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Clinical Prediction Guide |

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

Steven Smith, MD
[+] Article, Author, and Disclosure Information

Source of funding: In part, Pfizer Pharmaceuticals.

For correspondence: Dr. J.V. Selby, Division of Research, Kaiser Permanente, Oakland, CA, USA. E-mail jvs@dor.kaiser.org.

Ann Intern Med. 2002;136(3):117. doi:10.7326/ACPJC-2002-136-3-117
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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 data sets.

Setting: Kaiser Permanente health maintenance organization (HMO) in Oakland, California, United States.

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 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).

Description of prediction guide: 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.

Main outcome measures: Identification of patients at high short-term risk for macro- and microvascular, infectious, and metabolic complications.

Main results: Comparisons of the test properties of the various models for predicting each type of complication are summarized in the Table.

Conclusion: 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)*

Model 2Micro- and macrovascularInfectious diseaseMetabolic
Best models†‡72%73%2.680.3872%71%2.490.3983%70%2.790.24
Previous events72%72%2.570.3944%86%3.100.6533%99%24.60.68
3 variables‡71%73%2.630.4068%71%2.350.4575%70%2.520.35
Risk score‡74%70%2.470.3767%71%2.320.4682%71%2.860.25
HbA1c level‡31%70%1.040.9838%70%1.280.8859%70%1.970.59

*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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