Using In-depth History Screening as an Additional Method to Help Delabel Inappropriate β-Lactam Allergies
Bibliographic record
Abstract
To the Editor—We commend Blumenthal et al [1] for their study demonstrating that self-reported β-lactam allergies are associated with poorer outcomes in the perioperative setting. This work adds to the growing literature showing the harms secondary to the use of alternative second-line therapies, which are often broader, costlier, more toxic, and less effective [2, 3]. Blumenthal et al also described several approaches to verifying the unreliable self-reported β-lactam allergies in the perioperative setting, including routine skin testing and specialist consultation and exposure to test doses of cefazolin [4, 5]. Unfortunately, these resources are not available expeditiously in many healthcare centers. We would like to highlight an additional method to help delabel inappropriate β-lactam allergies that is available to all clinicians—that of using in-depth history screening [6]. At our site, each patient presenting to the preoperative clinic with a reported β-lactam allergy underwent a brief assessment by a nurse or pharmacist to clarify the nature, timing, and precise exposure eliciting the reported allergy. Each assessment was reviewed with an infectious diseases physician and patients were deemed safe to proceed with β-lactam prophylaxis if they did not describe a history of type I/immunoglobulin E–mediated reaction or other severe reaction. Antibiotic prophylaxis orders (with approval by the surgical team) were scheduled into the computerized order entry system to be given before the first incision of the upcoming operation. We found, that, among 485 patients with self-reported β-lactam allergy, only 117 (24%) reported a history consistent with anaphylaxis, a figure smaller than that determined by Blumenthal et al [1] (approximately 40%). Using our assessment, 277 patients (57%) ended up receiving β-lactam prophylaxis, with none subsequently experiencing adverse reactions. After implementation of this process at our institution, the overall use of alternative antibiotic prophylaxis at our institution among those reporting a β-lactam allergy decreased from 82% to 56%, and this decrease was directly associated with the number of monthly assessments. Because access to skin testing and allergist consultation is not readily available for the large volumes of elective surgeries performed yearly in most centers, this interdisciplinary approach can provide an efficient solution to the problem well demonstrated by Blumenthal et al A simple screening tool using the principles of prospective audit and feedback can increase the use of β-lactam perioperative prophylaxis without any adverse events and without the use of skin testing. Potential conflicts of interest. All authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".