AJMC publishes results showing big data analytics can predict risk of metabolic syndrome
Research published today in the American Journal of Managed Care demonstrates that analysis of patient records using state-of-the-art data analytics can predict future risk of metabolic syndrome. More than a third of the U.S. population has metabolic syndrome, a condition that can lead to chronic heart disease, stroke and diabetes. These conditions combine to account for almost 20 percent of overall health care costs in the U.S. The study was conducted by Aetna (NYSE: AET) and GNS Healthcare Inc. (GNS), a leading provider of big data analytics products and services in health care.
“This study demonstrates how integration of multiple sources of patient data can help predict patient-specific medical problems,” said lead author Dr. Gregory Steinberg, head of clinical innovation at Aetna Innovation Labs. “We believe the personalized clinical outreach and engagement strategies, informed by data from this study, can help improve the health of people with metabolic syndrome and reduce the associated costs.”
“The breakthrough in this study is that we are able to bring to light hyper-individualized patient predictions, including quantitatively identifying which individual patients are most at risk, which syndrome factors are most likely to push that patient past a threshold, and which interventions will have the greatest impact on that individual,” said Colin Hill, co-founder and CEO of GNS. “The GNS automated data analytics platform paired with Aetna’s deep clinical expertise produced these results on extremely large datasets in just three months, a testament to the ability of both groups.”
GNS analyzed data from nearly 37,000 members of one of Aetna’s employer customers who had voluntarily participated in screening for metabolic syndrome.
The data analyzed included medical claims records, demographics, pharmacy claims, lab tests and biometric screening results over a two-year period. For this study, the Aetna and GNS teams utilized two distinct analytical models:
A claims-based-only model to predict the probability of each of the five metabolic syndrome factors occurring for each study subject.
A second model based on both claims and biometric data to predict whether each study subject is likely to get worse, improve or stay the same for each metabolic syndrome factor.
Both analytical models predicted future risk of metabolic syndrome on both a population and an individual level, with ROC/AUC (receiver operating characteristic/area under the curve) varying from 0.80 and 0.88. The researchers were able to develop detailed risk profiles for individual participants, enabling a deep understanding of exactly which combination of the five metabolic syndrome factors each of the study subjects exhibit and are at risk for developing. For every Aetna member whose data was used in the study, the researchers used a scale that measures the percentage risk that individuals have of exhibiting each of the five metabolic syndrome factors. For example, in an individual patient who exhibited two of the five risk factors, researchers could predict which third factor is the most likely to develop.
Some people are genetically prone to develop insulin resistance or metabolic syndrome. Other people develop metabolic syndrome by:
Putting on excess body fat
Failing to get enough physical activity
Consuming a diet high in carborhydrates (more than 60 percent of daily caloric intake from carbs)
What groups are most likely to have metabolic syndrome?
Metabolic syndrome has become increasingly common in the United States. It’s estimated that about 70 million adults in the United States have it. Several factors increase the likelihood of acquiring metabolic syndrome:
Obesity and insulin resistance are two potential and important causes of metabolic syndrome. Excessive fat in and around the abdomen is most strongly associated with metabolic syndrome. However, the reasons abdominal obesity and metabolic syndrome seem to be linked are complex and not fully understood.
Metabolic syndrome is closely associated with a generalized metabolic disorder called insulin resistance, in which the body can’t use insulin efficiently. Some people are genetically predisposed to insulin resistance.
People who are not physically active are twice as likely to develop metabolic syndrome and its complications than people who exercise regularly.
The analytical models also helped identify individual variable impact on risk associated with adherence to prescribed medications, as well as adherence to routine, scheduled outpatient doctor visits. A scheduled, outpatient visit with a primary care physician lowers the one-year probability of having metabolic syndrome in nearly 90 percent of individuals. In addition, the study found that improving waist circumference and blood glucose yielded the largest benefits on patients’ subsequent risk and medical costs.
About Metabolic Syndrome
Metabolic syndrome is a group of risk factors: large waist size, high blood pressure, high triglycerides, low high-density lipoprotein (HDL)—or “good”—cholesterol and high blood sugar. Patients who exhibit three of these five factors are classified as having metabolic syndrome. Individuals who have metabolic syndrome are twice as likely to have a heart attack or stroke and are five times as likely to develop diabetes as those who do not. The rate of metabolic syndrome is on the rise, with over a third of U.S. adults having the condition. The chronic diseases that metabolic syndrome can lead to–heart disease, stroke and diabetes - together cause nearly 800,000 deaths per year in the U.S., alone. These diseases cost the health care system more than half a trillion dollars, or almost 20 percent of the $2.7 trillion the U.S. Centers for Disease Control and Prevention cites as the total cost of national health expenditures. (Sources: American Heart Association and American Diabetes Association)
About GNS Healthcare
GNS Healthcare is a big data analytics company that empowers payers, providers and pharmaceutical companies to make intelligent data-driven decisions. We unlock knowledge within complex data, enabling personalized, actionable predictions and precision targeting. For 15 years, GNS has been committed to developing and deploying the most sophisticated mathematical and computational platforms to help our partners improve health and reduce costs. http://www.GNSHealthcare.com
Aetna is one of the nation’s leading diversified health care benefits companies, serving an estimated 44 million people with information and resources to help them make better informed decisions about their health care. Aetna offers a broad range of traditional, voluntary and consumer-directed health insurance products and related services, including medical, pharmacy, dental, behavioral health, group life and disability plans, and medical management capabilities, Medicaid health care management services, workers’ compensation administrative services and health information technology products and services. Aetna’s customers include employer groups, individuals, college students, part-time and hourly workers, health plans, health care providers, governmental units, government-sponsored plans, labor groups and expatriates. For more information, see http://www.Aetna.com and the 2014 Aetna story about how Aetna is helping to build a healthier world.
David Santucci, 617-374-2347
Ethan Slavin, 860-273-6095
MacDougall Biomedical Communications
Lynnea Olivarez, 781-235-3060