학술논문
Evaluating the impact of alternative phenotype definitions on incidence rates across a global data network.
Document Type
Academic Journal
Author
Makadia R; OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY 10027, United States.; Global Epidemiology, Janssen Pharmaceutical Research and Development, LLC, Titusville, NJ 08560, United States.; Shoaibi A; OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY 10027, United States.; Global Epidemiology, Janssen Pharmaceutical Research and Development, LLC, Titusville, NJ 08560, United States.; Rao GA; OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY 10027, United States.; Global Epidemiology, Janssen Pharmaceutical Research and Development, LLC, Titusville, NJ 08560, United States.; Ostropolets A; OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY 10027, United States.; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10027, United States.; Rijnbeek PR; OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY 10027, United States.; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, 3000 CA, The Netherlands.; Voss EA; OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY 10027, United States.; Global Epidemiology, Janssen Pharmaceutical Research and Development, LLC, Titusville, NJ 08560, United States.; Duarte-Salles T; OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY 10027, United States.; Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, 08007, Spain.; Ramírez-Anguita JM; Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Barcelona, 08003, Spain.; Mayer MA; Management Control Department, Parc de Salut Mar (PSMAR), Barcelona, 08007, Spain.; Maljković F; Research and Development, Heliant d.o.o, Belgrade, 11000, Serbia.; Denaxas S; Institute of Health Informatics, University College London, London, NW1 2DA, United Kingdom.; British Heart Foundation Data Science Centre, HDR, London, NW1 2DA, United Kingdom.; Nyberg F; School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, 40530, Sweden.; Papez V; Institute of Health Informatics, University College London, London, NW1 2DA, United Kingdom.; Sena AG; OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY 10027, United States.; Global Epidemiology, Janssen Pharmaceutical Research and Development, LLC, Titusville, NJ 08560, United States.; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, 3000 CA, The Netherlands.; Alshammari TM; OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY 10027, United States.; College of Pharmacy, Prince Sattam Bin Abdulaziz University, Riyadh, 11942, Saudi Arabia.; Lai LYH; OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY 10027, United States.; Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, M13 9PL, United Kingdom.; Haynes K; Global Epidemiology, Janssen Pharmaceutical Research and Development, LLC, Titusville, NJ 08560, United States.; Suchard MA; OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY 10027, United States.; Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90025, United States.; Hripcsak G; OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY 10027, United States.; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10027, United States.; Ryan PB; OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY 10027, United States.; Global Epidemiology, Janssen Pharmaceutical Research and Development, LLC, Titusville, NJ 08560, United States.; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10027, United States.
Source
Publisher: Oxford University Press on behalf of the American Medical Informatics Association Country of Publication: United States NLM ID: 101730643 Publication Model: eCollection Cited Medium: Internet ISSN: 2574-2531 (Electronic) Linking ISSN: 25742531 NLM ISO Abbreviation: JAMIA Open Subsets: PubMed not MEDLINE
Subject
Language
English
Abstract
Objective: Developing accurate phenotype definitions is critical in obtaining reliable and reproducible background rates in safety research. This study aims to illustrate the differences in background incidence rates by comparing definitions for a given outcome.
Materials and Methods: We used 16 data sources to systematically generate and evaluate outcomes for 13 adverse events and their overall background rates. We examined the effect of different modifications (inpatient setting, standardization of code set, and code set changes) to the computable phenotype on background incidence rates.
Results: Rate ratios (RRs) of the incidence rates from each computable phenotype definition varied across outcomes, with inpatient restriction showing the highest variation from 1 to 11.93. Standardization of code set RRs ranges from 1 to 1.64, and code set changes range from 1 to 2.52.
Discussion: The modification that has the highest impact is requiring inpatient place of service, leading to at least a 2-fold higher incidence rate in the base definition. Standardization showed almost no change when using source code variations. The strength of the effect in the inpatient restriction is highly dependent on the outcome. Changing definitions from broad to narrow showed the most variability by age/gender/database across phenotypes and less than a 2-fold increase in rate compared to the base definition.
Conclusion: Characterization of outcomes across a network of databases yields insights into sensitivity and specificity trade-offs when definitions are altered. Outcomes should be thoroughly evaluated prior to use for background rates for their plausibility for use across a global network.
Competing Interests: R.M., A.S., G.R., E.A.V., A.G.S., K.H., and P.B.R. are employees of Janssen Pharmaceutical Company of Johnson & Johnson and shareholder of Johnson & Johnson. A.O. has received funding from the US National Institutes of Health and the US Food and Drug Administration. G.H. received support through a grant to Columbia University (NIH R01 LM006910). M.A.S. receives grants from the US Department of Veterans Affairs within the scope of this work, and grants and contracts from the US National Institutes of Health, US Food & Drug Administration, and Janssen Research & Development outside the scope of this work. P.R.R. has received funding from the Innovative Medicines Initiative (IMI) 2 Joint Undertaking (JU) under grant agreement No 806968. M.A.M., F.N., J.M.R.-A., F.M., T.D.-S., S.D., V.P., L.Y.H.L., and T.M.A. have no conflicts of interest to declare that are directly relevant to the contents of this study.
(© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)
Materials and Methods: We used 16 data sources to systematically generate and evaluate outcomes for 13 adverse events and their overall background rates. We examined the effect of different modifications (inpatient setting, standardization of code set, and code set changes) to the computable phenotype on background incidence rates.
Results: Rate ratios (RRs) of the incidence rates from each computable phenotype definition varied across outcomes, with inpatient restriction showing the highest variation from 1 to 11.93. Standardization of code set RRs ranges from 1 to 1.64, and code set changes range from 1 to 2.52.
Discussion: The modification that has the highest impact is requiring inpatient place of service, leading to at least a 2-fold higher incidence rate in the base definition. Standardization showed almost no change when using source code variations. The strength of the effect in the inpatient restriction is highly dependent on the outcome. Changing definitions from broad to narrow showed the most variability by age/gender/database across phenotypes and less than a 2-fold increase in rate compared to the base definition.
Conclusion: Characterization of outcomes across a network of databases yields insights into sensitivity and specificity trade-offs when definitions are altered. Outcomes should be thoroughly evaluated prior to use for background rates for their plausibility for use across a global network.
Competing Interests: R.M., A.S., G.R., E.A.V., A.G.S., K.H., and P.B.R. are employees of Janssen Pharmaceutical Company of Johnson & Johnson and shareholder of Johnson & Johnson. A.O. has received funding from the US National Institutes of Health and the US Food and Drug Administration. G.H. received support through a grant to Columbia University (NIH R01 LM006910). M.A.S. receives grants from the US Department of Veterans Affairs within the scope of this work, and grants and contracts from the US National Institutes of Health, US Food & Drug Administration, and Janssen Research & Development outside the scope of this work. P.R.R. has received funding from the Innovative Medicines Initiative (IMI) 2 Joint Undertaking (JU) under grant agreement No 806968. M.A.M., F.N., J.M.R.-A., F.M., T.D.-S., S.D., V.P., L.Y.H.L., and T.M.A. have no conflicts of interest to declare that are directly relevant to the contents of this study.
(© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)