Michele Romoli1, Paolo Eusebi1, Sabrina Siliquini1, Chiara Bedetti1, Paolo Calabresi2, Cinzia Costa3. 1. Neurology Clinic, University Hospital of Perugia, Perugia, Italy. 2. Neurology Clinic, University Hospital of Perugia, Perugia, Italy; IRCCS "Santa Lucia", Rome, Italy. 3. Neurology Clinic, University Hospital of Perugia, Perugia, Italy. Electronic address: cinzia.costa@unipg.it.
Abstract
INTRODUCTION: Adverse events (AEs) of antiepileptic drugs (AEDs) affect patient compliance and dropout. No questionnaire measuring AEs of AEDs is available for Italian-speaking people with epilepsy. Moreover, no questionnaire has been shown to predict patient dropout. OBJECTIVE: The aim of this study was to provide a validated Italian version of the Liverpool Adverse Events Profile (iLAEP) and to define iLAEP reliability in AE monitoring and dropout risk prediction. METHODS: The original LAEP was translated and tested for internal consistency and reliability. Patients with epilepsy who are on stable AED regimen completed the questionnaire as well as a 3-month follow-up to assess dropouts. RESULTS: Overall, 204 patients with epilepsy were enrolled (mean age: 47.1±21.5). High internal consistency (Cronbach's α=0.88) was demonstrated, and very quick completion time was registered (mean=9min). A 3-month follow-up was performed to assess treatment discontinuation and potential predictive value of the iLAEP score. Treatment was discontinued in 33.3% of the cohort. Moreover, iLAEP scores (mean=30.71) significantly differed between patients interrupting (39.15±5.66) and those prosecuting treatment (29.4±6.54, p<.001). A cutoff of 36.5 had an 85% accuracy in predicting treatment discontinuation (85% sensitivity, 79% specificity). Scores >36.5 were associated with a 20.27-fold increase in dropout relative risk (RR), with a 66% positive predictive value. CONCLUSIONS: The iLAEP represents a reliable, quick, and inexpensive assessment tool for patient-reported AEs of AEDs. An iLAEP cutoff of 36.5 differentiates patients unlikely to interrupt treatment from those more prone to stop AEDs in the following 3months. The iLAEP might help clinicians in weighting the risk of dropout and better tailor treatment to patients.
INTRODUCTION: Adverse events (AEs) of antiepileptic drugs (AEDs) affect patient compliance and dropout. No questionnaire measuring AEs of AEDs is available for Italian-speaking people with epilepsy. Moreover, no questionnaire has been shown to predict patient dropout. OBJECTIVE: The aim of this study was to provide a validated Italian version of the Liverpool Adverse Events Profile (iLAEP) and to define iLAEP reliability in AE monitoring and dropout risk prediction. METHODS: The original LAEP was translated and tested for internal consistency and reliability. Patients with epilepsy who are on stable AED regimen completed the questionnaire as well as a 3-month follow-up to assess dropouts. RESULTS: Overall, 204 patients with epilepsy were enrolled (mean age: 47.1±21.5). High internal consistency (Cronbach's α=0.88) was demonstrated, and very quick completion time was registered (mean=9min). A 3-month follow-up was performed to assess treatment discontinuation and potential predictive value of the iLAEP score. Treatment was discontinued in 33.3% of the cohort. Moreover, iLAEP scores (mean=30.71) significantly differed between patients interrupting (39.15±5.66) and those prosecuting treatment (29.4±6.54, p<.001). A cutoff of 36.5 had an 85% accuracy in predicting treatment discontinuation (85% sensitivity, 79% specificity). Scores >36.5 were associated with a 20.27-fold increase in dropout relative risk (RR), with a 66% positive predictive value. CONCLUSIONS: The iLAEP represents a reliable, quick, and inexpensive assessment tool for patient-reported AEs of AEDs. An iLAEP cutoff of 36.5 differentiates patients unlikely to interrupt treatment from those more prone to stop AEDs in the following 3months. The iLAEP might help clinicians in weighting the risk of dropout and better tailor treatment to patients.
Authors: Cinzia Costa; Elena Nardi Cesarini; Paolo Eusebi; David Franchini; Paola Casucci; Marcello De Giorgi; Carmen Calvello; Michele Romoli; Lucilla Parnetti; Paolo Calabresi Journal: Front Neurol Date: 2022-01-12 Impact factor: 4.003
Authors: Johann Philipp Zöllner; Nadine Conradi; Matthias Sauter; Markus Knuf; Susanne Knake; Gerhard Kurlemann; Thomas Mayer; Christoph Hertzberg; Astrid Bertsche; Ilka Immisch; Karl Martin Klein; Klaus Marquard; Sascha Meyer; Anna H Noda; Felix von Podewils; Hannah Schäfer; Charlotte Thiels; Bianca Zukunft; Susanne Schubert-Bast; Janina Grau; Laurent M Willems; Felix Rosenow; Jens-Peter Reese; Adam Strzelczyk Journal: Neurol Res Pract Date: 2021-06-28