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.
