ArticleFEMS microbes2026
Development of a clinical scoring tool using machine-learning prediction of monospot positivity in suspected infectious mononucleosis.
Article in FEMS microbes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Infectious mononucleosis is clinically difficult to distinguish from bacterial causes of tonsillitis/pharyngitis. First-line investigations include monospot testing, despite 63% sensitivity in certain cohorts. Therefore, national recommendations include repeating an initial negative monospot within five to seven days. A point-of-care clinical scoring tool could improve clinical outcomes, increase diagnostic accuracy and reduce unnecessary testing. We conducted a retrospective cohort study at Luton and Dunstable University Hospital including patients aged 15-24 presenting with sore throat, lymphadenopathy or fever between 01/01/2021 and 31/01/2024 who underwent monospot testing. Extracted data included demographics, observations, and laboratory results. Patients were randomly split into training (80%) and testing (20%) cohorts. Eleven parameters were used to develop four predictive models; classical multivariate logistic regression, machine-learning logistic regression with LIBLINEAR approximation, machine-learning decision tree classifier, and a simplified clinical risk-stratification model from machine-learning methods. Total of 278 presentations from 264 patients were included. The machine-learning decision tree classifier demonstrated superior performance, achieving 100% sensitivity, 98.0% specificity, and 98.2% diagnostic accuracy using only three parameters: lymphocyte count, neutrophil count, and alanine aminotransferase. The simplified clinical risk-stratification model demonstrated 83.3% sensitivity, 98.0% specificity, and overall 96.4% accuracy. All four models represent potential methods for developing clinical tools to predict monospot positivity. Our risk-stratification model showed significant promise as an easily memorisable, point-of-care clinical scoring tool. Using this, we propose an alternative diagnostic pathway with early counselling and de-escalation of antibiotics in high-risk cases, and reduced testing in low-risk cases; reducing population-level morbidity and epidemiological spread, whilst improving diagnostic accuracy and conserving resources.
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