Research has found that algorithms may be effective predictive tools for distinguishing the difference between alcohol-associated hepatitis and acute cholangitis, using a few simple variables and routinely available structured clinical information.

“This study was motivated by seeing many medical providers in the emergency department or ICU struggle to distinguish acute cholangitis and alcohol-associated hepatitis, which are very different conditions that can present similarly,” says Joseph Ahn, M.D., a third-year gastroenterology and hepatology fellow at Mayo Clinic in Rochester.

“We developed and trained machine-learning algorithms to distinguish the two conditions using some of the routinely available lab values that all of these patients should have,” Dr. Ahn says. “The machine-learning algorithms demonstrated excellent performances for discriminating the two conditions, with over 93% accuracy.”

The researchers analyzed electronic health records of 459 adult patients, diagnosed with acute cholangitis or alcohol-associated hepatitis, between 2010 and 2019. Ten routinely available laboratory values were collected at the time of hospital admission. After removal of patients whose data were incomplete, 260 patients with alcohol-associated hepatitis and 194 with acute cholangitis remained. This data was used to train eight machine-learning algorithms.

“The study highlights the potential for machine-learning algorithms to assist in clinical decision-making in cases of uncertainty,” says Dr. Ahn. “In some situations, the inability to obtain a reliable history from patients with altered mental status or lack of access to imaging modalities in underserved areas may force providers to make the determination based on a limited amount of objective data.”

“For patients, this would lead to improved diagnostic accuracy and reduce the number of additional tests or inappropriate ordering of invasive procedures, which may delay the correct diagnosis or subject patients to the risk of unnecessary complications.”