Diabetes Complications Severity Index and Risk of Mortality, Hospitalization, and Healthcare Utilization

Volume 14: 15-23 January 2008 Number 1

Diabetes Complications Severity Index and Risk of Mortality, Hospitalization, and Healthcare Utilization

Bessie Ann Young, MD, MPH; Elizabeth Lin, MD, MPH; Michael Von Korff, ScD; Greg Simon, MD, MPH; Paul Ciechanowski, MD, MPH; Evette J. Ludman, PhD; Siobhan Everson-Stewart, BA; Leslie Kinder, PhD; Malia Oliver, BA; Edward J. Boyko, MD, MPH; and Wayne J. Katon, MD

Objective: To determine whether the number and severity of diabetes complications are associated with increased risk of mortality and hospitalizations.

Study Design: Validation sample.

Methods: The Diabetes Complications Severity Index (DCSI) was developed from automated clinical baseline data of a primary care diabetes cohort and compared with a simple count of complications to predict mortality and hospitalizations. Cox proportional hazard and Poisson regression models were used to predict mortality and hospitalizations, respectively.

Results: Of 4229 respondents, 356 deaths occurred during 4 years of follow-up. Those with 1 complication did not have an increased risk of mortality, whereas those with 2 complications (hazard ratio [HR] = 1.90, 95% confidence interval [CI] = 1.27, 2.83), 3 complications (HR = 2.66, 95% CI = 1.77, 4.01), 4 complications (HR = 3.41, 95% CI = 2.18, 5.33), and ≥5 complications (HR = 7.18, 95% CI = 4.39, 11.74) had greater risk of death. Replacing the complications count with the DCSI showed a similar mortality risk. Each level of the continuous DCSI was associated with a 1.34-fold (95% CI = 1.28, 1.41) greater risk of death. Similar results were obtained for the association of the DCSI with risk of hospitalization. Comparison of receiver operating characteristic curves verified that the DCSI was a slightly better predictor of mortality than a count of complications (P <.0001).

Conclusion: Compared with the complications count, the DCSI performed slightly better and appears to be a useful tool for prediction of mortality and risk of hospitalization.

(Am J Manag Care. 2008;14:15-24)

Use of a Disease Severity Index for Evaluation of Healthcare Costs and Management of Comorbidities of Patients With Diabetes Mellitus

Diabetes Mellitus in Managed Care: Complications and Resource Utilization

Facing Type 2 Diabetes in the Healthcare Reform Era

Empiric Investigation on Direct Costs-of-Illness and Healthcare Utilization of Medicaid Patients With Diabetes Mellitus

Resource Utilization and Work or School Loss Reported by Patients with Diabetes: Experience in Diabetes Training Programs

Among the 20 million Americans with diabetes, microvascular and macrovascular complications result in enormous morbidity,^1-4 disability,⁵ and mortality.^6-8 Diabetes complications account for more than 35% of the estimated $91.8 billion in direct medical expenditures for this disease.⁹ Although much research has addressed individual complications, end-organ complications usually develop simultaneously or consecutively in a patient with diabetes rather than independently.¹⁰ Thus, an indicator that captures the severity or type of complications may be more powerful in predicting mortality and hospitalization than a simple count of complications.

Few attempts have been made to quantify the overall severity of diabetes complications in a reproducible fashion for risk assessment, or for prediction of mortality or future treatment needs and costs.¹¹ Risk equations have been developed to predict adverse cardiovascular outcomes^12,13 and to identify high-risk patients to target for intervention.¹⁴ However, these risk models considered only cardiovascular disease risk factors and not the broader array of diabetes complications that can now be assessed in large populations enrolled in health plans with automated medical records. Because it is well established that diabetes contributes to increased morbidity and mortality in the general population,^15-19 a logical next step is to use information relevant to the degree of progression of the disease to assess the level of risk for adverse outcomes, including hospitalization and mortality.

Given that healthcare organizations have limited resources to invest in creating disease management interventions for high-risk patients with diabetes, it is important to develop models to predict which patients are at highest risk of adverse medical outcomes. In this study, we sought to develop a method of assessing the level of risk for diabetes adverse outcomes, including hospitalization and mortality, from automated medical record data on diabetes complications. To quantify the severity of complications and to potentially better predict the risk of adverse outcomes, we developed and employed the Diabetes Complications Severity Index (DCSI). The DCSI is a 13-point scale scored from automated diagnostic, pharmacy, and laboratory data. We compared the DCSI with a simple count of diabetes complications to assess whether a severity index of diabetes complications based on clinical diagnoses would improve the prediction of adverse diabetes outcomes.

METHODS

Subjects and Study Design
The Pathways Epidemiology Study is a longitudinal, prospective, population-based cohort study designed to determine the adverse impact of depression in patients diagnosed with diabetes in a primary care population.²⁰ The study recruited patients from a large nonprofit health maintenance organization with more than 400 000 enrollees who receive medical care provided by 30 primary care clinics. Of the 30 clinics available, 9 clinics were selected for this study based on the following 3 criteria: (1) geographic location, (2) large diabetes population, and (3) the largest racial and ethnic diversity. Subjects were prospectively followed from the time of recruitment for the baseline survey (March 2001) to death or May 31, 2005.

Sample Recruitment
Enrollees were recruited from 9 primary care clinics as described in previous publications.^21-23 A total of 9063 baseline surveys were mailed to patients who met the inclusion criteria of the Group Health Diabetes Registry. This registry has been shown to capture all diabetic patients (both type 1 and 2) enrolled in our institution with administrative (hospitalization, clinic visit, laboratory, or pharmacy) evidence of diabetes.²⁴ Of the 9063 patients who received surveys, 1222 were found to be ineligible based on the following criteria: no diabetes present (259), gestational diabetes (8), cognitive impairment (80), too ill to participate (202), deceased (128), disenrolled/moving (444), language/hearing barriers (99), and other (2). Of the 7841 patients eligible, 4839 returned the surveys,²⁵ for a response rate of 62%. Of the patients who returned the surveys, 369 did not give permission to review medical records, and 201 had type 1 diabetes. The study was approved by the Group Health Institutional Review Board.

Predictors
The primary predictor of interest was the number and type of diabetes complications as an indicator of diabetes severity. Other predictors of interest associated with diabetes severity included diabetes type (1 or 2), duration, glycosylated hemoglobin (A1C) level, and insulin treatment. Because of the observational nature of this study, all A1C tests were performed at point-of-care interaction of the patient with the medical system at a single institutional laboratory, which uses the Roche-Boehringer Mannheim Immuno-inhibition assay performed on a Hitachi 917 machine. The test and standardization are methods certified by the Diabetes Control and Complications Trial, and the laboratory has maintained a long-term interassay precision of approximately 3.5% coefficient of variation at levels of 6.5% and 11.0%. Secondary model adjustment variables included age, race/ethnicity, sex, body mass index (BMI), and current smoking. A count of the complications, as determined by International Classification of Diseases, Ninth Revision (ICD- 9) codes (Table 1),²⁶ was utilized as 1 measure of the severity of diabetes morbidity and was compared with the DCSI to better represent the spectrum of concurrent complications and to provide a better representation of the diabetes severity casemix within our patient population.

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