| Autor: |
Dorajoo, Hui Xing Tan, Rachel Li Ting Lim, Pei San Ang, Belinda Pei Qin Foo, Yen Ling Koon, Jing Wei Neo, Amelia Jing Jing Ng, Siew Har Tan, Desmond Chun Hwee Teo, Mun Yee Tham, Aaron Jun Yi Yap, Nicholas Kai Ming Ng, Celine Wei Ping Loke, Li Fung Peck, Huilin Huang, Sreemanee Raaj |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Pharmacoepidemiology; Volume 2; Issue 3; Pages: 223-235 |
| ISSN: |
2813-0618 |
| DOI: |
10.3390/pharma2030019 |
| Popis: |
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 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 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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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