Jamie Gavin, Elson William, Kar Debasish, Wimalaratna Rashmi, Hoang Uy, Meza-Torres Bernardo, Forbes Anna, Hinton William, Anand Sneha, Ferreira Filipa, Byford Rachel, Ordonez-Mena Jose, Agrawal Utkarsh, de Lusignan Simon
Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, OX2 6ED, United Kingdom.
JAMIA Open. 2024 May 10;7(2):ooae034. doi: 10.1093/jamiaopen/ooae034. eCollection 2024 Jul.
To evaluate Phenotype Execution and Modelling Architecture (PhEMA), to express sharable phenotypes using Clinical Quality Language (CQL) and intensional Systematised Nomenclature of Medicine (SNOMED) Clinical Terms (CT) Fast Healthcare Interoperability Resources (FHIR) valuesets, for exemplar chronic disease, sociodemographic risk factor, and surveillance phenotypes.
We curated 3 phenotypes: Type 2 diabetes mellitus (T2DM), excessive alcohol use, and incident influenza-like illness (ILI) using CQL to define clinical and administrative logic. We defined our phenotypes with valuesets, using SNOMED's hierarchy and expression constraint language, and CQL, combining valuesets and adding temporal elements where needed. We compared the count of cases found using PhEMA with our existing approach using convenience datasets. We assessed our new approach against published desiderata for phenotypes.
The T2DM phenotype could be defined as 2 intensionally defined SNOMED valuesets and a CQL script. It increased the prevalence from 7.2% to 7.3%. Excess alcohol phenotype was defined by valuesets that added qualitative clinical terms to the quantitative conceptual definitions we currently use; this change increased prevalence by 58%, from 1.2% to 1.9%. We created an ILI valueset with SNOMED concepts, adding a temporal element using CQL to differentiate new episodes. This increased the weekly incidence in our convenience sample (weeks 26-38) from 0.95 cases to 1.11 cases per 100 000 people.
Phenotypes for surveillance and research can be described fully and comprehensibly using CQL and intensional FHIR valuesets. Our use case phenotypes identified a greater number of cases, whilst anticipated from excessive alcohol this was not for our other variable. This may have been due to our use of SNOMED CT hierarchy. Our new process fulfilled a greater number of phenotype desiderata than the one that we had used previously, mostly in the modeling domain. More work is needed to implement that sharing and warehousing domains.
评估表型执行与建模架构（PhEMA），使用临床质量语言（CQL）以及医学术语集（SNOMED）临床术语（CT）快速医疗保健互操作性资源（FHIR）值集来表达可共享的表型，用于典型的慢性病、社会人口统计学风险因素和监测表型。
我们使用CQL策划了3种表型：2型糖尿病（T2DM）、过度饮酒和流感样疾病（ILI）发病，以定义临床和管理逻辑。我们使用值集、SNOMED的层次结构和表达约束语言以及CQL来定义我们的表型，组合值集并在需要时添加时间元素。我们将使用PhEMA发现的病例数与我们使用便利数据集的现有方法进行了比较。我们根据已发表的表型要求评估了我们的新方法。
T2DM表型可定义为2个内涵定义的SNOMED值集和一个CQL脚本。其患病率从7.2%提高到了7.3%。过度饮酒表型由值集定义，这些值集在我们当前使用的定量概念定义中添加了定性临床术语；这一变化使患病率提高了58%，从1.2%提高到了1.9%。我们使用SNOMED概念创建了一个ILI值集，并使用CQL添加了时间元素以区分新发病例。这使我们便利样本（第26 - 38周）中的每周发病率从每10万人0.95例增加到了1.11例。
使用CQL和内涵FHIR值集可以全面且可理解地描述用于监测和研究的表型。我们的用例表型识别出了更多病例，虽然过度饮酒情况符合预期，但其他变量并非如此。这可能是由于我们使用了SNOMED CT层次结构。我们的新流程比我们之前使用的流程满足了更多的表型要求，主要是在建模领域。在实现共享和存储领域方面还需要更多工作。
