首页> 外文期刊>Neurology and therapy. >Understanding the Disease Course and Therapeutic Benefit of Tafamidis Across Real-World Studies of Hereditary Transthyretin Amyloidosis with Polyneuropathy: A Proof of Concept for Integrative Data Analytic Approaches
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Understanding the Disease Course and Therapeutic Benefit of Tafamidis Across Real-World Studies of Hereditary Transthyretin Amyloidosis with Polyneuropathy: A Proof of Concept for Integrative Data Analytic Approaches

机译:在遗传性运甲状腺素蛋白淀粉样变性病与多发性神经病的现实世界研究中了解塔法米的病程和治疗益处:综合数据分析方法的概念证明

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IntroductionHereditary transthyretin (TTR) amyloidosis with polyneuropathy (hATTR-PN) is a rare, autosomal dominant amyloidosis characterized primarily by progressive ascending sensorimotor neuropathy often associated with autonomic involvement. hATTR-PN is caused by a mutation in the TTR gene leading to protein misfolding and amyloid accumulation in peripheral nerves and vital organs. The latest global prevalence estimates point to 10,000 cases worldwide, with an upper end of about 40,000. Tafamidis has been approved in over 40 countries for delaying neurologic disease progression in early-stage hATTR-PN. Multiple observational studies have examined clinical outcomes in hATTR-PN patients treated with tafamidis in the routine clinical setting. Integrative data analysis (IDA) is a technique for optimally constructing synthetic treatment and control cohorts from multiple independent studies, which allows meta-analysis of patient-level data. Herein, we provide a proof of concept for the application of IDA to real-world and natural history hATTR-PN data. IDA permits increased understanding of outcomes in tafamidis-treated and untreated persons with hATTR-PN by optimally pooling all available information. MethodsSummary statistics corresponding to the Neuropathy Impairment Score-Lower Limb (NIS-LL) from five published studies were pooled, converted to change from baseline means and variances, and analyzed using IDA. IDA-based synthetic cohorts were generated by averaging across studies stratified on treatment versus control cohort. Trends in change from baseline in each study and the corresponding synthetic cohorts were plotted. Patient-level data were simulated from the synthetic cohort trends in a Monte Carlo simulation to highlight the ability to contrast synthetic cohort trends using the mixed model for repeated measures (MMRM). ResultsThe average sample size among the five studies was 71 (37–128) patients. The average NIS-LL trends indicated that tafamidis-treated patients experienced slower progression in neuropathy compared to untreated patients. Synthetic cohort trends reflected the trends observed in the contributing studies, while simultaneously shrinking the width of corresponding confidence bands. Monte Carlo simulation results demonstrated precise recovery of the synthetic cohort and time-dependent simulated NIS-LL means by the MMRM. DiscussionThis proof of concept demonstrates the utility of IDA-based synthetic cohorts for increased precision in characterizing and testing hypotheses about treatment outcomes and prognosis in hATTR-PN. FundingPfizer. Plain Language SummaryPlain language summary available for this article.
机译:引言遗传性甲状腺素转运蛋白(TTR)淀粉样变性伴多发性神经病(hATTR-PN)是一种罕见的常染色体显性淀粉样变性,主要特征是进行性上行感觉神经病变,通常与自主神经受累有关。 hATTR-PN是由TTR基因突变引起的,导致蛋白质错误折叠和淀粉样蛋白在周围神经和重要器官中积累。最新的全球患病率估计数表明全球有10,000例,上限约为40,000。 Tafamidis已被40多个国家批准用于延缓早期hATTR-PN中神经系统疾病的进展。多项观察性研究已经检查了在常规临床环境中用他法米司治疗的hATTR-PN患者的临床结局。集成数据分析(IDA)是一项用于根据多项独立研究优化构建综合治疗和控制队列的技术,该技术可对患者水平的数据进行荟萃分析。本文中,我们提供了将IDA应用于现实世界和自然历史hATTR-PN数据的概念证明。通过最佳地汇总所有可用信息,IDA可以提高对经过tafamidis治疗和未经治疗的hATTR-PN患者的预后的了解。方法汇总五项已发表研究的与神经病变减低评分(NIS-LL)相对应的摘要统计数据,将其转换为基线均值和方差的变化,并使用IDA进行分析。基于IDA的合成队列是通过对按治疗与对照队列分层的研究进行平均得出的。绘制了每个研究和相应的合成队列中与基线相比的变化趋势。在蒙特卡洛模拟中从合成队列趋势中模拟了患者水平的数据,以强调使用混合模型重复测量(MMRM)来对比合成队列趋势的能力。结果五项研究的平均样本量为71(37–128)位患者。 NIS-LL的平均趋势表明,与未接受治疗的患者相比,接受过他法米司治疗的患者的神经病变进展较慢。合成队列趋势反映了在贡献研究中观察到的趋势,同时缩小了相应置信带的宽度。蒙特卡洛模拟结果表明,MMRM可精确恢复合成队列,并具有时间依赖性,可模拟NIS-LL。讨论此概念证明证明了基于IDA的合成队列在表征和测试有关hATTR-PN中治疗结局和预后的假设方面提高了准确性。辉瑞公司。普通语言摘要本文提供了普通语言摘要。

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