Jv. Selby et al., Developing a prediction rule from automated clinical databases to identifyhigh-risk patients in a large population with diabetes, DIABET CARE, 24(9), 2001, pp. 1547-1555
OBJECTIVE - To develop and validate a prediction rule for identifying diabe
tic patients at high short-term risk of complications using automated data
in a large managed care organization.
RESEARCH DESIGN AND METHODS - Retrospective cohort analyses were performed
in 57,722 diabetic members of Kaiser Permanente, Northern California, aged
greater than or equal to 19 years, Data from 1994 to 1995 were used to mode
l risk for macro- and microvascular complications (n = 3,977), infectious c
omplications (n = 1,580), and metabolic complications (n = 316) during 1996
, Candidate predictors (n = 36) included prior inpatient and outpatient dia
gnoses, laboratory records, pharmacy records, utilization records, and surv
ey data. Using split-sample validation, the risk scores derived from logist
ic regression models in half of the population were evaluated in the second
half. Sensitivity, positive predictive value, and receiver operating chara
cteristics curves were used to compare scores obtained from full models to
those derived using simpler approaches.
RESULTS - History of prior complications or related outpatient diagnoses we
re the strongest predictors in each complications set. For patients without
previous events, treatment with insulin alone, serum creatinine - greater
than or equal to 13 mg/dl, use of two or more antihypertensive medications,
HbA(1c) > 10%, and albuminuria/microalbuminuria were independent predictor
s of two or all three complications. Several risk scores derived from multi
variate models were more efficient than simply targeting patients with elev
ated HbA(1c) levels for identifying high-risk patients.
CONCLUSIONS - Simple prediction rules based on automated clinical data are
useful in planning care management for populations with diabetes.