Diabetes case identification methods applied to electronic medical record systems: their use in HIV-infected patients.
OBJECTIVE
New onset diabetes mellitus type 2 is increasing among HIV-infected patients in the era of potent antiretroviral therapy. Accurately identifying HIV-infected patients with a diagnosis of diabetes in electronic medical record systems will facilitate the study of patients with this disease.


METHODS
We examined electronic medical record data for all patients who initiated care at an HIV clinic between 1/1/1997 and 12/31/2001 to identify potential cases of diabetes. Case identification methods included clinician-coded diagnoses, medications, and HbA1c and glucose levels. Diabetes diagnoses were verified by clinician documentation in an electronic medical record progress note. Test characteristics of each case identification method were calculated.


RESULTS
53 cases of diabetes were identified among the cohort of 1,441 patients. Use of clinician-coded diagnoses alone or combined with other methods was the most sensitive method for identifying diabetes cases. Clinician-coded diagnoses were also the best method as assessed by standard receiver operator characteristic plots. A significant attenuation of odds ratios for associations with diabetes were found for case identification methods with imperfect specificity such as serum glucose levels.


CONCLUSIONS
This study demonstrates that electronic medical record data can be used to accurately identify HIV-infected patients with diabetes. The optimal method applied will depend on the goals of a particular study.
Crane
Heidi M
HM
Department of Medicine, University of Washington, Seattle, USA. hcrane@u.washington.edu
Kadane
Joseph B
JB
Crane
Paul K
PK
Kitahata
Mari M
MM
D000046:Academic Medical Centers; D000328:Adult; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis* / Q000453:epidemiology*; D005260:Female; D015658:HIV Infections / Q000150:complications* / Q000453:epidemiology; D006801:Humans; D008297:Male; D016347:Medical Records Systems, Computerized; D008875:Middle Aged; D011237:Predictive Value of Tests; D015995:Prevalence; D012680:Sensitivity and Specificity; D014861:Washington