Development and internal validation of risk scores to diagnose infectious mononucleosis among college students

Abstract

Abstract Background Individual symptoms and signs of infectious mononucleosis (IM) are of limited value for diagnosis. Objective To develop and validate risk scores based on signs and symptoms with and without haematologic parameters for the diagnosis of IM. Design and setting Data 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. Method Independent 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. Results The 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. Conclusion We 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.