Personalizing Diabetes Treatment Targets

Rates of Deintensification of Blood Pressure and Glycemic Medication Treatment Based on Levels of Control and Life Expectancy in Older Patients With Diabetes Mellitus

Sussman JB, Kerr EA, Saini SD, et al.
JAMA Intern Med. 2015;175:1942-1949.

This retrospective cohort study used data from the US Veterans Health Administration to examine the rate at which physicians discontinued or deintensified therapy when their diabetes patients' glycemic or blood pressure (BP) levels were low enough to do so. Participants included one cohort of 211,667 patients with diabetes who were older than 70 years and were receiving active treatment for hypertension, and a second cohort of 179,991 patients receiving active treatment for hyperglycemia.

Patients were considered eligible for deintensification if they had low BP or a low glycated hemoglobin (A1c) level in their last measurement in 2012 (index measurement). Very low BP was defined as less than 120/65 mm Hg and moderately low as systolic BP of 120 to 129 mm Hg or diastolic BP less than 65 mm Hg. Very low A1c was defined as less than 6.0%, and moderately low as 6.0% to 6.4%. The main outcome measure was discontinuation or treatment dose reduction within 6 months after the index measurement. The investigators also examined whether life expectancy played a role in the rate of deintensification.

Weak Associations Between Deintensification and BP and A1c Levels

Of the 211,667 patients in the BP cohort, 49% had BP levels not considered low and for 15.1% of them therapy was deintensified. Among the 12% with moderately low BP, treatment was deintensified in 16.0%, while deintensification among the 38% with very low BP was still only 18.8%.

Results for the A1c cohort were somewhat more encouraging, but still lower than one would hope. Most (80%) had A1c levels not considered low and treatment was deintensified for 17.5%. Another 13% had moderately low A1c levels and treatment was deintensified in 20.9% of them, while deintensification among the 7% with very low A1c levels was 27.0%. Life expectancy was only weakly related to these deintensification rates.

Clinical Assessment of Individualized Glycemic Goals in Patients With Type 2 Diabetes: Formulation of an Algorithm Based on a Survey Among Leading Worldwide Diabetologists

Cahn A, Raz I, Kleinman Y, et al.
Diabetes Care. 2015;38:2293-2300.

Based on a worldwide survey of 244 key opinion-leading diabetologists, the authors developed an algorithm for calculating a target A1c. The survey contained two parts. First, from 11 parameters such as life expectancy, comorbidities, complications, disease duration, and functional attitude and treatment adherence, survey respondents ranked the factors they used to set their patients' glycemic targets according to their relative importance. Next, the authors presented six clinical vignettes, and asked the experts to suggest an appropriate glycemic target for each case.

The survey results were used to assign weights to the parameters, from which the algorithm was formulated. To validate the algorithm, the investigators presented three additional cases to 57 leading diabetologists who suggested glycemic targets. The investigators then compared those targets with the values computed by the algorithm.

A Simple Tool That Works

Survey responses indicated that "risk of hypoglycemia" and "life expectancy" were considered the most important parameters and thus received the highest weights. "Important comorbidities," "complications," and "cognitive function" were moderately important, while "disease duration" and "resources and support system" received the lowest weights.

To use the algorithm, a clinician simply scores each of these eight items as low, moderate, or high risk, and the algorithm generates an A1c target. In the six original cases as well as the three new cases, the algorithm suggested a target that was nearly identical to the A1c values recommended by the 57 experts.

Analysis and Commentary

Clinical decision-making is a tricky process, especially when the patient is complex. Because diabetes affects essentially every major organ and system of the human body, few diseases are more complex. To simplify treatment, professional organizations such as the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) develop guidelines and recommendations that are based on the best available evidence. When new evidence emerges that conflicts with existing guidelines, the complexity of treating complicated patients is amplified.

There is no better example than with diabetes, where lower glycemic and blood pressure levels proved not to be ideal for everyone,^[1,2,3] although there were subsets for whom lower targets were better. Even as far back as the United Kingdom Prospective Diabetes Study when the notion of tighter glycemic control was tested, cardiovascular disease outcomes were reduced for obese metformin-treated patients but not for the primary treatment group who received a sulfonylurea and insulin.^[4,5] This uncertainty coupled with an impossibly busy schedule likely prevents clinicians from adjusting therapies when warranted, as was shown in the study by Sussman and colleagues. Another possible explanation for lack of deintensification is that presenting patients seemed okay so clinicians chose the conservative path of leaving things alone. As Sussman and colleagues conclude, however, clinicians "should assess the potential harms of intensive therapy just as they do the benefits," a process that may demand new tools and a personalized approach.

Clearly, a more personalized approach to treatment targets is needed, and the ADA and EASD have acknowledged that reality since 2012.^[6] Yet little guidance on what personalized treatment targets should look like has been provided. Even with the now common use of electronic medical records (EMRs), reviewing a patient's medical history and accounting for their future needs while trying to appropriately weight the information to arrive at a single treatment target is nearly impossible. Decision tools such as the algorithm proposed by Cahn and colleagues could be of great help. This kind of algorithm could be provided in a standalone smartphone application, for example, or could even be programmed into an EMR so that all a clinician has to do is score a few risk parameters on a scale of one to three to calculate a glycemic target (although getting the patient to that target is another story).

An important concern with Cahn and colleagues' algorithm (and others that might be developed in similar ways) is that it is entirely based on expert opinion. There is no empirical data to verify that the identified A1c target is indeed the "right" one for the patient at hand. Furthermore, the very nature of personalization makes such evidence impossible to obtain. Because clinicians are scientists who are used to practicing evidence-based medicine, making such an important decision without clear-cut evidence may be uncomfortable. Still, the result could make the patient more comfortable. Isn't that the goal?

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