Summary of Pilot Grant Results

2021-2022

 

Defining donor characteristics for pediatric heart transplants

Impact Quantification of Donor Echocardiographic Data on Pediatric Heart Transplantation Recipient Outcomes

Investigators: Michael McCulloch, MD and Michael Porter, PhD

Heart transplantation is the standard of care for pediatric patients with end-stage heart failure or inoperable congenital defects, yet nearly 20 percent of patients with these conditions die while on the waitlist. To help increase the odds of successful pediatric heart transplants, Michael McCulloch, an associate professor and a pediatric cardiologist at UVA Children’s Hospital Heart Center, and Michael Porter, an associate professor of systems engineering in UVA’s School of Engineering and Applied Science, analyzed donor echocardiographic data to identify which donor characteristics contribute to positive heart transplant recipient outcomes.

The team successfully cleaned and merged all the UNOS databases, which has been a herculean effort considering the extent of data within each database and the sheer number of separate and distinct databases that existed. The team also extracted the echocardiographic data for 10,000 of the 12,000 donors provided to them from UNOS and entered the data into a REDCap database, which comprises the entirety of the pediatric donor data that the team elected to focus on in their initial work.

 

The investigators successfully competed for a Jefferson Trust grant that helped maintain their efforts while simultaneously writing an R21/R33 grant for the Agency for Healthcare Research and Quality (AHRQ) as discussed below.

This allowed the team to perform some initial analyses which were presented to the University of Virginia Pediatrics Department within the Grand Rounds lecture series.

The investigators are now in the process of producing their initial mathematical modeling efforts focused on determining the donor characteristics most consistently present in donors who were ACCEPTED vs REJECTED.

 

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Micheal McCuloch, MD

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Michael Porter, PhD

Designing an interactive training system for pediatric telemedicine cart operations incorporating augmented reality

Designing an Interactive Training System for Pediatric Telemedicine Cart Operators incorporating Augmented Reality

Investigators: Elham Morshedzadeh, PhD and Lydia Hartzell, MPH

Elham Morshedzadeh, an assistant professor of industrial design in Virginia Tech’s College of Architecture and Urban Studies; Andre Muelenaer, a professor of practice in Virginia Tech’s College of Engineering, and Carilion’s Lydia Hartzell, designed a robust and affordable training program to help improve telemedicine encounters for infants and pre-school children. They sought to provide a robust, feasible, and affordable training program integrating augmented reality, online and hands-on learning experience.

The team's research focused on how Augmented Reality (AR) can be employed in the healthcare training system to increase efficiency in Lean Healthcare (LH) and to improve telemedical visits for infants and children. They employed Telemedicine Carts which entails systems that integrate cameras, displays, and network access to bring remote physicians right to the side of the patient. This allows patients to communicate with a healthcare provider using technology, as opposed to physically visiting a doctor's office or hospital. To achieve this, the researchers proposed a training program via augmented reality, which will improve both online and in-person learning experiences.

 

AR represents digital information on top of real-world environments and in this study, it is utilized as interactive learning support to increase engagement and immersion in tele-healthcare. Digital and virtual objects (e.g., graphics, text, sounds) are superimposed on an existing environment to create an AR learning experience for telemedicine operators. The training system has been designed on the hints received from both contextual inquiry (Qualitative) and eye-tracking (Quantitative) techniques to inform the design solutions. The investigators used various tools for data collection from qualitative methods such as contextual inquiry, observation to a quantitative method, eye tracking to ensure triangulating the data. Combining eye-tracking techniques with other research techniques such as observation and contextual inquiry led to a holistic understanding of users’ needs and opportunities associated with AR training systems in telehealth care.

 

The researchers' next steps will focus on an AR Prototype and its immersive and intuitive experiences that benefit the four parties involved in this research (patients, parents, doctors, and nurses/operators).

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Elham Morshedzadeh, PhD

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Lydia Hartzell, MD

 

Measuring medication in patients with epilepsy

Effect of various neuroactive drugs on frequency distribution and phase coupling in various human brain regions- Neuroactive drugs and lntracranial EEG (iEEG) study

Investigators: Aashit Shah, MD and Sujith Vijayan, PhD

Aashit Shah, and a professor of internal medicine at the Virginia Tech Carilion School of Medicine, and Virginia Tech’s Sujith Vijayan, an assistant professor in the Virginia Tech College of Science’s School of Neuroscience  studied patients with intractable epilepsy who have been implanted with electrodes to determine the region responsible for their seizures. The team examined signals measured in epileptic patients undergoing intracranial electroencephalography. They reviewed intracranial electrical signals from various brain regions following administration of medications that work on the brain.

The investigators have thus far focused their efforts on the limbic system and frontal cortices. These structures are associated with emotional experiences and memories and are thought to be important in processing pain.

The team have observed the following:

 

Fentanyl and Hydrocodone, both opioids, impact spectral characteristics similarly, yet also have distinctive features as seen in their differential effects on limbic structures and frontal cortices. For example, in the left amygdala, a limbic structure, both drugs suppress lower frequency activity, but at the same time, fentanyl also increases higher frequency activity. In contrast, in the right superior frontal cortex, both drugs suppress lower frequency activity, but the exact frequencies that are suppressed within in this range are different from those suppressed in the amygdala, for both drugs. Furthermore, fentanyl does not increase higher frequency activity in the right superior frontal cortex.

 

The common features of the actions of these drugs in limbic and frontal regions could help to identify the invariant manners in which opioids act as a class of drugs. At the same time, their distinguishing features could help to pinpoint the mechanisms through which particular opioids affect the brain in different ways.

 

The investigators plan to continue analyzing these data. One area of focus will be an examination of the effects of all opioids versus a comparison drug commonly administered to patients during their stay, such as an antibiotic or anti-nausea medications. The investigators will also compare and contrast how opioids individually compare to one another as well as collectively and individually versus the effects of Fioricet (acetaminophen + butalbital + caffeine), a non-opioid pain medication. The team will also examine how the above comparisons are impacted by gender and prior history of addiction.

 

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Aashit Shah, MD

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Sujith Vijayan, PhD

 

Searching for genetic markers for celiac disease with machine learning

Use of machine learning image analysis and tissue transcriptomics to define clinically actionable celiac disease sub-types

Investigators: Sana Syed, MD and Suchitra Hourigan, MD

Sana Syed, an assistant professor in the UVA School of Medicine’s department of pediatrics, and Suchitra Hourigan, Inova Children’s Hospital’s vice chair of research and innovation sought to determine if machine learning is useful in diagnosing celiac disease sub-types. Currently, treatment and management of celiac disease involves gluten-free diet and is not intended to help assess the specific for the risk of patients developing other diseases. Syed and Hourigan investigated gut tissue biopsies as well as genetic markers of patients with celiac disease and type 1 diabetes and/or hypothyroidism.

In Aim 1, the investigators sought to leverage an existing machine learning small bowel image analysis platform to distinguish & predict celiac disease (CD) sub-types. They hypothesized that their model would identify a spectrum of known features (inflammatory cells, intestinal epithelial morphology) and novel features related to inflammation, tissue injury, and epithelial regeneration that will distinguish and enable the prediction of CD sub-types. The team’s existing CD image analysis platform was trained using duodenal biopsies at the time of diagnosis of CD without Type 1 Diabetes or hypothyroidism versus CD with either condition. Biopsy data originated from CD patients at INOVA and UVA. On visualization using saliency maps, they found distinctive histopathologic features detected by the model, including inflammatory cells, the morphology of intestinal epithelium, and entero-endocrine cells. The investigators scanned and digitized slides from all patients enrolled at both sites and labeled them using the Marsh scoring system. Two pathologists at UVA did this with a tie-breaker pathologist from an external site. The investigators then trained their machine learning model to differentiate between celiac disease and normal and Marsh scores 1-3. The team found distinctive differences in saliency maps between early and severe diseases.

 

Aim 2 and Aim 3 sought to explore the gene expression profiles for CD sub-types using Formalin-Fixed Paraffin-Embedded (FFPE) duodenal biopsy samples. Using archival FFPE samples for each CD sub-type, the investigators planned to extract and isolate RNA and sequence it to obtain differentially expressed genes. After consenting their enrolled population of transcriptomic sequencing, the team contacted an external vendor, GENEWIZ, who provided quotes for generating high-quality RNAseq. All patients diagnosed post-2015 were consented according to NIH guidelines. The investigators are currently in the process of shipping tissue blocks of these patients to GENEWIZ, and will use discretionary funds to complete Aims 2 and 3.

 

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Sana Syed, PhD

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Suchitra Hourigan, MD

 
 

Studying auditory therapy for Parkinson’s disease

Sound Sleep with Parkinson's: Nighttime Auditory Therapies to Improve Learning and Impede Disease Progression

Investigators: Della Williams, MD and Sujith Vijayan, PhD

Della Williams, a neurologist at Carilion Clinic and an assistant professor of internal medicine at the Virginia Tech Carilion School of Medicine partnered with Sujith Vijayan, to study auditory therapy for patients living with Parkinson’s disease. Their project sought to better understand the disease progression and if it can be slowed by providing some background noise to patients as they sleep.

In order to better pinpoint PD-related aberrant brain dynamics and develop non-invasive, innocuous interventions during sleep that improve motor learning in PD and potentially impede the progression of the disease itself, the following aims were pursued:

Aim 1: To strengthen the evidence for a linkage between aberrant sleep spindles in slow wave sleep and PD.

Aim 2: To evaluate the effect of auditory intervention during sleep on motor learning in PD patients.

The data collected so far indicate the following:

PD patients do not show overnight improvement in their reaction time in a finger tapping task.

PD patients spend a smaller percentage of their time in stage 3 non-rapid eye movement sleep (N3 sleep) than do control subjects.

There is less power in the slow wave frequency band in the frontal and central channels during N3 sleep in PD patients in comparison with control subjects.

There is less power in the spindling band in the frontal and central channels during N3 sleep in PD patients in comparison with control subjects.

There are indications of aberrant spindle-slow-wave coupling in PD patients.

 

In the coming months, the investigators plan to collect additional data to solidify these initial findings. The team also plans in the next step to see whether they can restore normal sleep dynamics in PD patients using auditory stimuli during sleep and improve their learning of motor tasks (e.g., the finger tapping task)—which would clearly have great clinical implications.

Once they have collected additional data, the team will apply for additional grant funding to support this line of research.

 

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Della Williams, MD

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Sujith Vijayan, PhD

 

When Can Patients Safely Drive After Rotator Cuff Repair?

Investigators: Peter Apel, MD and Miguel Perez, PhD

Despite more than 450,000 rotator cuff repairs performed every year, very little data exists guiding when a person can return to driving. Dr. Peter Apel, an assistant professor at Virginia Tech Carilion School of Medicine and a Carilion Clinic orthopedic surgeon, and Miguel Perez, an associate professor at the Virginia Tech Transportation Institute, lead this team. The team investigated how changes in driving skills can shorten the time patients are restricted from driving after surgery. 

The researchers were able to determine that the driving fitness of patients who had undergone rotator cuff repair surgery (RTCR) was non-inferior to a baseline preoperative reading. Specifically, patients showed no clinically significant negative impact on driving fitness as early as two weeks after RTCR. This has significant clinical implications, as it is currently common practice for physicians to restrict patient driving after RTCR until as far as six to eight weeks after surgery.

 

Next, the researchers plan (a) to publish their conclusions immediately in a major orthopaedic journal to disseminate these findings to the surgeons who routinely perform this procedure and (b) to utilize what they learned to investigate other forms of postoperative driving restrictions in orthopaedic surgery, such as after total knee arthroplasty.

 

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Peter Apel, MD

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Miguel Perez, PhD

Video courtesy of Carilion Clinic