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Learn how AI can help HCOs to promote early detection of MetS, improve patient self-management to slow or prevent the development of chronic diseases, and reduce the incidence of adverse events. Discover how AI can help to identify at-risk patients, surface patient-specific insights to improve care management, and predict avoidable acute care utilization.

Automatically ingest data from dozens of health data sources including...

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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ClosedLoop generates explainable predictions using

thousands of auto-generated, clinically relevant contributing factors

Hugh Stevens
42-Year-Old Male
Risk patient will develop metabolic syndrome
Risk Score Percentile
97
Impact on Risk  |  Contributing Factor
Value
+26% | Increase in Fasting Blood Glucose Levels (mg/dL)
80 to 105
+18% | BMI Level
29
+14% | Rise in Blood Pressure (mmHg)
130/90 to 150/100
+10% | Decline in Sleep Quality (episodes / week)
2 to 5

What Is 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 

 

Why It Matters

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

AI Presents an Opportunity

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. 


Did You Know…

  • 80 million adults in the U.S. meet the criteria for MetS¹
  • 60% higher annual utilization and costs on average are associated with MetS²
  • 5x individuals with MetS are five times more likely to develop diabetes mellitus¹
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Citations & Footnotes

1. Steinberg, Gregory B., et al. “Novel Predictive Models for Metabolic Syndrome Risk: A ‘Big Data’ Analytic Approach.” The American Journal of Managed Care, vol. 20, no. 6, Jun. 24. pp:221–228. Accessed 20 Mar. 2021. 

2. NHLBI. Metabolic Syndrome | NHLBI, NIH. Nih.gov. Published December 28, 2020. Accessed March 24, 2021. https://www.nhlbi.nih.gov/health-topics/metabolic-syndrome

3. O’Neill S, O’Driscoll L. Metabolic syndrome: a closer look at the growing epidemic and its associated pathologies. Obesity Reviews. 2014;16(1):1-12. doi:10.1111/obr.12229

4. Boudreau, D.M., et al. “Health Care Utilization and Costs by Metabolic Syndrome Risk Factors.” Metabolic Syndrome and Related Disorders, vol. 7, no. 4, Aug. 2009, pp. 305–314, doi:10.1089/met.2008.0070. Accessed 21 Mar. 2021.

5. Yu, Yu, et al. “Air Pollution, Noise Exposure, and Metabolic Syndrome – a Cohort Study in Elderly Mexican-Americans in Sacramento Area.” Environment International, vol. 134, Jan. 2020, p. doi:10.1016/j.envint.2019.105269. Accessed 21 Mar. 2021.

6. Lewis, S. J., et al. “Self-Reported Prevalence and Awareness of Metabolic Syndrome: Findings from SHIELD.” International Journal of Clinical Practice, vol. 62, no. 8, 29 Apr. 2008, pp. 1168–1176, doi:10.1111/j.1742-1241.2008.01770.x. Accessed 21 Mar. 2021.

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