Let us use each of the six steps we have identified in a systems analysis to better understand the problem of coronary artery disease.
Step 1: Identify influences—We know from reductionist research that there are multiple factors that increase the risk of coronary artery disease, including high blood pressure, high LDL cholesterol, low HDL cholesterol, abdominal obesity, diabetes, cigarette smoking, physical inactivity, family history, etc. Recognizing each of these factors has been an important part of addressing the problem of coronary artery disease. Further progress, however, requires us to think about how these interventions connect to each other.
Step 2: Estimate the relative strength of the influences—We need to estimate the relative strength or magnitude of the impact of each of the influences. We might estimate the relative risk for each of these factors, or we might classify their impacts as weak, moderate, or strong. In the case of coronary artery disease, each of these factors is considered of moderate importance with relative risks in the range of 2 to 4.
Step 3: Examine the interactions between factors—Examining the interaction between factors helps us understand what happens when two or more of the factors are present. Risk factors for disease may add together to increase the risk of disease, such as high blood pressure plus high LDL cholesterol and low HDL cholesterol. Alternatively, one factor, such as physical activity, may have a protective effect against coronary artery disease in and of itself. Interactions between factors may multiply the risk rather than resulting in an additive impact. Risk factors for coronary artery disease are usually assumed to add together rather than to multiply the impact. However, a combination of risk factors known as the metabolic syndrome has been shown to interact and greatly increase the risk. Metabolic syndrome includes increased waist circumference, low HDL cholesterol, elevated triglycerides, hypertension, and elevated fasting blood sugar. When all or a number of these risk factors occur together, they greatly magnify the probability of coronary artery disease as well as other large blood vessel diseases such as strokes.
Step 4: Identify feedback loops that lead to dynamic changes in the functioning of the system—Understanding how systems operate over time requires us to identify feedback mechanisms, or feedback loops, that alter the likelihood of disease or impact its outcome. For instance, increased weight, especially increased abdominal girth, may lead to increased LDL cholesterol, diabetes, reduced exercise, reduced HDL cholesterol, and increased blood pressure. Alternatively, multiple interventions focused on weight, exercise, blood sugar control, and treatment of hypertension may work together to have a surprisingly positive impact on the probability of coronary artery disease.e
Step 5: Identify bottlenecks—Bottlenecks imply that there are points in the system that need to be addressed in order for the other factors or influences to have their potential impacts. For instance, in coronary artery disease, if severe narrowing of the coronary arteries already exists, it is unlikely that interventions such as reducing blood sugar, reducing LDL cholesterol, increasing exercise, or stopping cigarette smoking are going to have a dramatic impact. If the bottleneck, the narrowed artery, can be addressed using angioplasty or surgery, attention to the other risk factors may have a much greater impact.
Step 6: Identify leverage points—The systems analysis that we have done so far suggests some leverage points where interventions may have greater than expected impacts. For instance, increasing exercise post angioplasty or surgery may be safer than when severe disease is present. Patients may also be highly motivated to exercise after having angioplasty or surgery. Exercise then might be effective in helping patients stop smoking cigarettes and reduce abdominal girth, as well as having an impact on HDL cholesterol and blood sugar.