The death of U.S. Senator Lindsey Graham from an aortic dissection has brought renewed attention to a rare but life-threatening medical emergency that doctors say requires immediate diagnosis and treatment to improve the chance of survival.

An aortic dissection occurs when a tear develops in the wall of the aorta, the body's largest artery, allowing blood to separate the layers of the vessel wall. Bill Novak, MD, a cardiologist at OSF HealthCare, says it’s like a crack in a pipe and it can get bigger and create a tear with increased pressure, including from high blood pressure. Although uncommon, with it occurring in about 3 to 3.5 per 100,0000 people, aortic dissection can quickly become fatal if it is not recognized and treated.

“The aorta is the main artery that leaves your heart, and if you have a tear in the aorta, it can be potentially life-threatening. It can cause a heart attack. It can cause stroke. It can cause heart failure, rupture of the aorta, and sudden death.”

Many aortic dissection symptoms overlap with other medical emergencies. Actor John Ritter died after his aortic dissection was misdiagnosed and it was treated as a heart attack.

Challenge to diagnose

"It's dangerous because it's relatively uncommon," says Dr. Novak. "It can sometimes be a challenge to diagnose since a lot of the symptoms overlap with heart attack and other conditions such as pulmonary embolism, and it's obviously a potentially life-threatening type of a problem."

Diagnosis requires immediate emergency imaging such as an MRI or an echocardiogram.

Anyone experiencing sudden, excruciating pain or other unexplained symptoms to seek emergency care immediately. Dr. Novak says while severe chest or back pain is often considered the hallmark symptom, people can also experience shortness of breath, fainting, heart failure or reduced blood flow to the arms or legs.

Treatment varies depending on the location and severity of the tear. Some patients require emergency surgery, either through an open surgical procedure or an endovascular approach, while others can be managed with medications (like beta-blockers) to strictly lower blood pressure and reduce the force of heart contractions. Surgery or stent placement may be needed if complications arise.

Dr. Novak says people at the greatest risk include older adults.

Know the risks

“How old you are is important. That increases your risk. If you smoke, that’s going to increase your risk and obviously treating your cholesterol is going to reduce the chance you’re going to develop certain types of plaque in your arteries which can also put you at a higher risk for having this problem.”

Senator Graham had a history of atherosclerosis (plaque build-up), also known as hardening of the arteries.

There are other people Dr. Novak points out who need to be monitored regularly, including those with conditions such as Marfan syndrome, Ehlers-Danlos syndrome, and Loeys-Dietz syndrome which weaken blood vessel walls and are the primary cause of aortic dissection in younger individuals.

“If you do have some risk factors such as a bicuspid valve, connective tissue disease, or an aneurysm, those things do have to be monitored on a regular basis."

An aneurysm is a bulging, weak area in the wall of a blood vessel. While it can occur in any blood vessel, it most often develops in an artery rather than a vein. An aortic aneurysm is the most common type.

While aortic dissection remains relatively rare, physicians say the national attention surrounding Graham's death serves as a reminder that recognizing warning signs and seeking emergency medical care without delay can save lives. Also, family history is important. If you have a close relative who has suffered from an aortic aneurysm or sudden arterial dissection, early testing and genetic counseling can be lifesaving.

Dr. Bill Novak is a cardiologist at OSF Cardiovascular Institute in Bloomington.

Video clips with Dr. Bill Novak

Sixteen research projects are sharing more than $1.6 million in funding through the Jump ARCHES research and development program. Projects funded focused on novel automation in health data and patient experience, precision medicine, digital tools to enhance patient engagement in health, wellness and treatment, plus improved treatment and diagnosis of neurologic disorders. The latest request for proposals also encouraged supporting underserved populations.

The Jump ARCHES program is a collaboration between OSF HealthCare, the University of Illinois Urbana-Champaign (UIUC), and the University of Illinois College of Medicine Peoria (UICOMP). The funding supports research involving clinicians, engineers and social scientists to rapidly develop technologies, devices, and treatment approaches that improve outcomes and reduce costs.

Spring 2023 Project Awards

STREAM-ED: Simulation to Refine, Enhance and Adapt Management of Emergency
William Bond, MD, OSF HealthCare
Hyojung Kang, PhD, University of Illinois Urbana-Champaign

This study aims to develop practical models combining machine learning, discrete event simulation, and optimization techniques to improve emergency department (ED) resource utilization and address ED overcrowding, which is exacerbated by the COVID-19 pandemic and staffing shortages.

Prototype: Intelligent Regulatory Change Management System
Scott Lowry, MHA, CHC, CCEP, OSF HealthCare
ChengXiang Zhai, PhD, University of Illinois Urbana-Champaign

This study proposes an Intelligent Regulatory Change Management (IRCM) System that uses natural language processing and artificial intelligence to track and evaluate public policy actions governing OSF HealthCare. This will enable compliance professionals to identify critical changes and determine appropriate courses of action, reducing manual review and improving quality, safety, privacy risk management and efficiency.

Machine Learning of Standardized DICOM Metadata from Imaging Datasets
Matthew Bramlet, MD, OSF HealthCare
Brad Sutton, PhD, University of Illinois Urbana-Champaign

This project aims to develop a machine learning-based algorithm that can categorize image parameters directly from signal intensity variations of 2D medical images to enable efficient pipelines for medical image segmentation. The proposed algorithm is expected to estimate patient and image-acquisition information by utilizing machine learning methods in situations where the DICOM header fields are incomplete or unreliable, ultimately allowing for automated characterization of unknown 3D DICOM imaging datasets.

Machine-Guided Staging of Neuroblastic Tumors of Patient Specific 3D Models
Daniel Robertson, MD, OSF HealthCare
Brad Sutton, PhD, University of Illinois Urbana-Champaign

The OSF HealthCare Children’s Hospital of Illinois is using segmentation services to create 3D models of neuroblastic tumors for pre-surgical planning. The hospital aims to transition from 2D imaging to 3D modeling to increase the reproducibility of staging analysis, establish a new standard for segmented models of neuroblastic tumors and develop machine-guided tools that can improve upon and automate current recommended image-defined risk factors staging.

Toward Machine-Learned Aortic Arch Measured Diameters
Matthew Bramlet, MD, OSF HealthCare
Brad Sutton, PhD, University of Illinois Urbana-Champaign

The original project aims to automate the segmentation and clinical measurement of aortic arch diameters from MRI imaging. The researchers leading this project have successfully completed several steps, including de-identification and curation of datasets, manual segmentation and the development of a novel method for automatically analyzing each aortic arch with promising results, indicating correlation between the automated and clinically derived measurements.

A Field Experiment to Evaluate the Efficacy of Convenient Health Kiosks
Ann Willemsen-Dunlap, CRNA, PhD, OSF HealthCare
Ujjal Mukherjee, PhD, University of Illinois Urbana-Champaign

This proposal outlines a field experiment to evaluate the efficacy of health kiosks supported by community health workers (CHWs) in delivering first line preventive health screenings to rural and underserved communities. The project is intended to lead to large-scale development and deployment of health kiosks with the goal of positively impacting social determinants of health and long-term health status of those served.

Contextualizing Nursing Needs for Development of Retention-Support App
Sheryl Emmerling, PhD, Rn, NEA-BC, OSF HealthCare
Ann-Perry Witmer, PhD, University of Illinois Urbana-Champaign

The goal of this project is to address the high turnover rate of new nurses by providing a digital app that offers personalized nursing support. The Contextual Engineering (CE) paradigm will be used to assess the needs and values of first-year nurses, including those who have left their positions, to inform the development of the app in the first phase of the project, with the goal of stabilizing the nursing staff, improving the quality of service and reducing operating costs.

Community Health Café: Engaging Digital Innovation and Community-Based Resources to Enhance Health Equities in Underserved Communities
Scott Barrows, MA, OSF HealthCare
Joe Bradley, PhD, MA, University of Illinois Urbana-Champaign

The purpose of the community health café is to provide digital access to health and health care resources, including links for assistance with the social determinants of health, health education and connections to public health in underserved communities. The eventual goal is a Medicaid telemedicine option with OSF OnCall. This proposal aims to address critical needs of underserved residents in vulnerable communities and is crucial for their health.

AI-Powered Brain Tumor Segmentation
Matthew Bramlet, MD, OSF HealthCare
Zhi-Pei Liang, PhD, University of Illinois Urbana-Champaign

This project aims to enhance the detection and monitoring of brain diseases. Phase 1 of the project focuses on accurate delineation and segmentation of brain tumors using a combination of structural and molecular multimodal brain imaging data and deep learning. The proposed work includes developing brain atlases for AI-powered brain image analysis, computational tools for automated tumor detection and segmentation and evaluating potential clinical applications.

Optimizing Pharmacologic Management of Behaviors in Patients with Autism
Adam Cross, MD, FAAP, OSF HealthCare
Ravishankar Iyer, PhD, University of Illinois Urbana-Champaign

This proposal aims to provide physicians with a machine learning model that assists in selecting appropriate medication and dosage strategies for patients with Autism Spectrum Disorder (ASD). By incorporating patient history, genetic information and clinician notes, the model will dynamically adapt the treatment protocol as the patient progresses, ensuring optimal choices for improved behavioral symptoms with a high degree of confidence.

Predicting Medication Non-Adherence in Type 2 Diabetes
Mary Stapel, MD, OSF HealthCare
Hyojung Kang, PhD, University of Illinois Urbana-Champaign

Medication adherence is crucial for managing diabetes, but disparities exist, particularly among racial/ethnic minorities and those with lower socioeconomic status. This proposal aims to use data-driven models to identify high-risk individuals and areas for non-adherence to diabetes medication, develop and validate prediction models and implement and evaluate them in clinical practice.

Knowledge Graph Construction with Large Language Models to Predict DKA Occurrence and Severity
Adam Cross, MD, FAAP, OSF HealthCare
Jimeng Sun, PhD, University of Illinois Urbana-Champaign

Diabetic ketoacidosis (DKA) hospitalizes over 50,000 American children annually, with underprivileged and underserved children at higher risk. This proposal aims to develop a predictive model using patient-specific knowledge graphs generated from clinical data extracted through name entity recognition and language modeling. Clinicians can use the model to identify high-risk diabetic patients and prevent DKA.