Development of a cardiac-centered frailty ontology

BackgroundA 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.ResultsThe 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.ConclusionsWe 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://bioportal.bioontology.org/ontologies/CCFO.

[1]  M. Tamburini,et al.  Health-related quality of life measures in cancer. , 2001, Annals of oncology : official journal of the European Society for Medical Oncology.

[2]  B S Linn,et al.  The Rapid Disability Rating Scale—2 , 1982, Journal of the American Geriatrics Society.

[3]  A. Mitnitski,et al.  Frailty in relation to the accumulation of deficits. , 2007, The journals of gerontology. Series A, Biological sciences and medical sciences.

[4]  M. Tinetti.  Performance‐Oriented Assessment of Mobility Problems in Elderly Patients , 1986, Journal of the American Geriatrics Society.

[5]  Paul Stolee,et al.  Frailty is associated with postoperative complications in older adults with medical problems. , 2009, Archives of gerontology and geriatrics.

[6]  Masafumi Kuzuya,et al.  Evaluation of Mini-Nutritional Assessment for Japanese frail elderly. , 2005, Nutrition.

[7]  T. Strandberg,et al.  Frailty in elderly people , 2007, The Lancet.

[8]  Nathan E Brummel,et al.  Frailty for Surgeons: Review of a National Institute on Aging Conference on Frailty for Specialists. , 2015, Journal of the American College of Surgeons.

[9]  J Charlet,et al.  Knowledge Representation and Management, It's Time to Integrate! , 2017, Yearbook of medical informatics.

[10]  M A Hlatky,et al.  A brief self-administered questionnaire to determine functional capacity (the Duke Activity Status Index). , 1989, The American journal of cardiology.

[11]  W. Alexander,et al.  American diabetes association. , 2010, P & T : a peer-reviewed journal for formulary management.

[12]  Stuart C. Shapiro,et al.  Natural language understanding for soft information fusion , 2013, Proceedings of the 16th International Conference on Information Fusion.

[13]  Catalina Martínez-Costa,et al.  Harmonizing SNOMED CT with BioTopLite: An Exercise in Principled Ontology Alignment , 2015, MedInfo.

[14]  Charlene R. Weir,et al.  Representation of Functional Status Concepts from Clinical Documents and Social Media Sources by Standard Terminologies , 2015, AMIA.

[15]  Irene Katzan,et al.  The Knowledge Program: an innovative, comprehensive electronic data capture system and warehouse. , 2011, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[16]  Kenneth Rockwood,et al.  Some mathematical models of frailty and their clinical implications , 2002 .

[17]  P. Haidet,et al.  Understanding Concordance in Patient-Physician Relationships: Personal and Ethnic Dimensions of Shared Identity , 2008, The Annals of Family Medicine.

[18]  Hairong Yu,et al.  Data extraction from electronic health records - existing tools may be unreliable and potentially unsafe. , 2013, Australian family physician.

[19]  Timo Mäkikallio,et al.  Everolimus-Eluting Stents or Bypass Surgery for Left Main Coronary Disease. , 2017, The New England journal of medicine.

[20]  C. Stehouwer,et al.  The frailty dilemma. Review of the predictive accuracy of major frailty scores. , 2012, European journal of internal medicine.

[21]  Pradeep Kumar Ray,et al.  Towards an ontology for data quality in integrated chronic disease management: A realist review of the literature , 2013, Int. J. Medical Informatics.

[22]  C. Sherbourne,et al.  The MOS 36-item Short-Form Health Survey (SF-36): III. Tests of data quality, scaling assumptions, and reliability across diverse patient groups. , 1994 .

[23]  Charlene R. Weir,et al.  Extracting frailty status for post surgical mortality prediction , 2018 .

[24]  Carl van Walraven,et al.  Association of the Hospital Volume of Frail Surgical Patients Cared for with Outcomes after Elective, Major Noncardiac Surgery: A Retrospective Population-based Cohort Study , 2017, Anesthesiology.

[25]  André Lamy,et al.  Everolimus-Eluting Stents or Bypass Surgery for Left Main Coronary Disease. , 2017, The New England journal of medicine.

[26]  Subashan Perera,et al.  Clinical Global Impression of Change in Physical Frailty: Development of a Measure Based on Clinical Judgment , 2004, Journal of the American Geriatrics Society.

[27]  Vishal Jain,et al.  Ontology Development and Query Retrieval using Protégé Tool , 2013 .

[28]  Kenneth Rockwood,et al.  Frailty status at admission to hospital predicts multiple adverse outcomes , 2017, Age and ageing.

[29]  Werner Ceusters,et al.  Analyzing SNOMED CT's Historical Data: Pitfalls and Possibilities , 2016, AMIA.

[30]  Fleur Mougin,et al.  Building a model for disease classification integration in oncology, an approach based on the national cancer institute thesaurus , 2017, J. Biomed. Semant..

[31]  Mathias Brochhausen,et al.  Towards a Consistent and Scientifically Accurate Drug Ontology , 2013, ICBO.

[32]  Aaron Aragaki,et al.  Frailty: Emergence and Consequences in Women Aged 65 and Older in the Women's Health Initiative Observational Study , 2005, Journal of the American Geriatrics Society.

[33]  M. Farkouh,et al.  Optimal approaches to diabetic patients with multivessel disease. , 2015, Trends in cardiovascular medicine.

[34]  Hua Xu,et al.  Data from clinical notes: a perspective on the tension between structure and flexible documentation , 2011, J. Am. Medical Informatics Assoc..

[35]  Werner Ceusters,et al.  An ontological analysis of diagnostic assertions in electronic healthcare records , 2015, ICBO.

[36]  Charlene R. Weir,et al.  Identification and Use of Frailty Indicators from Text to Examine Associations with Clinical Outcomes Among Patients with Heart Failure , 2016, AMIA.

[37]  Olivier Bodenreider,et al.  Circular hierarchical relationships in the UMLS: etiology, diagnosis, treatment, complications and prevention , 2001, AMIA.

[38]  J. Larson.  The MOS 36-Item Short form Health Survey , 1997, Evaluation & the health professions.

[39]  L. Fried,et al.  Frailty in older adults: evidence for a phenotype. , 2001, The journals of gerontology. Series A, Biological sciences and medical sciences.

[40]  S. Katz,et al.  12. Index of ADL. , 1976, Medical care.

[41]  D. Taggart,et al.  CABG or stents in coronary artery disease: end of the debate? , 2013, The Lancet.

[42]  José L. V. Mejino,et al.  A reference ontology for biomedical informatics: the Foundational Model of Anatomy , 2003, J. Biomed. Informatics.

[43]  Lawrence Hunter,et al.  Desiderata for ontologies to be used in semantic annotation of biomedical documents , 2011, J. Biomed. Informatics.

[44]  N. F. Noy,et al.  Ontology Development 101: A Guide to Creating Your First Ontology , 2001 .

[45]  F. Mahoney,et al.  FUNCTIONAL EVALUATION: THE BARTHEL INDEX. , 2018, Maryland state medical journal.

[46]  Stuart C. Shapiro,et al.  Use of background knowledge in natural language understanding for information fusion , 2015, 2015 18th International Conference on Information Fusion (Fusion).

[47]  J. Mckinlay,et al.  Non-medical influences on medical decision-making. , 1996, Social science & medicine.

[48]  I. McDowell,et al.  A global clinical measure of fitness and frailty in elderly people , 2005, Canadian Medical Association Journal.

[49]  Kenneth Rockwood,et al.  Frailty and Diabetes in Older Hospitalized Adults: What Drives Outcomes? , 2017 .

[50]  Werner Ceusters,et al.  Biomarkers in the Ontology for General Medical Science , 2015, MIE.

[51]  H. Holman,et al.  Measurement of patient outcome in arthritis. , 1980, Arthritis and rheumatism.

[52]  Sameer Bansilal,et al.  The Future REvascularization Evaluation in patients with Diabetes mellitus: optimal management of Multivessel disease (FREEDOM) trial: clinical and angiographic profile at study entry. , 2012, American heart journal.

[53]  Job G. Godino,et al.  Frailty assessment instruments: Systematic characterization of the uses and contexts of highly-cited instruments , 2016, Ageing Research Reviews.

[54]  Mandeep Singh,et al.  Importance of frailty in patients with cardiovascular disease. , 2014, European heart journal.

[55]  Karen J. Buth,et al.  Frail Patients Are at Increased Risk for Mortality and Prolonged Institutional Care After Cardiac Surgery , 2010, Circulation.

[56]  Richard Sutton,et al.  Limitations of the New York Heart Association functional classification system and self-reported walking distances in chronic heart failure , 2006, Heart.

[57]  Werner Ceusters,et al.  Toward an Ontological Treatment of Disease and Diagnosis , 2009, Summit on translational bioinformatics.

[58]  M. Lawton,et al.  Assessment of older people: self-maintaining and instrumental activities of daily living. , 1969, The Gerontologist.

[59]  M. Makary,et al.  Frailty as a predictor of surgical outcomes in older patients. , 2010, Journal of the American College of Surgeons.

[60]  Werner Ceusters,et al.  A Realism-Based Approach to the Evolution of Biomedical Ontologies , 2006, AMIA.

[61]  Charlene R. Weir,et al.  FRAILTY AND CARDIOVASCULAR SURGERY, DEEP NEURAL NETWORK VERSUS SUPPORT VECTOR MACHINE TO PREDICT DEATH , 2018 .

[62]  Kenneth Rockwood,et al.  Operationalization of Frailty Using Eight Commonly Used Scales and Comparison of Their Ability to Predict All‐Cause Mortality , 2013, Journal of the American Geriatrics Society.