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BackgroundClinical data offer the potential to advance patient care. Neonatal specialised care is a high-cost NHS service received by approximately 80,000 newborn infants each year.Objectives(1) To develop the use of routinely recorded operational clinical data from electronic patient records (EPRs), secure national coverage, evaluate and improve the quality of clinical data, and develop their use as a national resource to improve neonatal health care and outcomes. To test the hypotheses that (2) clinical and research data are of comparable quality, (3) routine NHS clinical assessment at the age of 2 years reliably identifies children with neurodevelopmental impairment and (4) trial-based economic evaluations of neonatal interventions can be reliably conducted using clinical data. (5) To test methods to link NHS data sets and (6) to evaluate parent views of personal data in research.DesignSix inter-related workstreams; quarterly extractions of predefined data from neonatal EPRs; and approvals from the National Research Ethics Service, Health Research Authority Confidentiality Advisory Group, Caldicott Guardians and lead neonatal clinicians of participating NHS trusts.SettingNHS neonatal units.ParticipantsNeonatal clinical teams; parents of babies admitted to NHS neonatal units.InterventionsIn workstream 3, we employed the Bayley-III scales to evaluate neurodevelopmental status and the Quantitative Checklist of Autism in Toddlers (Q-CHAT) to evaluate social communication skills. In workstream 6, we recruited parents with previous experience of a child in neonatal care to assist in the design of a questionnaire directed at the parents of infants admitted to neonatal units.Data sourcesData were extracted from the EPR of admissions to NHS neonatal units.Main outcome measuresWe created a National Neonatal Research Database (NNRD) containing a defined extract from real-time, point-of-care, clinician-entered EPRs from all NHS neonatal units in England, Wales and Scotland (n = 200), established a UK Neonatal Collaborative of all NHS trusts providing neonatal specialised care, and created a new NHS information standard: the Neonatal Data Set (ISB 1595) (seehttp://webarchive.nationalarchives.gov.uk/±/http://www.isb.nhs.uk/documents/isb-1595/amd-32–2012/index_html; accessed 25 June 2018).ResultsWe found low discordance between clinical (NNRD) and research data for most important infant and maternal characteristics, and higher prevalence of clinical outcomes. Compared with research assessments, NHS clinical assessment at the age of 2 years has lower sensitivity but higher specificity for identifying children with neurodevelopmental impairment. Completeness and quality are higher for clinical than for administrative NHS data; linkage is feasible and substantially enhances data quality and scope. The majority of hospital resource inputs for economic evaluations of neonatal interventions can be extracted reliably from the NNRD. In general, there is strong parent support for sharing routine clinical data for research purposes.LimitationsWe were only able to include data from all English neonatal units from 2012 onwards and conduct only limited cross validation of NNRD data directly against data in paper case notes. We were unable to conduct qualitative analyses of parent perspectives. We were also only able to assess the utility of trial-based economic evaluations of neonatal interventions using a single trial. We suggest that results should be validated against other trials.ConclusionsWe show that it is possible to obtain research-standard data from neonatal EPRs, and achieve complete population coverage, but we highlight the importance of implementing systematic examination of NHS data quality and completeness and testing methods to improve these measures. Currently available EPR data do not enable ascertainment of neurodevelopmental outcomes reliably in very preterm infants. Measures to maintain high quality and completeness of clinical and administrative data are important health service goals. As parent support for sharing clinical data for research is underpinned by strong altruistic motivation, improving wider public understanding of benefits may enhance informed decision-making.Future workWe aim to implement a new paradigm for newborn health care in which continuous incremental improvement is achieved efficiently and cost-effectively by close integration of evidence generation with clinical care through the use of high-quality EPR data. In future work, we aim to automate completeness and quality checks and make recording processes more ‘user friendly’ and constructed in ways that minimise the likelihood of missing or erroneous entries. The development of criteria that provide assurance that data conform to prespecified completeness and quality criteria would be an important development. The benefits of EPR data might be extended by testing their use in large pragmatic clinical trials. It would also be of value to develop methods to quality assure EPR data including involving parents, and link the NNRD to other health, social care and educational data sets to facilitate the acquisition of lifelong outcomes across multiple domains.Study registrationThis study is registered as PROSPERO CRD42015017439 (workstream 1) and PROSPERO CRD42012002168 (workstream 3).FundingThe National Institute for Health Research Programme Grants for Applied Research programme (£1,641,471). Unrestricted donations were supplied by Abbott Laboratories (Maidenhead, UK: £35,000), Nutricia Research Foundation (Schiphol, the Netherlands: £15,000), GE Healthcare (Amersham, UK: £1000). A grant to support the use of routinely collected, standardised, electronic clinical data for audit, management and multidisciplinary feedback in neonatal medicine was received from the Department of Health and Social Care (£135,494).

Background The National Neonatal Research Database (NNRD) is a rich repository of pre-defined clinical data extracted at regular intervals from point-of-care, clinician-entered electronic patient records on all admissions to National Health Service neonatal units in England, Wales, and Scotland. We describe population coverage for England and assess data completeness and accuracy. Methods We determined population coverage of the NNRD in 2008–2014 through comparison with data on live births in England from the Office for National Statistics. We determined the completeness of seven data items on the NNRD. We assessed the accuracy of 44 data items (16 patient characteristics, 17 processes, 11 clinical outcomes) for infants enrolled in the multi-centre randomised controlled trial, Probiotics in Preterm Study (PiPs). We compared NNRD to PiPs data, the gold standard, and calculated discordancy rates using predefined criteria, and sensitivity, specificity and positive predictive values (PPV) of binary outcomes. Results The NNRD holds complete population data for England for infants born alive from 25+0 to 31+6 (completed weeks) of gestation; and 70% and 90% for those born at 23 and 24 weeks respectively. Completeness of patient characteristics was over 90%. Data were linked for 2257 episodes of care received by 1258 of the 1310 babies recruited to PiPs. Discordancy rates were 85% for all outcomes; sensitivity ranged from 50–100%; PPV ranged from 58.8 (95% CI 40.8–75.4%) for porencephalic cyst to 99.7 (95% CI 99.2, 99.9%) for survival to discharge. Conclusions The completeness and quality of data held in the NNRD is high, providing assurance in relation to use for multiple purposes, including national audit, health service evaluations, quality improvement, and research.

To report on retinopathy of prematurity (ROP) screening compliance against a national guideline, factors associated with non-compliance and effect on ROP treatment.National cohort study using operational NHS data from the National Neonatal Research Database (NNRD) for the period 2009-2011.161 (94%) neonatal units in England.Infants born below 32 weeks' gestation and/or with a birth weight below 1501 g.ROP screening status ('on-time', 'early', 'late', 'unknown') and associated infant and neonatal unit characteristics, ROP treatment.The proportion of infants screened on-time increased over the study period (p0.001). Of 19 821 eligible infants, 7602 (38.4%) were recorded to have received ROP screening in accordance with the national guideline; 7474 (37.8%) received screening outside the recommended time period; data were missing for 4745 (16.7%) infants. For 16 411 infants in neonatal care during the recommended screening period, late screening was significantly associated with lower gestational age (relative risk ratio (RRR) (95% credible interval) for late versus on-time screening 0.83 (0.80 to 0.86) for each increased week of gestation) and care in a neonatal unit providing less than 500 days of intensive care per annum (2.48 (0.99 to 4.99)). Infants screened late were almost 40% more likely to receive ROP treatment (OR (95% CI) 1.36 (1.05 to 1.76)).Understanding organisational differences between neonatal units may help improve ROP screening. Patient-level electronic NHS clinical data offer opportunity for future rapid, low cost, population-based evaluations but require improved data entry.

OBJECTIVE: To describe birth weight and postnatal weight gain in a contemporaneous population of babies born

Aims The survival of preterm infants is a matter of wide public interest. Survival prediction is important for clinicians when advising parents and for risk adjustment when comparing providers. Prediction models are generally based on historical data, often from hospital rather than population-based cohorts. Here we demonstrate the use of near-contemporaneous electronic National Health Service (NHS) clinical data to provide a practical, up-to-date, web-based survival prediction tool for preterm infants admitted to neonatal units in England. We compared this with existing UK and US predictors and evaluated the change in survival over recent years for extremely preterm babies from 22 +0 –25 +6 week gestational age (GA) admitted to neonatal care, in comparison with previous published data. Methods Data for infants born ≤31+6 weeks GA that died or were discharged in 2009–2011 were received with Caldicott Guardian permission from English neonatal units in the UK Neonatal Collaborative. A multivariable logistic regression model was developed using known predictors. Discrimination and calibration were evaluated internally and on independent data. A web-based tool was written in Javascript. Survival was compared against data from the EPICure 2 study in 2006. Results There were 17,491 infants included in the cohort, of whom 16,164 (92%) survived. Birth weight, GA, sex, antenatal steroids, and multiple birth were factors included in the final model. The interactive tool is available online for open access. The model showed good discrimination internally (area under ROC curve (AUC) = 0.89, 95% CI 0.88 to 0.90) and on independent data (AUC = 0.87, 95% CI 0.82 to 0.91). Predictive performance was similar to previous UK models and improved over a US model. There has been no statistically significant increase in survival to discharge of admitted infants born at 22+0–25+6 weeks gestation in England since 2006 (Relative Risk 1.10, 95% CI 0.98 to 1.22, p = 0.11). Conclusions We have established the feasibility of employing contemporaneous, population-based, routinely collected, electronic NHS data for survival modelling for preterm babies admitted to neonatal care. The model is available in a web tool readily accessible to clinicians, parents, and healthcare managers and can be regularly updated to readily assess population changes in neonatal survival.

Introduction Growth monitoring is a cardinal precept of paediatric practice. The UK 1990 reference curves for preterm infants, based on cross sectional birth data from live and stillborn infants at different gestational ages, have been incorporated into the UK-WHO growth charts. These and related charts cannot be considered a standard as the optimum pattern of growth after preterm birth is unknown. Aims We aimed to construct birthweight centiles and illustrate the range of contemporaneous preterm postnatal growth to provide a population-based benchmark and a baseline for future examination of temporal trends. Methods With National Research Ethics Service approval, we constructed descriptive growth curves for babies born Results The birthweight centiles, based on 3172 infants, were similar to but slightly lower than UK-WHO. The gestation-specific growth curves were based on 57 746 weights from 2577 infants, with 96–514 infants and 2446–8107 weights per week of gestation (with 22/23 weeks combined). The figure 1 shows the fitted gestation-specific growth curves plotted against postmenstrual age. Weight consistently fell after birth with a greater fall for later gestations. Weight velocity stabilised after 3 weeks. Growth curves then tracked parallel to and between the second and ninth UK-WHO birthweight centiles, at a higher level in more mature infants. Conclusion The pattern of postnatal growth in very preterm infants is complex, dependent on gestation, and quite different from that represented by the birthweight chart. Longitudinal monitoring based on birthweight charts is likely to lead to inappropriate treatment decisions. A new form of chart is urgently needed to provide a basis for longitudinal monitoring of preterm growth.