Acute lyme infection presenting with amyopathic dermatomyositis and rapidly fatal interstitial pulmonary fibrosis: a case report

<p>Abstract</p> <p>Introduction</p> <p>Dermatomyositis has been described in the setting of lyme infection in only nine previous case reports. Although lyme disease is known to induce typical clinical findings that are observed in various collagen vascular diseases, to our knowledge, we believe that our case is the first presentation of acute lyme disease associated with amyopathic dermatomyositis, which was then followed by severe and fatal interstitial pulmonary fibrosis only two months later.</p> <p>Case presentation</p> <p>We present a case of a 64-year-old African-American man with multiple medical problems who was diagnosed with acute lyme infection after presenting with the pathognomonic rash and confirmatory serology. In spite of appropriate antimicrobial therapy for lyme infection, he developed unexpected amyopathic dermatomyositis and then interstitial lung disease.</p> <p>Conclusions</p> <p>This case illustrates a potential for lyme disease to produce clinical syndromes that may be indistinguishable from primary connective tissue diseases. An atypical and sequential presentation (dermatomyositis and interstitial lung disease) of a common disease (lyme infection) is discussed. This case illustrates that in patients who are diagnosed with lyme infection who subsequently develop atypical muscular, respiratory or other systemic complaints, the possibility of severe rheumatological and pulmonary complications should be considered.</p

Calculating Stage Duration Statistics in Multistage Diseases

Many human diseases are characterized by multiple stages of progression. While the typical sequence of disease progression can be identified, there may be large individual variations among patients. Identifying mean stage durations and their variations is critical for statistical hypothesis testing needed to determine if treatment is having a significant effect on the progression, or if a new therapy is showing a delay of progression through a multistage disease. In this paper we focus on two methods for extracting stage duration statistics from longitudinal datasets: an extension of the linear regression technique, and a counting algorithm. Both are non-iterative, non-parametric and computationally cheap methods, which makes them invaluable tools for studying the epidemiology of diseases, with a goal of identifying different patterns of progression by using bioinformatics methodologies. Here we show that the regression method performs well for calculating the mean stage durations under a wide variety of assumptions, however, its generalization to variance calculations fails under realistic assumptions about the data collection procedure. On the other hand, the counting method yields reliable estimations for both means and variances of stage durations. Applications to Alzheimer disease progression are discussed