Development of a machine learning algorithm based on administrative claims data for identification of ED anaphylaxis patient visits.

Background: Epidemiologic studies of anaphylaxis commonly rely on () codes to identify anaphylaxis cases, which may lead to suboptimal epidemiologic classification.

Objective: We sought to develop and assess the accuracy of a machine learning algorithm using codes and other administrative data compared with code-only algorithms to identify emergency department (ED) anaphylaxis visits.

Methods: We conducted a retrospective review of ED visits from January 2013 to September 2017.

Sulfonamide Drug Allergy.

Purpose Of Review: We sought to review past and current literature on sulfonamide drug allergy and distill it in a practical manner to assist the clinician, specifically focusing on cross-reactivity and desensitization.

Recent Findings: There do not appear to be consistent genetic markers to reliably predict features of or the presence hypersensitivity reactions. Recent evidence continues to alleviate early concerns cross-reactivity between sulfonamide antibiotics and non-antibiotics.

Allergy, Asthma and Immunology Training in Internal Medicine Residents.

Common allergic conditions such as allergic rhinitis, asthma and antibiotic allergies are frequently encountered by internal medicine physicians. These conditions are a significant source of health care utilization and morbidity. However, many internal medicine residency programs offer limited training in allergy and immunology.