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A comparison of phenotype definitions for diabetes mellitus.

Publication ,  Journal Article
Richesson, RL; Rusincovitch, SA; Wixted, D; Batch, BC; Feinglos, MN; Miranda, ML; Hammond, WE; Califf, RM; Spratt, SE
Published in: J Am Med Inform Assoc
December 2013

OBJECTIVE: This study compares the yield and characteristics of diabetes cohorts identified using heterogeneous phenotype definitions. MATERIALS AND METHODS: Inclusion criteria from seven diabetes phenotype definitions were translated into query algorithms and applied to a population (n=173 503) of adult patients from Duke University Health System. The numbers of patients meeting criteria for each definition and component (diagnosis, diabetes-associated medications, and laboratory results) were compared. RESULTS: Three phenotype definitions based heavily on ICD-9-CM codes identified 9-11% of the patient population. A broad definition for the Durham Diabetes Coalition included additional criteria and identified 13%. The electronic medical records and genomics, NYC A1c Registry, and diabetes-associated medications definitions, which have restricted or no ICD-9-CM criteria, identified the smallest proportions of patients (7%). The demographic characteristics for all seven phenotype definitions were similar (56-57% women, mean age range 56-57 years).The NYC A1c Registry definition had higher average patient encounters (54) than the other definitions (range 44-48) and the reference population (20) over the 5-year observation period. The concordance between populations returned by different phenotype definitions ranged from 50 to 86%. Overall, more patients met ICD-9-CM and laboratory criteria than medication criteria, but the number of patients that met abnormal laboratory criteria exclusively was greater than the numbers meeting diagnostic or medication data exclusively. DISCUSSION: Differences across phenotype definitions can potentially affect their application in healthcare organizations and the subsequent interpretation of data. CONCLUSIONS: Further research focused on defining the clinical characteristics of standard diabetes cohorts is important to identify appropriate phenotype definitions for health, policy, and research.

Duke Scholars

Published In

J Am Med Inform Assoc

DOI

EISSN

1527-974X

Publication Date

December 2013

Volume

20

Issue

e2

Start / End Page

e319 / e326

Location

England

Related Subject Headings

  • Phenotype
  • Middle Aged
  • Medical Informatics
  • Male
  • International Classification of Diseases
  • Humans
  • Female
  • Electronic Health Records
  • Diabetes Mellitus
  • Algorithms
 

Citation

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Richesson, R. L., Rusincovitch, S. A., Wixted, D., Batch, B. C., Feinglos, M. N., Miranda, M. L., … Spratt, S. E. (2013). A comparison of phenotype definitions for diabetes mellitus. J Am Med Inform Assoc, 20(e2), e319–e326. https://doi.org/10.1136/amiajnl-2013-001952
Richesson, Rachel L., Shelley A. Rusincovitch, Douglas Wixted, Bryan C. Batch, Mark N. Feinglos, Marie Lynn Miranda, W Ed Hammond, Robert M. Califf, and Susan E. Spratt. “A comparison of phenotype definitions for diabetes mellitus.J Am Med Inform Assoc 20, no. e2 (December 2013): e319–26. https://doi.org/10.1136/amiajnl-2013-001952.
Richesson RL, Rusincovitch SA, Wixted D, Batch BC, Feinglos MN, Miranda ML, et al. A comparison of phenotype definitions for diabetes mellitus. J Am Med Inform Assoc. 2013 Dec;20(e2):e319–26.
Richesson, Rachel L., et al. “A comparison of phenotype definitions for diabetes mellitus.J Am Med Inform Assoc, vol. 20, no. e2, Dec. 2013, pp. e319–26. Pubmed, doi:10.1136/amiajnl-2013-001952.
Richesson RL, Rusincovitch SA, Wixted D, Batch BC, Feinglos MN, Miranda ML, Hammond WE, Califf RM, Spratt SE. A comparison of phenotype definitions for diabetes mellitus. J Am Med Inform Assoc. 2013 Dec;20(e2):e319–e326.
Journal cover image

Published In

J Am Med Inform Assoc

DOI

EISSN

1527-974X

Publication Date

December 2013

Volume

20

Issue

e2

Start / End Page

e319 / e326

Location

England

Related Subject Headings

  • Phenotype
  • Middle Aged
  • Medical Informatics
  • Male
  • International Classification of Diseases
  • Humans
  • Female
  • Electronic Health Records
  • Diabetes Mellitus
  • Algorithms