Citations

Abstract Models that estimate the probability of an adverse clinical outcome require an operational cutoff to translate continuous estimated probabilities into discrete labels that can trigger clinical action. Although statistical methods identify ‘optimal’ cut-offs, threshold selection ultimately reflects value judgments regarding harm tolerance, resource allocation, and workflow feasibility. We describe a governance-informed approach to selecting a deployment threshold for an asthma exacerbation (AE) prediction model integrated into clinical workflows. Using prevalence-adjusted performance metrics and real-world provider capacity modeling, we evaluated multiple candidate thresholds and quantified downstream workload and missed-event trade-offs. We demonstrate that statistically optimal thresholds may produce operationally infeasible alert volumes or unacceptable miss rates. We propose a structured threshold governance framework integrating statistical performance, clinical utility, stakeholder input, and human oversight safeguards. This case illustrates how threshold decisions should be treated as organizational governance processes rather than purely technical optimizations.

Background Patients with self-reported antibiotic allergies have a higher cost of care, more frequent infections with resistant bacteria and worse health outcomes than patients without antibiotic allergies. Ultimately, less than 5% of patients who report a penicillin allergy have a clinically significant immune-mediated hypersensitivity reaction when tested. As 10%–30% of the population of pregnant patients are colonised for group B Streptococcus (GBS) and guidelines recommend penicillin as the treatment of choice for GBS, current recommendations support penicillin allergy testing in pregnant patients who report an allergy. Methods and intervention In this quality improvement project, nursing staff used an algorithm outlining inclusion and exclusion criteria to determine which patients were eligible to have penicillin allergy testing completed. Penicillin allergy testing consisted of a skin test using benzylpenicilloyl polylysine (Pre-Pen), penicillin G potassium, amoxicillin and alkaline hydrolysis mix (penicilloate) as a prick skin test, followed by intradermal skin test and finally an oral challenge with either amoxicillin or penicillin. Patient outcomes were analysed to evaluate the impact of the intervention. Results Of the 1266 patients receiving prenatal care during the intervention, 236 (19%) reported a history of penicillin allergy, and 212 if these were eligible for testing. 150 of the eligible patients were offered penicillin allergy testing. 101 patients (67%) completed testing and 49 (33%) declined testing. Seven patients (7%) had positive penicillin allergy testing, while 94 patients (93%) had negative penicillin allergy testing and were immediately de-labelled as penicillin allergic. Seventeen of the de-labelled patients subsequently tested positive for GBS colonisation, and all received intrapartum penicillin without adverse events. Conclusions Pursuing penicillin allergy testing for pregnant patients with reported penicillin allergy is a safe and feasible approach, allowing for allergy de-labelling and safe, guideline-driven antimicrobial therapy during subsequent labour and delivery hospitalisations. Cost-effectiveness of the allergy testing and impact on later episodes of care should be further investigated.

Background: Autoimmune progesterone dermatitis (APD) is a rare hypersensitivity disorder characterized by recurring dermatologic manifestations during the luteal phase of the menstrual cycle in women. Well-defined clinical and diagnostic criteria, outcomes measurements, and standard treatments are lacking. Methods: We performed a single-institution retrospective review of adult patients (older than 20 years at the time of diagnosis) with APD. Results: Fourteen patients were included with mean age of clinical onset of 34.3 ± 7.7 (range 24-54) years. There was a delay of 3.9 ± 5.5 (range 0.4-20) years between the onset of disease symptoms and diagnosis. The onset of APD was after exposure to exogenous progesterone in 9 of 14 patients. Progesterone skin test was performed in 9 patients and 6 were positive. Patients frequently presented with urticaria (9/14, 64.3%) and dermatitis (4/14, 28.6%). Continuous combined oral contraceptives (4/14, 28.6%), gonadotropin-releasing hormone agonist (3/14, 21.4%), and hysterectomy with bilateral salpingo-oophorectomy (2/14, 14.3%) were the most common attempted treatments with reliable outcomes. Conclusions: APD is a rare disorder which lacks universal diagnostic measures and criteria, contributing to a significant delay in diagnosis. Large-scale multicenter studies are needed to develop accurate tests, establish diagnostic criteria, and define treatment outcomes.

Background: Penicillin is the most common reported drug allergy. Previous literature suggests that there is increased prevalence of penicillin drug allergy in female patients in the outpatient setting. However, this is poorly described in the inpatient setting. Objective: This study was performed to determine whether female sex is an independent risk factor for penicillin allergy in the inpatient setting. Methods: A retrospective review of electronic medical records (January 1, 2001-December 31, 2017) was performed for patients with a history of penicillin allergy who underwent penicillin skin testing (PST). Each chart review included the age at initial skin testing, sex, medications, and medical co-morbidities. The study was approved by the institutional review board. Results: 30,883 patients underwent PST with 29,354 and 1,529 occurring in the outpatient and inpatient setting respectively. 170 patients tested positive with a ≥ 5x5 wheal. Of the 170 positive patients, 122 were female (72%) and 48 were male (28%). 15 patients tested positive in the inpatient setting. Of the 1506 adult patients tested in the inpatient setting, 809 were female and 697 were male. 12 females (92.3%) and 1 one male (7.7%) tested positive with a ≥ 5x5 wheal (OR-10.5; 95% CI-1.4-80.8; p-value=0.02). 23 pediatric patients were tested in the inpatient setting. Two pediatric male patients were positive and no female pediatric patients tested positive (OR-1.7; 95% CI-0.5-5.9; p-value=0.5). Conclusion: In the inpatient setting, adult females are 10 times more likely to have a positive PST compared to males. Female sex may be a potential risk factor for objective penicillin drug allergy in the inpatient setting.

Background Inhaled corticosteroids (ICSs) are important in asthma management, but there are concerns regarding associated risk of pneumonia. While studies in asthmatic adults have shown inconsistent results, this risk in asthmatic children is unclear. Objective Our aim was to determine the association of ICS use with pneumonia risk in asthmatic children. Methods A nested case-control study was performed in the Mayo Clinic Birth Cohort. Asthmatic children (<18 years) with a physician diagnosis of asthma were identified from electronic medical records of children born at Mayo Clinic from 1997 to 2016 and followed until 31 December 2017. Pneumonia cases defined by Infectious Disease Society of America were 1:1 matched with controls without pneumonia by age, sex and asthma index date. Exposure was defined as ICS prescription at least 90 days prior to pneumonia. Associations of ICS use, type and dose (low, medium and high) with pneumonia risk were analysed using conditional logistic regression. Results Of the 2108 asthmatic children eligible for the study (70% mild intermittent and 30% persistent asthma), 312 children developed pneumonia during the study period. ICS use overall was not associated with risk of pneumonia (adjusted OR: 0.94, 95% CI: 0.62 to 1.41). Poorly controlled asthma was significantly associated with the risk of pneumonia (OR: 2.03, 95% CI: 1.35 to 3.05; p<0.001). No ICS type or dose was associated with risk of pneumonia. Conclusion ICS use in asthmatic children was not associated with risk of pneumonia but poorly controlled asthma was. Future asthma studies may need to include pneumonia as a potential outcome of asthma management.

Abstract Background More than 90% of reported penicillin allergies are found inaccurate when formally assessed. These allergy labels lead to decreased utilization of first-line beta-lactam antibiotics, and adverse clinical outcomes. The objective of this study was to develop a multi-disciplinary approach to decrease inaccurate labeling among hospitalized patients with documented penicillin allergy. Methods A team of clinicians, pharmacists, and nurses utilized the DMAIC quality strategy to improve accuracy of penicillin allergy labeling. Allergic reactions were stratified to develop a penicillin allergy de-labeling algorithm (Figure 1). Admission to the intensive care unit (ICU) for anaphylaxis was defined as a balancing measure. We reviewed baseline data from patients with a documented penicillin allergy admitted to a single inpatient floor at Mayo Clinic, Rochester between June and October 2019. A cause and effect diagram was used to conduct a root cause analysis. The intervention was then applied to patients who reported penicillin allergy admitted to the same floor from November 2019 to January 2020. Study data were collected and basic descriptive statistics generated. Figure 1: Penicillin allergy delabeling algorithm Results 96 patients were included in the control group with mean age of 71 years (range 65–84 years) and 55% females. Breakdown of documented allergic reactions are represented in Figure 2. 58 (60%) received an antibiotic for a median duration of 1.5 days (IQR: 0 – 6). Of these, 7(12%) received penicillin-class antibiotics, and 41 (70.6%) received non-beta-lactam antibiotics. 2 (2%) of these patients were de-labeled without any penicillin skin tests. Detailed metrics of each PDSA cycle are shown in Table 1. During PDSA cycle 2, inaccurate penicillin documentation was removed in 9/19 (47.4%) of hospitalized patients. There were no ICU admissions for anaphylaxis. Figure 2: Graphic representation of proportion of type of documented allergic reactions to penicillin Table 1: Metrics and outcomes at baseline and during successive PDSA cycles Conclusion Various factors contribute to penicillin allergy mislabeling. Our comprehensive algorithm addresses nuances of penicillin allergic reactions and increased accurate penicillin allergy labeling in 47.4% of the cases. Beta-lactam use also increased to 37% through our pilot project while maintaining patient safety. A multi-disciplinary and patient-centered approach aligned with institutional workflows is necessary to improve patient outcomes. Disclosures All Authors: No reported disclosures

Introduction The lack of effective, consistent, reproducible and efficient asthma ascertainment methods results in inconsistent asthma cohorts and study results for clinical trials or other studies. We aimed to assess whether application of expert artificial intelligence (AI)-based natural language processing (NLP) algorithms for two existing asthma criteria to electronic health records of a paediatric population systematically identifies childhood asthma and its subgroups with distinctive characteristics. Methods Using the 1997–2007 Olmsted County Birth Cohort, we applied validated NLP algorithms for Predetermined Asthma Criteria (NLP-PAC) as well as Asthma Predictive Index (NLP-API). We categorised subjects into four groups (both criteria positive (NLP-PAC+/NLP-API+); PAC positive only (NLP-PAC+ only); API positive only (NLP-API+ only); and both criteria negative (NLP-PAC−/NLP-API−)) and characterised them. Results were replicated in unsupervised cluster analysis for asthmatics and a random sample of 300 children using laboratory and pulmonary function tests (PFTs). Results Of the 8196 subjects (51% male, 80% white), we identified 1614 (20%), NLP-PAC+/NLP-API+; 954 (12%), NLP-PAC+ only; 105 (1%), NLP-API+ only; and 5523 (67%), NLP-PAC−/NLP-API−. Asthmatic children classified as NLP-PAC+/NLP-API+ showed earlier onset asthma, more Th2-high profile, poorer lung function, higher asthma exacerbation and higher risk of asthma-associated comorbidities compared with other groups. These results were consistent with those based on unsupervised cluster analysis and lab and PFT data of a random sample of study subjects. Conclusion Expert AI-based NLP algorithms for two asthma criteria systematically identify childhood asthma with distinctive characteristics. This approach may improve precision, reproducibility, consistency and efficiency of large-scale clinical studies for asthma and enable population management.