Comparative Performance of 4 Penicillin-Allergy Prediction Strategies in a Large Cohort.
J Allergy Clin Immunol Pract
; 2024 Jul 20.
Article
em En
| MEDLINE
| ID: mdl-39038539
ABSTRACT
BACKGROUND:
A safe and pragmatic guide for labelling and delabelling patients with suspected penicillin allergy is mandatory.OBJECTIVE:
To compare the performance of 4 penicillin-allergy prediction strategies in a large independent cohort.METHODS:
We conducted a retrospective study for subjects presenting between January 2014 and December 2021 at the University Hospital of Montpellier, with a history of hypersensitivity to penicillins. The outcome targeted by the study was a positive penicillin-allergy test.RESULTS:
Of the 1,884 participants included, 382 (20.3%) had positive penicillin-allergy tests. The ENDA (European Network on Drug Allergy) and Blumenthal strategies yielded relatively high sensitivities and low specificities and, by design, did not misclassify any positive subjects with severe index reactions. The PEN-FAST <3 score had a negative predictive value of 90% (95% confidence interval [95% CI] 88%-91%), with a sensitivity of 66% (95% CI 62%-71%) and a specificity of 73% (95% CI 71%-75%), and incorrectly delabelled 18 subjects with anaphylaxis and 15 with other severe nonimmediate reactions. For the adapted Chiriac score, the specificity corresponding to 66% sensitivity was 73% (95% CI 70%-75%). Conversely, at a 73% specificity threshold, the sensitivity was 65% (95% CI, 61%-70%). Attempts to improve these prediction algorithms did not substantially enhance performance.CONCLUSIONS:
The ENDA and Blumenthal strategies are safe for high-risk subjects, but their delabelling effectiveness is limited, leading to unnecessary avoidance. Conversely, the PEN-FAST and Chiriac scores are performant in delabelling, but more frequently misclassify high-risk subjects with positive penicillin-allergy tests. Selection of the most appropriate tool requires careful consideration of the target population and the desired goal.
Texto completo:
1
Base de dados:
MEDLINE
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article