Predict Metabolic Syndrome

Metabolic syndrome (MetS) is a clustering of risk factors, including central obesity, insulin resistance, dyslipidemia, and hypertension that increases the risk of cardiovascular disease threefold and the risk of type 2 diabetes fivefold (1). People with MetS also often have other conditions, including excessive blood clotting and constant, low-grade inflammation throughout the body. MetS has also been associated with a plethora of cancers including breast, pancreatic, colon and liver cancer (2,3).

Size of the Problem

• 80 million adults in the U.S. meet the criteria for MetS (1).
• 60% higher annual utilization and costs on average are associated with MetS (2).
• 5x individuals with MetS are five times more likely to develop diabetes mellitus (1).

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Nearly one-third of U.S. adults—approximately 80 million people—meet the criteria for MetS (1). Such a high prevalence and potential for adverse outcomes imposes an enormous clinical and economic burden. Healthcare costs for individuals with MetS are 60% higher, and increase by another 24% for each additional risk factor (4). In total, the annual healthcare costs for people with MetS is estimated to exceed $220 billion (5). And yet, public awareness of MetS is alarmingly low. In a study of people with diabetes or at elevated risk for developing it, less than 15% indicated they had heard of the condition (6). Increased awareness and identification is paramount; an additional 104 million people are at risk for developing MetS (1).

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Fortunately, MetS is preventable and potentially reversible, but success depends on the ability to better identify people at risk for MetS and to individually-tailor medical treatments and health interventions that can improve outcomes. AI can help healthcare organizations (HCOs) identify individuals at-risk for developing MetS and provide critical insight into their specific risk factors. Predictive analytics can enable care teams to personalize interventions with self-management support, diet and exercise regimes, and greater continuity of care.

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• Vital Signs: Data indicating the status of the body’s vital and life-sustaining functions, with core vital signs including blood pressure, pulse, respiration rate, and body temperature.
• EHR Problem Lists: Data capturing the most important problems facing a patient, when it occurred and when it was resolved, and lists other illnesses, injuries and factors that affect their health.
• Lab Test Results: Data about important risk markers from tests used for diagnosis, monitoring therapy, or screening, with details about specific results and abnormal indicators.

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