Development and Validation of a Diagnostic Tool for the Timely Diagnosis of Patients with Systemic Mastocytosis

Background: Systemic mastocytosis (SM), a rare, clonal mast cell disease associated with heterogenous clinical presentation, is characterized by severe, debilitating and often unpredictable skin, gastrointestinal, neurocognitive, and systemic symptoms (including life-threatening anaphylaxis), long-term co-morbidities, and in Advanced SM (AdvSM), organ damage due to mast cell infiltration. SM consists of 2 distinct groups of variants, advanced and non-advanced: AdvSM includes aggressive SM (ASM), SM with an associated hematologic neoplasm (SM-AHN), and mast cell leukemia (MCL); non-AdvSM includes indolent SM (ISM) and smoldering SM (SSM). Arriving at a definitive diagnosis may take years and is one of the challenges of SM management. To aid clinicians in the identification of SM, we developed and validated a diagnostic algorithm using data from select community hematology practices in the United States. Methods: A sample of 209 patients (SM and control) was obtained from the Quality Cancer Care Alliance (QCCA), a network of 19 community oncology and hematology practices. Data collection consisted of patient characteristics, laboratory parameters, and signs and symptoms at presentation. General linear models (GLM) with a logit link function were used in a backwards elimination process with the p value set at < 0.05 to identify patient factors at presentation that were associated with a diagnosis of SM. A Dx scoring algorithm (range: 0-26) was then derived from the final model coefficients, with the intent of correctly differentiating between SM and non-SM patients. A receiver operating characteristic (ROC) curve analysis was then done to measure the Dx accuracy of the algorithm. Results: Data from 105 SM and 104 non-SM control (diagnosed with hematological cancers) patients were collected from QCCA. The SM cohort included patients with different SM subtypes, including: ISM (47.6%); ASM (9.5%); SM-AHN (19.0%); MCL (1.9%); and subtype not documented (21.9%). The factors identified as being predictive ofa correct diagnosis of SM were 1) patient age, 2) lymph node status, 3) absolute neutrophil count, and 4-7) the following symptoms within 30 days of presentation: diarrhea, rash, skin lesions, and unintended weight loss. In the algorithm, patients were assigned scores for each diagnostic factor they possessed [Figure 1]. The final score was then associated with an overall percent likelihood of a positive SM diagnosis. The area under the ROC curve was 0.89 (95%CI