Unraveling determinants of severe anaphylaxis — A cluster analysis from a large national hospitalization database

Luciana Kase Tanno , Pham Thao Van Luong, Megane Dieval, Caroline Dunoyer, Nicolas Molinari, Isabella Annesi-Maesano, Pascal Demoly. Journal of Allergy and Hypersensitivity Diseases Volume 1, January–March 2024, 100004

Abstract

Background

Understanding potential risk factors and phenotypes at-risk of anaphylaxis can tailor public health actions to prevent avoidable deaths. Therefore, our aim was to explore patterns of hospital admissions diagnosed with anaphylaxis in France from 2012 to 2021, across different age groups, using the national French hospital database and to evaluate potential determinants to propose phenotypes at-risk, with a particular view to severe cases.

Methods

This is a descriptive study, using data from the French Program for the Medicalization of Information Systems, from 2012 to 2021. We included all patients hospitalized for anaphylaxis using International Classification of Diseases (ICD)-10 codes listed as a primary diagnosis.

Potential risk factors were selected according to corresponding ICD codes, available as secondary data of hospitalization. Severe anaphylaxis (SA) was defined based on the need of admissions in intensive care units. To explore the patterns and characteristics, we employed cluster analysis using the clustering large applications algorithm. Charlson comorbidity index (CCI) was used to evaluate comorbidities.

Results

During the 9-year study period, 78,453 patients had 86,654 admissions due to anaphylaxis for all-causes. Most of the cases were non-severe anaphylaxis (93.7%), 5.9% were SA, and 0.4% evolved to fatal anaphylaxis (FA). Rate of admissions for SA was 0.08/100,000/year. Seasonal trend of cases of anaphylaxis due to Hymenoptera venoms were shown. Increased severity and risk of death were significantly higher in the elderly. Food-induced anaphylaxis is more frequent in childhood, but SA occurred at adolescence and early adulthood. The CCI was higher in SA and FA. By applying the clustering algorithm CLARA, we identified 3 clusters distributed according to the severity.

Conclusion

More than providing a snap shot of potential risk factors for anaphylaxis in France, we were able to suggest phenotypes of anaphylaxis of different severities and to propose actions based on our findings. Although further research is needed in the field, we strongly believe that the data presented may contribute to public health advancements to ensure quality of care of patients suffering from anaphylaxis.

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