Park, Miguel A.

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.

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.

Abstract Rationale Difficulty of asthma ascertainment and its associated methodologic heterogeneity have created significant barriers to asthma care and research. Objectives We evaluated the validity of an existing natural language processing (NLP) algorithm for asthma criteria to enable an automated chart review using electronic medical records (EMRs). Methods The study was designed as a retrospective birth cohort study using a random sample of 500 subjects from the 1997–2007 Mayo Birth Cohort who were born at Mayo Clinic and enrolled in primary pediatric care at Mayo Clinic Rochester. Performance of NLP-based asthma ascertainment using predetermined asthma criteria was assessed by determining both criterion validity (chart review of EMRs by abstractor as a gold standard) and construct validity (association with known risk factors for asthma, such as allergic rhinitis). Measurements and Main Results After excluding three subjects whose respiratory symptoms could be attributed to other conditions (e.g., tracheomalacia), among the remaining eligible 497 subjects, 51% were male, 77% white persons, and the median age at last follow-up date was 11.5 years. The asthma prevalence was 31% in the study cohort. Sensitivity, specificity, positive predictive value, and negative predictive value for NLP algorithm in predicting asthma status were 97%, 95%, 90%, and 98%, respectively. The risk factors for asthma (e.g., allergic rhinitis) that were identified either by NLP or the abstractor were the same. Conclusions Asthma ascertainment through NLP should be considered in the era of EMRs because it can enable large-scale clinical studies in a more time-efficient manner and improve the recognition and care of childhood asthma in practice.