Wang Jinlian, Li Hui, Liu Hongfang
The McWilliams School of Biomedical Informatics, Houston, TX, USA.
AMIA Jt Summits Transl Sci Proc. 2025 Jun 10;2025:607-613. eCollection 2025.
Although rare diseases (RD) are gaining priority in healthcare worldwide, developing research policies for studying them in public settings remains challenging due to the limited evidence available. Evidence generation is crucial for rare diseases, requiring systematic assessment of study quality across multiple sources. Given the scarcity of patients, literature and clinical trial data for orphan drugs, we developed RD-LIVES-a tool designed to automatically accelerate evidence collection from literature and clinical trials for systematic reviews and meta-analyses. This tool enhances our understanding of treatment outcomes, determines appropriate follow-up durations, and informs the required treatment impact size for new drugs. Using Idiopathic Pulmonary Fibrosis (IPF) as an example, we demonstrate how RD-LIVES automates evidence collection and element extraction. The results indicate that RD-LIVES plays a vital role in designing costly prospective trials and has the potential to increase the likelihood of successful trial outcomes.
尽管罕见病在全球医疗保健领域正日益受到重视,但由于可用证据有限,制定在公共环境中研究罕见病的研究政策仍具有挑战性。证据生成对于罕见病至关重要,需要对多个来源的研究质量进行系统评估。鉴于孤儿药的患者、文献和临床试验数据稀缺,我们开发了RD-LIVES——一种旨在自动加速从文献和临床试验中收集证据以进行系统评价和荟萃分析的工具。该工具增强了我们对治疗结果的理解,确定了适当的随访持续时间,并为新药所需的治疗影响大小提供了信息。以特发性肺纤维化(IPF)为例,我们展示了RD-LIVES如何自动收集证据和提取要素。结果表明,RD-LIVES在设计成本高昂的前瞻性试验中发挥着至关重要的作用,并且有可能增加试验成功的可能性。