Electronic Alerts for Heart Failure Prevention in Diabetes
NCT ID: NCT04791826
Last Updated: 2024-06-06
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
NA
1524 participants
INTERVENTIONAL
2021-03-25
2024-05-07
Brief Summary
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Detailed Description
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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.
Conditions
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Study Design
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RANDOMIZED
PARALLEL
TREATMENT
SINGLE
Study Groups
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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.
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 Alert
The CDS will not issue an on-screen alert.
No interventions assigned to this group
Interventions
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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.
Eligibility Criteria
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Inclusion Criteria
* Providers in a subspecialty Internal Medicine outpatient clinic encounter
* Providers in family medicine outpatient clinic encounter
Exclusion Criteria
* Patients with HF or on SGLT-2i
18 Years
ALL
No
Sponsors
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University of Texas Southwestern Medical Center
OTHER
Responsible Party
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Ambarish Pandey
Assistant Professor of Medicine
Locations
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University of Texas Southwestern Medical Center
Dallas, Texas, United States
Countries
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References
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Cavender MA, Steg PG, Smith SC Jr, Eagle K, Ohman EM, Goto S, Kuder J, Im K, Wilson PW, Bhatt DL; REACH Registry Investigators. Impact of Diabetes Mellitus on Hospitalization for Heart Failure, Cardiovascular Events, and Death: Outcomes at 4 Years From the Reduction of Atherothrombosis for Continued Health (REACH) Registry. Circulation. 2015 Sep 8;132(10):923-31. doi: 10.1161/CIRCULATIONAHA.114.014796. Epub 2015 Jul 7.
Benjamin EJ, Virani SS, Callaway CW, Chamberlain AM, Chang AR, Cheng S, Chiuve SE, Cushman M, Delling FN, Deo R, de Ferranti SD, Ferguson JF, Fornage M, Gillespie C, Isasi CR, Jimenez MC, Jordan LC, Judd SE, Lackland D, Lichtman JH, Lisabeth L, Liu S, Longenecker CT, Lutsey PL, Mackey JS, Matchar DB, Matsushita K, Mussolino ME, Nasir K, O'Flaherty M, Palaniappan LP, Pandey A, Pandey DK, Reeves MJ, Ritchey MD, Rodriguez CJ, Roth GA, Rosamond WD, Sampson UKA, Satou GM, Shah SH, Spartano NL, Tirschwell DL, Tsao CW, Voeks JH, Willey JZ, Wilkins JT, Wu JH, Alger HM, Wong SS, Muntner P; American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee. Heart Disease and Stroke Statistics-2018 Update: A Report From the American Heart Association. Circulation. 2018 Mar 20;137(12):e67-e492. doi: 10.1161/CIR.0000000000000558. Epub 2018 Jan 31. No abstract available.
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.
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.
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. doi: 10.1016/0002-9149(74)90089-7. No abstract available.
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.
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.
Related Links
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CV Risk Scores
Other Identifiers
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STU-2020-1300
Identifier Type: -
Identifier Source: org_study_id
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