Electronic Alerts for Heart Failure Prevention in Diabetes
Study Details
Study Description
Brief Summary
Type 2 diabetes mellitus (T2DM) is an independent risk factor for heart failure (HF) and is associated with significant morbidity and mortality. Recent therapeutic advances in pharmacotherapies, such as sodium-glucose cotransporter-2 inhibitors (SGLT2i), have shown to be beneficial in preventing HF among patients with T2DM. However, despite widely available risk prediction and stratification tools and evidence-based practice guidelines, SGLT-2i medications are under-prescribed in the United States. The proposed study is a pragmatic, single-center, randomized trial to test the feasibility and effectiveness of a clinical decision support (CDS) tool to alert providers and improve HF risk stratification in patients with T2DM.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
|
N/A |
Detailed Description
Type 2 diabetes mellitus (T2DM) is an independent risk factor for heart failure (HF) and is associated with significant morbidity and mortality. Even despite adequate glycemic control, individuals with T2DM face considerable risk of HF even in individuals without other significant risk factors. Moreover, individuals with both atherosclerotic cardiovascular disease and T2DM face up to a five-fold increased risk of HF and experience higher rates of mortality compared to age-matched controls. Thankfully, recent therapeutic advances in pharmacotherapies, such as sodium-glucose cotransporter-2 inhibitors (SGLT2i), have shown to be beneficial in preventing HF among patients with T2DM. Current guidelines by the American Diabetes Association and the joint American College of Cardiology/American Heart Association (ACC/AHA) both provide class I/A recommendations in initiating SGLT2i medication in individuals with T2DM and cardiovascular comorbidities for prevention of HF. Similarly, the Food and Drug Administration now indicates SGLT2i as a method to reduce the risk of HF hospitalization in adults with T2DM and established CV risk factors.
Unfortunately, SGLT2i are underused in patients with T2DM at risk for HF with ~5% of eligible patients treated with the medication. Risk-based approaches to identify patients who are at increased risk of developing adverse events is key to improve the use of evidence-based therapies and for efficient and cost-effective allocation of preventive strategies. Previous methods, such as the Pooled Cohort Equation, have been effective in guiding prescription of statin medications to at-risk patients. Similarly, alert-based clinical decision support tools have been used to help guide anticoagulation strategies in patients with atrial fibrillation. However, no such risk-based approach exists for implementation of goal-directed medical therapy for HF prevention in patients with T2DM.
The WATCH-DM risk score (Weight [body mass index], Age, hyperTension, Creatinine, HDL-C, Diabetes control [fasting plasma glucose] and QRS Duration, MI and CABG) is one such machine learning-based tool that was developed among participants of the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial.
The investigators used machine-learning methods and readily available clinical characteristics to derive the risk prediction model and has had excellent discrimination and calibration for estimating HF risk. For each risk factor level, patients are given a specific number of points. The sum of the points accounting for all risk factors included in the model is associated with 5-year risk of HF. There is a graded, dose-response relationship between the WATCH-DM risk score and risk of HF. For example, patients who had a WATCH-DM risk score of at least 11 had a 5-year risk of incident HF ≥9.2%.
This proposed trial will test the efficacy of a computer-based electronic alert (clinical decision support) notifying the provider that the patient is at an increased risk of developing heart failure. There currently are no developed or implemented alert systems notifying the provider that the patient is at an increased risk of heart failure. Similarly, there is no risk-based approach to implementation evidence-based T2DM therapies in patients at risk for HF. Currently, SGLT2i use is underutilized with ~5% of eligible patients current prescribed the medication. Clinical decision support tools may inform providers about a patient's risk of HF and may be useful to improve the use of SGLT2i therapies. Previous implementation strategies have been useful to guide statin medications in patients at risk for atherosclerotic cardiovascular events and anticoagulation strategies in patients with atrial fibrillation.
The current study will determine the impact of electronic alert-based CDS on prescription of SGLT2i medications in high-risk HF patients in the outpatient setting who are not being prescribed SGLT2i therapies. Investigators will not mandate a specific SGLT2i agent or regimen. Study investigators will provide options for SGLT2i medications to prevent HF and allow the provider to make the best choice based on their clinical judgement. If there is a contraindication to SGLT2i therapy, the provider can elect to omit the suggested therapy and provide an explanation for doing so. Data acquired throughout the study duration will also determine the impact of electronic alert-based CDS on the frequency of SGLT2i prescription patterns and incident HF events.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Experimental: Electronic Alert Each provider in the alert group will receive an on-screen notification regarding the patient's increased risk of HF in diabetes and the lack of an active order for SGLT2i therapy. |
Behavioral: On-screen electronic alert
On-screen computer-based alert notifying the provider that the patient is at an increased risk of developing HF based on the WATCH-DM risk score and associated guideline recommendation for preventive management of these patients.
|
| No Intervention: No Alert The CDS will not issue an on-screen alert. |
Outcome Measures
Primary Outcome Measures
- Frequency of prescription of SGLT-2i medication at outpatient clinic visits [30 days]
Prescription rate of SGLT-2i medication (no. of patients prescribed SGLT2 / no. of eligible patients)
Secondary Outcome Measures
- Adherence of prescription of SGLT-2i medication at outpatient clinic visit [6 months]
Defined as continued prescription and adherence to a SGLT-2i medication at 6 months after initial prescription.
- Adherence of prescription of SGLT-2i medication at outpatient clinic visit [12 months]
Defined as continued prescription and adherence to a SGLT-2i medication at 12 months after initial prescription.
Other Outcome Measures
- Frequency of incident HF [12 months]
Frequency of incident HF
Eligibility Criteria
Criteria
Inclusion Criteria:
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Providers in a General Internal Medicine outpatient clinic encounter
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Providers in a subspecialty Internal Medicine outpatient clinic encounter
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Providers in family medicine outpatient clinic encounter
Exclusion Criteria:
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Providers in an inpatient hospital encounter
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Patients with HF or on SGLT-2i
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | University of Texas Southwestern Medical Center | Dallas | Texas | United States | 75390 |
Sponsors and Collaborators
- University of Texas Southwestern Medical Center
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Additional Information:
Publications
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- Kannel WB, Hjortland M, Castelli WP. Role of diabetes in congestive heart failure: the Framingham study. Am J Cardiol. 1974 Jul;34(1):29-34.
- McAllister DA, Read SH, Kerssens J, Livingstone S, McGurnaghan S, Jhund P, Petrie J, Sattar N, Fischbacher C, Kristensen SL, McMurray J, Colhoun HM, Wild SH. Incidence of Hospitalization for Heart Failure and Case-Fatality Among 3.25 Million People With and Without Diabetes Mellitus. Circulation. 2018 Dec 11;138(24):2774-2786. doi: 10.1161/CIRCULATIONAHA.118.034986.
- Segar MW, Vaduganathan M, Patel KV, McGuire DK, Butler J, Fonarow GC, Basit M, Kannan V, Grodin JL, Everett B, Willett D, Berry J, Pandey A. Machine Learning to Predict the Risk of Incident Heart Failure Hospitalization Among Patients With Diabetes: The WATCH-DM Risk Score. Diabetes Care. 2019 Dec;42(12):2298-2306. doi: 10.2337/dc19-0587. Epub 2019 Sep 13.
- Standl E, Schnell O, McGuire DK. Heart Failure Considerations of Antihyperglycemic Medications for Type 2 Diabetes. Circ Res. 2016 May 27;118(11):1830-43. doi: 10.1161/CIRCRESAHA.116.306924. Review.
- Zelniker TA, Wiviott SD, Raz I, Im K, Goodrich EL, Bonaca MP, Mosenzon O, Kato ET, Cahn A, Furtado RHM, Bhatt DL, Leiter LA, McGuire DK, Wilding JPH, Sabatine MS. SGLT2 inhibitors for primary and secondary prevention of cardiovascular and renal outcomes in type 2 diabetes: a systematic review and meta-analysis of cardiovascular outcome trials. Lancet. 2019 Jan 5;393(10166):31-39. doi: 10.1016/S0140-6736(18)32590-X. Epub 2018 Nov 10. Erratum in: Lancet. 2019 Jan 5;393(10166):30.
- STU-2020-1300