Low-risk cefalexin allergies are associated with inpatient prescribing of second-line non-beta-lactam antibiotics

Abstract

Background Cefalexin is a commonly prescribed oral antibiotic, with a similar side chain to amoxicillin. The objectives of this study were to (1) describe the frequency and nature of previously recorded cefalexin adverse reaction (AR) labels in the electronic medical record (EMR) in a medical inpatient cohort, (2) evaluate the accuracy of these labels and (3) examine the association between a cefalexin allergy label and the antibiotics prescribed during an inpatient admission. Methods Consecutive admissions under general medicine in a tertiary hospital over a 1-year period were included in this retrospective cohort study. Data regarding cefalexin adverse reaction (AR) history, and antibiotics prescribed during admission were collected from the EMR. Cefalexin allergy descriptions were reviewed using expert criteria to determine whether the described reaction was most consistent with allergy or intolerance. Results The number of admissions included in this study was 12,134. Of the 224 (1.9%) admissions with a recorded cefalexin AR, 196 (87.5%) had a label of allergy and 28 (12.5%) of intolerance. Following the application of expert criteria, 43 (21.9%) of cefalexin allergy labels were found to be consistent with intolerance. The presence of a low-risk cefalexin allergy was associated with an increased likelihood of receiving non-penicillin antibiotics including clindamycin and ciprofloxacin. Conclusion Cefalexin AR are common, and frequently incorrectly classified in the electronic medical record with consequences for in-hospital antibiotic prescribing and antimicrobial stewardship.

Artificial intelligence–enabled penicillin allergy delabelling: an implementation study

“…Finally, similar strategies may also be investigated for other drug ARs, such as cefalexin allergies, which also influence prescribing practices. 11 This AI implementation study has provided evidence for the potential utility of a deep learning email-based alert in the evaluation of inaccurate penicillin allergy labels. Following establishment, such an approach could be maintained with minimal cost, although the alert burden of clinicians should be considered.…”