Nawar Shara, PhD

In collaboration with Children’s National Hospital and Johns Hopkins University, MedStar Health Research Institute will undertake a series of studies of a new device that could revolutionize our understanding of pain.   If you have ever been asked to describe your pain by pointing to a scale of grimacing faces, you know our understanding of pain is somewhat limited. A new collaborative project funded by the Advanced Research Projects Agency for Health (ARPA-H) could fundamentally change how we think about pain—from a feeling patients are asked to describe to a physiological response we can objectively measure. The project led by principal investigator Julia Finkel, M.D. is a collaboration between Children’s National Hospital, Johns Hopkins University, and MedStar Health Research Institute, specifically the Center for Biostatistics, Informatics, and Data Science (CBIDS) MedStar-Georgetown Collaborative Center for Artificial Intelligence in Healthcare Research and Education (AI CoLab). We’re working together to advance the AlgometRx Nociometer, a device that could help providers more precisely, objectively measure and understand patients’ pain.  Statistics from the Centers for Disease Control and Prevention reveal that about 1 in 5 adults in the U.S. lives with chronic pain. Pain is subjective, and asking patients to assess their pain can lead to poor outcomes—it oversimplifies the many different types of pain people can experience. It reduces our inability to provide high-quality care and lowers the quality of life for many people. Pain is acknowledged as a primary driver of the opioid crisis, which claimed more than 80,000 lives from overdose in 2023. Pain is a crisis, with healthcare costs estimated to range from $261 billion to $300 billion. If you include lost work and wages, the total cost to society jumps from $560 billion to $635 billion—more than the cost of cancer and diabetes combined.  This exciting collaboration, Sprint for Women’s Health, is funded by the Advanced Research Projects Agency for Health (ARPA-H), a research funding agency supporting significant healthcare advances. Our work could lead to a transformative medical breakthrough that could revolutionize how we think about and respond to pain.  Exploring this breakthrough technology encompasses a five-year series of studies and activities to explore, understand, and prepare the sociometer for approval by the Food and Drug Administration.  Women and girls are more likely to have chronic pain conditions than men or boys and often report more severe pain and more skepticism from providers. One of the project’s goals is to enhance our understanding of how gender affects pain, allowing for improved care for everyone. CIBDS is playing an essential role in this collaborative project. Our team has specialized expertise in working with electronic health records and extensive experience with large sets of real-world data. We are committed to designing studies and validating the resulting data to ensure that this important technology can be studied rigorously and efficiently. More specifically, our team will:​ Support study design for several pilot and pivotal studies. Lead the analysis and interpretation of the results for all the pilot studies and clinical studies Pain involves several processes in the body, which can be different for everyone. It comes in three main types:​ Normal nociceptive pain: Pain that follows an injury or surgery Neuropathic pain: When nerves are the source of pain, such as in diabetic neuropathy Inflammatory pain: Pain associated with inflammation, such as in lupus or inflammatory bowel disease Chronic pain can also lead to a fourth type—nociplastic pain. Fibromyalgia is one example of this type of pain, in which changes to the central nervous system lead to abnormal sensory processing. It can be challenging to communicate how pain feels, especially for patients who may not have speech or enough vocabulary, such as young children or adults with dementia. Communication barriers can be a source of diagnostic and care modification errors that could significantly impact treatment. Our collaborators have identified a non-invasive way to assess pain by measuring how a patient’s pupils dilate in response to mild stimulation. The device can illuminate, like never before, which type of pain a patient has and its severity.  This project and this device are inspiring. Early pilot studies have demonstrated its effectiveness, and we are now finalizing the study design for more robust explorations of its ability to measure the different types of pain so patients can get precise treatment. At CBIDS, our expertise in high-quality statistical methods, clinical informatics, data science, and the development of health information technology applications makes us in-demand collaborators. Our work is to engineer (or reengineer) the best biomedical research, no matter which discipline or disease area it addresses.  We are pleased to be a part of this vital project, and our teams are working to ensure top-of-the-line research and data analysis help make the AlgometRx Nociometer a reality for patients and providers everywhere. Meaningful collaborations like this, with funding that recognizes the transformational nature of this project, can make a significant difference in patients’ lives.​

MedStar Health Research Institute’s study suggests a machine learning algorithm could help identify patients at risk of cardiovascular disease earlier in pregnancy, opening a window to intervene.   Our research team assessed a new machine learning algorithm and determined that it can spot birthing individuals’ risk of cardiovascular conditions an average of 56.8 days before typical diagnosis!  With grant funding from the National Institutes of Health, MedStar Health Research Institute evaluated a tool by Invaryant called Healthy Outcomes for All Pregnancy Experiences – Cardiovascular Risk Assessment Technology (HOPE-CAT). Our research, which has been accepted for publication, shows this technology can identify heart risk in pregnancy early so providers can help sooner. The U.S. has the highest rate of deaths during pregnancy of any high-income nation in the world. The 2021 rate of 32.9 deaths per 100,000 live births is more than 10 times the rate of other similar nations.   Machine learning has helped find patients at high risk for other complications and predicted readmission for blood pressure-related complications. In our study, we are among the first to investigate how this technology can be applied to understanding maternal heart risk, which is the leading cause of death among pregnant women.   To study HOPE-CAT, we collaborated with to identify two categories of risk factors for the algorithm to assess: Static: Factors that remain the same, such as race, ethnicity, age, medical history, and family Variable: Things that change more frequently, such as blood pressure, heart rate, and symptoms like headache and shortness of breath The algorithm used these factors and the anonymous electronic health records (EHR) of 6,069 patients ages 18-40 with more than one pregnancy-related visit who delivered between 2017 and 2020. HOPE-CAT created two risk profiles: Standard risk and high risk. Specific indicators of high risk include: High resting heart rate High systolic blood pressure High respiratory rate Low oxygen saturation Dyspnea (shortness of breath) Orthopnea (difficulty breathing when lying down, but not when standing) As soon as HOPE-CAT was trained, we cleaned and standardized the EHR data from different records systems into a single virtual server environment. The algorithm examined this data one encounter at a time, from a patient’s first visit through delivery and beyond.  When HOPE-CAT identified a risk, it generated a profile for that patient and encounter. Linking this profile to the anonymous patient outcomes of cardiovascular conditions allowed us to compare the results. For example, if it detects that a patient had symptoms of kidney disease on day 114 and the EHR shows a diagnosis of kidney disease on day 144, the difference is 28 days. We conducted in-depth manual reviews to cross-check each result.  The outcome of our research is clear: HOPE-CAT can effectively identify the risk of cardiovascular disease an average of 56.8 days earlier than the first date of diagnosis. How much earlier depends upon the condition: Myocardial Infarction (heart attack): Average of 65.7 days earlier Preeclampsia: Average of 60.2 days earlier Eclampsia: Average of 46.2 days earlier Cerebral infarction: Average of 42.3 days HELLP Syndrome: Average of 34 days earlier Acute kidney disease and failure: Average of 25.1 days earlier Thromboembolism: Average of 15.3 days earlier Cardiomyopathy: Average of 13.9 days earlier Heart failure: Average of 13.6 days earlier Among the 5,238 patients with one or more risk factors, HOPE-CAT identified 1,716 high-risk profiles. Of these, 604 de-duplicated records had cardiovascular outcomes.  Early and effective heart risk screening with machine learning technologies could help improve pregnancy outcomes. The earlier we can identify risk, the sooner specialists can help. In places with fewer healthcare resources, earlier identification of risk could mean earlier referrals to specialists.   HOPE-CAT and technologies like it could help providers deliver more data-driven care, reducing patient morbidity and costs associated with hospitalization while lowering stress on healthcare systems. More work is needed to ensure this tool is effective in a real-world setting, so that additional research will be done. These exciting results provide foundational evidence that machine learning technologies could unlock solutions to improve birthing patients’ heart health.

MedStar Health investigators study how applying algorithms to electronic health records can find red flags in a pregnant patient’s medical information that may put them at risk for maternal health complications.   Despite myriad medical advances, maternal mortality is still a challenge in the U.S. It is estimated that 70% of maternal deaths and life-threating complications are preventable.   According to the Centers for Disease Control & Prevention, the maternal mortality rate was 23.8 deaths per 100,000 live births in 2020, compared with 20.1 in 2019. For Black women, the rate was 2.9 times higher than among white women.   As part of our system-wide initiative to improve maternal and infant health care, MedStar Health Research Institute has embarked on a clinical study to determine how machine learning can help identify patients at risk of cardiovascular complications in pregnancy—and potentially prevent more maternal deaths. This research was published in JMIR Publications earlier this year.    Machine learning is a type of artificial intelligence that specializes in data interpretation, using algorithms to simulate human learning and incrementally improving accuracy. Originating from concepts devised by the mathematician Alan Turing in 1935, today’s technology lets the data talk to us, revealing trends and allowing us to analyze data from multiple sources simultaneously—such as disparate electronic medical records (EMRs).   Using an adapted version of the machine learning tool, Health Outcomes for all Pregnancy Experiences–Cardiovascular-Risk Assessment Technology (HOPE-CAT) by Invaryant, our joint research team analyzed a large set of retrospective EMR data from patients who were pregnant, some of whom developed cardiovascular conditions.   We confirmed that machine learning models can effectively identify cardiovascular risk in pregnant patients early enough to potentially avoid complications. For example, we could determine which patients were at risk of preeclampsia—life-threatening high blood pressure in pregnancy—up to 60 days before they were diagnosed.   This type of crucial information, delivered in an easy-to-access format, could provide timely opportunities for clinicians to open a dialogue about how patients can manage their heart health during and after pregnancy.   Click to Tweet  When patients seek care from different health centers, their EMR data can become fragmented. Unstandardized data, such as free-form text fields, are difficult to assess for manual risk stratification, reducing the ability to easily identify red flags with the patients’ health. These issues are especially prevalent in maternal health care, as pregnant patients receive care from providers in clinic and hospital settings that may not be related or connected, exacerbating maternal morbidity and mortality.   By extracting, standardizing, and analyzing retrospective data from 32,409 patients in multiple Cerner EMRs using an adapted version of the HOPE-CAT tool, we were able to identify early signs of risk such as elevated blood pressure, shortness of breath, and chest pain among patients who ultimately developed new or worsening cardiovascular conditions during pregnancy.   Collated into an artificial neural network, trends identified by the tool could alert providers to real-time risk factors early enough to intervene and improve a patient’s chances for positive outcomes or even prevent more serious health issues during and after pregnancy.   All the while, HOPE-CAT will sift through a growing body of diverse data, improving its predictive capabilities and identifying previously unrecognized risk-related patterns clinicians can act upon. And with the next phase of our research, we are working to make these opportunities even easier for clinicians to access.    MedStar Health Research Institute is working to include additional types of hard-to-quantify data into the HOPE-CAT tool. Using natural language processing (NLP) technology, we can extract and analyze actionable data from unstructured formats, such as provider notes.   While more research is needed, implementing NLP-driven risk stratification at the clinical level could be a major step toward health equity, providing amalgamated insights into- factors like access to food, housing, and other social determinants of health that often are captured in these free-text fields.   There is great power in data. Though machine learning will never replace a provider’s clinical empathy and expertise, it can ease the burden of data analysis at the point of care, affording patients more quality face time with providers.     And with providers and technology working together to identify potential risk factors earlier in pregnancy, we are one step closer to truly changing the way we care for birthing individuals and achieving our goal of ensuring that everyone in our community has a healthy pregnancy and early transition to parenthood.