Heart failure risk in diabetics can now be predicted by machine learning derived score

A novel machine learning derived risk score can predict the risk of heart failure (HF) in patients with type 2 diabetes, a recent study published in the journal Diabetes Care has found. For predicting the risk, this device integrates readily available clinical, electrocardiographic and laboratory variables.

The study was also presented at the Heart Failure Society of America Annual Scientific Meeting in Philadelphia.

Type 2 diabetes is a global epidemic that is expected to affect over 592 million people globally by 2035, a dramatic increased from 382 million people with diabetes mellitus in 2013, a prevalence that is likely to be underestimated. Type 2 diabetes patients are at more than double the risk of developing heart failure resulting in disability or death among such patients.

Earlier this month, late-breaking trial results revealed that a new class of medications known as SGLT2 inhibitors may be helpful for patients with heart failure. These therapies may also be used in patients with diabetes to prevent heart failure from occurring in the first place. However, a way of accurately identifying which diabetes patients are most at risk for heart failure remains elusive.

Matthew W. Segar, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, and colleagues conducted this study with an objective to develop and validate a novel, machine learning–derived model to predict the risk of heart failure among patients with type 2 diabetes mellitus.

"We hope that this risk score can be useful to clinicians on the ground -- primary care physicians, endocrinologists, nephrologists, and cardiologists -- who are caring for patients with diabetes and thinking about what strategies can be used to help them," said co-first author Muthiah Vaduganathan, a cardiologist at the Brigham.

"Our risk score provides a novel prediction tool to identify patients who face a heart failure risk in the next five years," said Dr Segar, a resident physician at UT Southwestern. "By not requiring specific clinical cardiovascular biomarkers or advanced imaging, this risk score is readily integrable into bedside practice or electronic health record systems and may identify patients who would benefit from therapeutic interventions."

The risk score -- called WATCH-DM was developed by the data leveraged from 8,756 diabetes patients. The patients were enrolled in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. It included a total of 147 variables consisting of demographics, clinical information, laboratory data and more. Machine learning methods were used that were capable of handling multidimensional data to determine the top-performing predictors of heart failure.

Also Read: Type 2 diabetes may increase repeat hospitalisation and death risk in heart failure patients

Key findings include:

• Over the course of almost five years, 319 patients (3.6 per cent) developed heart failure.


• The team identified the 10 top-performing predictors of heart failure, which make up the WATCH-DM risk score: weight (BMI), age, hypertension, creatinine, HDL-C, diabetes control (fasting plasma glucose), QRS duration, myocardial infarction and coronary artery bypass grafting. Patients with the highest WATCH-DM scores faced a five-year risk of heart failure approaching 20 per cent.

Also Read: Diabetes increases heart failure risk predominantly in women: Diabetologia

The study draws strength from its large sample size and the high rate of heart failure, but the authors note that their findings may be constrained by certain limitations. ACCORD was conducted between 1999 and 2009, and predictors of heart failure may have evolved since the trial's conclusion. In addition, while the risk score was accurate in predicting one form of heart failure -- that with reduced ejection fraction -- it fell short for predicting the second form of heart failure -- that with preserved ejection fraction. Future studies will be needed to develop specific risk scores for predicting the latter among the general population and among patients with diabetes.

Importantly, the WATCH-DM risk score is now available as an online tool for clinicians to use. As a next step, the research team is working to integrate the risk score into electronic health record systems at both the Brigham and UT Southwestern to facilitate its practical use.

In addition to the tool's usefulness for clinicians, Vaduganathan also sees a key message from the study for patients with diabetes who are concerned about their risk of heart failure.

"It's important to look at these 10 variables and reflect on them," said Vaduganathan. "For individual patients, these are important messages to think about when assessing personal risk. BMI was one of the top predictors of heart failure risk, which reinforces the idea that long-term excess weight may increase the future risk for heart failure. We hope this work highlights ways to intervene -- both through lifestyle changes and through the use of SGLT2 inhibitors -- to delay or even entirely prevent heart failure."

"This risk tool is an important step in the right direction to promote the prevention of heart failure in patients with type 2 diabetes. It can be readily used as part of clinical care of patients with type 2 diabetes and integrated with the electronic medical records to inform physicians about the risk of heart failure in their patients and guide use of effective preventive strategies," said Ambarish Pandey, MD, MSCS, a preventive cardiologist at UT Southwestern and the senior author of this study.

To read the complete study log on to https://doi.org/10.2337/dc19-0587