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

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.

A Quantitative Study of the Decision Threshold for the Diagnosis of Infectious Mononucleosis.

BACKGROUND: Ordering a serologic test for infectious mononucleosis (IM) in all young patients with sore throat is costly and impractical. The test threshold to determine when to order a diagnostic test for IM based on the patient's symptoms has not been previously studied. OBJECTIVE: To determine the test threshold for IM in the management of patients with sore throat. DESIGN AND SETTING: Online surveys were sent to a convenience sample of US primary care clinicians regarding their decision making about whether or not to order a test for IM in a patient with sore throat. METHOD: 7 clinical vignettes were created, each with a different combinations of symptoms and signs. The probability of IM for each vignette was estimated by the investigator based on the number of symptoms present to generate a plausible range of disease probabilities. Clinicians were then asked to decide whether to test or not test for IM, and mixed-effect logistic regression was used to determine the test threshold for IM where half of physicians chose to test and half chose not to test. RESULTS: A total of 117 clinicians provided responses for a total of 819 clinical vignettes. The overall test threshold for IM as estimated using the logistic regression was 9.5% (95% CI: 8.2% to 10.9%). The test threshold for clinicians practicing greater than 10 years was significantly higher than for those practicing less or equal to 10 years (10.5% vs 7.3%, P = .02). No significant differences between specialties and practice sites were found with respect to the test threshold. CONCLUSION: This study identified a test threshold for IM of approximately 10% based on realistic clinical vignettes. This threshold was stable regarding the clinician's specialty and practice sites and could be used in the development of a clinical prediction rule to determine the cutoff for low- versus high-risk groups.

College health as a partner in the care of pediatric cancer survivors.

OBJECTIVE: This study reviews an initiative to educate providers on pediatric cancer survivor care and to establish a cancer survivor registry in a college health center. PARTICIPANTS: PARTICIPANTS were University of Georgia (UGA) college health providers. METHODS: Providers attended lectures on survivor care and were encouraged to register on Cancer SurvivorLink. Changes in provider familiarity and practice were measured using baseline and follow-up surveys. A survivor registry was created using health entrance forms and medical records abstraction. RESULTS: Twenty-four providers registered on SurvivorLink, and 16 completed both surveys. Familiarity with survivor care (p = .003) and a survivor health care plan (p = .016) increased. Likelihood to deliver survivor care increased (p = .01). UGA follows 95 survivors; 71 diagnosed at < 21 years. Among survivors diagnosed at < 21 years, 91% reported their diagnosis on entrance forms. CONCLUSIONS: Through education and optimization of health informatics, college health centers can identify and provide survivor care to this medically vulnerable population.