Quality-adjusted life years gained or lost by a treatment that leads to a 1% reduction in hemoglobin A1c level (from 8.5% to 7.5%) across 4 age groups and views of the burden of treatment.
Variability in gains in QALYs from a 1% reduction in hemoglobin A1c (HbA1c) level for various age, utility, and starting HbA1c values.
eTable 1. Net Gains in Quality Adjusted Life Expectancy, by Age, Starting A1c, and Treatment Disutility: 15% Reduction in CHD Events per 1 Point Reduction in A1c; No Effect of Albuminuria on CHD Risk
eTable 2. Net Gains in Quality Adjusted Life Expectancy, by Age, Starting A1c, and Treatment Disutility: 15% Reduction in CHD Events per 1 Point Reduction in A1c; Direct Effect of Albuminuria on CHD Risk
eAppendix. Technical Appendix: The Effects of Patients’ Risks and Preferences on Health Gains With Glucose Lowering in Type 2 Diabetes
Vijan S, Sussman JB, Yudkin JS, Hayward RA. Effect of Patients’ Risks and Preferences on Health Gains With Plasma Glucose Level Lowering in Type 2 Diabetes Mellitus. JAMA Intern Med. 2014;174(8):1227-1234. doi:10.1001/jamainternmed.2014.2894
Copyright 2014 American Medical Association. All Rights Reserved. Applicable FARS/DFARS Restrictions Apply to Government Use.
Type 2 diabetes mellitus is common, and treatment to correct blood glucose levels is standard. However, treatment burden starts years before treatment benefits accrue. Because guidelines often ignore treatment burden, many patients with diabetes may be overtreated.
To examine how treatment burden affects the benefits of intensive vs moderate glycemic control in patients with type 2 diabetes.
Design, Setting, and Participants
We estimated the effects of hemoglobin A1c (HbA1c) reduction on diabetes outcomes and overall quality-adjusted life years (QALYs) using a Markov simulation model. Model probabilities were based on estimates from randomized trials and observational studies. Simulated patients were based on adult patients with type 2 diabetes drawn from the National Health and Nutrition Examination Study.
Glucose lowering with oral agents or insulin in type 2 diabetes.
Main Outcomes and Measures
Main outcomes were QALYs and reduction in risk of microvascular and cardiovascular diabetes complications.
Assuming a low treatment burden (0.001, or 0.4 lost days per year), treatment that lowered HbA1c level by 1 percentage point provided benefits ranging from 0.77 to 0.91 QALYs for simulated patients who received a diagnosis at age 45 years to 0.08 to 0.10 QALYs for those who received a diagnosis at age 75 years. An increase in treatment burden (0.01, or 3.7 days lost per year) resulted in HbA1c level lowering being associated with more harm than benefit in those aged 75 years. Across all ages, patients who viewed treatment as more burdensome (0.025-0.05 disutility) experienced a net loss in QALYs from treatments to lower HbA1c level.
Conclusions and Relevance
Improving glycemic control can provide substantial benefits, especially for younger patients; however, for most patients older than 50 years with an HbA1c level less than 9% receiving metformin therapy, additional glycemic treatment usually offers at most modest benefits. Furthermore, the magnitude of benefit is sensitive to patients’ views of the treatment burden, and even small treatment adverse effects result in net harm in older patients. The current approach of broadly advocating intensive glycemic control should be reconsidered; instead, treating patients with HbA1c levels less than 9% should be individualized on the basis of estimates of benefit weighed against the patient’s views of the burdens of treatment.
Intensive glycemic control is a standard of care for many organizations, and achieving a hemoglobin A1c (HbA1c) level of less than 7% is a quality measure often used to profile physicians and health plans.1,2 Lowering HbA1c level delays the onset and slows the progression of early microvascular disease.3,4 However, trials have found no significant reductions in clinically relevant end points such as vision loss, end-stage renal disease (ESRD), and amputation with 10 years of improved glycemic control.3 Observational studies and disease modeling suggest that benefits in these major outcomes will eventually accrue but typically take 2 or more decades to manifest.5,6 Effects on macrovascular end points, such as heart attacks and strokes, have varied widely between trials, but meta-analyses suggest that glycemic control may convey a small reduction in nonfatal events.7- 12
Whenever treatments have limited or delayed benefits, the burden and risks of treatment become particularly important.13- 20 Most glycemic medications have unwanted effects, such as weight gain, hypoglycemia, or gastrointestinal adverse effects.3,21 Moreover, clinicians are now faced with an expanding arsenal of diabetes mellitus treatments that have varying adverse effects and a risk of treatment-related harm, such as that found in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial and suggested by meta-analyses of rosiglitazone use.18- 20 Treatment burden and adverse effects can have an appreciable negative impact on patient quality of life.13,22- 24 Decisions made in chronic diseases often lead to lifelong therapy, allowing these undesired effects to accrue over a long period. These considerations have led many guidelines to recommend that patient preferences, age, and health status be taken into account when glucose management targets are set for patients with type 2 diabetes mellitus.2,16,25 However, putting these concepts into practice has been limited because of the lack of quantitative estimation of the benefits and burdens of treating various HbA1c levels with different glycemic medications. We sought to quantify the advantages of using a tailored approach for intensifying glycemic control and to examine thresholds at which treatment decisions become sensitive to the level of treatment burden (quantified as disutility, a small loss in quality of life).
We used an updated version of a previously published Markov model of diabetes outcomes to examine the benefits of glycemic control.6,15,26- 28 The model considers microvascular and cardiovascular diabetes complications, specifically examining the impact of risk factor levels on their development and progression. We have previously published estimates for benefits from blood pressure and lipid level treatment15 and focus here on quantifying the benefits of HbA1c level reduction. Because we are challenging existing paradigms of treatment, we chose somewhat optimistic assumptions of the benefits of glycemic control as detailed herein.
Detailed model specifications are available in the eAppendix (in the Supplement); we describe key parameter estimates briefly here. We modeled risk of early microvascular and neuropathic diabetes complications primarily using estimates drawn from the UK Prospective Diabetes Study (UKPDS)3 because a patient-level meta-analysis for microvascular complications has yet to be published, and other trials have short-term follow-up. We modeled progression through the intermediate steps as measured in the UKPDS: risk of progression to photocoagulation, risk of microalbuminuria and proteinuria, and risk of neuropathy. The relationship between these risks and HbA1c level is defined by assuming a constant relative risk across the spectrum of HbA1c levels (Table 1). This implies a log-linear relationship between HbA1c level and microvascular outcomes, a well-established finding in observational studies.29,35,36 Estimates for rates of progression from intermediate to end-stage microvascular complications were drawn from clinical trials, and from observational studies when necessary.37- 45
Pretreatment risks of coronary heart disease (CHD) and stroke were estimated using the Framingham risk estimator reported by Anderson et al.46 Estimates of the distribution of risk factors in the US diabetic population were obtained from the 2009 to 2010 National Health and Nutrition Examination Survey data.47 These risks were then input into the model in a nonstationary fashion (changing with time).
There is substantial uncertainty regarding the relationship between CHD risk and HbA1c level. A series of meta-analyses have converged around a consensus that lowering HbA1c level reduces the risk of nonfatal CHD events but not cardiovascular or total mortality.7- 11 We therefore assumed that CHD event risk is reduced by 15% per 1 percentage point change in HbA1c level. A 15% CHD event risk reduction is the effect size seen in rigorous observational studies12 and is similar to the non–statistically significant reduction in myocardial infarction risk seen in the UKPDS 33 and in the long-term follow-up study of the UKPDS.3,5
Quantifying and comparing the impact of disease complications and treatment burdens on overall patient quality of life is generally done using the concept of utility, measured on a scale with 1 indicating perfect health and 0 indicating death. For example, a commonly used health utility for visual loss is 0.6930,31—blindness is estimated to reduce quality of life compared with perfect health by 31% (disutility = 0.31), equivalent to approximately 113 days of high-quality life lost per year. We generally followed prior models in selecting health utility values for disease complications (Table 1).30,44,48
We did not specify a baseline burden of treatment; rather, we examined the effects across a range, based on analyses of glycemic treatment disutilities.22- 24 Insulin therapy is the best studied; disutility estimates range between 0.02 and 0.12, equivalent to the loss of 7 to 44 days of quality of life per year, whereas for oral therapies the reported treatment burdens are smaller and are driven primarily by adverse effects. For example, a weight gain of 3%, which typically occurs in patients taking sulfonylureas and thiazolidinediones, has a mean attributed disutility of 0.04, and gastrointestinal adverse effects, which can occur in patients taking metformin, also have a disutility of 0.04. We examined a conservative range of treatment disutility, from 0.001 to 0.05 (ie, a utility range of 0.95 to 0.999 for those in otherwise normal health, or a loss of 0.4 to 18 days of high-quality life per year). This range of estimates was based largely on existing data, such as that outlined for insulin, or for the act of taking a daily pill (0.001).49,50 In order to remain optimistic about our estimates of treatment benefit, we chose neither to discount future events nor to consider out-of-pocket costs or the potential for adverse events inherent in using newer treatments.
We examined the effects of a treatment that lowers HbA1c level by 1%, a typical response to glucose-lowering therapies.51 We examined how starting HbA1c levels affected the benefits of HbA1c lowering, but we assumed that HbA1c levels greater than 9% would be routinely treated. We also examined 2 specific treatment scenarios. In the first, a 45-year-old with a new diabetes diagnosis with an HbA1c level of 8.5% is prescribed metformin, resulting in an HbA1c level reduction of 1.5 points to 7.0%. Using an adverse effect profile based on clinical reports, we assumed that persistent gastrointestinal adverse effects occurred in 10% of individuals, with a disutility of 0.04 for those who experienced the effects (for a mean loss of 0.004, or 1.46 days of high-quality life per year).23 Minor hypoglycemia was assumed in 0.4% of patients each year, with a disutility of 0.0122,23,52; in combination with gastrointestinal adverse effects, this produces a total mean disutility of 0.00404, or approximately 1.47 days lost of high-quality life per year. In a second scenario, we examined the impact of switching this patient to insulin therapy if their HbA1c level increased to 8.5% over a 10-year period, similar to that seen in the UKPDS; insulin therapy was assumed to reduce HbA1c level by 1.0%.3,53 Daily injections themselves have an estimated disutility of approximately 0.03.22 After adding in a disutility for a mean 0.5% weight gain per year over 10 years (a cumulative disutility of 0.007 per year), a minor hypoglycemia rate of 2% per year (disutility = 0.01), and a major hypoglycemia rate of 0.2% per year (disutility = 0.03),3,23 insulin therapy had a total mean disutility of 0.0372, or 13.6 days lost of high-quality life per year.
We conducted sensitivity analyses of all variables in the model across a broad range of assumptions. The key variables that we identified as critical were age at diagnosis, pretreatment HbA1c level, and treatment disutility. We also found that any benefits from reduction in CHD events due to lower rates of albuminuria were important, particularly if it was assumed that these benefits were additive with the assumed 15% reduction in CHD events with a 1-point reduction in HbA1c level.
Our primary outcome was quality-adjusted life years (QALYs); we also examined the risk reductions in individual end points for our 2 treatment scenarios. We tested model predictions by comparing them with reported outcomes in the literature. Although there are no reports of observed QALYs, we were able to compare life expectancy predictions with those based on UKPDS actuarial projections across age and HbA1c level strata.54 We additionally compared model predictions of complication rates with those seen in the Steno-2 study.55
Our model predictions of life expectancy aligned well with those predicted from the UKPDS, as did estimates of individual complication rates seen in the Steno-2 study (see eAppendix in the Supplement).
In our best-case scenario (improving HbA1c level lowers CHD event risk, and treatment has minimal patient burden and/or adverse effects), we found substantial benefits to lowering HbA1c level, particularly among younger individuals (eTable 1 in the Supplement). For example, in a 45-year-old, lifetime treatment from an HbA1c level of 8.5% to 7.5% produces a gain of 0.906 QALYs. This benefit is smaller in older individuals, declining to a gain of 0.269 QALYs at age 65 years and 0.104 QALYs at age 75 years. Although these benefits are slightly less in a patient with a starting HbA1c level of 7.5%, as long as treatment risks and treatment burden remain very small (disutility of 0.001, equivalent to 0.3 days of high-quality life lost per year), all age groups receive some benefit.
The patient perception of the level of treatment burden has a profound impact on the net benefits of HbA1c level lowering (Figure 1). For example, in an otherwise favorable scenario (diabetes onset at age 45 years, and a 15% risk reduction in CHD events per unit decrease in HbA1c level), a high treatment burden of 0.05 (equivalent to 18.2 days of high-quality life lost per year, a level often reported by people receiving insulin22- 24) outweighs all benefits of glycemic control. Indeed, the model predicts that patients will lose between 0.653 and 0.818 QALYs even when treatments improve glycemic control by 1%. The treatment burden at which reducing HbA1c level by 1 point results in net harm ranges between 0.01 and 0.05, depending on other key factors such as patient age and pretreatment HbA1c level (eTable 1 in the Supplement).
To provide a sense of the relative efficiency and the required duration of treatment, we also estimated the number of QALYs gained per 100 years of treatment. These estimates can be seen in Table 2. In the best-case scenario, 3.47 QALYs are gained per 100 treatment-years when HbA1c level is reduced from 8.5% to 7.5% with a low burden and/or adverse effect treatment started in a 45-year-old.
We also examined 2 representative treatment scenarios, 1 examining the impact of starting metformin therapy and another of starting insulin therapy (Table 3). Metformin therapy was assumed to be started at diagnosis and reduced HbA1c level from 8.5% to 7.0%. Metformin, which has relatively small treatment disutility because it does not cause weight gain and has minimal risks of hypoglycemia, produces benefits across the age spectrum (ranging from 0.148 QALYs in a 75-year-old to 1.2 QALYs in a 45-year-old). The reductions in individual end points are also shown in Table 3; for example, the absolute risk reduction in ESRD risk is almost 10 times greater for a 45-year-old (0.065) than in a 75-year-old (0.007).
In our second example, insulin therapy was started after 10 years of use of oral agents after a gradual rise in HbA1c level from 6.5% to 8.5%; insulin use reduced HbA1c level back to 7.5%.53 In contrast to the consistent benefits of metformin therapy, the switch to insulin produces a negative effect on QALYs across all age groups; that is, the adverse effects and burdens of treatment, on the basis of literature estimates and accumulated over time, outweigh the benefits of improved glycemic control. In addition to insulin’s higher treatment burden, the treatment benefit is lower in this scenario because of smaller HbA1c level reduction relative to metformin therapy and because the patient is now 10 years older, reducing the time available to achieve benefit. For example, the absolute risk reduction in ESRD is 0.013 when insulin therapy is initiated at age 55 years, as compared with the 0.065 from starting metformin therapy 10 years earlier.
Figure 2 shows the results of varying key parameters in our sensitivity analyses. The estimates of QALYs gained per year of treatment are shown for a representative patient, 55 years old with an HbA1c level of 8.5%, a treatment disutility of 0.01, and with an expected 15% reduction in CHD event risk per 1-point reduction in HbA1c level. Each parameter is varied across a reasonable range; larger effects are demonstrated by a greater change in QALYs across the ranges of the variables. The importance of treatment burden relative to the other variables is apparent.
Additional sensitivity analyses showed that our results were robust to changes in most other model parameters (eAppendix in the Supplement). A key additional parameter was the relationship between albuminuria and cardiovascular events. If albuminuria is causally related to higher CHD event rates and this effect is additive to the assumed 15% reduction posited for a 1% change in HbA1c level, then the benefits of glycemic control are larger (eTable 2 in the Supplement). However, it is more likely that any effect of prevention of albuminuria is already captured by the assumption that HbA1c level reduction leads to a reduction in CHD event risk.
A growing body of research has accepted that the benefit of diabetes treatment is complicated by variation in patient clinical circumstances and treatment burden, so that no single HbA1c level target is appropriate for all patients.14- 17,25,56,57 However, putting this concept into practice has been limited; our results help to inform the decision-making process for patients and clinicians. We found that once moderate control of HbA1c level (9%) is achieved, patient views of the burdens of treatment are the most important factor in the net benefit of glucose-lowering treatments. Thus, higher-quality decision making is best achieved by individualizing treatment decisions (“What are the burdens and benefits of prescribing a new medication for this patient?”), not solely by individualizing HbA1c level targets (“What should this patient’s HbA1c level target be?”).58
Although in this article we used QALYs to facilitate comparing different potential disease complications and treatment burdens, clinicians may find our results for outcome-specific (eg, myocardial infarction, ESRD) absolute risk reductions to be more useful for thinking about individual patient decisions. These estimates of the potential benefits of HbA1c level reduction can provide clinicians a means of considering and balancing treatment benefits with the burdens of glucose-lowering treatments. Although there is no consensus on the optimal approach to implementing shared decision making in practice, having fairly concrete estimates of treatment benefit is particularly necessary because evidence suggests that most clinicians vastly overestimate treatment benefits and few consider treatment burden explicitly.59
In addition to providing information to aid clinical decision making, our results challenge the wisdom of the current HbA1c level–centered approach to quality measures and clinical research. Instead of current recommendations and performance measures based on achievement of specific HbA1c level goals, our results suggest that quality of diabetes care is more accurately defined by assessing whether high-benefit treatment is provided and whether an informed decision-making process is used when potential benefits are more modest.60 Implementing such measures will be more challenging than current approaches, but assessing high-priority care is already feasible through medical record review. With the spread of the electronic medical record, measures of high-benefit care can be automated because data on HbA1c levels, current diabetes medications, and age are readily available in most electronic medical records. In addition to identifying whether high-value treatment is provided appropriately, the electronic medical record can also facilitate and document shared decision making without making treatments with uncertain net benefit into standards of care.61,62 Eddy has argued that an intervention should be considered a “standard” only if there is “virtual unanimity among patients about the overall desirability (or undesirability) of the outcomes” (emphasis ours).63(p3081) Our results show that given variability in individual preferences, it is unlikely that there will be “virtual unanimity” for most glycemic treatment decisions. For example, even for a 45-year-old with an HbA1c level of 8.5%, insulin therapy can easily result in net harm for someone with a moderate dislike of insulin treatment (disutility = 0.05).24
Our finding that older patients experience smaller benefits from glycemic control is not unexpected,6,14,44,48,64 but the degree is noteworthy. We estimate that the expected gain in QALYs for a 1-point change in HbA1c level in a 75-year-old is 0.06 years (22 days), even with the favorable assumption that glycemic control’s cardiovascular benefit extends to the elderly.
Glycemic medications continue to be approved and marketed almost entirely on the basis of whether they help achieve HbA1c level targets. Our findings provide further reason to favor evaluating diabetes medications with clinically relevant end points rather than HbA1c level alone, as has been suggested in recent trials, and argues against using new, expensive medications with minimal safety data based on achieving HbA1c level targets.58,65 Our findings also support the importance of developing and approving medications that both are safe and have fewer adverse effects and inconvenience because for many patients, treatment burden is the primary consideration in determining the net benefit of treatment.
Given how influential treatment burden is in our study, it is important to note that these burdens are not easy to quantify.22- 24 For this reason, we ran the models using treatment burden as a variable, ascribing particular values to particular treatments only for the purpose of the scenarios in Table 3. However, we were conservative in our estimates; 1 study reported a mean disutility for insulin (0.12) that was more than twice the level at which all of the patient groups have a net loss of quality of life with treatment.24 Although the act of taking a pill has little to no burden for most patients, adverse effects such as weight gain and hypoglycemia can confer substantial burden.
The main limitation of our study relates to inherent uncertainties in the literature. Our results are robust to the ranges found in the literature for disease progression and for treatment effects. There are some scenarios that we do not consider, such as assuming a greater than average reduction in CHD event risk from treatment of younger patients. But our optimistic assumption of 15% CHD event risk reduction per 1% HbA1c level lowering, combined with our lack of discounting, is likely to result in our results being somewhat biased toward favoring glucose-lowering treatment, particularly in light of data accruing from meta-analyses,7- 11 from Food and Drug Administration approval standards, and from outcome studies of incretin-based and other therapies.66- 69
In our base case, we did not model the potential increase in mortality seen in ACCORD.18 Inclusion of this in scenarios for the most intensive targets (HbA1c level 7.5% to 6.5%) led to net harm in all patients, but the implications of ACCORD remain controversial. We also did not model any direct medication effects, such as the mortality benefit from metformin use observed in the UKPDS that was independent of HbA1c level reduction.21 Finally, we used median survival, and because the health of the median person is quite good in all age groups examined in our study, it is important to note that our results by age mainly apply to those in relatively good health. Those with reduced life expectancy due to comorbidities are likely to receive less benefit than those reported in the tables.
There are important known mediators of diabetes complications other than HbA1c level. Our results show that the benefits of blood pressure and statin therapy dwarf those for glycemic control; for example, in previous analyses using the same model, we found that simvastatin, prescribed at 20 mg/d in the highest-benefit patients, led to more than 30 QALYs gained per 100 years of treatment.15 This is more than 8 times higher than the highest-benefit group achieves with glycemic control (Table 2).
We found that net treatment benefits of glycemic treatments vary widely depending on a patient’s age at diagnosis, their pretreatment HbA1c level, and most importantly, a patient’s view of the burden of the specific treatment being considered. Because of this, using HbA1c level treatment targets alone to guide patient decision making is a fundamentally flawed strategy; instead, each glycemic treatment decision should be individualized, mostly on the basis of patients’ views of the burdens of therapy, with age and initial level of glycemic control important but secondary considerations. Thus, shared decision making, in which patient preferences are specifically elicited and considered, appears to be the best approach to making most decisions about glycemic management in patients with type 2 diabetes. This study provides a starting point for the implementation of such an approach. To make optimal decisions, clinicians and patients will need decision support and incentives to engage in discussions that incorporate patients’ values.70 Currently, we are failing our patients by not recognizing that their preferences and views of treatment burden are the most important factor in helping them make glycemic treatment decisions that are best for them.
Accepted for Publication: May 12, 2014.
Corresponding Author: Sandeep Vijan, MD, MS, Ann Arbor Veterans Affairs Health Services Research and Development Service Center for Clinical Management Research, North Campus Research Complex, 2800 Plymouth Rd, Bldg 16, Room 344E, Ann Arbor, MI 48109-2800 (email@example.com).
Published Online: June 30, 2014. doi:10.1001/jamainternmed.2014.2894.
Author Contributions: Dr Vijan had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Vijan, Yudkin, Hayward.
Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: Vijan, Yudkin.
Critical revision of the manuscript for important intellectual content: Sussman, Yudkin, Hayward.
Statistical analysis: Vijan, Sussman, Hayward.
Obtained funding: Vijan.
Administrative, technical, or material support: Vijan, Sussman.
Study supervision: Vijan.
Conflict of Interest Disclosures: None reported.
Funding/Support: Financial support was provided via grants from the Department of Veterans Affairs Health Services Research and Development Service (IIR 06-253) and Quality Enhancement Research Initiative (QUERI DIB 98-001) and the Michigan Center for Diabetes Translational Research (National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health [P60 DK-20572]).
Role of the Sponsors: The funding agencies had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.