New VA tool uses artificial intelligence to predict COVID-19 patient mortality

New VA tool uses artificial intelligence to predict COVID-19 patient mortality

July 12, 2021

Tim Strebel is a two-time winner of the Department of Veterans Affairs “Shark Tank” Award, which honors innovative practices, for developing two software packages to automate the jobs of those who work in prosthetics. Now, in what Strebel calls the “most significant” work in his seven-year VA career, he’s developed a tool that uses artificial intelligence to calculate the risk of a COVID-19 patient dying within 120 days of diagnosis.

The hope is that clinicians can use those predictions to improve the treatment of their patients. The tool is being piloted at 13 VA medical centers.

Strebel, a computer programmer focusing on health informatics at the Washington DC VA Medical Center, and his colleagues described the tool in a paper published June 9 in BMJ Health Care & Informatics.

Strebel created the tool in collaboration with VA’s National Artificial Intelligence Institute (NAII), which was launched in 2019 to develop AI research and development to support Veterans, their families, survivors, and caregivers. He worked with NAII Director Dr. Gil Alterovitz, a specialist in bioinformatics, and Dr. Christos Makridis, a senior researcher advisor at NAII. The institute is a joint initiative between the VA’s Office of Research and Development and the Secretary’s Center for Strategic Partnerships.

Artificial intelligence increasingly used in health care

Artificial intelligence (AI) uses computers to simulate human thinking, especially in applications involving large amounts of data. AI is common in the commercial technology sector and is increasingly being used in health care. VA uses AI for purposes such as reducing Veterans’ wait times, identifying Veterans at high risk of suicide, and helping doctors interpret the results of cancer lab tests and choose the best therapies.

Strebel’s tool creates a report that provides AI-generated 120-day mortality risk scores in both inpatient and outpatient settings. The report is based on two models.

The first model assesses conditions about a patient that are known before he or she enters a hospital. It relies heavily on age, body mass index (BMI), and co-existing health conditions that can be found in a Veteran’s electronic health record. Body mass index is a measure of body fat based on one’s height and weight. A high or low BMI may signal health problems.

“It’s no surprise that age and BMI are the most predictive factors for mortality in COVID-19 patients,” Strebel said. “An overwhelming amount of concurrent research confirms this. While a few comorbidities on their own are predictive of mortality, such as diabetes and dementia, we’ve found that the amount of and severity of comorbidities in a patient is the best way to use them to predict mortality.”

A second set of models used to predict inpatient mortality considers many of the same factors from the outpatient models, in addition to Veterans’ lab work and vital signs taken at admission. These extra data points “drastically improve” the accuracy of the model, according to Strebel.

“It would be ideal to have this information for every patient diagnosed with COVID-19,” he said. “But this isn’t practical. Bringing every patient in for these tests could increase the risk of transmission and present logistical challenges. That is why we provide the outpatient models to try and provide clinicians with an additional perspective of who may be at risk based on the information we already know about the patient to promote early treatment.”

However, he added, “One of the biggest challenges in any AI effort is bias. While age is no doubt one of the leading predictors of death, there are always exceptions. Some patients in their 90s survive COVID-19. Conversely, some really young patients die from COVID-19. In those rare cases, our models may show that older Veterans are at higher risk of death than they may actually be simply because of their age. Usually when this is the case, other strong factors such as BUN [blood urea nitrogen] or lymphocytes may be within a healthy range. To help reduce biased decision making, for each of our models we provide additional models that are mirror copies, except we take age out.”

The BUN test measures the amount of nitrogen in a person’s blood that comes from the waste product urea. It indicates how well one’s kidneys are working. Lymphocytes are one of several types of white blood cells, a key part of the immune system.

A model for other AI initiatives

The tool, Strebel says, will be used in its pilot form until its computing resources are needed to pilot other models, such as those that can identify and treat patients with long-term COVID symptoms, including organ failure and chronic lung conditions. The tool could also serve as a model for VA artificial intelligence initiatives that apply to conditions and viruses beyond COVID-19, such as suicide prevention.

AI has the potential to greatly improve clinical experiences and patient outcomes, Strebel notes, but the results must be accessible, interpretable, and actionable.

“The COVID-19 prognostic tool is only the beginning of a broader series of tools that we are in the process of piloting,” Makridis of NAII says. “Given the complexity and expansiveness of medical history data, it’s almost impossible for clinicians to keep track of everything. Our goal is to empower clinician decision-making by creating AI-driven tools that allow them to assess a patient’s risk factor for a particular outcome and identify the primary contributing factors, thereby allowing them to provide more effective and personalized treatments.

“We’ll learn more about the potential of artificial intelligence as time goes on.”

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