Heart FailureNCT04791826
Electronic Alerts for Heart Failure Prevention in Diabetes
A study testing On-screen electronic alert in people with Heart Failure and Diabetes Mellitus, Type 2, run by University of Texas Southwestern Medical Center at 1 site in United States.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Heart Failure, Diabetes Mellitus, Type 2
- What is being tested?
- On-screen electronic alert
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,524 participants
- Where
- 1 siteUnited States
- Study dates
- 25 Mar 2021to 7 May 2024
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Providers in a General Internal Medicine outpatient clinic encounter
- Providers in a subspecialty Internal Medicine outpatient clinic encounter
- Providers in family medicine outpatient clinic encounter
Who cannot join
- Providers in an inpatient hospital encounter
- Patients with HF or on SGLT-2i
Trial locations and contacts
1 site in 1 country.
United States1 site
University of Texas Southwestern Medical Center
Dallas, Texas, 75390
About this study
Official title: Evaluation of an EMR-Based Clinical Decision Support Tool for the Implementation of Guideline-Directed Therapies for Prevention of Heart Failure Among High-Risk Patients With Type 2 Diabetes
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.
Read the 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.
- Conditions
- Heart Failure, Diabetes Mellitus, Type 2
- Being tested
- On-screen electronic alert
- Keywords
- Clinical Decision Support
- Medical subject headings
- Heart Failure, Diabetes Mellitus, Type 2
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
On-screen electronic alertBehavioural
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.
| Study group | Role | What they receive |
|---|---|---|
| Electronic Alert | Experimental | 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. Receives: Behavioral: On-screen electronic alert |
| No Alert | No intervention | The CDS will not issue an on-screen alert. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Frequency of prescription of SGLT-2i medication at outpatient clinic visits
Prescription rate of SGLT-2i medication (no. of patients prescribed SGLT2 / no. of eligible patients)
Measured: 30 days
Secondary outcomes (2)
Adherence of prescription of SGLT-2i medication at outpatient clinic visit
Defined as continued prescription and adherence to a SGLT-2i medication at 6 months after initial prescription.
Measured: 6 months
Adherence of prescription of SGLT-2i medication at outpatient clinic visit
Defined as continued prescription and adherence to a SGLT-2i medication at 12 months after initial prescription.
Measured: 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Mar 2021
- Study start25 Mar 2021
- Primary completion31 Jul 2022
- Study completion7 May 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (7)Papers the sponsor has linked to this study.
- Other trials for Heart FailureStudies of the same condition, open and under way.
- More trials run by University of Texas Southwestern Medical CenterThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- The intervention in an EMR-based clinical decision support tool that informs providers of the HF risk among patients with type 2 DM that are being seen by the provider in an outpatient setting. Based on the 5-year HF risk as estimated by the WATCH-DM score or existing biomarker levels, the providers will be provided guidance regarding the use of SGLT-2i to modify the HF risk.
- Masking
- Single blind
- Who is masked
- Care provider
- Enrolment
- 1,524 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Texas Southwestern Medical Center
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Ambarish Pandey, University of Texas Southwestern Medical Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04791826
- Sponsor's protocol ID
- STU-2020-1300
- Status last verified
- Jun 2024
- First submitted
- 5 Mar 2021
- Last updated
- 6 Jun 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 26152709Benjamin 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.
PubMed 29386200McAllister 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.
PubMed 29950404Standl 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.
PubMed 27230644Kannel 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.
PubMed 4835750Zelniker 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.
PubMed 30424892Segar 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.
PubMed 31519694
Study at a glance
From the registry record, last updated by the sponsor on 6 Jun 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluation of an EMR-Based Clinical Decision Support Tool for the Implementation of Guideline-Directed Therapies for Prevention of Heart Failure Among High-Risk Patients With Type 2 Diabetes
- ClinicalTrials.gov ID
- NCT04791826
- Acronym / sponsor ID
- STU-2020-1300
- Conditions
- Heart Failure, Diabetes Mellitus, Type 2
- Keywords
- Clinical Decision Support
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 1,524 participants (actual)
- Lead sponsor
- University of Texas Southwestern Medical CenterUniversity, hospital or other
- Study start
- 25 Mar 2021 (actual)
- Primary completion
- 31 Jul 2022 (actual)
- Study completion
- 7 May 2024 (actual)
- First posted
- 10 Mar 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (care provider)
- Number of arms
- 2
- Model description
- The intervention in an EMR-based clinical decision support tool that informs providers of the HF risk among patients with type 2 DM that are being seen by the provider in an outpatient setting. Based on the 5-year HF risk as estimated by the WATCH-DM score or existing biomarker levels, the providers will be provided guidance regarding the use of SGLT-2i to modify the HF risk.
| Arm | Type | Receives |
|---|---|---|
| Electronic Alert | Experimental | Behavioral: On-screen electronic alertEach 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. |
| No Alert | No intervention | –The CDS will not issue an on-screen alert. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- Providers in a General Internal Medicine outpatient clinic encounter
- Providers in a subspecialty Internal Medicine outpatient clinic encounter
- Providers in family medicine outpatient clinic encounter
Key exclusion criteria (2 listed)
- Providers in an inpatient hospital encounter
- Patients with HF or on SGLT-2i
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| On-screen electronic alertOn-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. | Behavioural | Electronic Alert |
Primary outcome
Frequency of prescription of SGLT-2i medication at outpatient clinic visits
Time frame: 30 days
Prescription rate of SGLT-2i medication (no. of patients prescribed SGLT2 / no. of eligible patients)
2 secondary outcomes
Adherence of prescription of SGLT-2i medication at outpatient clinic visit
Time frame: 6 months
Adherence of prescription of SGLT-2i medication at outpatient clinic visit
Time frame: 12 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Jun 2024.
- Background: PubMed 26152709Cavender 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.
- Background: PubMed 29386200Benjamin 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.
- Background: PubMed 29950404McAllister 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.
- Background: PubMed 27230644Standl 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.
- Background: PubMed 4835750Kannel 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.
- Background: PubMed 30424892Zelniker 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.
- Background: PubMed 31519694Segar 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.
- CV Risk ScoresLink provided by the sponsor.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing On-screen electronic alert in people with Heart Failure and Diabetes Mellitus, Type 2, run by University of Texas Southwestern Medical Center at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 6 Jun 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Frequency of prescription of SGLT-2i medication at outpatient clinic visits (time frame: 30 days). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04791826, which also lists 7 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 6 Jun 2024.