Personalised selection of medication for newly diagnosed adult epilepsy: study protocol of a first-in-class, double-blind, randomised controlled trial.

IF 2.3 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL BMJ Open Pub Date : 2025-04-05 DOI:10.1136/bmjopen-2024-086607
Daniel Thom, Richard Shek-Kwan Chang, Natasha A Lannin, Zanfina Ademi, Zongyuan Ge, David Reutens, Terence O'Brien, Wendyl D'Souza, Piero Perucca, Sandra Reeder, Armin Nikpour, Chong Wong, Michelle Kiley, Jacqui-Lyn Saw, John-Paul Nicolo, Udaya Seneviratne, Patrick Carney, Dean Jones, Ernest Somerville, Clare Stapleton, Emma Foster, Lata Vadlamudi, David N Vaughan, James Lee, Tania Farrar, Mark Howard, Robert Sparrow, Zhibin Chen, Patrick Kwan
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Abstract

Introduction: Selection of antiseizure medications (ASMs) for newly diagnosed epilepsy remains largely a trial-and-error process. We have developed a machine learning (ML) model using retrospective data collected from five international cohorts that predicts response to different ASMs as the initial treatment for individual adults with new-onset epilepsy. This study aims to prospectively evaluate this model in Australia using a randomised controlled trial design.

Methods and analysis: At least 234 adult patients with newly diagnosed epilepsy will be recruited from 14 centres in Australia. Patients will be randomised 1:1 to the ML group or usual care group. The ML group will receive the ASM recommended by the model unless it is considered contraindicated by the neurologist. The usual care group will receive the ASM selected by the neurologist alone. Both the patient and neurologists conducting the follow-up will be blinded to the group assignment. Both groups will be followed up for 52 weeks to assess treatment outcomes. Additional information on adverse events, quality of life, mood and use of healthcare services and productivity will be collected using validated questionnaires. Acceptability of the model will also be assessed.The primary outcome will be the proportion of participants who achieve seizure-freedom (defined as no seizures during the 12-month follow-up period) while taking the initially prescribed ASM. Secondary outcomes include time to treatment failure, time to first seizure after randomisation, changes in mood assessment score and quality of life score, direct healthcare costs, and loss of productivity during the treatment period.This trial will provide class I evidence for the effectiveness of a ML model as a decision support tool for neurologists to select the first ASM for adults with newly diagnosed epilepsy.

Ethics and dissemination: This study is approved by the Alfred Health Human Research Ethics Committee (Project 130/23). Findings will be presented in academic conferences and submitted to peer-reviewed journals for publication.

Trial registration number: ACTRN12623000209695.

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为新确诊的成人癫痫患者个性化选择药物:首次同类双盲随机对照试验的研究方案。
新诊断癫痫的抗癫痫药物(asm)的选择在很大程度上仍然是一个反复试验的过程。我们开发了一个机器学习(ML)模型,使用从五个国际队列收集的回顾性数据,预测不同asm作为新发癫痫个体成人初始治疗的反应。本研究旨在采用随机对照试验设计在澳大利亚对该模型进行前瞻性评价。方法和分析:将从澳大利亚的14个中心招募至少234名新诊断为癫痫的成年患者。患者将按1:1随机分配到ML组或常规护理组。ML组接受模型推荐的ASM,除非神经科医生认为这是禁忌。常规护理组将接受由神经科医生单独选择的ASM。进行随访的患者和神经科医生都不知道分组分配。两组均将随访52周以评估治疗结果。将使用有效的问卷收集有关不良事件、生活质量、情绪和保健服务使用情况以及生产力的其他信息。模型的可接受性也将被评估。主要结果将是参与者在服用最初规定的ASM时实现癫痫发作自由(定义为在12个月的随访期间无癫痫发作)的比例。次要结局包括治疗失败的时间、随机化后第一次癫痫发作的时间、情绪评估评分和生活质量评分的变化、直接医疗保健费用和治疗期间生产力的损失。这项试验将为ML模型的有效性提供一级证据,作为神经科医生为新诊断的成人癫痫患者选择首个ASM的决策支持工具。伦理和传播:本研究得到阿尔弗雷德健康人类研究伦理委员会(130/23项目)的批准。研究结果将在学术会议上发表,并提交给同行评议的期刊发表。试验注册号:ACTRN12623000209695。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BMJ Open
BMJ Open MEDICINE, GENERAL & INTERNAL-
CiteScore
4.40
自引率
3.40%
发文量
4510
审稿时长
2-3 weeks
期刊介绍: BMJ Open is an online, open access journal, dedicated to publishing medical research from all disciplines and therapeutic areas. The journal publishes all research study types, from study protocols to phase I trials to meta-analyses, including small or specialist studies. Publishing procedures are built around fully open peer review and continuous publication, publishing research online as soon as the article is ready.
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