AI Tool Helps Identify Heart Failure Risk in Diabetes Patients
By HospiMedica International staff writers Posted on 25 Oct 2024 |

Diabetic cardiomyopathy is a heart condition marked by abnormal changes in the structure and function of the heart, which increases the risk of heart failure in patients. Defining this condition has been challenging due to its asymptomatic early stages and the varied effects it can have on the heart. Machine learning has emerged as a tool to identify high-risk patients, potentially providing a more nuanced approach compared to traditional diagnostic methods. Researchers have now created a machine learning model capable of identifying patients with diabetic cardiomyopathy. The findings, published in the European Journal of Heart Failure, present a data-driven strategy to detect a high-risk diabetic cardiomyopathy phenotype, facilitating early interventions that could help prevent heart failure in this vulnerable group.
Phenotypes refer to the observable physical characteristics of individuals that confer specific biological traits. Researchers at UT Southwestern Medical Center (Dallas, TX, USA) analyzed data from the Atherosclerosis Risk in Communities cohort, which consisted of over 1,000 participants with diabetes but no prior history of cardiovascular disease. By examining a set of 25 echocardiographic parameters and cardiac biomarkers, the team identified three patient subgroups. One of these subgroups, comprising 27% of the cohort, was classified as the high-risk phenotype. Patients in this group showed significantly elevated levels of NT-proBNP, a biomarker associated with heart stress, along with abnormal heart remodeling features such as increased left ventricular mass and impaired diastolic function. Notably, the five-year incidence of heart failure in this subgroup was 12.1%, which was considerably higher than that in the other groups.
Following these findings, the researchers developed a deep neural network classifier to identify diabetic cardiomyopathy. When validated on additional cohorts, the model detected between 16% and 29% of diabetic patients as having the high-risk phenotype. These patients consistently displayed a higher incidence of heart failure. Clinically, this model could assist in targeting intensive preventive therapies, such as SGLT2 inhibitors, which are medications used to manage Type 2 diabetes, to those patients who are most likely to benefit. It may also enhance clinical trials focused on heart failure prevention strategies in diabetic patients. By offering a new method to identify individuals at risk for heart failure, the model could enable earlier and more proactive interventions, thereby improving patient outcomes and influencing future research in cardiovascular care.
“This research is noteworthy because it uses machine learning to provide a comprehensive characterization of diabetic cardiomyopathy – a condition that has lacked a consensus definition – and identifies a high-risk phenotype that could guide more targeted heart failure prevention strategies in patients with diabetes,” said senior author Ambarish Pandey, M.D., Associate Professor of Internal Medicine in the Division of Cardiology at UT Southwestern. “This builds on our previous work that evaluated the prevalence and prognostic implications of diabetic cardiomyopathy in community-dwelling adults. It extends those efforts by using machine learning to identify a more specific high-risk cardiomyopathy phenotype.”
Channels
Artificial Intelligence
view channel
Innovative Risk Score Predicts Heart Attack or Stroke in Kidney Transplant Candidates
Heart researchers have utilized an innovative risk assessment score to accurately predict whether patients being evaluated for kidney transplants are at risk for future major cardiac events, such as a... Read more
AI Algorithm Detects Early-Stage Metabolic-Associated Steatotic Liver Disease Using EHRs
Liver disease, which is treatable when detected early, often goes unnoticed until it reaches advanced stages. Metabolic-associated steatotic liver disease (MASLD), the most prevalent form of liver disease,... Read moreSurgical Techniques
view channel
Easy-To-Apply Gel Could Prevent Formation of Post-Surgical Abdominal Adhesions
Surgical adhesions are a frequent and often life-threatening complication following open or laparoscopic abdominal surgery. These adhesions develop in the weeks following surgery as the body heals.... Read more
Groundbreaking Leadless Pacemaker to Prevent Invasive Surgeries for Children
Leadless pacemakers marked a significant advancement in cardiac care, primarily because traditional pacemakers are dependent on leads, which are prone to breakage over time. Currently, two FDA-approved... Read morePatient Care
view channel
Portable Biosensor Platform to Reduce Hospital-Acquired Infections
Approximately 4 million patients in the European Union acquire healthcare-associated infections (HAIs) or nosocomial infections each year, with around 37,000 deaths directly resulting from these infections,... Read more
First-Of-Its-Kind Portable Germicidal Light Technology Disinfects High-Touch Clinical Surfaces in Seconds
Reducing healthcare-acquired infections (HAIs) remains a pressing issue within global healthcare systems. In the United States alone, 1.7 million patients contract HAIs annually, leading to approximately... Read more
Surgical Capacity Optimization Solution Helps Hospitals Boost OR Utilization
An innovative solution has the capability to transform surgical capacity utilization by targeting the root cause of surgical block time inefficiencies. Fujitsu Limited’s (Tokyo, Japan) Surgical Capacity... Read more
Game-Changing Innovation in Surgical Instrument Sterilization Significantly Improves OR Throughput
A groundbreaking innovation enables hospitals to significantly improve instrument processing time and throughput in operating rooms (ORs) and sterile processing departments. Turbett Surgical, Inc.... Read moreHealth IT
view channel
Printable Molecule-Selective Nanoparticles Enable Mass Production of Wearable Biosensors
The future of medicine is likely to focus on the personalization of healthcare—understanding exactly what an individual requires and delivering the appropriate combination of nutrients, metabolites, and... Read more
Smartwatches Could Detect Congestive Heart Failure
Diagnosing congestive heart failure (CHF) typically requires expensive and time-consuming imaging techniques like echocardiography, also known as cardiac ultrasound. Previously, detecting CHF by analyzing... Read morePoint of Care
view channel
Handheld, Sound-Based Diagnostic System Delivers Bedside Blood Test Results in An Hour
Patients who go to a doctor for a blood test often have to contend with a needle and syringe, followed by a long wait—sometimes hours or even days—for lab results. Scientists have been working hard to... Read more
Smartphone-Enabled, Paper-Based Quantitative Diagnostic Platform Transforms POC Testing
Point-of-care diagnostics are crucial for public health, offering rapid, on-site testing that enables prompt diagnosis and treatment. This is especially valuable in remote or underserved regions where... Read moreBusiness
view channel
Becton Dickinson to Spin Out Biosciences and Diagnostic Solutions Business
Becton, Dickinson and Company (BD, Franklin Lakes, NJ, USA), has announced that its board of directors has unanimously authorized BD management to pursue a plan to separate BD's Biosciences and Diagnostic... Read more