Algorithms to define diabetes type using data from administrative databases: A systematic review of the evidence
Authors' conclusion
Algorithms based on multiple diabetes diagnostic codes and insulin use are the most accurate approaches to distinguish type 1 from type 2 diabetes using administrative data. Approaches with more than one criterion may also increase sensitivity in distinguishing diabetes type.
Study context
- Population
- Studies evaluating algorithms defining T1D and T2D in administrative databases; Embase and MEDLINE, January 2000–January 202324 studies
- Comparison
- Algorithms to distinguish T1D and T2D in administrative data, against a range of reference standards
- Outcome
- Diagnostic performance of algorithms to distinguish T1D and T2D
Methodological detailsQUADAS quality assessment
- QUADAS quality assessment
Pooled result
As reported in the abstract
Algorithms using multiple diabetes diagnostic codes alone: sensitivity and specificity for T1D
>92.1%
Algorithms using multiple diabetes diagnostic codes alone: sensitivity and specificity for T2D
>86.9%
Top 10-performing algorithms for T1D featuring multiple criteria
70%
Top 10-performing algorithms for T2D featuring multiple criteria
100%
Further pooled results
Study quality
- Studies with low risk of bias and low applicability concerns across all domains
- 19 of 24
Original publication
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