- The program Diabetes PHD, powered by Archimedes software, estimates absolute risk and absolute risk reductions for individual patients and is a useful decision-support tool (C).
- For most patients with type 2 diabetes who are older than 50 years, recommend aspirin and exercise as first-line interventions (C).
Strength of recommendation (SOR)
- Good-quality patient-oriented evidence
- Inconsistent or limited-quality patient-oriented evidence
- Consensus, usual practice, opinion, disease-oriented evidence, case series
Background Benefits of interventions are usually reported as relative risk reductions. Absolute risk reductions (ARRs)—most relevant to individual patients—are reported less often.
Objectives Estimate ARRs for interventions in a patient with diabetes mellitus.
Methods We used the Archimedes Risk Assessment Tool to estimate 10-year risks of myocardial infarction (MI), cerebrovascular accident (CVA), end-stage renal disease (ESRD), blindness, foot ulceration, and amputation, and to estimate the ARRs associated with controlling blood pressure (BP), blood sugar, and low-density lipoprotein (LDL) cholesterol levels; moderate exercise; and taking aspirin and a beta-blocker. our hypothetical base case was a 65-year-old white man. Three other hypothetical patients were a 50-year-old white man, a 65-year-old white woman, and a 65-year-old black man. Each patient had a 5-year history of diabetes mellitus, a sedentary lifestyle, body mass index (BMI) of 28 kg/m2, BP of 140/90 mm Hg, LDL of 120 mg/dl, high-density lipoprotein (HDL) of 45 mg/dL, and glycosylated hemoglobin (HbA1c) of 10%.
Results For the base case, the risks of MI (22.3%) and CVA (14.4%) far exceeded the risks of ESRD, blindness, and amputation. ARRs for interventions to reduce MI risk were: aspirin, 6.8%; HbA1c to 7%, 5.1%; moderate exercise, 2.7%; BP to 130/80 mm Hg, 1.4%; and LDL to 100 mg/dl, 1.4%. The female patient had a lower ARR for aspirin and a greater ARR for exercise. The black male patient had greater ARRs for both aspirin and exercise. Estimates were similar for CVA.
Conclusion Patients resembling our base case and its variations would probably benefit more from aspirin and moderate exercise than from all other interventions combined.
If you’re accustomed to telling patients with diabetes how different interventions may reduce their risk of macro-and microvascular complications, our study’s findings may alter your approach to the next patient.
Standard guidelines recommend controlling hyperglycemia, hypertension, and dyslipidemia, advocating moderate exercise and weight control, and treating with aspirin, angiotensin-converting enzyme (ACE) inhibitors, and β-blockers.1-3 Published benefits of these interventions most commonly come from clinical trials and are usually reported as relative risk reductions (RRR). However, the true benefit for an individual—the absolute risk reduction (ARR)—depends on that person’s baseline risk, the duration of a selected treatment, and the RRR associated with the treatment. Because RRR is often numerically larger than ARR, some patients may mistakenly perceive an intervention’s benefit to be greater than it actually is.
The purpose of this study was 2-fold: (1) to estimate the ARR for common diabetes interventions by analyzing a model case and variations of the case, thereby giving physicians a better sense of potential outcomes with these interventions; and (2) to demonstrate the potential utility of our evaluation method for practice.
How we estimated RRR and ARR
Diabetes risk engines use customized software designed to calculate the probable occurrence of different disease complications and how certain interventions might decrease those probabilities. Among the better known risk engines are the United Kingdom Prospective Diabetes Study (UKPDS) Risk Engine,4 the CDC/RTI Diabetes Cost-Effectiveness Model,5 and the Global Diabetes Model (GDM).6
Risk engines are generally of 1 of 2 types. The first type uses regression equations to analyze data from a single study. An example is the UKPDS Risk Engine, based on data from the United Kingdom Prospective Diabetes Study.
The second type uses Markov modeling, a method that describes the progression of diabetes through transition states. A simulated patient moves from 1 state of a disease to another at defined intervals based on transitional probabilities. Treatment impacts are analyzed according to their effects on these probabilities. Examples are the CDC/RTI Diabetes Cost-Effectiveness Model and the GDM.
The unique risk engine we used. We obtained absolute risk estimates for adverse events in a simulated patient using Diabetes PHD (Personal Health Decisions), a risk engine available online through the American Diabetes Association (ADA) Web site.7 The Diabetes PHD engine uses a software program called Archimedes, which differs from all other engines in several important ways.