Priorities for child health research across the UK and Ireland

BackgroundThe General and Adolescent Paediatric Research Network in the UK and Ireland (GAPRUKI) was established in 2016. The aims of GAPRUKI are to unite general paediatricians around the UK and Ireland, to develop research ideas and protocols, and facilitate delivery of multicentre research.ObjectivesTo undertake a research prioritisation exercise among UK and Ireland general paediatricians.MethodsThis was a four-phase study using a modified Delphi survey. The first phase asked for suggested research priorities. The second phase developed ideas and ranked them in priority. In the third phase, priorities were refined; and the final stage used the Hanlon Prioritisation Process to agree on the highest priorities.ResultsIn phase one, there were 250 questions submitted by 61 GAPRUKI members (66% of the whole membership). For phase two, 92 priorities were scored by 62 members and the mean Likert scale (1–7) scores ranged from 3.13 to 5.77. In a face-to-face meeting (phases three and four), 17 research questions were identified and ultimately 14 priorities were identified and ranked. The four priorities with the highest ranking focused on these three respiratory conditions: asthma, bronchiolitis and acute wheeze. Other priorities were in the diagnosis or management of constipation, urinary tract infection, fever, gastro-oesophageal reflux and also new models of care for scheduled general paediatric clinics.ConclusionResearch priorities for child health in the UK and Ireland have been identified using a robust methodology. The next steps are for studies to be designed and funded to address these priorities.

Patients with idiopathic pulmonary fibrosis have poor clinical outcomes with COVID-19 disease: a propensity matched multicentre research network analysis

IntroductionOutcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in patients with pre-existing idiopathic pulmonary fibrosis (IPF) remain understudied, and it is unknown if IPF is an independent predictor of worse disease course. Herein, we report the clinical outcomes in a large cohort of 251 patients with COVID-19 in the setting of known IPF. Outcomes were compared with a propensity matched cohort of patients with COVID-19 without IPF.MethodsAnalysis of a federated multicentre research network TriNetX was performed including patients more than 16 years of age diagnosed with SARS-CoV-2 infection. Outcomes in patients diagnosed as positive for SARS-CoV-2 infection with concurrent IPF were compared with a propensity matched cohort of patients without IPF.ResultsA total of 311 060 patients with SARS-CoV-2 infection on the research network were identified, 251 patients (0.08%) carried a diagnosis of IPF. Mean age of patients with IPF was 68.30±12.20 years, with male predominance (n=143, 56.97%). Comorbidities including chronic lower respiratory diseases, diabetes mellitus, ischaemic heart disease and chronic kidney disease were more common in patients with IPF when compared with the non-IPF cohort. After propensity matching, higher rates of composite primary outcome (death or mechanical ventilation) at 30 and 60 days, as well as need for hospitalisation, critical care, and acute kidney injury were observed in the IPF cohort.ConclusionPoor outcomes of COVID-19 disease were observed in patients with IPF after robust matching of confounders. Our data confirm that patients with IPF constitute a high-risk cohort for poor outcomes related to COVID-19 disease.

Challenges of developing, conducting, analysing and reporting a COVID-19 study as the COVID-19 pandemic unfolds: an online co-autoethnographic study

ObjectivesTo capture the complexities and unique experience of a newly formed multidisciplinary and multicentre research team developing and deploying a COVID-19 study and to identify lessons learnt.DesignCo-autoethnographic study.SettingStaff at two UK academic institutions, a national charity and two major UK hospitals.ParticipantsResearchers, clinicians, academics, statisticians and analysts, patient and public involvement representatives and national charity.MethodsThe sampling frame was any content discussed or shared between research team members (emails, meeting minutes, etc), standard observational dimensions and reflective interviews with team members. Data were thematically analysed.ResultsData from 34 meetings and >50 emails between 17 March and 5 August 2020 were analysed. The analysis yielded seven themes with ‘Managing our stress’ as an overarching theme.ConclusionsMutual respect, flexibility and genuine belief that team members are doing the best they can under the circumstances are essential for completing a time-consuming study, requiring a rapid response during a pandemic. Acknowledging and managing stress and a shared purpose can moderate many barriers, such as the lack of face-to-face interactions, leading to effective team working.

Data quality methods through remote source data verification auditing: results from the Congenital Cardiac Research Collaborative

Background: Multicentre research databases can provide insights into healthcare processes to improve outcomes and make practice recommendations for novel approaches. Effective audits can establish a framework for reporting research efforts, ensuring accurate reporting, and spearheading quality improvement. Although a variety of data auditing models and standards exist, barriers to effective auditing including costs, regulatory requirements, travel, and design complexity must be considered. Materials and methods: The Congenital Cardiac Research Collaborative conducted a virtual data training initiative and remote source data verification audit on a retrospective multicentre dataset. CCRC investigators across nine institutions were trained to extract and enter data into a robust dataset on patients with tetralogy of Fallot who required neonatal intervention. Centres provided de-identified source files for a randomised 10% patient sample audit. Key auditing variables, discrepancy types, and severity levels were analysed across two study groups, primary repair and staged repair. Results: Of the total 572 study patients, data from 58 patients (31 staged repairs and 27 primary repairs) were source data verified. Amongst the 1790 variables audited, 45 discrepancies were discovered, resulting in an overall accuracy rate of 97.5%. High accuracy rates were consistent across all CCRC institutions ranging from 94.6% to 99.4% and were reported for both minor (1.5%) and major discrepancies type classifications (1.1%). Conclusion: Findings indicate that implementing a virtual multicentre training initiative and remote source data verification audit can identify data quality concerns and produce a reliable, high-quality dataset. Remote auditing capacity is especially important during the current COVID-19 pandemic.

Development of a convolutional neural network to differentiate among the etiology of similar appearing pathological B lines on lung ultrasound: a deep learning study

ObjectivesLung ultrasound (LUS) is a portable, low-cost respiratory imaging tool but is challenged by user dependence and lack of diagnostic specificity. It is unknown whether the advantages of LUS implementation could be paired with deep learning (DL) techniques to match or exceed human-level, diagnostic specificity among similar appearing, pathological LUS images.DesignA convolutional neural network (CNN) was trained on LUS images with B lines of different aetiologies. CNN diagnostic performance, as validated using a 10% data holdback set, was compared with surveyed LUS-competent physicians.SettingTwo tertiary Canadian hospitals.Participants612 LUS videos (121 381 frames) of B lines from 243 distinct patients with either (1) COVID-19 (COVID), non-COVID acute respiratory distress syndrome (NCOVID) or (3) hydrostatic pulmonary edema (HPE).ResultsThe trained CNN performance on the independent dataset showed an ability to discriminate between COVID (area under the receiver operating characteristic curve (AUC) 1.0), NCOVID (AUC 0.934) and HPE (AUC 1.0) pathologies. This was significantly better than physician ability (AUCs of 0.697, 0.704, 0.967 for the COVID, NCOVID and HPE classes, respectively), p<0.01.ConclusionsA DL model can distinguish similar appearing LUS pathology, including COVID-19, that cannot be distinguished by humans. The performance gap between humans and the model suggests that subvisible biomarkers within ultrasound images could exist and multicentre research is merited.

Consensus exercise identifying priorities for research in the field of general surgery of childhood in the UK

Background The evidence base underlying clinical practice in children’s general surgery is poor and high-quality collaborative clinical research is required to address current treatment uncertainties. The aim of this study was, through a consensus process, to identify research priorities for clinical research in this field amongst surgeons who treat children. Methods Questions were invited in a scoping survey amongst general surgeons and specialist paediatric surgeons. These were refined by the study team and subsequently prioritized in a two-stage modified Delphi process. Results In the scoping survey, a total of 226 questions covering a broad scope of children’s elective and emergency general surgery were submitted by 76 different clinicians. These were refined to 71 research questions for prioritization. A total of 168 clinicians took part in stage one of the prioritization process, and 157 in stage two. A ‘top 10’ list of priority research questions was generated for both elective and emergency general surgery of childhood. These cover a range of conditions and concepts, including inguinal hernia, undescended testis, appendicitis, abdominal trauma and enhanced recovery pathways. Conclusion Through consensus amongst surgeons who treat children, 10 priority research questions for each of the elective and emergency fields have been identified. These should provide a basis for the development of high-quality multicentre research projects to address these questions, and ultimately improve outcomes for children requiring surgical care.

Heterogeneity-aware and communication-efficient distributed statistical inference

In multicentre research, individual-level data are often protected against sharing across sites. To overcome the barrier of data sharing, many distributed algorithms, which only require sharing aggregated information, have been developed. The existing distributed algorithms usually assume the data are homogeneously distributed across sites. This assumption ignores the important fact that the data collected at different sites may come from various subpopulations and environments, which can lead to heterogeneity in the distribution of the data. Ignoring the heterogeneity may lead to erroneous statistical inference. In this paper, we propose distributed algorithms which account for the heterogeneous distributions by allowing site-specific nuisance parameters. The proposed methods extend the surrogate likelihood approach (Wang et al., 2017; Jordan et al., 2018) to the heterogeneous setting by applying a novel density ratio tilting method to the efficient score function. The proposed algorithms maintain the same communication cost as existing communication-efficient algorithms. We establish a non-asymptotic risk bound for the proposed distributed estimator and its limiting distribution in the two-index asymptotic setting which allows both sample size per site and the number of sites to go to infinity. In addition, we show that the asymptotic variance of the estimator attains the Cramér-Rao lower bound when the number of sites is in rate smaller than the sample size at each site. Finally, we use simulation studies and a real data application to demonstrate the validity and feasibility of the proposed methods.

Respiratory function and respiratory complications in spinal cord injury: protocol for a prospective, multicentre cohort study in high-income countries

IntroductionPneumonia is one of the leading complications and causes of death after a spinal cord injury (SCI). After a cervical or thoracic lesion, impairment of the respiratory muscles decreases respiratory function, which increases the risk of respiratory complications. Pneumonia substantially reduces patient’s quality of life, may prolong inpatient rehabilitation time, increase healthcare costs or at worse, lead to early death. Respiratory function and coughing can be improved through various interventions after SCI, but the available evidence as to which aspect of respiratory care should be optimised is inconclusive. Furthermore, ability of respiratory function parameters to predict pneumonia risk is insufficiently established. This paper details the protocol for a large-scale, multicentre research project that aims to evaluate the ability of parameters of respiratory function to predict and understand variation in inpatient risk of pneumonia in SCI.Methods and analysisRESCOM, a prospective cohort study, began recruitment in October 2016 across 10 SCI rehabilitation centres from Australia, Austria, Germany, the Netherlands and Switzerland. Inpatients with acute SCI, with complete or incomplete cervical or thoracic lesions, 18 years or older and not/no more dependent on 24-hour mechanical ventilation within the first 3 months after injury are eligible for inclusion. The target sample size is 500 participants. The primary outcome is an occurrence of pneumonia; secondary outcomes include pneumonia-related mortality and quality of life. We will use the longitudinal data for prognostic models on inpatient pneumonia risk factors.Ethics and disseminationThe study has been reviewed and approved by all local ethics committees of all participating centres. Study results will be disseminated to the scientific community through peer-reviewed journals and conference presentations, to the SCI community, other stakeholders and via social media, newsletters and engagement activities.Trial registration detailsClinicalTrials.gov NCT02891096.