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Belmont AP, Stone CA, Guyer AC, Edelman EJ, Trubiano JA. A call to address penicillin allergy labels in patients with hematopoietic stem cell transplants: How to avoid rash decisions. Transpl Infect Dis 2024:e14350. [PMID: 39101669 DOI: 10.1111/tid.14350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Accepted: 07/11/2024] [Indexed: 08/06/2024]
Abstract
Among patients with hematopoietic stem cell transplants, infections, particularly multidrug-resistant infections, pose a grave threat. In this setting, penicillin allergy labels are both common and harmful. Though the majority of patients who report penicillin allergy can actually tolerate penicillin, penicillin allergy labels are associated with use of alternative antibiotics, which are often more broad spectrum, less effective, and more toxic. In turn, they are associated with more severe infections, multidrug-resistant infections, Clostridium difficile, and increased mortality. Evaluating penicillin allergy labels can immediately expand access to preferred therapeutic options, which are critical to care in patients with recent hematopoietic stem cell transplants. Point-of-care assessment and clinical decision tools now exist to aid the nonallergist in assessment of penicillin allergy. This can aid in expanding use of other beta-lactam antibiotics and assist in risk-stratifying patients to determine a testing strategy. In patients with low-risk reaction histories, direct oral challenges can be employed to efficiently delabel patients across clinical care settings. We advocate for multidisciplinary efforts to evaluate patients with penicillin allergy labels prior to transplantation.
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Affiliation(s)
- Ami P Belmont
- Section of Rheumatology, Allergy and Immunology, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Cosby A Stone
- Division of Allergy, Pulmonary, and Critical Care, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Autumn C Guyer
- Allergy and Immunology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - E Jennifer Edelman
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Social and Behavioral Sciences, Yale School of Public Health, New Haven, Connecticut, USA
| | - Jason A Trubiano
- Centre for Antibiotic Allergy and Research, Department of Infectious Diseases and Immunology, Austin Health, Heidelberg, Victoria, Australia
- Department of Infectious Diseases, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, Victoria, Australia
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Núñez R, Doña I, Cornejo-García JA. Predictive models and applicability of artificial intelligence-based approaches in drug allergy. Curr Opin Allergy Clin Immunol 2024; 24:189-194. [PMID: 38814733 DOI: 10.1097/aci.0000000000001002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2024]
Abstract
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.
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Affiliation(s)
- Rafael Núñez
- Allergy Research Group, Biomedical Research Institute of Malaga (IBIMA)-BIONAND Platform
| | - Inmaculada Doña
- Allergy Research Group, Biomedical Research Institute of Malaga (IBIMA)-BIONAND Platform
- Allergy Unit, Malaga Regional University Hospital, Malaga
- Inflammatory Diseases Network (RICORS, RD21/0002/0008, Instituto de Salud Carlos III), Málaga, Spain
| | - José Antonio Cornejo-García
- Allergy Research Group, Biomedical Research Institute of Malaga (IBIMA)-BIONAND Platform
- Allergy Unit, Malaga Regional University Hospital, Malaga
- Inflammatory Diseases Network (RICORS, RD21/0002/0008, Instituto de Salud Carlos III), Málaga, Spain
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Hanniet A, Puyraveau M, Castelain F, Pelletier F, Aubin F. Efficacy of the PEN-FAST score in a French cohort of patients with reported allergy to penicillins. FRONTIERS IN ALLERGY 2024; 5:1439698. [PMID: 39139756 PMCID: PMC11319814 DOI: 10.3389/falgy.2024.1439698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Accepted: 07/10/2024] [Indexed: 08/15/2024] Open
Abstract
Introduction Various clinical decision-making tools for penicillin allergy have been developed to guide delabeling strategies. Objective To evaluate the penicillin allergy PEN-FAST decision score in a retrospective cohort of patients, adults and children, with penicillin-reported allergy. Methods This monocentric retrospective cohort included patients with penicillin-reported allergy. All patients underwent penicillin allergy testing using skin tests and/or drug challenge. The PEN-FAST score sensitivity, specificity, negative (NPV) and positive (PPV) predictive values, and the area under the receiver operating characteristics curve (AUC) were calculated. Results Two hundred and fourteen patients were included (64 children and 150 adults). Allergy was confirmed in 52 cases (24%). A PEN-FAST score <3 points showed a poor discrimination capacity for the whole population (AUC = 0.66; 95% CI: 0.58-0.75), while it demonstrated a better discrimination capacity in the adults group (AUC = 0.71; 95% CI: 0.63-0.80). The sensitivity to identify penicillin allergy using this cutoff of less than 3 points was 0.67 (95% CI: 0.52-0.80); specificity, 0.58 (95% CI: 0.48-0.68); PPV, 0.43 (95% CI: 0.32-0.55); and NPV, 0.78 (95% CI: 0.68-0.87). Conclusions Although our data confirm a rather good discrimination value of a PEN-FAST score <3 points, its low negative predictive value (78%) did not advocate for its use as an accurate, simple and cost-effective clinical decision-making tool to effectively reduce the number of penicillin skin tests required before direct oral challenge. Further studies are required to improve the predictive capacity of the PEN-FAST score.
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Affiliation(s)
- Anatole Hanniet
- Allergology Unit, Departement of Dermatology, University Hospital, Besançon, France
| | - Marc Puyraveau
- Methodology and Statistics Unit, University Hospital, Besançon, France
| | - Florence Castelain
- Allergology Unit, Departement of Dermatology, University Hospital, Besançon, France
| | - Fabien Pelletier
- Allergology Unit, Departement of Dermatology, University Hospital, Besançon, France
| | - François Aubin
- Allergology Unit, Departement of Dermatology, University Hospital, Besançon, France
- INSERM UMR 1098 RIGHT, University of Besançon, Besançon, France
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Ghiordanescu IM, Ciocănea-Teodorescu I, Molinari N, Jelen A, Al-Ali O, Schrijvers R, Demoly P, Chiriac AM. Comparative Performance of 4 Penicillin-Allergy Prediction Strategies in a Large Cohort. THE JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY. IN PRACTICE 2024:S2213-2198(24)00743-8. [PMID: 39038539 DOI: 10.1016/j.jaip.2024.07.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 07/08/2024] [Accepted: 07/14/2024] [Indexed: 07/24/2024]
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.
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Affiliation(s)
- Ileana-Maria Ghiordanescu
- Carol Davila University of Medicine and Pharmacy, Bucharest, Romania; IDESP (The Desbrest Institute of Epidemiology and Public Health), University of Montpellier-INSERM (National Institute of Health and Medical Research), Montpellier, France.
| | - Iuliana Ciocănea-Teodorescu
- Carol Davila University of Medicine and Pharmacy, Bucharest, Romania; Victor Babeș National Institute of Pathology, Bucharest, Romania
| | - Nicolas Molinari
- IDESP (The Desbrest Institute of Epidemiology and Public Health), University of Montpellier-INSERM (National Institute of Health and Medical Research), Montpellier, France; Division of Medical Information, La Colombiere University Hospital of Montpellier, Montpelier, France
| | - Anais Jelen
- Department of Pulmonology, Division of Allergy, Arnaud de Villeneuve, University Hospital of Montpellier, Montpellier, France
| | - Omar Al-Ali
- Department of Pulmonology, Division of Allergy, Arnaud de Villeneuve, University Hospital of Montpellier, Montpellier, France; Transversal Unit of Allergology, University Hospital of Nîmes, Nîmes, France
| | - Rik Schrijvers
- Department of Microbiology, Immunology and Transplantation, Allergy and Clinical Immunology Research Group, KU, Leuven, Belgium
| | - Pascal Demoly
- IDESP (The Desbrest Institute of Epidemiology and Public Health), University of Montpellier-INSERM (National Institute of Health and Medical Research), Montpellier, France; Department of Pulmonology, Division of Allergy, Arnaud de Villeneuve, University Hospital of Montpellier, Montpellier, France
| | - Anca Mirela Chiriac
- IDESP (The Desbrest Institute of Epidemiology and Public Health), University of Montpellier-INSERM (National Institute of Health and Medical Research), Montpellier, France; Department of Pulmonology, Division of Allergy, Arnaud de Villeneuve, University Hospital of Montpellier, Montpellier, France
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Gonzalez-Estrada A, Park MA, Accarino JJO, Banerji A, Carrillo-Martin I, D'Netto ME, Garzon-Siatoya WT, Hardway HD, Joundi H, Kinate S, Plager JH, Rank MA, Rukasin CRF, Samarakoon U, Volcheck GW, Weston AD, Wolfson AR, Blumenthal KG. Predicting Penicillin Allergy: A United States Multicenter Retrospective Study. THE JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY. IN PRACTICE 2024; 12:1181-1191.e10. [PMID: 38242531 DOI: 10.1016/j.jaip.2024.01.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 12/29/2023] [Accepted: 01/07/2024] [Indexed: 01/21/2024]
Abstract
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.
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Affiliation(s)
- Alexei Gonzalez-Estrada
- Division of Pulmonary, Allergy, and Sleep Medicine, Department of Medicine, Mayo Clinic, Jacksonville, Fla
| | - Miguel A Park
- Division of Allergic Diseases, Department of Internal Medicine, Mayo Clinic, Rochester, Minn
| | - John J O Accarino
- Division of Rheumatology, Allergy, and Immunology, Department of Medicine, Massachusetts General Hospital, Boston, Mass
| | - Aleena Banerji
- Division of Rheumatology, Allergy, and Immunology, Department of Medicine, Massachusetts General Hospital, Boston, Mass; Harvard Medical School, Boston, Mass
| | - Ismael Carrillo-Martin
- Division of Pulmonary, Allergy, and Sleep Medicine, Department of Medicine, Mayo Clinic, Jacksonville, Fla
| | - Michael E D'Netto
- Division of Allergic Diseases, Department of Internal Medicine, Mayo Clinic, Rochester, Minn
| | - W Tatiana Garzon-Siatoya
- Division of Pulmonary, Allergy, and Sleep Medicine, Department of Medicine, Mayo Clinic, Jacksonville, Fla
| | - Heather D Hardway
- Digital Innovation Lab, Department of Health Sciences Research, Mayo Clinic, Jacksonville, Fla
| | - Hajara Joundi
- Division of Pulmonary, Allergy, and Sleep Medicine, Department of Medicine, Mayo Clinic, Jacksonville, Fla
| | - Susan Kinate
- Division of Allergy, Asthma, and Clinical Immunology, Department of Medicine, Mayo Clinic, Scottsdale, Ariz
| | - Jessica H Plager
- Department of Medicine, Massachusetts General Hospital, Boston, Mass
| | - Matthew A Rank
- Division of Allergy, Asthma, and Clinical Immunology, Department of Medicine, Mayo Clinic, Scottsdale, Ariz; Section of Allergy, Immunology, Division of Pulmonary, Phoenix Children's Hospital, Phoenix, Ariz
| | - Christine R F Rukasin
- Division of Allergy, Asthma, and Clinical Immunology, Department of Medicine, Mayo Clinic, Scottsdale, Ariz; Section of Allergy, Immunology, Division of Pulmonary, Phoenix Children's Hospital, Phoenix, Ariz
| | - Upeka Samarakoon
- Division of Rheumatology, Allergy, and Immunology, Department of Medicine, Massachusetts General Hospital, Boston, Mass
| | - Gerald W Volcheck
- Division of Allergic Diseases, Department of Internal Medicine, Mayo Clinic, Rochester, Minn
| | - Alexander D Weston
- Digital Innovation Lab, Department of Health Sciences Research, Mayo Clinic, Jacksonville, Fla
| | - Anna R Wolfson
- Division of Rheumatology, Allergy, and Immunology, Department of Medicine, Massachusetts General Hospital, Boston, Mass; Harvard Medical School, Boston, Mass
| | - Kimberly G Blumenthal
- Division of Rheumatology, Allergy, and Immunology, Department of Medicine, Massachusetts General Hospital, Boston, Mass; Harvard Medical School, Boston, Mass.
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Kunz Coyne AJ, Holger D, Kennedy E, Connell M, Binienda J, Giuliano C, Bailey EM. Penicillin allergy reassessment for treatment improvement: A dental office tool to support appropriate penicillin allergy labeling. J Am Dent Assoc 2024:S0002-8177(24)00055-2. [PMID: 38520419 DOI: 10.1016/j.adaj.2023.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/14/2023] [Accepted: 12/28/2023] [Indexed: 03/25/2024]
Abstract
BACKGROUND Dental appointments offer an opportunity to evaluate a documented penicillin (PCN) allergy and determine whether the patient might be a candidate for medical reassessment of their allergy. The authors gathered feedback on the Penicillin Allergy Reassessment for Treatment Improvement (PARTI) tool, designed to enhance dentist-patient communications regarding PCN allergies. METHODS From January 2022 through May 2023, the authors conducted a mixed-methods study, collecting focus group data from patients with PCN allergies and surveying health care workers (HCWs) regarding the PARTI tool. Feedback focused on reassessment procedures, patient-centered allergy information, and medical records updates. Thematic analysis was used for focus group data. RESULTS The study included 15 patients in focus groups and 50 HCW survey respondents representing diverse US regions. Patient demographic characteristics included varied races, the mean age was 52 years, and most of the patients were female (53.3%). Most patients had health care interactions within the preceding year, at which 86.6% of patients were asked about drug allergies. HCW respondents primarily consisted of pharmacists (30%) and dentists, dental hygienists, and dental assistants (28%). Feedback on the PARTI tool was constructive, with both patients and HCWs recognizing its potential benefits and providing insights for improvement. Many HCWs (68%) highlighted the importance of step 3 of the PARTI tool, that is, the section on PCN allergy testing. Feedback from participants was incorporated into the final PARTI tool. CONCLUSIONS Patient and HCW feedback on the PARTI tool was used to finalize a tool for the dental office to provide to patients who are candidates for PCN allergy reassessment. The feedback will also be used to inform an upcoming pilot study in US dental offices, focused on the process for PCN allergy reassessment and health record documentation. PRACTICAL IMPLICATIONS Deploying the PARTI tool in dental offices is pivotal, as mislabeling patients with PCN allergies could have severe consequences, such as hindering the prescription of lifesaving antibiotics for conditions like endocarditis, in the future. This implementation not only enhances communication between dentists and patients, but it is also crucial for ensuring improved patient safety and maintaining accurate medical records among health care settings.
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Blanchard F, Guegan S, Chouchana L, Canouï E, Charlier C. β-Lactam allergy management: A practical approach for clinical decision-making. Anaesth Crit Care Pain Med 2024; 43:101371. [PMID: 38467240 DOI: 10.1016/j.accpm.2024.101371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 02/21/2024] [Accepted: 03/01/2024] [Indexed: 03/13/2024]
Affiliation(s)
- Florian Blanchard
- Antimicrobial Stewardship Team GH Paris Centre, Cochin Port Royal Hospital, APHP, Paris, France; Sorbonne University, GRC 29, AP-HP, DMU DREAM, Department of Anesthesiology and Critical Care, Pitié-Salpêtrière Hospital, Paris, France
| | - Sarah Guegan
- Department of Dermatology, Cochin Port Royal Hospital, APHP, Paris, France; Université Paris Cité, Paris, France
| | - Laurent Chouchana
- Regional Center of Pharmacovigilance, Department of Pharmacology, Cochin Port Royal Hospital, APHP, Paris, France
| | - Etienne Canouï
- Antimicrobial Stewardship Team GH Paris Centre, Cochin Port Royal Hospital, APHP, Paris, France
| | - Caroline Charlier
- Antimicrobial Stewardship Team GH Paris Centre, Cochin Port Royal Hospital, APHP, Paris, France; Université Paris Cité, Paris, France; NRC Listeria, WHO-collaborative center, Biology of Infection Unit, Inserm 1117, Institut Pasteur, Paris, France.
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Wolfson AR, Blumenthal KG, Guyer A, Ramsey A, Dowden AM. Penicillin Allergy Evaluation Should Be Performed Proactively in Patients With a Penicillin Allergy Label. THE JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY. IN PRACTICE 2023; 11:3626-3628. [PMID: 37838278 DOI: 10.1016/j.jaip.2023.09.045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 09/19/2023] [Indexed: 10/16/2023]
Abstract
Penicillin allergy is the most common drug allergy in the US population. A penicillin allergy label is associated with poor patient outcomes including increased hospital length of stay, increased perioperative infections, and overall increased mortality. A penicillin allergy evaluation accurately identifies approximately 9 of 10 patients who, despite reporting a history of penicillin allergy, can receive penicillins safely. Penicillin allergy evaluations should be offered proactively to healthy patients during routine visits, including children and pregnant women, in advance of antibiotic need.
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