Arch Pediatr Adolesc Med

Objective To develop a novel diagnostic algorithm for Lyme disease among children with facial palsy by integrating public health surveillance data with traditional clinical predictors. Design Retrospective cohort study. Setting Children’s Hospital Boston emergency department,1995–2007 Patients 264 children under age 20 years presenting with peripheral facial palsy who were evaluated for Lyme disease Main outcome measures Multivariate regression was used to identify independent clinical and epidemiologic predictors of Lyme facial palsy. Results 65% of children from high-risk counties during Lyme season tested positive, compared to 5% of children without geographic or seasonal risk factors present. Among patients with both seasonal and geographic risk factors, 80% with one clinical risk factor (fever or headache) and 100% with two clinical factors had Lyme. Factors independently associated with Lyme facial palsy were presentation from June-November (odds ratio 25, 95% CI 8.3–113), residence in a county where the most recent three year average Lyme incidence exceeded 4 cases/100,000 (18, 6.5–69), fever (3.9, 1.5–11), and headache (2.7, 1.3–5.8). Clinical experts correctly treated 68/94 (72%) patients with Lyme facial palsy, but a tool incorporating geographical and seasonal risk identified all 94 cases. Conclusions Most clinicians intuitively integrate geographic information into Lyme disease management, but we demonstrate quantitatively how formal use of geographically-based incidence in a clinical algorithm improves diagnostic accuracy. These findings demonstrate potential for improved outcomes from investments in health information technology that foster bidirectional communication between public health and clinical settings.

21199982

PMC3644029

Journal Article

CDC Public Access