• Xinyan Cai, Mark H Ebell, Garth Russo, Kevin K Dobbin, and Jose F Cordero.
• Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA, United States.
• Fam Pract. 2023 Mar 28; 40 (2): 261267261-267.

BackgroundIndividual symptoms and signs of infectious mononucleosis (IM) are of limited value for diagnosis.ObjectiveTo develop and validate risk scores based on signs and symptoms with and without haematologic parameters for the diagnosis of IM.Design And SettingData were extracted from electronic health records of a university health centre and were divided into derivation (9/1/2015-10/31/2017) and a prospective temporal internal validation (11/1/2017-1/31/2019) cohort.MethodIndependent predictors for the diagnosis of IM were identified in univariate analysis using the derivation cohort. Logistic regression models were used to develop 2 risk scores: 1 with only symptoms and signs (IM-NoLab) and 1 adding haematologic parameters (IM-Lab). Point scores were created based on the regression coefficients, and patients were grouped into risk groups. Primary outcomes were area under the receiver operating characteristic curve (AUROCC) and classification accuracy.ResultsThe IM-NoLab model had 4 predictors and identified a low-risk group (7.9% with IM) and a high-risk group (22.2%) in the validation cohort. The AUROCC was 0.75 in the derivation cohort and 0.69 in the validation cohort. The IM-Lab model had 3 predictors and identified a low-risk group (3.6%), a moderate-risk group (12.5%), and a high-risk group (87.6%). The AUROCC was 0.97 in the derivation cohort and 0.93 in the validation cohort.ConclusionWe derived and internally validated the IM-NoLab and IM-Lab risk scores. The IM-Lab score in particular had very good discrimination and have the potential to reduce the need for diagnostic testing for IM.© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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