Desiderata for the development of next-generation electronic health record phenotype libraries

Chapman, Martin; Mumtaz, Shahzad; Rasmussen, Luke V.; Karwath, Andreas; Gkoutos, Georgios V.; Gao, Chuang; Thayer, Daniel; Pacheco, Jennifer A.; Parkinson, Helen; Richesson, Rachel L.; Jefferson, Emily; Denaxas, Spiros; Ćurčin, Vasa

doi:10.1093/gigascience/giab059

Cited by 22 publications

(23 citation statements)

References 31 publications

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“…Overall, ongoing work is needed to improve metadata collection for phenotype algorithms, with some groundwork proposed in the field. [Chapman2021, Alper2022]…”

Section: Discussionmentioning

confidence: 99%

Characterizing Variability of EHR-Driven Phenotype Definitions

Brandt

Kho

Luo

et al. 2022

Preprint

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Objective Analyze a publicly available sample of rule-based phenotype definitions to characterize and evaluate the types of logical constructs used. Materials & Methods A sample of 33 phenotype definitions used in research and published to the Phenotype KnowledgeBase (PheKB), that are represented using Fast Healthcare Interoperability Resources (FHIR) and Clinical Quality Language (CQL) was analyzed using automated analysis of the computable representation of the CQL libraries. Results Most of the phenotype definitions include narrative descriptions and flowcharts, while few provide pseudocode or executable artifacts. Most use 4 or fewer medical terminologies. The number of codes used ranges from 5 to 6865, and value sets from 1 to 19. We found the most common expressions used were literal, data, and logical expressions. Aggregate and arithmetic expressions are the least common. Expression depth ranges from 4 to 27. Discussion Despite the range of conditions, we found that all of the phenotype definitions consisted of logical criteria, representing both clinical and operational logic, and tabular data, consisting of codes from standard terminologies and keywords for natural language processing. The total number and variety of expressions is low, which may be to simplify implementation, or authors may limit complexity due to data availability constraints. Conclusion The phenotypes analyzed show significant variation in specific logical, arithmetic and other operators, but are all composed of the same high-level components, namely tabular data and logical expressions. A standard representation for phenotype definitions should support these formats and be modular to support localization and shared logic.

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“…Overall, ongoing work is needed to improve metadata collection for phenotype algorithms, with some groundwork proposed in the field. [Chapman2021, Alper2022]…”

Section: Discussionmentioning

confidence: 99%

Characterizing Variability of EHR-Driven Phenotype Definitions

Brandt

Kho

Luo

et al. 2022

Preprint

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“… 17 Given the benefits of a concrete, unambiguous phenotype definition, we hope that phenotype repository managers will encourage the inclusion of computable definitions, in addition to providing APIs to allow integrating with their repositories. 18 …”

Section: Discussionmentioning

confidence: 99%

Development of a repository of computable phenotype definitions using the clinical quality language

2021

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Objective The objective of this study is to create a repository of computable, technology-agnostic phenotype definitions for the purposes of analysis and automatic cohort identification. Materials and Methods We selected phenotype definitions from PheKB and excluded definitions that did not use structured data or were not used in published research. We translated these definitions into the Clinical Quality Language (CQL) and Fast Healthcare Interoperability Resources (FHIR) and validated them using code review and automated tests. Results A total of 33 phenotype definitions met our inclusion criteria. We developed 40 CQL libraries, 231 value sets, and 347 test cases. To support these test cases, a total of 1624 FHIR resources were created as test data. Discussion and Conclusion Although a number of challenges were encountered while translating the phenotypes into structured form, such as requiring specialized knowledge, or imprecise, ambiguous, and conflicting language, we have created a repository and a development environment that can be used for future research on computable phenotypes.

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“…High-quality phenotypes must be portable, accessible, and reproducible. A number of phenotype libraries have been developed or are undergoing development [29] in order to collect and store validated phenotype definitions. Our LC phenotype is available to download from BioPortal, where researchers can use it to produce their own executable script.…”

Section: Principal Findingsmentioning

confidence: 99%

Developing a Long COVID Phenotype for Postacute COVID-19 in a National Primary Care Sentinel Cohort: Observational Retrospective Database Analysis

Mayer¹,

Meza-Torres²,

Okusi³

et al. 2022

JMIR Public Health Surveill

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Background Following COVID-19, up to 40% of people have ongoing health problems, referred to as postacute COVID-19 or long COVID (LC). LC varies from a single persisting symptom to a complex multisystem disease. Research has flagged that this condition is underrecorded in primary care records, and seeks to better define its clinical characteristics and management. Phenotypes provide a standard method for case definition and identification from routine data and are usually machine-processable. An LC phenotype can underpin research into this condition. Objective This study aims to develop a phenotype for LC to inform the epidemiology and future research into this condition. We compared clinical symptoms in people with LC before and after their index infection, recorded from March 1, 2020, to April 1, 2021. We also compared people recorded as having acute infection with those with LC who were hospitalized and those who were not. Methods We used data from the Primary Care Sentinel Cohort (PCSC) of the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) database. This network was recruited to be nationally representative of the English population. We developed an LC phenotype using our established 3-step ontological method: (1) ontological step (defining the reasoning process underpinning the phenotype, (2) coding step (exploring what clinical terms are available, and (3) logical extract model (testing performance). We created a version of this phenotype using Protégé in the ontology web language for BioPortal and using PhenoFlow. Next, we used the phenotype to compare people with LC (1) with regard to their symptoms in the year prior to acquiring COVID-19 and (2) with people with acute COVID-19. We also compared hospitalized people with LC with those not hospitalized. We compared sociodemographic details, comorbidities, and Office of National Statistics–defined LC symptoms between groups. We used descriptive statistics and logistic regression. Results The long-COVID phenotype differentiated people hospitalized with LC from people who were not and where no index infection was identified. The PCSC (N=7.4 million) includes 428,479 patients with acute COVID-19 diagnosis confirmed by a laboratory test and 10,772 patients with clinically diagnosed COVID-19. A total of 7471 (1.74%, 95% CI 1.70-1.78) people were coded as having LC, 1009 (13.5%, 95% CI 12.7-14.3) had a hospital admission related to acute COVID-19, and 6462 (86.5%, 95% CI 85.7-87.3) were not hospitalized, of whom 2728 (42.2%) had no COVID-19 index date recorded. In addition, 1009 (13.5%, 95% CI 12.73-14.28) people with LC were hospitalized compared to 17,993 (4.5%, 95% CI 4.48-4.61; P<.001) with uncomplicated COVID-19. Conclusions Our LC phenotype enables the identification of individuals with the condition in routine data sets, facilitating their comparison with unaffected people through retrospective research. This phenotype and study protocol to explore its face validity contributes to a better understanding of LC.

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Desiderata for the development of next-generation electronic health record phenotype libraries

Cited by 22 publications

References 31 publications

Characterizing Variability of EHR-Driven Phenotype Definitions

Characterizing Variability of EHR-Driven Phenotype Definitions

Development of a repository of computable phenotype definitions using the clinical quality language

Developing a Long COVID Phenotype for Postacute COVID-19 in a National Primary Care Sentinel Cohort: Observational Retrospective Database Analysis

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