Steven Smith, MD
Smith S. Several simple rules predicted complications in high-risk patients with diabetes. Ann Intern Med. 2002;136:117. doi: 10.7326/ACPJC-2002-136-3-117
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Published: Ann Intern Med. 2002;136(3):117.
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 data sets.
Kaiser Permanente health maintenance organization (HMO) in Oakland, California, United States.
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 data set included 28 838 patients (mean age 61 y, 53% men), and the validation data set included 28 884 patients (mean age 61 y, 52% men).
A “best” model and 4 simpler approaches were derived: the previous events strategy (identifies patients with previous events or related outpatient diagnoses during the baseline period), the first 3 variables of the “best” model, the numerical risk score (a summed score obtained by replacing significant model coefficients with integer values: 1.0 for a significant multivariate odds ratio [OR] between 1.1 and 1.49, 2.0 for an OR between 1.50 and 1.99, and 3.0 for an OR ≥ 2, with corresponding negative numbers for significant ORs < 1.0), and ranking on the basis of average HbA1c level during baseline.
Identification of patients at high short-term risk for macro- and microvascular, infectious, and metabolic complications.
Comparisons of the test properties of the various models for predicting each type of complication are summarized in the Table.
Simple prediction rules were better than HbA1c levels for identifying patients with diabetes who were at high short-term risk for complications.
Test properties of 5 models for predicting complications in diabetes (validation data set)*
*Sens = sensitivity; spec = specificity. Diagnostic terms defined in Glossary. Data on specificity, +LR, and −LR provided by author.
†The “best” models for predicting complications included predictors from the following categories: patient demographics, previous diagnoses of complications, metabolic measurements, medications, and health care utilization measures.
‡Cut point of patients with the highest 30% of predicted risk scores.
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Cardiology, Coronary Risk Factors, Diabetes, Endocrine and Metabolism.
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