Although attention has rightly been paid to the risks of overtreatment of hyperglycemia in older adults (hypoglycemia, treatment burden, possibly increased mortality), untreated or undertreated hyperglycemia also has risks, even in patients with life expectancy too short to be impacted by the development of chronic complications. Blood glucose levels consistently over the renal threshold for glycosuria (∼180–200 mg/dL, but can vary) increase the risks for dehydration, electrolyte abnormalities, urinary incontinence, dizziness, and falls. Hyperglycemic hyperosmolar syndrome is a particularly severe complication of unrecognized or undertreated hyperglycemia in older adults. Although it is appropriate to relax glycemic targets for older patients with a history of hypoglycemia, a high burden of comorbidities, and limited life expectancy, goals that minimize severe hyperglycemia are indicated for almost all patients.
Life expectancy
A central concept in geriatric diabetes care guidelines is that providers should base decisions regarding treatment targets or interventions on life expectancy . Patients whose life expectancy is limited (e.g., <5 years, <10 years) are considered unlikely to benefit from intensive glucose control, for example, whereas those with longer life expectancy may be appropriate candidates for this intervention. An observation supporting this concept is that cumulative event curves for the intensive and conventional glycemic control arms of the UKPDS separated after the 9-year mark.
National Vital Statistics life table estimates of average life expectancy for adults of specific ages, sexes, and races (105) may not apply to older adults with diabetes, who have shorter life expectancies than the average older adult. Mortality prediction models that account for variables such as comorbidities and functional status can serve as the basis for making more refined life expectancy estimates . Mortality prediction models specific to diabetes exist but were not designed to inform treatment decisions . A limitation of existing mortality models is that they can help to rank patients by probability of death, but these probabilities must still be transformed into a life expectancy for a particular older diabetic patient.
Simulation models can help transform mortality prediction into a usable life expectancy. One such model estimated the benefits of lowering A1C from 8.0 to 7.0% for hypothetical older diabetic patients with varying levels of age, comorbidity, and functional status . A combination of multiple comorbid illnesses and functional impairments was a better predictor of limited life expectancy and diminished benefits of intensive glucose control than age alone. This model suggests that life expectancy averages less than 5 years for patients aged 60–64 years with seven additional index points (points due to comorbid conditions and functional impairments), aged 65–69 years with six additional points, aged 70–74 years with five additional points, and aged 75–79 years with four additional points. An example of comorbid illnesses is the diagnosis of cancer, which confers two points, whereas an example of a functional impairment is the inability to bathe oneself, conferring two points.
Shared decision making
In light of the paucity of data for diabetes care in older adults, treatment decisions are frequently made with considerable uncertainty. Shared decision making has been advocated as an approach to improving the quality of these so-called preference-sensitive medical decisions . Key components of the shared decision-making approach are 1) establishing an ongoing partnership between patient and provider, 2) information exchange, 3) deliberation on choices, and 4) deciding and acting on decisions.
When asked about their health care goals, older diabetic patients focus most on their functional status and independence . A key component of improving communication in the clinical setting may be finding congruence between patient goals and the biomedical goals on which clinicians tend to focus. Discussions eliciting and incorporating patients’ preferences regarding treatments and treatment targets may be difficult when patients do not understand the significance of risk factors or the value of risk reduction. Thus, providers must first educate patients and their caregivers about what is known about the role of risk factors in the development of complications and then discuss the possible harms and benefits of interventions to reduce these risk factors.
Equally important is discussing the actual medications that may be needed to achieve treatment goals because patients may have strong preferences about the treatment regimen. In a study of patient preferences regarding diabetes complications and treatments, end-stage complications had the greatest perceived burden on quality of life; however, comprehensive diabetes treatments had significant negative perceived quality-of-life effects, similar to those of intermediate complications. Preferences for each health state varied widely among patients, and this variation was not related to health status , implying that the preferences of an individual patient cannot be assumed to be known based on health status.
Many older adults rely on family members or friends to help them with their treatment decisions or to implement day-to-day treatments. In the case of the older person with cognitive deficits, the family member or friend may in fact be serving as a surrogate decision maker. Prior studies of older cognitively intact patients have shown that surrogate decision makers often report treatment preferences for the patient that have little correlation with the patient’s views , highlighting the importance of eliciting patient preferences whenever possible.
Racial and ethnic disparities
Among older adults, African Americans and Hispanics have higher incidence and prevalence of type 2 diabetes than non-Hispanic whites, and those with diagnosed diabetes have worse glycemic control and higher rates of comorbid conditions and complications . The Institute of Medicine found that although health care access and demographic variables account for some racial and ethnic disparities, there are persistent, residual gaps in outcomes attributed to differences in the quality of care received . There is clearly a need for more research into the disparities in diabetes, particularly to understand the full impact of quality improvement programs and culturally tailored interventions among vulnerable older adults with diabetes.
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