The Effect of Comorbid Illness and Functional Status on the Expected Benefits of Intensive Glucose Control in Older Patients with Type 2 Diabetes: A Decision Analysis

Context Whether tight glucose control benefits elderly patients with type 2 diabetes is subject to debate because some studies show adverse outcomes with tight control. Contribution This computer model estimated the net benefits of treating to a hemoglobin A1c level of 7% versus 7.9% among individuals 60 to 80 years of age with various life expectancies and suggests modest benefits of tight control, ranging from 51 to 116 additional quality-adjusted days. Benefits decreased as age increased and life expectancy decreased, supporting a relaxation of hemoglobin A1c targets for elderly people with comorbid illness. Caution The mortality index that the investigators used to predict life expectancy in the model is not appropriate for predicting an individual patient's life expectancy. The Editors Intensive glucose control (hemoglobin A1c [HbA1c] level <7% [1]) decreases the risk for multiple complications in patients with diabetes compared with moderate glucose control (HbA1c level about 7.9%) (24). The importance of intensive glucose control has led to public health efforts to improve the delivery of diabetes care (5). Despite its promise, the benefits of intensive glucose control remain uncertain for the heterogeneous population of older diabetic patients. This uncertainty, reflected in the wide variation in practice by clinical specialty (6, 7) and the different therapeutic recommendations from leaders in the field of diabetes (810), arises from a lack of clinical trial data evaluating the benefits of long-term intensive glucose control in older patients, especially those with substantial comorbid illnesses or functional impairments. The first guideline to acknowledge the unique care considerations of older diabetic patients (11) recommended an individualized approach to diabetes care that multiple medical organizations have since endorsed (1214). A central concept introduced in that guideline is that providers should consider targeting glucose control levels on the basis of life expectancy. Patients whose life expectancy is less than 5 years are considered unlikely to benefit from intensive glucose control, whereas patients with longer life expectancy are thought to be good candidates for intensive glucose control. Although these recommendations represent a conceptual advance in diabetes care, they have received little evaluation. Comorbid illness and functional status are well known determinants of life expectancy (15, 16); however, the extent to which these characteristics influence the expected benefits of intensive glucose control is unknown. In addition, there are concerns about using limited life expectancy as a sole means of determining glucose control. Many older patients with limited life expectancy may also have prolonged duration of diabetes, a clinical characteristic that may increase the expected benefits of intensive control. How these competing characteristics might interact and influence decisions is unknown. One approach for gaining insight into these questions is to use existing clinical evidence in decision analyses (1719). An advantage of decision analysis is that it allows examination of clinical questions for patients that might typically be excluded from clinical trials. Recent advances in prediction models in diabetes (20) and geriatrics (21) enable us to evaluate how comorbid illnesses and functional status may alter the expected benefits of intensive glucose control in older type 2 diabetic patients. Methods This decision analysis is an integration of multiple prediction models from the fields of diabetes and geriatrics. We housed all prediction models in the structure of an existing model of diabetes complications, the National Institutes of Health Model (17, 22). This Monte Carlo simulation model is framed by simultaneous progression of disease through individual diabetes-related complications and death (Figure 1). Within a 1-year cycle, patients move from 1 disease state to another or stay in the current disease state until death or age 95 years. The model is run for 10000 iterations for each specific model setting, such as population characteristics or glucose level, with each iteration representing a patient life. The model was constructed by using Microsoft Excel 2000 (Microsoft, Seattle, Washington) and @Risk 4.0 (Palisades, Newfield, New York). Figure 1. Patient flow through 1 cycle of the model. Hypothetical patients move through the model from left to right for each cycle length (1 year). On the basis of initial clinical characteristics, patients are subject to the risk for various diabetes-related complications and death. Patients who survive a given year repeat the cycle until death. In the following sections, we describe the individual prediction models, the population of interest, the comparison treatments, the outcomes of interest, and sensitivity analyses. For details, see the Appendix Table. Appendix Table. Base-Case Model Assumptions Diabetes Complications The diabetes complication models are derived from the UKPDS (United Kingdom Prospective Diabetes Study) results (3, 20, 23, 24). The UKPDS study group developed prediction models for all major diabetes-related complications. These models have been internally validated and externally validated with cardiovascular trial data (25). We could not use the UKPDS prediction model for end-stage renal disease because this model does not include glucose control as a predictor. We instead modeled the development of microalbuminuria and proteinuria, which are linked to the intensity of glucose control (19, 26). For probabilities under moderate control, we used prediction models developed by using optimization procedures to fit observations from the UKPDS control group to a functional form used in the original National Institutes of Health model (27) (Appendix Table). To determine the transition probabilities for intensive glucose control, we used a multiplier derived by comparing the overall UKPDS results for individual complications. We used probabilities from an observational study for the transition between proteinuria and end-stage renal disease (28). Incorporating Functional Status and Comorbid Illness into Background Mortality We used mortality rates from a 4-year mortality index developed from the Health and Retirement Study (21), rather than mortality rates from life tables (17, 22, 29). This index was developed and validated with a split-sample approach. The index has a total score of 26, and each comorbid illness or functional impairment contributes 1 to 2 points to the index score. To calculate background mortality rates for the general population, we subtracted cardiovascular mortality rates for the general population from the mortality rates associated with each index score (30). We then multiplied these mortality rates by 2.75, as in a previous study (22), to reflect the higher background mortality rates for patients with diabetes. We included points for age group and sex in the baseline index score for each hypothetical subgroup. We then systematically increased the mortality index score by as much as 14 additional points. Apart from these changes, we retained the National Institutes of Health model assumptions about death from other specific causes (3133). Population We performed simulations for hypothetical patients 60 to 80 years of age who had type 2 diabetes and no history of diabetes-related complications. We assumed the patients to have the demographic and clinical characteristics of diabetic patients older than age 60 years, as described in the NHANES (National Health and Nutrition Examination Surveys) from 1999 to 2002 (34). Hypothetical cohorts were divided into 5-year age groups with a uniform age distribution, which correspond to major groupings of older diabetic patients. We also varied the duration of diabetes for such patients (new onset, 0 to 5 years, 5 to 10 years, and 10 to 15 years). We assumed that the population had the sex and ethnic distributions observed in NHANES. We did not assume any additional effect of race on complication rates because the major ethnic minority groups included in the UKPDS data do not completely correspond with those in the United States. Comparison Treatments We compared the projected health effects of moderate glucose control (HbA1c level, 7.9%) and intensive glucose control (HbA1c level, 7.0%) (11, 35) We assumed that patients maintained these glucose control levels throughout their lives. In sensitivity analyses, we also compared HbA1c levels of 9.0% versus 7.9% and HbA1c levels of 7.0% versus 6.5% (36). Other Treatment Assumptions We held all other elements of care constant in the 2 scenarios (3739). We selected cardiovascular risk factor levels for hypothetical patients from the age-, race-, and sex-specific subgroup distributions for systolic blood pressure, diastolic blood pressure, total cholesterol, high-density lipoprotein cholesterol, and smoking for diabetic patients in NHANES (19992002). Outcomes Outcomes of interest included the lifetime incidence of individual complications and life expectancy. We report the benefits of intensive versus moderate glucose control as an average absolute risk reduction in complications and added days of life. The primary outcome of interest was the average difference in quality-adjusted days. To calculate quality-adjusted benefits, we used utility weights for major complications used in previous analyses (19, 27, 4044). We assumed no disutility of life with different treatments (45). When multiple health states occurred, we used the minimum health state method (46). Sensitivity Analysis The UKPDS prediction models assume that the glucose level is a modifiable risk factor for coronary heart disease in type 2 diabetes, when in fact this remains a highly debated and studied topic (47). To assess the effect of this assumption, we replaced the UKPDS models for coronary heart disease and stroke with Framingham models (48, 49). W