Assessing electronic health record phenotypes against gold-standard diagnostic criteria for diabetes mellitus

Journal of the American Medical Informatics Association

Volumn 24, Issue e1, 2017, Pages e121-e128

  Spratt, Susan E ^a   Pereira, Katherine ^b   Granger, Bradi B ^b   Batch, Bryan C ^a   Phelan, Matthew ^c   Pencina, Michael ^a,c   Miranda, Marie Lynn ^d   Boulware, Ebony ^a   Lucas, Joseph E ^e   Nelson, Charlotte L ^c   Neely, Benjamin ^c   Goldstein, Benjamin A ^a,c   Barth, Pamela ^a   Richesson, Rachel L ^b   Riley, Isaretta L ^a   Corsino, Leonor ^a   McPeek Hinz, Eugenia R ^a   Rusincovitch, Shelley ^a   Green, Jennifer ^a   Barton, Anna Beth ^a   Kelley, Carly ^f   Hyland, Kristen ^f   Tang, Monica ^f   Elliott, Amanda ^f   Ruel, Ewa ^f   Clark, Alexander ^f   Mabrey, Melanie ^f   Morrissey, Kay Lyn ^f   Rao, Jyothi ^f   Hong, Beatrice ^f   Pierre Louis, Marjorie ^f   Kelly, Katherine ^f   Jelesoff, Nicole ^f   more..

^a DUKE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b DUKE UNIVERSITY   (United States)

^c DUKE CLINICAL RESEARCH INSTITUTE   (United States)

^d BAYLOR COLLEGE OF MEDICINE   (United States)

^e Duke University   (United States)

^f Duke Translational Medicine Institute ^*

Author keywords

diabetes identification; diabetes registries; EHR phenotypes

Indexed keywords

ARTICLE; DIABETIC PATIENT; ELECTRONIC HEALTH RECORD; GLUCOSE METABOLISM; HUMAN; HYPERGLYCEMIA; IMPAIRED GLUCOSE TOLERANCE; INTERMETHOD COMPARISON; MEDICAL RECORD REVIEW; NON INSULIN DEPENDENT DIABETES MELLITUS; PHENOTYPE; SENSITIVITY AND SPECIFICITY; ALGORITHM; BLOOD; DIABETES MELLITUS;

GLYCOSYLATED HEMOGLOBIN; HEMOGLOBIN A1C PROTEIN, HUMAN;

ALGORITHMS; DIABETES MELLITUS; DIABETES MELLITUS, TYPE 2; ELECTRONIC HEALTH RECORDS; GLYCATED HEMOGLOBIN A; HUMANS; PHENOTYPE; SENSITIVITY AND SPECIFICITY;

EID: 85031024709     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1093/jamia/ocw123     Document Type: Article

References (30)

1. American Diabetes Association. Economic costs of diabetes in the U.S in 2012. Diabetes Care. 2013;36:1033-46.
2. The Diabetes Control and Complications Trial Research Group. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med. 1993;329:977-86.
3. Centers for Disease Control and Prevention. National Diabetes Statistics Report, 2014: Estimates of Diabetes and Its Burden in the United States. Atlanta, GA: US Department of Health and Human Services; 2014. http:// www.cdc.gov/diabetes/pubs/statsreport14/national-diabetes-report-web. pdf. Accessed November 2, 2015.
4. American Diabetes Association. Classification and diagnosis of diabetes. Diabetes Care. 2016;39 (Suppl 1): S13-22.
5. Richesson RL, Rusincovitch SA, Wixted D, et al. A comparison of phenotype definitions for diabetes mellitus. J Am Med Inform Assoc. 2013;20:e319-26.
6. Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System. http://www.cdc.gov/brfss/. Accessed May 8, 2015.
7. US Department of Health and Human Services. HITECH Act Enforcement Interim Final Rule. http://www.hhs.gov/hipaa/for-professionals/specialtopics/ HITECH-act-enforcement-interim-final-rule/index.html. Accessed February 16, 2016.
8. Nichols GA, Schroeder EB, Karter AJ, et al. Trends in diabetes incidence among 7 million insured adults, 2006-2011: the SUPREME-DM project. Am J Epidemiol. 2015;181:32-9.
9. Holt TA, Stables D, Hippisley-Cox J, et al. Identifying undiagnosed diabetes: cross-sectional survey of 3.6 million patients' electronic records. Br J Gen Pract. 2008;58:192-6.
10. Vinker S, Fogelman Y, Elhayany A, et al. Usefulness of electronic databases for the detection of unrecognized diabetic patients. Cardiovasc Diabetol. 2003;2:13.
11. Kudyakov R, Bowen J, Ewen E, et al. Electronic health record use to classify patients with newly diagnosed versus preexisting type 2 diabetes: infrastructure for comparative effectiveness research and population health management. Popul Health Manag. 2012;15:3-11.
12. Baus A, Wood G, Pollard C, et al. Registry-based diabetes risk detection schema for the systematic identification of patients at risk for diabetes in West Virginia primary care centers. Perspect Health Inf Manag. 2013;10:1f. eCollection 2013.
13. Ho ML, Lawrence N, van Walraven C, et al. The accuracy of using integrated electronic health care data to identify patients with undiagnosed diabetes mellitus. J Eval Clin Pract. 2012;18:606-11.
14. Mishra NK, Son RY, Arnzen JJ. Towards automatic diabetes case detection and ABCS protocol compliance assessment. ClinMed Res. 2012;10:106-21.
15. Schultz S, Seddig T, Hanser S, et al. Checking coding completeness by mining discharge summaries. Stud Health Technol Inform. 2011;169:594-8.
16. Klompas M, Eggleston E, McVetta J, et al. Automated detection and classification of type 1 versus type 2 diabetes using electronic health record data. Diabetes Care. 2013;36:914-21.
17. Morris AD, Boyle DI, MacAlpine R, et al. The diabetes audit and research in Tayside Scotland (DARTS) study: electronic record linkage to create a diabetes register. DARTS/MEMO Collaboration. BMJ. 1997;315:524-8.
18. Chamany S, Silver LD, Bassett MT, et al. Tracking diabetes: New York City's A1C Registry. Milbank Q. 2009;87:547-70.
19. Spratt SE, Batch BC, Davis LP, et al. Methods and initial findings from the Durham Diabetes Coalition: integrating geospatial health technology and community interventions to reduce death and disability. J Clin Transl Endocrinol. 2015;2:26-36.
20. Nichols GA, Desai J, Elston Lafata J, et al. Construction of a multisite DataLink using electronic health records for the identification, surveillance, prevention, and management of diabetes mellitus: the SUPREMEDM project. Prev Chronic Dis. 2012;9:E110.
21. Desai JR Wu P, Nichols GA, et al. Diabetes and asthma case identification, validation, and representativeness when using electronic health data to construct registries for comparative effectiveness and epidemiologic research. Med Care. 2012;50 (Suppl):S30-5.
22. Vogt TM, Elston-Lafata J, Tolsma D, et al. The role of research in integrated healthcare systems: the HMO Research Network. Am J Manag Care. 2004;10:643-648.
23. Pacheco JA, Thompson W. Type 2 Diabetes Mellitus Electronic Medical Record Case and Control Selection Algorithms. 2011. https://phekb.org/ sites/phenotype/files/T2DM-algorithm.pdf. Accessed November 2, 2015.
24. Begg CB, Greenes RA. Assessment of diagnostic tests when disease verification is subject to selection bias. Biometrics. 1983;39:207-15.
25. Richesson RL, Barth P, Nelson CL, et al. A Chart Review System for the Validation of Computable Phenotypes in Diabetes. Poster presented at the American Medical Informatics Association (AMIA) Joint Summits for Translational Research. San Francisco; March 2015.
26. REDCap website. https://redcap.vanderbilt.edu/. Accessed November 2, 2015.
27. Cronin AM, Vickers AJ. Statistical methods to correct for verification bias in diagnostic studies are inadequate when there are few false negatives: a simulation study. BMC Med Res Methodol. 2008;8:75.
28. Pepe, MS. The Statistical Evaluation of Medical Tests for Classification and Prediction (Oxford Statistical Science Series). New York: Oxford University Press; 2003.
29. Seidu S, Davies MJ, Mostafa S, et al. Prevalence and characteristics in coding, classification and diagnosis of diabetes in primary care. Postgrad Med J. 2014;90:13-17.
30. Liaw ST, Taggart J, Yu H, et al. Data extraction from electronic health records-existing tools may be unreliable and potentially unsafe. Aust Fam Physician. 2013;42:820-3.