Acute cholangitis is a potentially life-threatening bacterial infection that often is associated with gallstones. Symptoms include fever, jaundice, right upper quadrant pain, and elevated liver enzymes. While these may seem like distinctive, telltale symptoms, they are similar to those of a much different condition: alcohol-associated hepatitis. This challenges emergency department staff and other health care professionals who need to diagnose and treat patients with liver enzyme abnormalities and systemic inflammatory responses.

New Mayo Clinic research finds that machine-learning algorithms can help health care staff distinguish the two conditions. In an article “Machine Learning Techniques Differentiate Alcohol-Associated Hepatitis From Acute Cholangitis in Patients With Systemic Inflammation and Elevated Liver Enzymes,” published in Mayo Clinic Proceedings, scientists show how algorithms may be effective predictive tools using a few variables and routinely available structured clinical information.

“[The objective of the research was to] develop machine learning algorithms (MLAs) that can differentiate patients with acute cholangitis (AC) and alcohol-associated hepatitis (AH) using simple laboratory variables. A study was conducted of 459 adult patients admitted to Mayo Clinic, Rochester, with AH (n¼265) or AC (n¼194) from January 1, 2010, to December 31, 2019. Ten laboratory variables (white blood cell count, hemoglobin, mean corpuscular volume, platelet count, aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase, total bilirubin, direct bilirubin, albumin) were collected as input variables.

“Eight supervised MLAs (decision tree, naive Bayes, logistic regression, k-nearest neighbor, support vector machine, artificial neural networks, random forest, gradient boosting) were trained and tested for classification of AC vs AH. External validation was performed with patients with AC (n¼213) and AH (n¼92) from the MIMIC-III database. A feature selection strategy was used to choose the best 5-variable combination. There were 143 physicians who took an online quiz to distinguish AC from AH using the same 10 laboratory variables alone.

“The MLAs demonstrated excellent performances with accuracies up to 0.932 and area under the curve (AUC) up to 0.986. In external validation, the MLAs showed comparable accuracy up to 0.909 and AUC up to 0.970. Feature selection in terms of information-theoretic measures was effective, and the choice of the best 5-variable subset produced high performance with an AUC up to 0.994. Physicians did worse, with mean accuracy of 0.790. Conclusion: Using a few routine laboratory variables, MLAs can differentiate patients with AC and AH and may serve valuable adjunctive roles in cases of diagnostic uncertainty.

“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, MD, a third-year gastroenterology and hepatology fellow at Mayo Clinic in Rochester, and first author of the study. “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. The machine-learning algorithms demonstrated excellent performances for discriminating the two conditions, with over 93% accuracy.

The researchers analyzed electronic health records of 459 patients older than age 18 who were admitted to Mayo Clinic in Rochester between Jan. 1, 2010, and Dec. 31, 2019. The patients were diagnosed with acute cholangitis or alcohol-associated hepatitis. Ten routinely available laboratory values were collected at the time of admission. After removal of patients whose data were incomplete, 260 patients with alcohol-associated hepatitis and 194 with acute cholangitis remained. These data were used to train eight machine-learning algorithms.

Analyzed electronic health records

The researchers also externally validated the results using a cohort of ICU patients who were seen at Beth Israel Deaconess Medical Center in Boston between 2001 and 2012. The algorithms also outperformed physicians who participated in an online survey, which is described in the article.

“The study highlights the potential for machine-learning algorithms to assist in clinical decision-making in cases of uncertainty,” says Ahn. “There are many instances of gastroenterologists receiving consults for urgent endoscopic retrograde cholangiopancreatography in patients who initially deny a history of alcohol use but later turn out to have alcohol-associated hepatitis. 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.”

If the machine-learning algorithms can be made easily accessible with an online calculator or smartphone app, they may help health care staff who are urgently presented with an acutely ill patient with abnormal liver enzymes, according to the study.

“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,” according to Ahn.

A team from the department of computer science at Hanyang University in Seoul, South Korea, were also involved in the research study.

To read about other examples of how machine learning is impacting biotech R&D and manufacturing see Machine Learning Tool Advances Research on Rheumatoid and Osteoarthritis, Novel Machine Learning Tool Helps Discover Genetic Risk Factors for ALS, and Optimizing Biodrug Formulations with Machine Learning.


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