and switch in secondary data sources: A case study on anti-seizure medications in Danish registries

Research output: Contribution to journalJournal articleResearchpeer-review

Standard

A machine-learning guided method for predicting add-on and switch in secondary data sources : A case study on anti-seizure medications in Danish registries. / Breitenstein, Peter Suhr; Mahmoud, Israa; Al-Azzawi, Fahed; Shakibfar, Saeed; Sessa, Maurizio.

In: Frontiers in Pharmacology, Vol. 13, 954393, 2022.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Breitenstein, PS, Mahmoud, I, Al-Azzawi, F, Shakibfar, S & Sessa, M 2022, 'A machine-learning guided method for predicting add-on and switch in secondary data sources: A case study on anti-seizure medications in Danish registries', Frontiers in Pharmacology, vol. 13, 954393. https://doi.org/10.3389/fphar.2022.954393

APA

Breitenstein, P. S., Mahmoud, I., Al-Azzawi, F., Shakibfar, S., & Sessa, M. (2022). A machine-learning guided method for predicting add-on and switch in secondary data sources: A case study on anti-seizure medications in Danish registries. Frontiers in Pharmacology, 13, [954393]. https://doi.org/10.3389/fphar.2022.954393

Vancouver

Breitenstein PS, Mahmoud I, Al-Azzawi F, Shakibfar S, Sessa M. A machine-learning guided method for predicting add-on and switch in secondary data sources: A case study on anti-seizure medications in Danish registries. Frontiers in Pharmacology. 2022;13. 954393. https://doi.org/10.3389/fphar.2022.954393

Author

Breitenstein, Peter Suhr ; Mahmoud, Israa ; Al-Azzawi, Fahed ; Shakibfar, Saeed ; Sessa, Maurizio. / A machine-learning guided method for predicting add-on and switch in secondary data sources : A case study on anti-seizure medications in Danish registries. In: Frontiers in Pharmacology. 2022 ; Vol. 13.

Bibtex

@article{f6fe588d9634495197e05b05ef59bbfe,
title = "A machine-learning guided method for predicting add-on and switch in secondary data sources: A case study on anti-seizure medications in Danish registries",
abstract = "Purpose: There is a lack of available evidence regarding the treatment pattern of switches and add-ons for individuals aged 65 years or older with epilepsy during the first years from the time they received their first anti-seizure medication because of the lack of valid methods. Therefore, this study aimed to develop an algorithm for identifying switches and add-ons using secondary data sources for anti-seizure medication users. Methods: Danish nationwide databases were used as data sources. Residents in Denmark between 1996 and 2018 who were diagnosed with epilepsy and redeemed their first prescription for anti-seizure medication after epilepsy diagnosis were followed up for 730 days until the end of the follow-up period, death, or emigration to assess switches and add-ons occurred during the follow-up period. The study outcomes were the overall accuracy of the classification of switch or add-on of the newly developed algorithm. Results: In total, 15870 individuals were included in the study population with a median age of 72.9 years, of whom 52.0% were male and 48.0% were female. A total of 988 of the 15879 patients from the study population were present during the 730-day follow-up period, and 988 individuals (6.2%) underwent a total of 1485 medication events with co-exposure to two or more anti-seizure medications. The newly developed algorithmic method correctly identified 9 out of 10 add-ons (overall accuracy 92%) and 9 out of 10 switches (overall accuracy 88%). Conclusion: The majority of switches and add-ons occurred early during the first 2 years of disease and according to clinical recommendations. The newly developed algorithm correctly identified 9 out of 10 switches/add-ons.",
author = "Breitenstein, {Peter Suhr} and Israa Mahmoud and Fahed Al-Azzawi and Saeed Shakibfar and Maurizio Sessa",
note = "Copyright {\textcopyright} 2022 Breitenstein, Mahmoud, Al-Azzawi, Shakibfar and Sessa.",
year = "2022",
doi = "10.3389/fphar.2022.954393",
language = "English",
volume = "13",
journal = "Frontiers in Pharmacology",
issn = "1663-9812",
publisher = "Frontiers Research Foundation",

}

RIS

TY - JOUR

T1 - A machine-learning guided method for predicting add-on and switch in secondary data sources

T2 - A case study on anti-seizure medications in Danish registries

AU - Breitenstein, Peter Suhr

AU - Mahmoud, Israa

AU - Al-Azzawi, Fahed

AU - Shakibfar, Saeed

AU - Sessa, Maurizio

N1 - Copyright © 2022 Breitenstein, Mahmoud, Al-Azzawi, Shakibfar and Sessa.

PY - 2022

Y1 - 2022

N2 - Purpose: There is a lack of available evidence regarding the treatment pattern of switches and add-ons for individuals aged 65 years or older with epilepsy during the first years from the time they received their first anti-seizure medication because of the lack of valid methods. Therefore, this study aimed to develop an algorithm for identifying switches and add-ons using secondary data sources for anti-seizure medication users. Methods: Danish nationwide databases were used as data sources. Residents in Denmark between 1996 and 2018 who were diagnosed with epilepsy and redeemed their first prescription for anti-seizure medication after epilepsy diagnosis were followed up for 730 days until the end of the follow-up period, death, or emigration to assess switches and add-ons occurred during the follow-up period. The study outcomes were the overall accuracy of the classification of switch or add-on of the newly developed algorithm. Results: In total, 15870 individuals were included in the study population with a median age of 72.9 years, of whom 52.0% were male and 48.0% were female. A total of 988 of the 15879 patients from the study population were present during the 730-day follow-up period, and 988 individuals (6.2%) underwent a total of 1485 medication events with co-exposure to two or more anti-seizure medications. The newly developed algorithmic method correctly identified 9 out of 10 add-ons (overall accuracy 92%) and 9 out of 10 switches (overall accuracy 88%). Conclusion: The majority of switches and add-ons occurred early during the first 2 years of disease and according to clinical recommendations. The newly developed algorithm correctly identified 9 out of 10 switches/add-ons.

AB - Purpose: There is a lack of available evidence regarding the treatment pattern of switches and add-ons for individuals aged 65 years or older with epilepsy during the first years from the time they received their first anti-seizure medication because of the lack of valid methods. Therefore, this study aimed to develop an algorithm for identifying switches and add-ons using secondary data sources for anti-seizure medication users. Methods: Danish nationwide databases were used as data sources. Residents in Denmark between 1996 and 2018 who were diagnosed with epilepsy and redeemed their first prescription for anti-seizure medication after epilepsy diagnosis were followed up for 730 days until the end of the follow-up period, death, or emigration to assess switches and add-ons occurred during the follow-up period. The study outcomes were the overall accuracy of the classification of switch or add-on of the newly developed algorithm. Results: In total, 15870 individuals were included in the study population with a median age of 72.9 years, of whom 52.0% were male and 48.0% were female. A total of 988 of the 15879 patients from the study population were present during the 730-day follow-up period, and 988 individuals (6.2%) underwent a total of 1485 medication events with co-exposure to two or more anti-seizure medications. The newly developed algorithmic method correctly identified 9 out of 10 add-ons (overall accuracy 92%) and 9 out of 10 switches (overall accuracy 88%). Conclusion: The majority of switches and add-ons occurred early during the first 2 years of disease and according to clinical recommendations. The newly developed algorithm correctly identified 9 out of 10 switches/add-ons.

U2 - 10.3389/fphar.2022.954393

DO - 10.3389/fphar.2022.954393

M3 - Journal article

C2 - 36438810

VL - 13

JO - Frontiers in Pharmacology

JF - Frontiers in Pharmacology

SN - 1663-9812

M1 - 954393

ER -

ID: 327322996