Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
Ann Clin Transl Neurol. 2024 Jul;11(7):1681-1690. doi: 10.1002/acn3.52059. Epub 2024 Jun 12.
BACKGROUND/OBJECTIVES: Epileptiform activity (EA), including seizures and periodic patterns, worsens outcomes in patients with acute brain injuries (e.g., aneurysmal subarachnoid hemorrhage [aSAH]). Randomized control trials (RCTs) assessing anti-seizure interventions are needed. Due to scant drug efficacy data and ethical reservations with placebo utilization, and complex physiology of acute brain injury, RCTs are lacking or hindered by design constraints. We used a pharmacological model-guided simulator to design and determine the feasibility of RCTs evaluating EA treatment.
In a single-center cohort of adults (age >18) with aSAH and EA, we employed a mechanistic pharmacokinetic-pharmacodynamic framework to model treatment response using observational data. We subsequently simulated RCTs for levetiracetam and propofol, each with three treatment arms mirroring clinical practice and an additional placebo arm. Using our framework, we simulated EA trajectories across treatment arms. We predicted discharge modified Rankin Scale as a function of baseline covariates, EA burden, and drug doses using a double machine learning model learned from observational data. Differences in outcomes across arms were used to estimate the required sample size.
Sample sizes ranged from 500 for levetiracetam 7 mg/kg versus placebo, to >4000 for levetiracetam 15 versus 7 mg/kg to achieve 80% power (5% type I error). For propofol 1 mg/kg/h versus placebo, 1200 participants were needed. Simulations comparing propofol at varying doses did not reach 80% power even at samples >1200.
Our simulations using drug efficacy show sample sizes are infeasible, even for potentially unethical placebo-control trials. We highlight the strength of simulations with observational data to inform the null hypotheses and propose use of this simulation-based RCT paradigm to assess the feasibility of future trials of anti-seizure treatment in acute brain injury.
背景/目的：癫痫样活动（EA），包括发作和周期性模式，会使急性脑损伤患者（例如，蛛网膜下腔出血[aSAH]）的预后恶化。需要评估抗癫痫干预措施的随机对照试验（RCT）。由于缺乏药物疗效数据和对安慰剂使用的伦理保留意见，以及急性脑损伤的复杂生理学，因此缺乏 RCT 或受到设计限制。我们使用药理学模型指导的模拟器来设计和确定评估 EA 治疗的 RCT 的可行性。
在一项单中心成年人（年龄＞18 岁）aSAH 和 EA 的队列研究中，我们使用机制药代动力学-药效学框架，利用观察性数据来模拟治疗反应。随后，我们模拟了左乙拉西坦和丙泊酚的 RCT，每个 RCT 都有三个治疗组，分别模拟临床实践和一个额外的安慰剂组。使用我们的框架，我们模拟了治疗组中 EA 轨迹。我们使用双机器学习模型从观察性数据中预测了作为基线协变量、EA 负担和药物剂量函数的出院改良 Rankin 量表。我们使用各治疗臂之间的结果差异来估计所需的样本量。
左乙拉西坦 7mg/kg 与安慰剂相比，样本量为 500 例；左乙拉西坦 15mg/kg 与 7mg/kg 相比，样本量超过 4000 例，以达到 80%的效力（5%的Ⅰ类错误率）。对于丙泊酚 1mg/kg/h 与安慰剂相比，需要 1200 名参与者。甚至在样本量超过 1200 例时，比较不同剂量丙泊酚的模拟也未能达到 80%的效力。
我们使用药物疗效进行的模拟表明，即使是潜在不道德的安慰剂对照试验，样本量也是不可行的。我们强调了使用观察性数据进行模拟的优势，可以为零假设提供信息，并提出使用这种基于模拟的 RCT 范式来评估急性脑损伤中抗癫痫治疗的未来试验的可行性。
