Scoring the risk of having systemic mastocytosis in adult patients with mastocytosis in the skin

Abstract Background: Mastocytosis in adults often presents with skin lesions. A bone marrow biopsy (BMB) is necessary to confirm or exclude the presence of systemic mastocytosis (SM) in these cases. When a BMB is not performed the provisional diagnosis is mastocytosis in the skin (MIS). No generally accepted scoring system has been established to estimate the risk of SM in these patients. Objective: To develop a risk score to predict SM in adults with MIS. Methods: We examined 1145 patients with MIS from the European Competence Network on Mastocytosis (ECNM) registry who underwent a BMB. 944 patients had SM and 201 patients had cutaneous mastocytosis (CM); 63.7% were female, 36.3% were male. Median age was 44\ub113.3 years. The median serum tryptase level amounted to 29.3\ub181.9 ng/ml. We established a multivariate regression model using the whole population of patients as a training and validation set (bootstrapping). A risk score was developed and validated with receiver operating curves. Results: In the multivariate model, the tryptase level (p<0.001), constitutional/cardiovascular symptoms (p=0.014) and bone symptoms/osteoporosis (p<0.001) were independent predictors of SM (p<0.001, sensitivity 90.7%, specificity 69.1%). A 6-point risk score was established (risk, 10.7-98.0%) and validated. Conclusions: Using a large dataset of the ECNM registry we created a risk score to predict the presence of SM in patients with MIS. Although the score will need further validation in independent cohorts, our score seems to discriminate safely between patients with SM and with pure CM

Cumulative complexity: a functional, patient-centered model of patient complexity can improve research and practice

&lt;b&gt;Objective&lt;/b&gt; To design a functional, patient-centered model of patient complexity with practical applicability to analytic design and clinical practice. Existing literature on patient complexity has mainly identified its components descriptively and in isolation, lacking clarity as to their combined functions in disrupting care or to how complexity changes over time.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Study design and setting&lt;/b&gt; The authors developed a cumulative complexity model, which integrates existing literature and emphasizes how clinical and social factors accumulate and interact to complicate patient care. A narrative literature review is used to explicate the model.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Results&lt;/b&gt; The model emphasizes a core, patient-level mechanism whereby complicating factors impact care and outcomes: the balance between patient workload of demands and patient capacity to address demands. Workload encompasses the demands on the patient's time and energy, including demands of treatment, self-care, and life in general. Capacity concerns ability to handle work (e.g., functional morbidity, financial/social resources, literacy). Workload-capacity imbalances comprise the mechanism driving patient complexity. Treatment and illness burdens serve as feedback loops, linking negative outcomes to further imbalances, such that complexity may accumulate over time.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Conclusion&lt;/b&gt; With its components largely supported by existing literature, the model has implications for analytic design, clinical epidemiology, and clinical practice