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. 2024 Jun 6;14(6):e080126.
doi: 10.1136/bmjopen-2023-080126.

New data-driven method to predict the therapeutic indication of redeemed prescriptions in secondary data sources: a case study on antiseizure medications users aged ≥65 identified in Danish registries

Israa Mahmoud  1 , Vera Battini  1   2 , Carla Carnovale  2 , Emilio Clementi  2 , Kristian Kragholm  3 , Maurizio Sessa  4
Affiliations

New data-driven method to predict the therapeutic indication of redeemed prescriptions in secondary data sources: a case study on antiseizure medications users aged ≥65 identified in Danish registries

Israa Mahmoud et al. BMJ Open. .

Abstract

Objectives: We aimed to develop a new data-driven method to predict the therapeutic indication of redeemed prescriptions in secondary data sources using antiepileptic drugs among individuals aged ≥65 identified in Danish registries.

Design: This was an incident new-user register-based cohort study using Danish registers.

Setting: The study setting was Denmark and the study period was 2005-2017.

Participants: Participants included antiepileptic drug users in Denmark aged ≥65 with a confirmed diagnosis of epilepsy.

Primary and secondary outcome measures: Sensitivity served as the performance measure of the algorithm.

Results: The study population comprised 8609 incident new users of antiepileptic drugs. The sensitivity of the algorithm in correctly predicting the therapeutic indication of antiepileptic drugs in the study population was 65.3% (95% CI 64.4 to 66.2).

Conclusions: The algorithm demonstrated promising properties in terms of overall sensitivity for predicting the therapeutic indication of redeemed antiepileptic drugs by older individuals with epilepsy, correctly identifying the therapeutic indication for 6 out of 10 individuals using antiepileptic drugs for epilepsy.

Keywords: aged; epidemiology; epilepsy.

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Conflict of interest statement

Competing interests: None declared.

Figures

Figure 1
Figure 1
Overview of the steps for the developed algorithm. AEDs, antiepileptics drugs; TDM, therapeutic drug monitoring. Some of the strategies used in the algorithm required the use of a specific study design to avoid epidemiological biases. For example, in steps 5 to 7, it is necessary to look at events that occurred during the entire follow-up period. Therefore, to avoid conditioning on a future event, it was not possible to use a cohort design. This should not be seen as a limitation of the algorithm as currently, the evidence-based pyramid of evidence as explained by Murad al. emphasizes comparability of evidence between cohort and case-control studies.
Figure 2
Figure 2
Flowchart of the developed algorithm applied using the incident new-user study design to predict the therapeutic indication of redeemed antiseizure medications by older individuals with epilepsy. AEDs, antiepileptics drugs; TDM, therapeutic drug monitoring. Index date, first redeemed antiseizure medication following epilepsy hospitalization.
Figure 3
Figure 3
Performance of the developed algorithm for predicting the use of antiseizure medications for epilepsy. Step 3=treatment patterns of antiseizure medications compatible for nonepileptic disorders; Step 5= therapeutic substitution and add-on, Step 6= epileptic surgical procedures and examinations; Step 6b= AEDs therapeutic drug monitoring; Total= overall algorithm/corresponding to step 7 (recorded therapeutic indication for redeemed antiseizure medications in the Danish National Prescription Registry). AEDs, Antiepileptic drugs; N, number.
Figure 4
Figure 4
Cumulative hazard plot for the overall mortality in older individuals with epilepsy identified using the incident new-user study design. Blue line (cohort 1): individuals with predicted therapeutic indication of epilepsy for their first redeemed antiseizure medication after the hospitalization of epilepsy. Red line (cohort 2): individuals with unpredicted therapeutic indication for their first redeemed antiseizure medication after the hospitalization of epilepsy. P value calculated using the Gray test.
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