Machine learning approach predicts heart failure outcome risk

Researchers from the University of Virginia (UVA) have developed a machine learning tool designed to assess and predict adverse outcome risks for patients with advanced heart failure with reduced ejection fraction (HFrEF), according to a recent study published in the American Heart Journal.

The research team indicated that risk models for HFrEF exist, but few are capable of addressing the challenge of missing data or incorporating invasive hemodynamic data, limiting their ability to provide personalized risk assessments for heart failure patients.

“Heart failure is a progressive condition that affects not only quality of life but quantity as well,” explained Sula Mazimba, MD, an associate professor of medicine at UVA and cardiologist at UVA Health, in the news release. "All heart failure patients are not the same. Each patient is on a spectrum along the continuum of risk of suffering adverse outcomes. Identifying the degree of risk for each patient promises to help clinicians tailor therapies to improve outcomes.”

Outcomes like weakness, fatigue, swollen extremities and death are of particular concern for heart failure patients, and the risk model is designed to stratify the risk of these events.

The tool was built using anonymized data pulled from thousands of patients enrolled in heart failure clinical trials funded by the National Institutes of Health (NIH) National Heart, Lung and Blood Institute (NHLBI).

Patients in the training and validation cohorts were categorized into five risk groups based on left ventricular assist device (LVAD) implantation or transplantation, rehospitalization within six months of follow-up and death, if applicable.

To make the model robust in the presence of missing data, the researchers trained it to predict patients’ risk categories using either invasive hemodynamics alone or a feature set incorporating noninvasive hemodynamics data.

Prediction accuracy for each category was determined separately using area under the curve (AUC).

Overall, the model achieved high performance across all five categories. The AUCs ranged from 0.896 +/- 0.074 to 0.969 +/- 0.081 for the invasive hemodynamics feature set and 0.858 +/- 0.067 to 0.997 +/- 0.070 for the set incorporating all features.

The research team underscored that the inclusion of hemodynamic data significantly aided the model’s performance.

“This model presents a breakthrough because it ingests complex sets of data and can make decisions even among missing and conflicting factors,” said Josephine Lamp, a doctoral researcher in the UVA School of Engineering’s Department of Computer Science. “It is really exciting because the model intelligently presents and summarizes risk factors reducing decision burden so clinicians can quickly make treatment decisions.”

The researchers have made their tool freely available online for researchers and clinicians in the hopes of driving personalized heart failure care.

In pursuit of personalized and precision medicine, other institutions are also turning to machine learning.

Last week, a research team from Clemson University shared how a deep learning tool can help researchers better understand how gene-regulatory network (GRN) interactions impact individual drug response.

GRNs map the interactions between genes, proteins and other elements. These insights are crucial for exploring how genetic variations influence a patient’s phenotypes – such as drug response. However, many genetic variants linked to disease are in areas of DNA that don’t directly code for proteins, creating a challenge for those investigating the role of these variants in individual health.

The deep learning-based Lifelong Neural Network for Gene Regulation (LINGER) tool helps address this by using single-cell multiome data to predict how GRNs work, which can shed light on disease drivers and drug efficacy.