Phenotyping Diabetes Mellitus on Aggregated Electronic Health Records from Disparate Health Systems

Phenotyping Diabetes Mellitus on Aggregated Electronic Health Records from Disparate Health Systems

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Background: Identifying patients with diabetes mellitus (DM) is often performed in epidemiological studies using electronic health records (EHR), but currently available algorithms have features that limit their generalizability.Methods: We developed a rule-based algorithm to determine DM status using the nationally aggregated EHR database.The algorithm was validated on two chart-reviewed samples (n = 2813) of (a) patients with atrial fibrillation (AF, n = 1194) and (b) randomly sampled hospitalized patients (n = 1619).

Results: DM diagnosis codes alone resulted in a sensitivity of 77.0% and 83.4% in the AF and ealisboa.com random hospitalized samples, respectively.

The proposed algorithm combines blood glucose values and DM medication usage with diagnostic codes and exhibits sensitivities between 96.9% and 98.0%, while positive predictive values (PPV) ranged between 61.

1% and 75.6%.Performances were comparable across sexes, but a lower specificity was observed in younger patients (below 65 versus 65 and above) in both validation samples (75.

8% vs.90.8% and grandpas best 60.

6% vs.88.8%).

The algorithm was robust for missing laboratory data but not for missing medication data.Conclusions: In this nationwide EHR database analysis, an algorithm for identifying patients with DM has been developed and validated.The algorithm supports quantitative bias analyses in future studies involving EHR-based DM studies.