Predictors of Late Asthmatic Response

Avila, Pedro C.; Segal, Mark R.; Wong, Hofer; Boushey, Homer A.; Fahy, John V.

doi:10.1164/ajrccm.161.6.9909056

Cited by 23 publications

(2 citation statements)

References 16 publications

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“…Factors influencing the LAR are not as well characterized as the factors influencing an EAR. Higher non‐specific bronchial hyperresponsiveness seems to be associated with occurrence of LAR, 14,15,18 especially for cat exposure, 17 but this link was not always confirmed 9,13,19,20 . Higher bronchial allergen sensitivity (i.e., a lower allergen PD20) is sometimes, but not always, 10,15,20 associated with a more frequent LAR 13,17 .…”

Section: Introductionmentioning

confidence: 96%

Rhinoconjunctivitis severity induced by cat exposure influences early and late asthmatic responses: Evidence from an environmental exposure chamber

Piotin,

Godet,

Domis

et al. 2024

Clin Experimental Allergy

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BackgroundThe impact of allergic rhinoconjunctivitis on the early (EAR) and late asthmatic response (LAR) has yet to be assessed during optimal allergen exposure conditions.ObjectiveWe aimed to assess predictive factors of the EAR and LAR and to evaluate the relation between rhinitis, conjunctivitis and asthma induced by cat allergen exposure in an environmental exposure chamber (EEC).MethodsData from two cohort studies involving asthmatic patients with cat allergy who performed a cat allergen exposure challenge in ALYATEC EEC were analysed. Spirometry, visual analogue scale (VAS) for asthma, VAS for rhinitis, Total Nasal Symptoms Score, Total Ocular Symptoms Score (TOSS), Rhinoconjunctivitis Total Symptoms Score and Abelson score were used to assess asthma, rhinitis and conjunctivitis during and after exposure.ResultsAn EAR occurred in 65.1% of patients, 32.1% of whom had a LAR. The diameter of the prick test to cat allergens and non‐specific bronchial hypersensitivity level were independent risk factors for EAR (p < .05). No independent risk factors for LAR were identified. Rhinoconjunctivitis severity during exposure correlated with the asthma VAS during EAR and LAR (p < .05). Allergen exposure time needed to trigger an EAR correlated with the Abelson score during exposure (p < .05). The asthma VAS and TOSS during exposure correlated with faster LAR occurrence (p < .05).ConclusionPrick test size and non‐specific bronchial hypersensitivity level were confirmed as independent predictive factors of EAR during allergen exposure in an EEC. This study demonstrated the relation between the severity of rhinitis, conjunctivitis and asthma induced by allergen exposure for both EAR and LAR.

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Section: Introductionmentioning

confidence: 96%

Rhinoconjunctivitis severity induced by cat exposure influences early and late asthmatic responses: Evidence from an environmental exposure chamber

Piotin,

Godet,

Domis

et al. 2024

Clin Experimental Allergy

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“… 2 Despite having a reproducible type‐2 immune response to inhaled allergen in both SR and DR subgroups, 1 no baseline biomarkers related to the type‐2 immune response can presently predict the airway response in an individual (e.g. 3 ). In contrast, some studies have shown differences in neutrophil‐related biomarkers 4 , 5 and less neutrophils in the blood between DR and SR. 6 However, the neutrophil‐related differences within the airway compartments and how they may contribute to the dual allergic response have not been investigated.…”

Section: Introductionmentioning

confidence: 99%

Neutrophil phenotypes in bronchial airways differentiate single from dual responding allergic asthmatics

Burg

Stenberg

Ekstedt

et al. 2022

Clin Experimental Allergy

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Introduction: Allergic asthmatics with both an early (EAR) and a late allergic reaction (LAR) following allergen exposure are termed 'dual responders' (DR), while 'single responders' (SR) only have an EAR. Mechanisms that differentiate DR from SR are largely unknown, particularly regarding the role and phenotypes of neutrophils.Therefore, we aimed to study neutrophils in DR and SR asthmatics.Methods: Thirty-four allergic asthmatics underwent an inhaled allergen challenge, samples were collected before and up to 24 h post-challenge. Cell differentials were counted from bronchial lavage, alveolar lavage and blood; and tissue neutrophils were quantified in immune-stained bronchial biopsies. Lavage neutrophil nuclei lobe segmentation was used to classify active (1-4 lobes) from suppressive neutrophils (≥5 lobes). Levels of transmigration markers: soluble (s)CD62L and interleukin-1Ra, and activity markers: neutrophil elastase (NE), DNA-histone complex and dsDNA were measured in lavage fluid and plasma. Results:Compared with SR at baseline, DR had more neutrophils in their bronchial airways at baseline, both in the lavage (p = .0031) and biopsies (p = .026) and elevated bronchial neutrophils correlated with less antitransmigratory IL-1Ra levels (r = −0.64).DR airways had less suppressive neutrophils and more 3-lobed (active) neutrophils (p = .029) that correlated with more bronchial lavage histone (p = .020) and more plasma NE (p = .0016). Post-challenge, DR released neutrophil extracellular trap factors in the blood earlier and had less pro-transmigratory sCD62L during the late phase (p = .0076) than in SR. Conclusion:DR have a more active airway neutrophil phenotype at baseline and a distinct neutrophil response to allergen challenge that may contribute to the development of an LAR. Therefore, neutrophil activity should be considered during targeted diagnosis and bio-therapeutic development for DR.

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A comparison of regression trees, logistic regression, generalized additive models, and multivariate adaptive regression splines for predicting AMI mortality

Austin

2006

Statistics in Medicine

133

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Clinicians and health service researchers are frequently interested in predicting patient-specific probabilities of adverse events (e.g. death, disease recurrence, post-operative complications, hospital readmission). There is an increasing interest in the use of classification and regression trees (CART) for predicting outcomes in clinical studies. We compared the predictive accuracy of logistic regression with that of regression trees for predicting mortality after hospitalization with an acute myocardial infarction (AMI). We also examined the predictive ability of two other types of data-driven models: generalized additive models (GAMs) and multivariate adaptive regression splines (MARS). We used data on 9484 patients admitted to hospital with an AMI in Ontario. We used repeated split-sample validation: the data were randomly divided into derivation and validation samples. Predictive models were estimated using the derivation sample and the predictive accuracy of the resultant model was assessed using the area under the receiver operating characteristic (ROC) curve in the validation sample. This process was repeated 1000 times-the initial data set was randomly divided into derivation and validation samples 1000 times, and the predictive accuracy of each method was assessed each time. The mean ROC curve area for the regression tree models in the 1000 derivation samples was 0.762, while the mean ROC curve area of a simple logistic regression model was 0.845. The mean ROC curve areas for the other methods ranged from a low of 0.831 to a high of 0.851. Our study shows that regression trees do not perform as well as logistic regression for predicting mortality following AMI. However, the logistic regression model had performance comparable to that of more flexible, data-driven models such as GAMs and MARS.

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Predictors of Late Asthmatic Response

Cited by 23 publications

References 16 publications

Rhinoconjunctivitis severity induced by cat exposure influences early and late asthmatic responses: Evidence from an environmental exposure chamber

Rhinoconjunctivitis severity induced by cat exposure influences early and late asthmatic responses: Evidence from an environmental exposure chamber

Neutrophil phenotypes in bronchial airways differentiate single from dual responding allergic asthmatics

A comparison of regression trees, logistic regression, generalized additive models, and multivariate adaptive regression splines for predicting AMI mortality

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