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How To Overcome The Complexities of Treatment Decisions For Your Patients with Type 2 Diabetes

Apr 28, 2020
Editor: Steve Freed, R.PH., CDE

Author: Dr. Bradley Eilerman and Len Testa

Part 3: Trade Offs — Clinical Decision Making for Patients with Type 2 Diabetes

Part 1 | Part 2


In the previous series of articles, we discussed the complexity of clinical decision making and the role the cost plays into that decision. Cost is only one dimension of the complex process of deciding the appropriate next steps in the treatment of type 2 diabetes. In this article, we will discuss the other aspects of clinical decision making, and how clinicians can think about the trade-offs involved in making medication choices. 

Despite recent controversies, glucose control remains the primary goal of diabetes treatment. How glucose control is defined and measured is a subject of growing discussion in the diabetes world. Hemoglobin A1c has provided our long-term estimate of glucose control, in part because it is easily quantifiable and, in part, due to its association with substantial outcomes trials like UKPDS. Advances in glucose monitoring, including improving technology in continuous glucose monitoring, suggest glucose variability and avoidance of hypoglycemia are central to glucose control. 

Because of its importance, glucose control provides an excellent discussion example of trade-offs in antihyperglycemic medicine. For example, subcutaneous insulin and sulfonylurea provide relatively healthy glucose-lowering, with a coinciding increased risk of hypoglycemia. This is countered by newer agents like DPP-4 inhibitors and dopamine agonists, which produce modest lowering with little chance of lows. 

The area of non–glucose-related effects is of growing interest in decision-making in type 2 diabetes. For many thought leaders, slowing the disease progression provides the motivation for the order in which drugs should be selected. This perspective balances the long-term impact on beta-cell health against short-term benefits in glucoselowering. 

Closely related to this is the regimen’s impact on non-glucose metabolic disease like obesity, hypertension, and fatty liver. Also, FDA-mandated cardiovascular outcomes trials bring a unique level of relevance to individuals at high-risk for cardiovascular events. More and more, new diabetes agents bring benefits beyond glucoselowering — again, providing contrast with sulfonylurea and subcutaneous insulin. 

Adverse events provide a fourth dimension in diabetes decision-making. The impact of adverse events can account for health-threatening contraindications such as TZD-related edema in decompensated heart failure. More commonly, adverse events like diarrhea have low-impact on mortality but extremely high impact on the quality of life.  Experienced clinicians learn that these types of adverse events have variable impact on individuals in terms of both severity and tolerability, and ultimately adherence. The myriad benefits of inexpensive Metformin, for example, maybe negated by frequent intolerable diarrhea. 

Ease of implementation and complexity remains the final aspect of decision making to consider. The nearconstant impact of diabetes in the day-to-day aspect of life is a frequent complaint of patients over time. When medication provides an additional intrusion on the patient’s routine, adherence to their diabetes plan becomes very difficult. This is particularly evident in basalbolus insulin therapy, which requires both frequent dosing and quantification of carbohydrate intake for optimal outcomes. This stands in contrast to GLP-1 agonists, which offer once-daily and once-weekly administration. 

We’ve just listed five decision-making categories: glucose control, the ability to slow disease progression, impact on non-glucose metabolic diseases, adverse events, and ease of implementation. Comparisons within any one of these decision-making categories can be transparent. The complexity comes when these categories have to be considered together. 

In every choice in medication in diabetes, a clinician needs to consider cost, glucoselowering, non-glucose benefits, non-glucose adverse events, and complexity. This has to be done for insulin and 11 non-insulin classes, not to mention multiple drugs within each class. The result creates a question for the clinician and patient to consider together, such as: 

Is a GLP-1, which has a $150 cost at the pharmacy, worth superior A1c lowering, cardiovascular benefit, and weight loss against a $20 TZD with moderate A1c efficacy, weight gain, and a possible negative impact on the bone? 

As different patients have different values and decisions, choosing a treatment regimen should be a shared exercise between provider and patient. 

Once again, complete examination of all issues present is difficult, if not impossible, to achieve during the 10 to 20 minutes afforded most office visits. Often, clinical decision making devolves to inconsistency and habit, which ultimately results in suboptimal outcomes. In the next and final article, we will discuss how advances in information technology can help clinicians navigate through these choices in the form of clinical decision support, with a final presentation of a decision support system in active clinical trials that have already demonstrated benefit in type 2 diabetes. 


Next Week: Part 4 Decision support – The first three articles in this series focused on what makes treating type 2 diabetes so difficult: the overwhelming number of medicines and combinations; finding effective treatments that work with our patients’ insurance coverage and household budgets; and incorporating factors beyond glucose control, such as body weight, adherence, and side effects, into our decisions (above). In Part 4 of this series – Decision Support – we discuss how those problems can be represented in ways that computers can understand. With this we can take advantage of computers’ fast number-crunching capabilities to produce treatment recommendations that are effective, affordable, and sustainable, with other potential benefits.


This article was updated on April 28, 2020. Originally published Jan. 27, 2018.

Bradley Eilerman MD MHI obtained his medical degree from the University of Kentucky in 2001. He completed his internship at Vanderbilt University Medical Center in 2001 and his residency in Internal Medicine/Pediatrics at the University of Cincinnati in 2005. Dr. Eilerman went on to complete a fellowship in Diabetes, Endocrinology and Metabolism in 2008. He finished his Master’s in Health Informatics in 2016. Dr. Ellerman works as the lead physician, director of clinical research, and clinical endocrinologist for the St. Elizabeth Physician’s group where he cares for a patient base of approximately 2,500. He is licensed to practice medicine in the state of Kentucky.

Len Testa is a computer scientist at GlucosePATH.com, focusing on the combinatorial optimization engine and architecture. Len’s other work includes optimization software that has helped millions of people minimize their waits in line at Disney World. (He’s happy to talk about that, too.) That said, it’s widely acknowledged that Len is the “pretty face” of a much more talented group of people, and that “pretty face” is charitable, at best.