Several simple rules predicted complications in high-risk patients with diabetes.

Abstract

(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 . [OpenUrl][1][Abstract/FREE Full Text][2] 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? A cohort of patients, randomly split into derivation and validation datasets. Kaiser Permanente health maintenance organization (HMO) in Oakland, California, USA. 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). A “best” model and 4 simpler … [1]: {openurl}?query=rft.jtitle%253DDiabetes%2BCare%26rft.stitle%253DDiabetes%2BCare%26rft.issn%253D0149-5992%26rft.aulast%253DSelby%26rft.auinit1%253DJ.%2BV.%26rft.volume%253D24%26rft.issue%253D9%26rft.spage%253D1547%26rft.epage%253D1555%26rft.atitle%253DDeveloping%2Ba%2BPrediction%2BRule%2BFrom%2BAutomated%2BClinical%2BDatabases%2Bto%2BIdentify%2BHigh-Risk%2BPatients%2Bin%2Ba%2BLarge%2BPopulation%2BWith%2BDiabetes%26rft_id%253Dinfo%253Adoi%252F10.2337%252Fdiacare.24.9.1547%26rft_id%253Dinfo%253Apmid%252F11522697%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/ijlink?linkType=ABST&journalCode=diacare&resid=24/9/1547&atom=%2Febmed%2F7%2F3%2F96.1.atom