Dolin Robert H, Arafat Waddah, Heale Bret S E, Shenvi Edna, Chamala Srikar
Elimu Informatics, El Cerrito, CA.
UT Southwestern, Dallas, TX.
AMIA Annu Symp Proc. 2025 May 22;2024:359-367. eCollection 2024.
: Clinical trials play a crucial role in precision cancer care. Patients generally learn of trials from their physician, and physician recognition of potential matches can be enhanced through decision support tools. But automated trial matching remains challenging, particularly for molecular eligibility criteria. : We assessed the feasibility of FHIR Genomics plus CQL to enable trial matching, particularly for molecular criteria. : We developed a prototype that included (1) encoded trial criteria in CQL; (2) synthetic patient clinical and genomic data; (3) trial eligibility computation. : We found that even complex molecular eligibility criteria can be represented in CQL given that the semantics of a criterion are formalized in base FHIR specifications. The proof of concept "CQL for Clinical Trials Matching" is available at [https://elimu.io/downloads/]. : Proof of concept work suggests FHIR and CQL as viable options for enhancing clinical trial matching.
临床试验在精准癌症治疗中发挥着关键作用。患者通常从其医生处了解到试验信息，并且通过决策支持工具可以增强医生对潜在匹配情况的识别。但自动化试验匹配仍然具有挑战性，尤其是对于分子入选标准而言。
我们评估了FHIR基因组学加CQL实现试验匹配的可行性，特别是对于分子标准。
我们开发了一个原型，其中包括：（1）用CQL编码的试验标准；（2）合成的患者临床和基因组数据；（3）试验资格计算。
我们发现，鉴于标准的语义在基础FHIR规范中已形式化，即使是复杂的分子入选标准也可以用CQL表示。概念验证“用于临床试验匹配的CQL”可在[https://elimu.io/downloads/]获取。
概念验证工作表明，FHIR和CQL是增强临床试验匹配的可行选择。
