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Clinical prediction guide
Several simple rules predicted complications in high risk patients with diabetes

https://doi.org/10.1136/ebm.7.3.96

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    (2001) Diabetes Care 24, 1547. Selby JV, Karter AJ, Ackerson LM, et al.. Developing a prediction rule from automated clinical databases to identify high-risk patients in a large population with diabetes.. Aug;. :. –55.
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 QUESTION: What is the accuracy of a prediction rule for identifying patients with diabetes mellitus who are at high short term risk for macro- and microvascular events, infectious disease, and metabolic complications?

    Design

    A cohort of patients, randomly split into derivation and validation datasets.

    Setting

    Kaiser Permanente health maintenance organization (HMO) in Oakland, California, USA.

    Patients

    57 722 members of the HMO who were ≥ 19 years of age, had diabetes, and were continuously enrolled in the health plan during the 2 year baseline period. The derivation dataset included 28 838 patients (mean age 61 y, 53% men), and the validation dataset included 28 884 patients (mean age 61 y, 52% men).

    Description of prediction guide

    A “best” model and 4 simpler …

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    Footnotes

    • Source of funding: in part, Pfizer Pharmaceuticals.

    • For correspondence: Dr J V Selby, Division of Research, Kaiser Permanente, Oakland, CA, USA. jvs{at}dor.kaiser.org.