OBJECTIVE -To develop and validate a prediction rule for identifying diabetic 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 Ն19 years. Data from 1994 to 1995 were used to model risk for macro-and microvascular complications (n ϭ 3,977), infectious complications (n ϭ 1,580), and metabolic complications (n ϭ 316) during 1996. Candidate predictors (n ϭ 36) included prior inpatient and outpatient diagnoses, laboratory records, pharmacy records, utilization records, and survey data. Using split-sample validation, the risk scores derived from logistic regression models in half of the population were evaluated in the second half. Sensitivity, positive predictive value, and receiver operating characteristics 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 were the strongest predictors in each complications set. For patients without previous events, treatment with insulin alone, serum creatinine Ն1.3 mg/dl, use of two or more antihypertensive medications, HbA 1c Ͼ10%, and albuminuria/microalbuminuria were independent predictors of two or all three complications. Several risk scores derived from multivariate models were more efficient than simply targeting patients with elevated 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. A pproximately 4% of most managed care populations have diabetes (1,2), but these patients account for nearly 12% of total health care expenditures (2). These costs also reflect catastrophic events in the lives of patients, because a large fraction of total costs result from hospitalization for disease complications (2). Recognizing the burden of this illness, many managed care organizations have developed intensive diabetes care programs, featuring multidisciplinary clinics or nurse case management (3-5) to improve pharmacotherapy, preventive screening, and support for selfcare. However, inclusion of all diabetic patients in intensive disease management programs, including those at low risk for complications, would diminish the costeffectiveness of these programs. Clinical prediction rules (6) are tools created by combining information from clinical data, usually using multivariate analyses, to estimate the probability of an outcome for individual patients. When applied to an entire population of members with diabetes, a prediction rule could be used to identify and rank members by their level of risk for complications. Despite the frequent availability of rich automated clinical data in health plan systems (7), prediction rules have not been widely used for diabetes. Instead, many programs focus solely on poor control of HbA 1c levels to identify those in need of more intensive intervention. This study seeks to develop and test a prediction rule using automated clinical data that can be applied at the population level to improve this strategy. Several approaches are compared to identify the simplest rule that efficiently identifies high-risk patients. METHODS -This report is based on a retrospective cohort analysis conducted in the Northern California Kaiser Permanente Diabetes Registry. Kaiser Permanente, which is a group model health maintenance organization (HMO), had ϳ2.5 million enrollees during the study period. The registry (2) is an ongoing epidemiological cohort of all HMO members with diabetes identified from four automated databases: pharmacy prescriptions for diabetes medications, abnormal HbA 1c values (Ն6.7%) in laboratory files, primary hospital discharge diagnoses of diabetes, and emergency department records of diabetes as the reason for visit. During the study period, this registry had a sensitivity of 90% when matched against Ͼ1,500 self-reported diabetic patients who responded to two large mailed
E.g., Selby et al. REF developed a prediction rule from clinical databases to identify high-risk patients in a large population with diabetes.
Developing a Prediction Rule From Automated Clinical Databases to Identify High-Risk Patients in a Large Population With Diabetes
Diabetes care
Diabetes care
Medicine