Early detection and counselling intervention of asthma symptoms in preschool children: study design of a cluster randomised controlled trial

Randomised trials conducted using cohorts: a scoping review

INTRODUCTION

Cohort studies generate and collect longitudinal data for a variety of research purposes. Randomised controlled trials (RCTs) increasingly use cohort studies as data infrastructures to help identify and recruit trial participants and assess outcomes.

OBJECTIVE

To examine the extent, range and nature of research using cohorts for RCTs and describe the varied definitions and conceptual boundaries for RCTs using cohorts.

DESIGN

Scoping review.

DATA SOURCES

Searches were undertaken in January 2021 in MEDLINE (Ovid) and EBM Reviews-Cochrane Methodology Registry (Final issue, third Quarter 2012).

ELIGIBILITY CRITERIA

Reports published between January 2007 and December 2021 of (a) cohorts used or planned to be used, to conduct RCTs, or (b) RCTs which use cohorts to recruit participants and/or collect trial outcomes, or (c) methodological studies discussing the use of cohorts for RCTs.

DATA EXTRACTION AND SYNTHESIS

Data were extracted on the condition being studied, age group, setting, country/continent, intervention(s) and comparators planned or received, unit of randomisation, timing of randomisation, approach to informed consent, study design and terminology.

RESULTS

A total of 175 full-text articles were assessed for eligibility. We identified 61 protocols, 9 descriptions of stand-alone cohorts intended to be used for future RCTs, 39 RCTs using cohorts and 34 methodological papers.The use and scope of this approach is growing. The thematics of study are far-ranging, including population health, oncology, mental and behavioural disorders, and musculoskeletal conditions.Authors reported that this approach can lead to more efficient recruitment, more representative samples, and lessen disappointment bias and crossovers.

CONCLUSION

This review outlines the development of cohorts to conduct RCTs including the range of use and innovative changes and adaptations. Inconsistencies in the use of terminology and concepts are highlighted. Guidance now needs to be developed to support the design and reporting of RCTs conducted using cohorts.

Machine learning model for classification of predominantly allergic and non-allergic asthma among preschool children with asthma hospitalization

OBJECTIVE

Asthma is the most frequent chronic airway illness in preschool children and is difficult to diagnose due to the disease's heterogeneity. This study aimed to investigate different machine learning models and suggested the most effective one to classify two forms of asthma in preschool children (predominantly allergic asthma and non-allergic asthma) using a minimum number of features.

METHODS

After pre-processing, 127 patients (70 with non-allergic asthma and 57 with predominantly allergic asthma) were chosen for final analysis from the Frankfurt dataset, which had asthma-related information on 205 patients. The Random Forest algorithm and Chi-square were used to select the key features from a total of 63 features. Six machine learning models: random forest, extreme gradient boosting, support vector machines, adaptive boosting, extra tree classifier, and logistic regression were then trained and tested using 10-fold stratified cross-validation.

RESULTS

Among all features, age, weight, C-reactive protein, eosinophilic granulocytes, oxygen saturation, pre-medication inhaled corticosteroid + long-acting beta2-agonist (PM-ICS + LABA), PM-other (other pre-medication), H-Pulmicort/celestamine (Pulmicort/celestamine during hospitalization), and H-azithromycin (azithromycin during hospitalization) were found to be highly important. The support vector machine approach with a linear kernel was able to diffrentiate between predominantly allergic asthma and non-allergic asthma with higher accuracy (77.8%), precision (0.81), with a true positive rate of 0.73 and a true negative rate of 0.81, a F1 score of 0.81, and a ROC-AUC score of 0.79. Logistic regression was found to be the second-best classifier with an overall accuracy of 76.2%.

CONCLUSION

Predominantly allergic and non-allergic asthma can be classified using machine learning approaches based on nine features.

Supplemental data for this article is available online at at www.tandfonline.com/ijas .

Demographic and neonatal risk factors for childhood asthma in the USA

OBJECTIVE

Asthma is the most common chronic condition diagnosed among children worldwide according to the World Health Organization (WHO). This study evaluated on a longitudinal basis prospectively collected medical records for demographic and neonatal information among United States (US) children diagnosed with childhood asthma in comparison to controls.

DESIGN

The Vaccine Safety Datalink (VSD) database was examined to identify cases (n = 5907) diagnosed with International Classification of Disease, ninth revision (ICD-9) healthcare provider diagnosed childhood asthma (493.xx) and controls (n = 11,662).

PATIENTS

All cases and controls were health maintenance organization (HMO)-enrolled from birth until diagnosis or sufficient time to ensure that they were unlikely to receive a diagnosis, respectively.

MAIN OUTCOME MEASURES

Child's gestational age in weeks at birth, birth weight in grams, maternal age in years at birth, Appearance-Pulse-Grimace-Activity-Respiration (APGAR) score at 1 minute and 5 minutes following birth, gender, and race.

RESULTS

The study results revealed childhood asthma was diagnosed significantly more frequently among males than females, and significantly more frequently among minority populations (Black > Hispanic > Native American > Asian) than White populations. Cases diagnosed with childhood asthma had significantly decreased mean values for the following neonatal risk factors: gestational age, maternal age, birth weight, and APGAR scores at 1 and 5 minutes following birth in comparison to controls.

CONCLUSIONS

This study offers healthcare providers important demographic and neonatal factors significantly associated with childhood asthma, and should help aid in the early diagnosis and treatment of childhood asthma.

Association of Mycoplasma pneumoniae infection with increased risk of asthma in children

The present study was conducted to investigate the relationship between Mycoplasma pneumoniae (MP) infection and the risk of asthma among children by detecting the rate of MP immunoglobulin M (MP-IgM) and the eosinophil (EOS) count. A total of 139 asthmatic children were enrolled as the case group and assigned into three groups: Group A (aged <3 years, n=42), group B (aged 3–8 years, n=45) and group C (aged >8 years, n=52). Additionally, 115 healthy children were enrolled in the control group. Enzyme-linked immunosorbent assay was used to measure the MP-IgM-positive rate. EOS count was detected in the experimental and control groups by using a hemocytometer analyzer. A meta-analysis was performed by using the Comprehensive Meta-Analysis version 2.0 software. The positive rates of the MP-IgM and EOS count in the experimental group were significantly higher than those in control group (both P<0.001). Furthermore, the asthmatic children in group C had a higher MP-IgM-positive rate and EOS count as compared to those in groups A and B, respectively (all P<0.05). Results from groups A and B were not statistically significant (all P>0.05). The meta-analysis further confirmed that asthmatic children had a higher MP-IgM-positive rate as compared to the healthy controls (P<0.001). Age-stratified analysis revealed that the MP-IgM-positive rate in asthmatic children aged ≥8 and <8 years was significantly higher than that in the healthy controls (P=0.003 and P<0.001). Asthmatic children had a higher MP-IgM-positive rate and EOS count as compared with controls, suggesting that the MP infection may be closely associated with the risk of asthma. Additionally, the positive rate of MP-IgM may indicate an important biological marker in predicting the development of asthma.

Parental celiac disease and risk of asthma in offspring: a Danish nationwide cohort study

Objective

The incidences of celiac disease (CD) and asthma are increasing and the two conditions are associated in individuals. Risk of asthma may be passed on to the next generation through shared risk factors. We examined whether parental CD is associated with risk of asthma in offspring.

Methods

We conducted a population-based Danish nationwide cohort study, using medical databases, covering the period 1 January 1979 to 31 December 2009. For each child with a parental history of CD, we randomly sampled 100 children without this history from the children born in the same calendar year. We used Cox proportional-hazards regression to estimate incidence rate ratios for asthma, adjusting for measured covariates.

Results

We identified 1,107 children with a parental history of CD and 110,700 children without this parental history. During up to 32 years of follow-up, 6,125 children received a hospital diagnosis of asthma. The adjusted incidence rate ratio for asthma associated with a parental history of CD was 1.09 (95% confidence interval: 0.86–1.39) and was similar for maternal and paternal CD. Inclusion of asthma-medication in the definition of asthma did not substantially change the results.

Conclusion

There was no convincing evidence of an increased risk of asthma among offspring of parents with CD.

Evaluation of systematic assessment of asthma-like symptoms and tobacco smoke exposure in early childhood by well-child professionals: a randomised trial

OBJECTIVES

This study aimed to evaluate the effectiveness of systematic assessment of asthma-like symptoms and environmental tobacco smoke (ETS) exposure during regular preventive well-child visits between age 1 and 4 years by well-child professionals.

METHODS

Sixteen well-child centres in Rotterdam, the Netherlands, were randomised into 8 centres where the brief assessment form regarding asthma-like symptoms and ETS exposure was used and 8 centres that applied usual care. 3596 and 4179 children (born between April 2002 and January 2006) and their parents visited the intervention and control centres, respectively. At child's age 6 years, physician-diagnosed asthma ever, wheezing, fractional exhaled nitric oxide (FeNO), airway resistance (Rint), health-related quality of life (HRQOL) and ETS exposure at home ever were measured. Linear mixed models were applied.

RESULTS

No differences in asthma, wheezing, FeNO, Rint or HRQOL measurements between intervention and control group were found using multilevel regression in an intention-to-treat analysis (p>0.05). Children of whom the parents were interviewed by using the brief assessment form at the intervention well-child centres had a decreased risk on ETS exposure at home ever, compared to children who visited the control well-child centres, in an explorative per-protocol analysis (aOR = 0.71, 95% CI:0.59-0.87).

CONCLUSIONS

Systematic assessment and counselling of asthma-like symptoms and ETS exposure in early childhood by well-child care professionals using a brief assessment form was not effective in reducing the prevalence of physician-diagnosed asthma ever and wheezing, and did not improve FeNO, Rint or HRQOL at age 6 years. Our results hold some promise for interviewing parents and using information leaflets at well-child centres to reduce ETS exposure at home in preschool children.

TRIAL REGISTRATION

Controlled-Trials.com ISRCTN15790308.

Predicting asthma in preschool children with asthma symptoms: study rationale and design

Background

In well-child care it is difficult to determine whether preschool children with asthma symptoms actually have or will develop asthma at school age. The PIAMA (Prevention and Incidence of Asthma and Mite Allergy) Risk Score has been proposed as an instrument that predicts asthma at school age, using eight easy obtainable parameters, assessed at the time of first asthma symptoms at preschool age. The aim of this study is to present the rationale and design of a study 1) to externally validate and update the PIAMA Risk Score, 2) to develop an Asthma Risk Appraisal Tool to predict asthma at school age in (specific subgroups of) preschool children with asthma symptoms and 3) to test implementation of the Asthma Risk Appraisal Tool in well-child care.

Methods and design

The study will be performed within the framework of Generation R, a prospective multi-ethnic cohort study. In total, consent for postnatal follow-up was obtained from 7893 children, born between 2002 and 2006. At preschool age the PIAMA Risk Score will be assessed and used to predict asthma at school age. Discrimination (C-index) and calibration will be assessed for the external validation. We will study whether the predictive ability of the PIAMA Risk Score can be improved by removing or adding predictors (e.g. preterm birth). The (updated) PIAMA Risk Score will be converted to the Asthma Risk Appraisal Tool- to predict asthma at school age in preschool children with asthma symptoms. Additionally, we will conduct a pilot study to test implementation of the Asthma Risk Appraisal Tool in well-child care.

Discussion

Application of the Asthma Risk Appraisal Tool in well-child care will help to distinguish preschool children at high- and low-risk of developing asthma at school age when asthma symptoms appear.

This study will increase knowledge about the validity of the PIAMA risk score and might improve risk assessment of developing asthma at school age in (specific subgroups of) preschool children, who present with asthma symptoms at well-child care.