8 research outputs found

    Food anaphylaxis diagnostic marker compilation in machine learning design and validation.

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    BackgroundTraditional food allergy assessment of anaphylaxis remains limited in accuracy and accessibility. Current methods of anaphylaxis risk assessment are costly with low predictive accuracy. The Tolerance Induction Program (TIP) for anaphylactic patients undergoing TIP immunotherapy produced large-scale diagnostic data across biosimilar proteins, which was used to develop a machine learning model for patient-specific and allergen-specific anaphylaxis assessment. In explanation of construct, this work describes the algorithm design for assignment of peanut allergen score as a quantitative measure of anaphylaxis risk. Secondarily, it confirms the accuracy of the machine learning model for a specific cohort of food anaphylactic children.Methods and resultsMachine learning model design for allergen score prediction utilized 241 individual allergy assays per patient. Accumulation of data across total IgE subdivision served as the basis of data organization. Two regression based Generalized Linear Models (GLM) were utilized to position allergy assessment on a linear scale. The initial model was further tested with sequential patient data over time. A Bayesian method was then used to improve outcomes by calculating the adaptive weights for the results of the two GLMs of peanut allergy score prediction. A linear combination of both provided the final hybrid machine learning prediction algorithm. Specific analysis of peanut anaphylaxis within one endotype model is estimated to predict the severity of possible anaphylactic reaction to peanut with a recall of 95.2% on a dataset of 530 juvenile patients with various food allergies, including but not limited to peanut allergy. Receiver Operating Characteristic analysis yielded over 99% AUC (area under curve) results within peanut allergy prediction.ConclusionsMachine learning algorithm design established from comprehensive molecular allergy data produces high accuracy and recall in anaphylaxis risk assessment. Subsequent design of additional food protein anaphylaxis algorithms is needed to improve the precision and efficiency of clinical food allergy assessment and immunotherapy treatment

    Beta-blocker management of refractory hemoptysis in cystic fibrosis: a novel treatment approach

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    Background/objective: Recurrent hemoptysis is a debilitating complication of cystic fibrosis (CF) and likely results from mucosal erosions into abnormal bronchial blood vessels due to chronic respiratory infection. We hypothesize that the use of beta-blockade will decrease mean arterial pressure resulting in lower bronchial artery blood flow and, subsequently, decrease the frequency and severity of hemoptysis, rate of hospitalizations, and usage of intravenous antibiotics. Methods: Retrospective chart review was performed on 12 CF patients with recurrent hemoptysis, aged 13–40 years old, along with a follow-up telephone survey to assess the effectiveness of beta-blockade for hemoptysis, tolerance of inhaled respiratory medications, activity tolerance, and potential adverse effects. A beta-blocker, specifically atenolol, was initiated in all subjects within 24 hours after experiencing recurrent hemoptysis episodes. Results: A majority of patients (72.7%) had complete cessation of hemoptysis. There were significant decreases in the frequency of hemoptysis ( p = 0.02) and the amount of hemoptysis ( p = 0.004). The rate of hospitalizations significantly decreased from 1.33 to 0.67 ( p = 0.05) after initiation of atenolol. There was a trend toward statistical significance in the reduction of intravenous antibiotics use ( p = 0.08). No statistical difference was found when comparing the pre- and post-treatment means of forced expiratory volume in 1-second ( p = 0.59). Very minimal adverse effects were observed with only one patient reporting intermittent facial flushing. Conclusion: Beta-blockade, particularly with atenolol, appears to successfully treat, if not resolve, recurrent hemoptysis refractory to conservative therapy in CF. Beta-blocker therapy appears to maintain an effective safety profile in CF

    Immune Response to SARS-CoV-2 in an Asymptomatic Pediatric Allergic Cohort

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    Disease-specific COVID-19 pediatric comorbidity has not been studied effectively to date. Atopy and food anaphylaxis disease states require improved characterization of SARS-CoV-2 infection risk. To provide the first such characterization, we assessed serum samples of a highly atopic, food anaphylactic, asymptomatic pediatric cohort from across the US during the height of the pandemic. From our biobank, 172 pediatric patient serum samples were characterized specific to atopic, food anaphylactic, and immunologic markers in the US at the beginning of the pandemic, from 1 February to 20 April 2020. Clinical and demographic data were further analyzed in addition to sample analysis for SARS-CoV-2 IgM and IgG ELISA. SARS-CoV-2 antibody results were positive in six patients (4%). Nearly half of the pediatric patients had a history of asthma (49%). Total IgE, total IgG, and IgG1-3 were similar in those positive and negative to SARS-CoV-2. Median total IgG4 in the SARS-CoV-2 positive group was nearly three times (p-value = 0.02) that of the negative group. Atopy controller medications did not confer additional benefit. Our data suggest that food anaphylaxis and highly atopic children are not at increased risk for SARS-CoV-2 seropositivity. This specific population appears either at equal or potentially less risk than the general population. Total and specific IgG4 may be a novel predictor of SARS-CoV-2 infection risk specific to the allergic pediatric population
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