http://archpedi.jamanetwork.com/ en-us Mon, 24 Dec 2012 00:00:00 GMT Tue, 01 Jan 2013 00:46:50 GMT Silverchair editor@archpedi.jamanetwork.com webmaster@archpedi.jamanetwork.com http://archpedi.jamanetwork.com/article.aspx?articleID=384158 Sat, 01 Jan 2011 00:00:00 GMT Fine AM, Brownstein JS, Nigrovic LE, et al. <span class="paragraphSection"><div class="boxTitle">Objective</div>To develop a novel diagnostic algorithm for Lyme disease among children with facial palsy by integrating public health surveillance data with traditional clinical predictors.<div class="boxTitle">Design</div>Retrospective cohort study.<div class="boxTitle">Setting</div>Children's Hospital Boston emergency department, 1995-2007.<div class="boxTitle">Patients</div>Two hundred sixty-four children (aged <20 years) with peripheral facial palsy who were evaluated for Lyme disease.<div class="boxTitle">Main Outcome Measures</div>Multivariate regression was used to identify independent clinical and epidemiologic predictors of Lyme disease facial palsy.<div class="boxTitle">Results</div>Lyme diagnosis was positive in 65% of children from high-risk counties in Massachusetts during Lyme disease season compared with 5% of those without both geographic and seasonal risk factors. Among patients with both seasonal and geographic risk factors, 80% with 1 clinical risk factor (fever or headache) and 100% with 2 clinical factors had Lyme disease. Factors independently associated with Lyme disease facial palsy were development from June to November (odds ratio, 25.4; 95% confidence interval, 8.3-113.4), residence in a county where the most recent 3-year average Lyme disease incidence exceeded 4 cases per 100 000 (18.4; 6.5-68.5), fever (3.9; 1.5-11.0), and headache (2.7; 1.3-5.8). Clinical experts correctly treated 68 of 94 patients (72%) with Lyme disease facial palsy, but a tool incorporating geographic and seasonal risk identified all 94 cases.<div class="boxTitle">Conclusions</div>Most physicians 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.</span> 165 1 61 67 10.1001/archpediatrics.2010.250 http://archpedi.jamanetwork.com/article.aspx?articleID=384158