Development of a cardiac-centered frailty ontology

• Published in: Journal of biomedical semantics 2019-01, Vol.10 (1), p.3-3, Article 3
• Main Authors: Doing-Harris, Kristina, Bray, Bruce E, Thackeray, Anne, Shah, Rashmee U, Shao, Yijun, Cheng, Yan, Zeng-Treitler, Qing, Garvin, Jennifer H, Weir, Charlene
• Format: Article
• Language: English
• Subjects: Activities of daily living; Adults; Arthritis; Automation; Biological Ontologies; Cardiac Surgical Procedures; Cardiology; Children; Clinical Decision-Making; Comorbidity; Decision making; Decisions; Elderly; Electronic Health Records; Electronic medical records; Electronic records; Frailty; Frailty - surgery; Gerontology; Government agencies; Heart; Heart failure; Heart surgery; Humans; Knowledge representation; Mapping; Measuring instruments; Medical examination; Medical records; Medical research; Natural Language Processing; Older people; Ontology; Patients; Programming languages; Quality of life; SNOMED-CT; Surgery
• ISSN: 2041-1480; 2041-1480
• DOI: 10.1186/s13326-019-0195-3

A Cardiac-centered Frailty Ontology can be an important foundation for using NLP to assess patient frailty. Frailty is an important consideration when making patient treatment decisions, particularly in older adults, those with a cardiac diagnosis, or when major surgery is a consideration. Clinicians often report patient's frailty in progress notes and other documentation. Frailty is recorded in many different ways in patient records and many different validated frailty-measuring instruments are available, with little consistency across instruments. We specifically explored concepts relevant to decisions regarding cardiac interventions. We based our work on text found in a large corpus of clinical notes from the Department of Veterans Affairs (VA) national Electronic Health Record (EHR) database. The full ontology has 156 concepts, with 246 terms. It includes 86 concepts we expect to find in clinical documents, with 12 qualifier values. The remaining 58 concepts represent hierarchical groups (e.g., physical function findings). Our top-level class is clinical finding, which has children clinical history finding, instrument finding, and physical examination finding, reflecting the OGMS definition of clinical finding. Instrument finding is any score found for the existing frailty instruments. Within our ontology, we used SNOMED-CT concepts where possible. Some of the 86 concepts we expect to find in clinical documents are associated with the properties like ability interpretation. The concept ability to walk can either be able, assisted or unable. Each concept-property level pairing gets a different frailty score. Each scored concept received three scores: a frailty score, a relevance to cardiac decisions score, and a likelihood of resolving after the recommended intervention score. The ontology includes the relationship between scores from ten frailty instruments and frailty as assessed using ontology concepts. It also included rules for mapping ontology elements to instrument items for three common frailty assessment instruments. Ontology elements are used in two clinical NLP systems. We developed and validated a Cardiac-centered Frailty Ontology, which is a machine-interoperable description of frailty that reflects all the areas that clinicians consider when deciding which cardiac intervention will best serve the patient as well as frailty indications generally relevant to medical decisions. The ontology owl file is available on Bioportal at http://bioport