Role of clinical history in beta-lactam hypersensitivity

Predictive models and applicability of artificial intelligence-based approaches in drug allergy

PURPOSE OF REVIEW

Drug allergy is responsible for a huge burden on public healthcare systems, representing in some instances a threat for patient's life. Diagnosis is complex due to the heterogeneity of clinical phenotypes and mechanisms involved, the limitations of in vitro tests, and the associated risk to in vivo tests. Predictive models, including those using recent advances in artificial intelligence, may circumvent these drawbacks, leading to an appropriate classification of patients and improving their management in clinical settings.

RECENT FINDINGS

Scores and predictive models to assess drug allergy development, including patient risk stratification, are scarce and usually apply logistic regression analysis. Over recent years, different methods encompassed under the general umbrella of artificial intelligence, including machine and deep learning, and artificial neural networks, are emerging as powerful tools to provide reliable and optimal models for clinical diagnosis, prediction, and precision medicine in different types of drug allergy.

SUMMARY

This review provides general concepts and current evidence supporting the potential utility of predictive models and artificial intelligence branches in drug allergy diagnosis.

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.

How to Define and Manage Low-Risk Drug Allergy Labels

Risk stratification in drug allergy implies that specific risk categories (eg, low, moderate, and high) classify historical drug hypersensitivity reactions. These risk categories can be based on reaction phenotypic characteristics, the timing of the reaction and evaluation, the required reaction management, and individual characteristics. Although a multitude of frameworks have been described in the literature, particularly for penicillin allergy labels, there has yet to be a global consensus, and approaches continue to vary between allergy centers. Immune-mediated drug allergies can sometimes be confirmed using skin testing, but a negative drug challenge is required to demonstrate tolerance and remove the allergy from the electronic health record ("delabel" the allergy). Even for quintessential IgE-mediated drug allergy, penicillin allergy, recent data reveal that a direct oral challenge, without prior skin testing, is an appropriate diagnostic strategy in those who are considered low-risk. Drug allergy pathogenesis and clinical manifestations may vary depending on the culprit drug, and as such, the optimal approach should be based on risk stratification that considers individual patient and reaction characteristics, the likely hypersensitivity reaction phenotype, the drug class, and the patient's clinical needs. This article will describe low-risk drug allergy labels, focusing on β-lactam and sulfonamide antibiotics, nonsteroidal anti-inflammatory drugs, iodinated contrast media, and common chemotherapeutics. This review will also address practical management approaches using currently available risk stratification and clinical decision tools.

Approach for delabeling beta-lactam allergy in children

A considerable number of pediatric patients treated with beta-lactam (BL) antibiotics develop delayed onset of skin rashes during the course of treatment. Although the most frequent cause of these symptoms is infectious, many cases are labeled as allergic reactions to these drugs. BL allergy labels could have a negative impact, as they imply avoidance of this group of drugs and the use of second-line antibiotics, leading to a potential increase in adverse effects and the utilization of less effective therapies. This constitutes a major public health concern and economic burden, as the use of broad-spectrum antibiotics can result in multidrug-resistant organisms and prolonged hospital stays. Therefore, it is crucial to delabel patients during childhood to avoid false labeling in adult life. Although the label of BL allergy is among the most frequent causes of allergy referral, its management remains controversial, and new diagnostic perspectives are changing the paradigm of managing BL allergies in children. Traditionally, drug provocation testing (DPT) was exclusively performed in patients who had previously obtained negative results from skin tests (STs). However, the sensitivity of STs is low, and the role of in vitro testing in the pediatric population is not well defined. Recent studies have demonstrated the safety of direct DPT without prior ST or serum tests for pediatric patients who report a low-risk reaction to BLs, which is cost-effective. However, there is still a debate on the optimal allergic workup to be performed in children with a benign immediate reaction and the management of children with severe cutaneous adverse drug reactions. In this review, we will discuss the impact of the label of BL allergy and the role of the different tools currently available to efficiently address BL allergy delabeling in children.

Potential delayed and/or missed STI diagnoses among outpatients presenting with lower genitourinary tract symptoms: a real-world database study

OBJECTIVES

Sexually transmitted infection (STI) diagnosis is complicated as these infections can present with lower genitourinary tract symptoms (LGUTS) that overlap with other disorders, i.e. urinary tract infections (UTIs). The study's objective was to determine potential missed STI diagnoses from patients presenting with LGUTS in the US between January 2010 and December 2019.

METHODS

The de-identified insurance claims data from the IBM® MarketScan® Research Databases were collected from patients (14-64 years old) who presented with LGUTS, which could be caused by an STI. A 'GAP' cohort was created, consisting of episodes with potentially delayed STI (Chlamydia trachomatis [CT]/Neisseria gonorrhoeae [NG]) treatment. The intention was to capture episodes where an STI was not initially suspected. Four subgroups were defined depending on the treatment received (fluoroquinolone; azithromycin and/or doxycycline; cephalosporins; gentamicin and azithromycin).

RESULTS

The GAP cohort consisted of 833,574 LGUTS episodes from the original cohort (23,537,812 episodes). Post-index CT/NG testing was carried out for 4.6% and 5.4% of the episodes from men and women, respectively. There were ≥2 return visits for 16.1% and 15.8% of the episodes from men and women, respectively. A substantial percentage of episodes from men (52.1%) and women (68.3%) were diagnosed with a UTI and/or acute cystitis at the index prior to receiving post-index STI treatment. Other top conditions diagnosed at index for men were dysuria (25.8% of the episodes), orchitis/epididymitis (14.3% of the episodes), and acute prostatitis (10.1% of the episodes), and for women were dysuria (24.2% of the episodes), vaginitis/vulvitis/vulvovaginitis (11.7% of the episodes), and cervicitis (3.3% of the episodes).

CONCLUSION

These findings highlight delayed STI antibiotic treatment and low rates of CT/NG testing, suggesting late STI consideration and suboptimal diagnosis. Additionally, our study illustrates the importance of accurately diagnosing and treating STIs in patients with LGUTS and associated conditions, to avoid antibiotic misuse and complications from delayed administration of appropriate treatment.

Evaluating the PEN-FAST Clinical Decision-making Tool to Enhance Penicillin Allergy Delabeling

This cohort study evaluated the diagnostic accuracy of PEN-FAST as a clinical decision-making tool to enhance penicillin allergy evaluation.

The state and consideration for skin test of β-lactam antibiotics in pediatrics

β-lactam antibiotics are the most frequently used drugs and the most common drugs that cause allergic reactions in pediatrics. The occurrence of some allergic reactions can be predicted by skin testing, especially severe adverse reactions such as anaphylactic shock. Thus, penicillin and cephalosporin skin tests are widely used to predict allergic reactions before medication in pediatrics. However, false-positive results from skin tests were more often encountered in pediatrics than in adults. In fact, many children labeled as allergic to β-lactam are not allergic to the antibiotic, leading to the use of alternative antibiotics, which are less effective and more toxic, and the increase of antibiotic resistance. There has been controversy over whether β-lactam antibiotics should be tested for skin allergies before application in children. Based on the great controversy in the implementation of β-lactam antibiotic skin tests, especially the controversial cephalosporin skin tests in pediatrics, the mechanism and reasons of anaphylaxis to β-lactam antibiotics, the significance of β-lactam antibiotic skin tests, the current state of β-lactam antibiotic skin tests at home and abroad, and the problems of domestic and international skin tests were analyzed to determine a unified standard of β-lactam antibiotic skin tests in pediatrics to prevent and decrease adverse drug reactions, avoid waste of drugs, and a large amount of manpower and material resource consumption.

New diagnostıc perspectives in the management of pediatrıc beta-lactam allergy

Since overdiagnosis of beta-lactam (BL) allergy is common in the pediatric population, delabeling is a critical part of antimicrobial stewardship. Undesirable consequences of inaccurate BL allergy labeling can be handled by incorporating traditional delabeling or newer risk-based strategies into antibiotic stewardship programs. Conventional assessment of BL allergy relies upon a stepwise algorithm including a clinical history with skin testing followed by drug provocation tests (DPTs). However, a growing number of studies highlighted the suboptimal diagnostic value of skin testing in children. Recently, there has been a paradigm shift in the practice of BL allergy assessment due to recent challenging data which emphasize the safety and accuracy of direct DPTs in children with a suspicion of non-immediate mild cutaneous reactions such as maculopapular eruption, delayed urticaria, and possibly also for benign immediate reactions such as urticaria/angioedema. Identifying low-risk BL allergy patients, in whom skin tests can be skipped and proceeding directly to DPTs could be safe, has become a hot topic in recent years. New risk stratification and predictive modeling studies that have the potential to better predict BL allergy risk status have recently been introduced into the field of drug allergy, particularly in adults. However, in contrast to adults, risk assessment studies in children are rare, and optimal risk definitions are controversial. In the coming years, promising potential methods to elucidate the predictors of BL allergy in children will require multidimensional approaches that may include predictive analytics, artificial intelligence techniques, and point-of-care clinical decision tools.