870:
Systemic Mastocytosis (SM) and Mast Cell Activation Syndrome (MCAS); How Do They Differ?
Monday, March 5, 2018
South Hall A2 (Convention Center)
Catherine Randa Weiler, MD PhD FAAAAI, Rabe E Alhurani, MBBS, MS, Joseph H. Butterfield, MD FAAAAI, Rohit Divekar, MBBS PhD

RATIONALE:

There are limited data outlining unbiased differences between symptoms and signs of patients with systemic mastocytosis (SM) and those with idiopathic mast cell activation syndrome (MCAS). We hypothesized that using a mathematical model will provide unbiased information differentiating between the two disorders.

METHODS:

Electronic medical records (2003-2012) at our institution were retrospectively reviewed. Patients with bone marrow biopsy proven SM and those with MCAS, whose diagnosis fulfills the 2012 consensus diagnostic criteria, were included. Each chart was individually reviewed. Data from 45 SM and 44 MCAS patient charts regarding the presence or absence of specific symptoms were used for this analysis. Categorical data were transformed into format suitable for use in generation of the network map. An unsupervised bipartite network model of co-occurring symptoms and patients was generated using Gephi (v0.9). Conditional overlay of SM or MCAS was applied after network model was generated allowing for unsupervised exploration of pattern of symptoms.

RESULTS:

Visual inspection of the network revealed differential symptoms associations with SM and MCAS. Syncope, forgetfulness, weight loss, reflux disease, depression, anemia, lymphopenia, eosinophilia, osteopenia, bone fractures, adenopathy and organomegally, were associated with SM. In contrast, urticaria, angioedema, hypotension, dermatographia, bloating, belching, hiccups, rhinorrhea, sneezing and wheezing had a greater association with MCAS.

CONCLUSIONS:

There is a clear difference in the presentation of patients with SM and those with MCAS. Unsupervised bipartite network analytical model provided some insight into those differences. This mathematical model will proof invaluable in the study of larger sample sizes.