Details:

Alternative Title:
Diabetes Care
Publisher's site:
http://dx.doi.org/10.2337/dc12-0964
Personal Author:
Klompas, Michael ; Eggleston, Emma ; McVetta, Jason ; Lazarus, Ross ; Li, Lingling ; ... More +
Klompas, Michael ; Eggleston, Emma ; McVetta, Jason ; Lazarus, Ross ; Li, Lingling ; Platt, Richard Less -
Description:
OBJECTIVE

To create surveillance algorithms to detect diabetes and classify type 1 versus type 2 diabetes using structured electronic health record (EHR) data.

RESEARCH DESIGN AND METHODS

We extracted 4 years of data from the EHR of a large, multisite, multispecialty ambulatory practice serving ∼700,000 patients. We flagged possible cases of diabetes using laboratory test results, diagnosis codes, and prescriptions. We assessed the sensitivity and positive predictive value of novel combinations of these data to classify type 1 versus type 2 diabetes among 210 individuals. We applied an optimized algorithm to a live, prospective, EHR-based surveillance system and reviewed 100 additional cases for validation.

RESULTS

The diabetes algorithm flagged 43,177 patients. All criteria contributed unique cases: 78% had diabetes diagnosis codes, 66% fulfilled laboratory criteria, and 46% had suggestive prescriptions. The sensitivity and positive predictive value of ICD-9 codes for type 1 diabetes were 26% (95% CI 12–49) and 94% (83–100) for type 1 codes alone; 90% (81–95) and 57% (33–86) for two or more type 1 codes plus any number of type 2 codes. An optimized algorithm incorporating the ratio of type 1 versus type 2 codes, plasma C-peptide and autoantibody levels, and suggestive prescriptions flagged 66 of 66 (100% [96–100]) patients with type 1 diabetes. On validation, the optimized algorithm correctly classified 35 of 36 patients with type 1 diabetes (raw sensitivity, 97% [87–100], population-weighted sensitivity, 65% [36–100], and positive predictive value, 88% [78–98]).

CONCLUSIONS

Algorithms applied to EHR data detect more cases of diabetes than claims codes and reasonably discriminate between type 1 and type 2 diabetes.


Subject:
[+]
Adolescent
Adult
Aged
Aged, 80 And Over
Child
Child, Preschool
Diabetes Mellitus, Type 1
Diabetes Mellitus, Type 2
Electronic Health Records
Female
Humans
Infant
Infant, Newborn
Male
Middle Aged
Original Research
Young Adult

Source:
Diabetes Care. 2013; 36(4):914-921.
Pubmed ID:
23193215
Pubmed Central ID:
PMC3609529
Document Type:
Journal Article
Funding:
1P01-HK-00088/HK/PHITPO CDC HHS/United States
Collection(s):
CDC Public Access
Main Document Checksum:
[+]
urn:sha256:f5d881661fd36f193542cb3b1e27633248f2dcaa530d113795f957c0114bdd74
File Type:

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