Abstract

We present a 15-year-old female patient with an anaphylactic reaction to a jellyfish sting, sustained while surfing in the Mediterranean Sea. She experienced immediate difficulty in breathing, hoarseness and itching and was taken by ambulance to the emergency department, receiving intramuscular adrenaline on the way. She presented with periorbital swelling and facial edema and improved with systemic steroids and antihistamines. She was discharged 2 days later with allergy service follow up at our institution. This is the first case report documenting anaphylaxis due to Mediterranean jellyfish envenomation.

Abstract

We have proposed a new scoring system (Anaphylaxis SCoring Aichi: ASCA) for a quantitative evaluation of the anaphylactic reaction that is observed in an oral food challenge (OFC). Furthermore, the TS/Pro (Total Score of ASCA/cumulative protein dose) can be a marker to represent the overall severity of a food allergy. We aimed to develop a prediction model for a severe allergic reaction that is provoked in a boiled egg white challenge. We used two separate datasets to develop and validate the prediction model, respectively. The development dataset included 198 OFCs, that tested positive. The validation dataset prospectively included 140 consecutive OFCs, irrespective of the result. A ‘severe reaction’ was defined as a TS/Pro higher than 31 (the median score of the development dataset). A multivariate logistic regression analysis was performed to identify the factors associated with a severe reaction and develop the prediction model. The following four factors were independently associated with a severe reaction: ovomucoid specific IgE class (OM-sIgE: 0–6), aged 5 years or over, a complete avoidance of egg, and a total IgE < 1000 IU/mL. Based on these factors, we made a simple scoring prediction model. The model showed good discrimination in a receiver operating characteristic analysis, area under the curve (AUC) = 0.84 in development dataset, AUC = 0.85 in validation dataset. The prediction model significantly improved the AUC in both datasets compared to OM-sIgE alone. This simple scoring prediction model was useful for avoiding risky OFC.