Safety of penicillin allergy skin testing in patients with low lung volumes before lung transplant

Predicting Penicillin Allergy: A United States Multicenter Retrospective Study

BACKGROUND

Using the reaction history in logistic regression and machine learning (ML) models to predict penicillin allergy has been reported based on non-US data.

OBJECTIVE

We developed ML positive penicillin allergy testing prediction models from multisite US data.

METHODS

Retrospective data from 4 US-based hospitals were grouped into 4 datasets: enriched training (1:3 case-control matched cohort), enriched testing, nonenriched internal testing, and nonenriched external testing. ML algorithms were used for model development. We determined area under the curve (AUC) and applied the Shapley Additive exPlanations (SHAP) framework to interpret risk drivers.

RESULTS

Of 4777 patients (mean age 60 [standard deviation: 17] years; 68% women, 91% White, and 86% non-Hispanic) evaluated for penicillin allergy labels, 513 (11%) had positive penicillin allergy testing. Model input variables were frequently missing: immediate or delayed onset (71%), signs or symptoms (13%), and treatment (31%). The gradient-boosted model was the strongest model with an AUC of 0.67 (95% confidence interval [CI]: 0.57-0.77), which improved to 0.87 (95% CI: 0.73-1) when only cases with complete data were used. Top SHAP drivers for positive testing were reactions within the last year and reactions requiring medical attention; female sex and reaction of hives/urticaria were also positive drivers.

CONCLUSIONS

An ML prediction model for positive penicillin allergy skin testing using US-based retrospective data did not achieve performance strong enough for acceptance and adoption. The optimal ML prediction model for positive penicillin allergy testing was driven by time since reaction, seek medical attention, female sex, and hives/urticaria.

Bring it on again: antimicrobial stewardship in transplant infectious diseases: updates and new challenges

Advancement in solid organ transplantation and hematopoietic stem cell transplant continues to improve the health outcomes of patients and widens the number of eligible patients who can benefit from the medical progress. Preserving the effectiveness of antimicrobials remains crucial, as otherwise transplant surgeries would be unsafe due to surgical site infections, and the risk of sepsis with neutropenia would preclude stem cell transplant. In this review, we provide updates on three previously discussed stewardship challenges: febrile neutropenia, Clostridioides difficile infection, and asymptomatic bacteriuria. We also offer insight into four new stewardship challenges: the applicability of the "shorter is better" paradigm shift to antimicrobial duration; antibiotic allergy delabeling and desensitization; colonization with multidrug-resistant gram-negative organisms; and management of cytomegalovirus infections. Specifically, data are accumulating for "shorter is better" and antibiotic allergy delabeling in transplant patients, following successes in the general population. Unique to transplant patients are the impact of multidrug-resistant organism colonization on clinical decision-making of antibiotic prophylaxis in transplant procedure and the need for antiviral stewardship in cytomegalovirus. We highlighted the expansion of antimicrobial stewardship interventions as potential solutions for these challenges, as well as gaps in knowledge and opportunities for further research.

The Impact of Reported β-Lactam Allergy on Clinical Outcomes and Antibiotic Use Among Solid Organ Transplant Recipients

Background

Reported β-lactam allergies (BLAs) are common and frequently inaccurate, but there are limited data on the clinical implications of BLA among solid organ transplant (SOT) recipients. We examined the impact of BLA on clinical outcomes and antibiotic use among SOT recipients.

Methods

This retrospective cohort study included adult patients undergoing single-organ heart, kidney, liver, lung, or pancreas transplant at a United States academic medical center from 1 April 2017 to 31 December 2020. Demographic and clinical data were collected from the electronic health record. Multivariate median regression was performed to evaluate the association between BLA and days alive and out of the hospital in the first 180 days posttransplant (DAOH180). Multivariate logistic regression was performed to evaluate the association between BLA and antibiotic use.

Results

Among 1700 SOT recipients, 285 (16.8%) had a BLA at the time of transplant. BLA was not associated with DAOH180 (adjusted median difference, -0.8 days [95% confidence interval {CI}, -2.7 to 1.2]; P = .43). Patients with BLA were more likely to receive intravenous vancomycin (adjusted odds ratio [aOR], 1.8 [95% CI, 1.3-2.6]; P < .001), clindamycin (aOR, 9.9 [95% CI, 5.1-18.9]; P < .001), aztreonam (aOR, 19.6 [95% CI, 5.9-64.4]; P < .001), fluoroquinolones (aOR, 3.8 [95% CI, 2.8-5.0]; P < .001), or aminoglycosides (aOR, 3.9 [95% CI, 2.5-6.2]; P < .001).

Conclusions

BLA was associated with use of β-lactam alternative antibiotics but not DAOH180 among SOT recipients.

Drug Allergy Delabeling Programs: Recent Strategies and Targeted Populations

Drug allergy delabeling programs have become an essential element of antibiotic stewardship. Development of delabeling programs involves careful selection of target patient population, thoughtful design of delabeling approach, stakeholder engagement, assembly of key team members, implementation, and evaluation of clinical and safety outcomes. Recent programs have targeted patients thought to be most likely to benefit from removal of inaccurate antibiotic allergy labels, those with β-lactam antibiotic allergies and high-risk populations likely to need β-lactam antibiotics as first-line treatment. This review provides an overview of current risk stratification methods and β-lactam cross-reactivity data and summarizes how different inpatient and outpatient delabeling programs have used these concepts in delabeling algorithms. β-Lactam delabeling programs for inpatients, pediatric patients, and programs utilizing telehealth have been implemented with good outcomes. This review also focuses on delabeling programs for high-risk populations likely to benefit from first-line β-lactam antibiotics. These populations include perioperative, prenatal, and immunocompromised patients. Delabeling programs have been successful in the inpatient and outpatient settings at enabling appropriate antibiotic use. This article reviews delabeling strategies utilized by these programs with a focus on highlighting elements key to their success and future areas for innovation.