Dataset Information

Individual risk assessment tool for school-age asthma prediction in UK birth cohort.

ABSTRACT:

Background

Current published asthma predictive tools have moderate positive likelihood ratios (+LR) but high negative likelihood ratios (-LR) based on their recommended cut-offs, which limit their clinical usefulness.

Objective

To develop a simple clinically applicable asthma prediction tool within a population-based birth cohort.

Method

Children from the Manchester Asthma and Allergy Study (MAAS) attended follow-up at ages 3, 8 and 11 years. Data on preschool wheeze were extracted from primary-care records. Parents completed validated respiratory questionnaires. Children were skin prick tested (SPT). Asthma at 8/11 years (school-age) was defined as parentally reported (a) physician-diagnosed asthma and wheeze in the previous 12 months or (b) ?3 wheeze attacks in the previous 12 months. An asthma prediction tool (MAAS APT) was developed using logistic regression of characteristics at age 3 years to predict school-age asthma.

Results

Of 336 children with physician-confirmed wheeze by age 3 years, 117(35%) had school-age asthma. Logistic regression selected 5 significant risk factors which formed the basis of the MAAS APT: wheeze after exercise; wheeze causing breathlessness; cough on exertion; current eczema and SPT sensitisation(maximum score 5). A total of 281(84%) children had complete data at age 3 years and were used to test the MAAS APT. Children scoring ?3 were at high risk of having asthma at school-age (PPV > 75%; +LR 6.3, -LR 0.6), whereas children who had a score of 0 had very low risk(PPV 9.3%; LR 0.2).

Conclusion

MAAS APT is a simple asthma prediction tool which could easily be applied in clinical and research settings.

SUBMITTER: Wang R

PROVIDER: S-EPMC6446726 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Publications

Individual risk assessment tool for school-age asthma prediction in UK birth cohort.

Wang Ran R Simpson Angela A Custovic Adnan A Foden Phil P Belgrave Danielle D Murray Clare S CS

Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology 20190104 3

<h4>Background</h4>Current published asthma predictive tools have moderate positive likelihood ratios (+LR) but high negative likelihood ratios (-LR) based on their recommended cut-offs, which limit their clinical usefulness.<h4>Objective</h4>To develop a simple clinically applicable asthma prediction tool within a population-based birth cohort.<h4>Method</h4>Children from the Manchester Asthma and Allergy Study (MAAS) attended follow-up at ages 3, 8 and 11 years. Data on preschool wheeze were e ...[more]

PMID: 30447026

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Project description:Previous research suggests statistical associations between season of birth (SOB) with prevalence of neurobehavioral disorders such as schizophrenia and bipolar disorder, personality traits such as novelty and sensation seeking, and suicidal behavior. These effects are thought to be mediated by seasonal differences in perinatal photoperiod, which was recently shown to imprint circadian clock neurons and behavior in rodents. However, it is unknown whether SOB is associated with any measurable differences in the normal human adult brain, and whether individual SOB can be deduced based on phenotype. Here I show that SOB predicts morphological differences in brain structure, and that MRI scans carry spatially distributed information allowing significantly above chance prediction of an individual's SOB. Using an open source database of over 550 structural brain scans, Voxel-Based Morphometry (VBM) analysis showed a significant SOB effect in the left superior temporal gyrus (STG) in males (p=0.009, FWE whole-brain corrected), with greater gray matter volumes in fall and winter births. A cosinor analysis revealed a significant annual periodicity in the left STG gray matter volume (Zero Amplitude Test: p<5×10(-7)), with a peak towards the end of December and a nadir towards the end of June, suggesting that perinatal photoperiod accounts for this SOB effect. Whole-brain VBM maps were used as input features to multivariate machine-learning based analyses to classify SOB. Significantly greater than chance prediction was achieved in females (overall accuracy 35%, p<0.001), but not in males (overall accuracy 26%, p=0.45). Pairwise binary classification in females revealed that the highest discrimination was obtained for winter vs. summer classification (peak area under the ROC curve=0.71, p<0.0005). Discriminating regions included fusiform and middle temporal gyrus, inferior and superior parietal lobe, cerebellum, and dorsolateral and dorsomedial prefrontal cortex. Results indicate that SOB is detectable with MRI, imply that SOB exerts effects on the developing human brain that persist through adulthood, and suggest that neuroimaging may be a valuable intermediate phenotype in bridging the gap between SOB and personality and neurobehavioral disorders.

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Dataset Information

Individual risk assessment tool for school-age asthma prediction in UK birth cohort.

Background

Objective

Method

Results

Conclusion

Publications

Individual risk assessment tool for school-age asthma prediction in UK birth cohort.

Similar Datasets

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