Using Big Data Analytics to Predict Metabolic Syndrome

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A new study by Aetna’s Innovation Labs and GNS Healthcare uses “big data” analytics to predict patients at risk for metabolic syndrome. Their results show that lowering waist size and blood glucose have the largest health benefits and cause the biggest reduction in medical costs. Regular doctor visits and medication adherence reduces the one-year probability of having metabolic syndrome in nearly 90% of people.

Metabolic syndrome is the name for a group of five risk factors that raise your risk for heart disease and other health problems, such as diabetes and stroke:

• Large waist size
• high blood pressure
• high triglycerides
• low high-density lipoprotein (HDL or “good”cholesterol)
• high blood sugar

Patients who exhibit three of these five factors are classified as having metabolic syndrome. People who have metabolic syndrome are 2x as likely to have a heart attack or stroke and are 5x as likely to develop diabetes as those who don’t. All together, these conditions account for ~20% of healthcare costs in the United States. As a managed healthcare company, Aetna is very interested in personalized interventions that could reduce risk and decrease costs associated with metabolic syndrome.

Aetna’s Innovation Labs collaborated with GNS Healthcare to build computer models and evaluate information from 37,000 members of one of Aetna’s employer customers. The information included medical and pharmacy claims, demographics, lab tests and biometric screening results (blood pressure and cholesterol) over a two-year period.

Two analytical models were used in the study:

1. a claims-based-only model to predict the likelihood of each of the five metabolic syndrome factors occurring for each subject
2. a model based on both claims and biometric data to predict whether each study subject is likely to improve, stay the same, or get worse for each metabolic syndrome factor

The models were used to predict future risk of metabolic syndrome on both a population and an individual level. Detailed risk profiles were built for each subject that included which combination of the five metabolic syndrome factors that person exhibited and are at risk for developing.

The models were also be used to create personalized exercise, weight management, and care management programs. The study found that:

• reduction in waist size and blood glucose had the largest health benefits and the greatest decrease in medical costs
• having regular doctor visits and appropriate use of prescription medicines helped people change their risk factors; having a routine, scheduled outpatient visit reduced the one-year probability of having metabolic syndrome in nearly 90% of people.

Results from the study are published in the June issue of the American Journal of Managed Care.

Following the study, several Aetna employer customers began using the metabolic syndrome reporting and prediction capabilities described. Once people are identified as at risk for metabolic syndrome, they receive  specific suggestions for how to reduce their risk. Aetna expects the metabolic syndrome predictor will be more broadly available to Aetna employer customers in 2015.

Sources: Aetna, GNS Healthcare

References

1. Steinberg et al. Novel Predictive Models for Metabolic Syndrome Risk: A “Big Data” Analytic Approach. Am J Manag Care. 2014 Jun 26.
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