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.
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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.”
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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.
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. […]
Read more – speciality.medicaldialogues.in
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.
Copyright by speciality.medicaldialogues.in
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.”
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
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.
Key findings include:
Read more – speciality.medicaldialogues.in
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