Person:

Zurakowski, David

Background: The significance of hypergammaglobulinemia as a marker of immune activation is unknown, as a differential diagnosis for hypergammaglobulinemia in children has not been adequately established. The goal of this study was to identify conditions associated with hypergammaglobulinemia in children, with the hypothesis that elevated immunoglobulin levels may precede or predict the development of autoimmune conditions. Methods: We reviewed the medical records for all children with IgG level ≥2000 mg/dL treated at a tertiary care children’s hospital from January 1, 2000 through December 31, 2009. We compared clinical and laboratory features of these patients, and developed an algorithm to predict the likelihood of underlying autoimmunity based on these characteristics. Results: After excluding children who had received IVIG, a total of 442 patients with hypergammaglobulinemia were identified. Of these, nearly half had autoimmune conditions, most frequently systemic lupus erythematosus and lupus-related disorders. Autoimmune gastrointestinal disorders such as inflammatory bowel disease were also common. Infectious diseases were the next largest category of diseases, followed with much less frequency by malignant, drug-related, and other conditions. In comparison with non-autoimmune conditions, patients with autoimmune disease had higher IgG levels, lower white blood cell counts, lower hemoglobin values, and lower C-reactive protein (CRP) levels. Multivariable logistic regression confirmed that CRP (P = 0.002), white blood cell count (P < 0.001), hemoglobin (P = 0.015), and female gender (P < 0.001) are independent risk factors for autoimmune disease in patients with high IgG levels. Conclusions: In a cohort of pediatric patients at a tertiary care children’s hospital, hypergammaglobulinemia was most commonly associated with autoimmune diseases. In female patients with hypergammaglobulinemia, the presence of leukopenia, anemia, and normal CRP was 95% predictive of underlying autoimmune disease.

Background: The number of trained specialists world-wide is insufficient to serve all children with pediatric rheumatologic disorders, even in the countries with robust medical resources. We evaluated the potential of diagnostic decision support software (DDSS) to alleviate this shortage by assessing the ability of such software to improve the diagnostic accuracy of non-specialists. Methods: Using vignettes of actual clinical cases, clinician testers generated a differential diagnosis before and after using diagnostic decision support software. The evaluation used the SimulConsult® DDSS tool, based on Bayesian pattern matching with temporal onset of each finding in each disease. The tool covered 5405 diseases (averaging 22 findings per disease). Rheumatology content in the database was developed using both primary references and textbooks. The frequency, timing, age of onset and age of disappearance of findings, as well as their incidence, treatability, and heritability were taken into account in order to guide diagnostic decision making. These capabilities allowed key information such as pertinent negatives and evolution over time to be used in the computations. Efficacy was measured by comparing whether the correct condition was included in the differential diagnosis generated by clinicians before using the software (“unaided”), versus after use of the DDSS (“aided”). Results: The 26 clinicians demonstrated a significant reduction in diagnostic errors following introduction of the software, from 28% errors while unaided to 15% using decision support (p < 0.0001). Improvement was greatest for emergency medicine physicians (p = 0.013) and clinicians in practice for less than 10 years (p = 0.012). This error reduction occurred despite the fact that testers employed an “open book” approach to generate their initial lists of potential diagnoses, spending an average of 8.6 min using printed and electronic sources of medical information before using the diagnostic software. Conclusions: These findings suggest that decision support can reduce diagnostic errors and improve use of relevant information by generalists. Such assistance could potentially help relieve the shortage of experts in pediatric rheumatology and similarly underserved specialties by improving generalists’ ability to evaluate and diagnose patients presenting with musculoskeletal complaints. Trial registration ClinicalTrials.gov ID: NCT02205086 Electronic supplementary material The online version of this article (doi:10.1186/s12969-016-0127-z) contains supplementary material, which is available to authorized users.