Coagulopathy as initial manifestation of concomitant celiac disease and cystic fibrosis: a case report

<p>Abstract</p> <p>Introduction</p> <p>Celiac disease and cystic fibrosis have many common manifestations, such as malabsorption, steatorrhea and growth failure, and were for many years recognized as one clinical entity. Since their recognition as two separate diseases, their co-existence in a patient has been described sporadically; around 20 cases have been described in the literature. Taking into consideration the incidences of the two diseases, the chance of them occurring together is one in 2,000,000 in the general population.</p> <p>Case presentation</p> <p>We describe the case of a five-year-old boy of Turkish ethnicity with both celiac disease and cystic fibrosis, who presented initially with a skin hemorrhage. The diagnosis of celiac disease was made with a positive serum anti-tissue transglutaminase antibody test and the presence of HLA-DQ2 heterodimer, and confirmed on histology with small intestinal villous atrophy. A positive sweat test confirmed the diagnosis of associated cystic fibrosis.</p> <p>To the best of our knowledge there has been no previous report of this rare presentation of associated celiac disease and cystic fibrosis.</p> <p>Conclusion</p> <p>The clinical significance of this case is the consideration of malabsorption with both celiac disease and cystic fibrosis in patients who present with unexplained coagulopathy.</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