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.

Fatal ampicillin-sulbactam anaphylaxis in a 34-year-old male.

A 34-year-old man receiving his first dose of ampicillin-sulbactam for osteomyelitis in a hospital setting experienced fatal drug-induced anaphylaxis.

Drug allergy in older adults: A study from the United States Drug Allergy Registry.

BACKGROUND: Older adults have an increased risk of adverse drug reactions and negative effects associated with alternative antibiotic use. Although the number of antibiotic allergies reported increases with age, the characteristics and outcomes of older adults receiving drug allergy assessment are unknown. OBJECTIVE: To assess the characteristics and outcomes of drug allergy evaluations in older adults. METHODS: We considered patients aged above or equal to 65 years enrolled in the United States Drug Allergy Registry (USDAR), a US multisite prospective cohort (January 16, 2019 to February 28, 2022). Data were summarized using descriptive statistics. RESULTS: Of 1678 USDAR participants from 5 sites, 406 older adults aged above or equal to 65 years (37% 65-69 years, 37% 70-74 years, 16% 75-79 years, and 10% ≥80 years) received 501 drug allergy assessments. USDAR older adults were primarily of female sex (69%), White (94%), and non-Hispanic (98%). Most USDAR older adults reported less than or equal to 1 infections per year (64%) and rated their general health as good, very good, or excellent (80%). Of 296 (59%) penicillin allergy assessments in USDAR older adults, 286 (97%) were disproved. Other drug allergy assessments included sulfonamide (n = 41, 88% disproved) and cephalosporin (n = 20, 95% disproved) antibiotics. All 41 drug allergy labels in USDAR participants aged above or equal to 80 years and all 80 penicillin allergy labels in USDAR men aged above or equal to 65 years were disproved. CONCLUSION: Older adults represented a quarter of USDAR participants but were neither racially nor ethnically diverse and were generally healthy without considerable antibiotic need. Most older adults presented for antibiotic allergy assessments, the vast majority of which were disproved. Drug allergy assessments may be underutilized in the older adults who are most vulnerable to the harms of unconfirmed antibiotic allergy labels.

Potential role of mitochondrial superoxide decreasing ferrochelatase and heme in coronary artery soluble guanylate cyclase depletion by angiotensin II.

Oxidation of the soluble guanylate cyclase (sGC) heme promotes loss of regulation by nitric oxide (NO) and depletion of sGC. We hypothesized that angiotensin II (ANG II) stimulation of mitochondrial superoxide by its type 1 receptor could function as a potential inhibitor of heme biosynthesis by ferrochelatase, and this could decrease vascular responsiveness to NO by depleting sGC. These processes were investigated in a 24-h organoid culture model of bovine coronary arteries (BCA) with 0.1 µM ANG II. Treatment of BCA with ANG II increased mitochondrial superoxide, depleted mitochondrial superoxide dismutase (SOD2), ferrochelatase, and cytochrome oxidase subunit 4, and sGC, associated with impairment of relaxation to NO. These processes were attenuated by organoid culture with 8-bromo-cGMP and/or δ-aminolevulinic acid (a stimulator of sGC by protoporphyrin IX generation and heme biosynthesis). Organoid culture with Mito-TEMPOL, a scavenger of mitochondrial matrix superoxide, also attenuated ANG II-elicited ferrochelatase depletion and loss of relaxation to NO, whereas organoid culture with Tempol, an extramitochondrial scavenger of superoxide, attenuated the loss of relaxation to NO by ANG II, but not ferrochelatase depletion, suggesting cytosolic superoxide could be an initiating factor in the loss of sGC regulation by NO. The depletion of cytochrome oxidase subunit 4 and sGC (but not catalase) suggests that sGC expression may be very sensitive to depletion of heme caused by ANG II disrupting ferrochelatase activity by increasing mitochondrial superoxide. In addition, cGMP-dependent activation of protein kinase G appears to attenuate these ANG II-stimulated processes through both preventing SOD2 depletion and increases in mitochondrial and extramitochondrial superoxide.