Pre-Diabetes Cardiovascular (CV) Care (Pre-Diabetes Wizard)

Nearly one in three adults has prediabetes, a condition that substantially increases the risk of heart attacks and stroke. The increased cardiovascular risk associated with prediabetes can be effectively managed by lifestyle changes or medication therapy, but recent data shows few prediabetes patients are treated effectively. In this project, we will adapt, implement, and evaluate a proven electronic health record-linked, web-based clinical decision support system to identify patients with prediabetes and provide prioritized treatment recommendations to patients and providers in a rural health system. The results of the project will provide a template for implementation of more efficient and effective rural healthcare and have the potential to substantially and improve cardiovascular quality of care and clinical outcomes of millions of rural Americans with prediabetes.

Nearly one in three adults in rural communities has prediabetes, a condition that increases the risk of heart attacks and stroke but can be managed by use of metformin, lifestyle interventions, and control of major cardiovascular (CV) risk factors. However, current prediabetes care is characterized by: (a) delayed recognition of prediabetes; (b) patient unawareness of effective treatment options for prediabetes; (c) poor control of concomitant major CV risk factors; (d) very low rates of metformin initiation; and (e) low rates of follow up to assess ongoing effectiveness of prediabetes management.1-3 Increased use of electronic health records (EHR) in rural communities now provides a new opportunity to improve awareness and management of prediabetes and to reduce these patients' significant CV risk burden. In this project, we implement and evaluate an EHR-linked, Web-based clinical decision support (CDS) system that identifies patients with prediabetes and provides patients and their primary care providers personalized, evidence-based CDS and follow up to reduce risk of heart attacks or stroke. To accomplish this objective, we randomly allocate 30 primary care clinics with their 450 primary care providers and estimated 17,000 prediabetes patients to one of two intervention arms: Usual Care; or else the prediabetes CDS to optimize management and follow up of prediabetes patients with uncontrolled CV risk factors. Random-effects models assess intervention impact on: (a) American College of Cardiology/American Heart Association (ACC/AHA) pooled CV risk; (b) major CV risk factors (blood pressure, lipids, HbA1c, smoking, and BMI); (c) use of evidence-based drugs, including metformin, and lifestyle interventions to manage prediabetes; and (d) patient and provider satisfaction with the intervention strategy. We also conduct a state-of-the-art cost and a cost-effectiveness analysis of the interventions relative to usual care. The Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework, supplemented by the Consolidated Framework for Implementation Research (CFIR), is used to assess implementation processes and outcomes in a rural/urban health system. The results of the project will provide a template for implementation of personalized CDS tools in rural and urban health settings, resulting in more efficient and effective rural healthcare that can be broadly applied across many clinical domains, incorporates patient treatment preferences, and has the potential to substantially improve the quality of CV care and clinical outcomes of millions of Americans with prediabetes residing in medically underserved areas.

In the Intervention arm, primary care providers will be provided with an EHR-linked, Web-based clinical decision support system that identifies patients with prediabetes and provides patients and their primary care providers personalized, evidence-based CDS and follow up to reduce risk of heart attacks or stroke, optimizing management and follow up of pre-diabetes patients with uncontrolled CV risk factors.

In the No Intervention arm, patients receive usual care from their primary care clinic and care providers.

an EHR-linked, Web-based clinical decision support (CDS) system that identifies patients with prediabetes and provides patients and their primary care providers personalized, evidence-based CDS and follow up to reduce risk of heart attacks or stroke

10-year cardiovascular risk was calculated at the index and all post-index patient encounters. A comparison of the difference in model-estimated annual rate of change in cardiovascular risk in Clinical Decision Support versus Usual Care clinics tested the primary efficacy hypothesis. The American College of Cardiology/American Heart Association (ACC/AHA) pooled CV risk calculator provides sex- and race-specific 10-year risk estimates for the first ASCVD event for black & white men & women aged 40-79 years. Variables included in risk assessment equations: age, total cholesterol, high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP) diabetes mellitus (diabetes), and current smoking status. 10-year risk for ASCVD categorized as: Low (<5%), Borderline (5% to 7.4%), Intermediate (7.5% to 19.9%), High (≥20%). Numerator: Rate of change in Clinical Decision Support. Denominator: Rate of change in Usual Care. Relative Risk < 1 supports primary hypothesis.

Inclusion Criteria: Pre-Diabetes Diagnosis or Clinical Lab Values and One or more of the following CV risk factors: Current smoking, Hypertension, elevated LDL, or BMI not within normal range, AND at least one subsequent primary care visit to a randomized clinic 12 to 24 months after the index visit. Exclusion Criteria: Evidence of Diabetes in the previous 12 months Pregnant Hospice care or Chemotherapy

Karve A, Hayward RA. Prevalence, diagnosis, and treatment of impaired fasting glucose and impaired glucose tolerance in nondiabetic U.S. adults. Diabetes Care. 2010 Nov;33(11):2355-9. doi: 10.2337/dc09-1957. Epub 2010 Aug 19.

Saman DM, Allen CI, Freitag LA, Harry ML, Sperl-Hillen JM, Ziegenfuss JY, Haapala JL, Crain AL, Desai JR, Ohnsorg KA, O'Connor PJ. Clinician perceptions of a clinical decision support system to reduce cardiovascular risk among prediabetes patients in a predominantly rural healthcare system. BMC Med Inform Decis Mak. 2022 Nov 19;22(1):301. doi: 10.1186/s12911-022-02032-z.

Pratt R, Saman DM, Allen C, Crabtree B, Ohnsorg K, Sperl-Hillen JM, Harry M, Henzler-Buckingham H, O'Connor PJ, Desai J. Assessing the implementation of a clinical decision support tool in primary care for diabetes prevention: a qualitative interview study using the Consolidated Framework for Implementation Science. BMC Med Inform Decis Mak. 2022 Jan 15;22(1):15. doi: 10.1186/s12911-021-01745-x.