USING AI TO PREDICT IBD FLARE-UPS

At the VA's Ann Arbor Healthcare System in Michigan, gastroenterologist Akbar Waljee, MD, is building a better way to predict flare-ups in symptoms associated with inflammatory bowel disease (IBD).

More than a million Americans – including, according to a VA study, more than 60,000 to 80,000 veterans (2000-2019) – suffer from IBD, an umbrella term for chronic conditions that include Crohn’s disease and ulcerative colitis. According to the Crohn’s and Colitis Foundation, IBD-related hospitalizations and outpatient drug therapies cost between $11 billion and $28 billion annually.

Periods of symptom flares and remission are typical of IBD, and flare-ups are often painful and debilitating enough to require hospitalization, surgery, or treatment with steroids – which can involve side effects and increase the risk of other disorders, such as infections, bone loss, blood clots, and high blood pressure, among other side effects. In some cases, IBD can lead to lifethreatening complications such as blood clotting and liver damage.

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Biomarkers that help predict IBD flares are most commonly identified through blood or stool tests, which are expensive and vary widely in availability and accuracy. Better predictive models, Waljee believes, would help patients avoid disabling aggravations of this disease and keep them in remission – and in remission, avoid long periods of ineffective or unnecessary therapies with other drugs. By keeping veterans out of hospitals, a better predictor could also greatly reduce IBD-associated health care costs.

Waljee is exceptionally capable of devising this new predictive tool: In addition to being a staff physician, he’s an investigator in the Ann Arbor VA’s Center for Clinical Management Research (CCMR). With research health scientist Wyndy Wiitala, PhD, he co-directs the CCMR’s Prediction Modeling Unit (PMU), which uses machine learning to collect and analyze patient data for the purpose of informing clinical decisions.

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Using machine-learning algorithms to analyze patient record data, including histories of flares and commonly available bloodwork values, the PMU team, along with University of Michigan statistician Ji Zhu, PhD, came up with a set of criteria for deciding which patients to watch more closely and which may need to begin taking non-steroidal medications to forestall flares. Because some of these drugs take two to three months to take effect, Waljee said, it’s important to identify a coming flare at least three months in advance. “We decided to take all of the relevant information from a patient’s prior history, the longitudinal data, and then predict their need for steroids in the next few months,” he said.

The model produced by the Ann Arbor team – which is updated over time to integrate new patient data – has outperformed traditional tests, predicting flare-ups among veteran patients with about 80 percent accuracy. The next step, Waljee said, will be to validate the model in an external cohort of patients, and then to develop a platform for deploying the model throughout the VA.