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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.

In 2015, the Department of Health in England announced an ambition to reduce 'brain injuries occurring during or soon after birth'. We describe the development of a pragmatic case definition and present annual incidence rates.Retrospective cohort study using data held in the National Neonatal Research Database (NNRD) extracted from neonatal electronic patient records from all National Health Service (NHS) neonatal units in England, Wales and Scotland. In 2010-2011, population coverage in the NNRD was incomplete, hence rate estimates are presented as a range; from 2012, population coverage is complete, and rates (95% CIs) are presented. Rates are per 1000 live births.NHS neonatal units in England.Infants admitted for neonatal care; denominator: live births in England.'Brain injuries occurring at or soon after birth' defined as infants with seizures, hypoxic-ischaemic encephalopathy, stroke, intracranial haemorrhage, central nervous system infection and kernicterus and preterm infants with cystic periventricular leucomalacia.In 2010, the lower estimate of the rate of 'Brain injuries occurring at or soon after birth' in England was 4.53 and the upper estimate was 5.19; in 2015, the rate was 5.14 (4.97, 5.32). For preterm infants, the population incidence in 2015 was 25.88 (24.51, 27.33) and 3.47 (3.33, 3.62) for term infants. Hypoxic-ischaemic encephalopathy was the largest contributor to term brain injury, and intraventricular/periventricular haemorrhage was the largest contributor to preterm brain injury.Annual incidence rates for brain injuries can be estimated from data held in the NNRD; rates for individual conditions are consistent with published rates. Routinely recorded clinical data can be used for national surveillance, offering efficiencies over traditional approaches.

ObjectiveTo analyse survival trends and regional variation for very preterm infants admitted to neonatal care.SettingAll neonatal units in England.PatientsInfants born at 22+0–31+6 weeks+daysgestational age (GA) over 2008–2014 and admitted to neonatal care; published data for admitted infants 22+0–25+6 weeks+days GA in 1995 and 2006, and for live births at 22+0–31+6 weeks+days GA in 2013.MethodsWe obtained data from the National Neonatal Research Database. We used logistic regression to model survival probability with birth weight, GA, sex, antenatal steroid exposure and multiple birth included in the risk adjustment model and calculated annualpercentage change (APC) for trends using joinpoint regression. We evaluated survival over a 20-year period for infants ResultsWe identified 50 112 eligible infants. There was an increase in survival over 2008–2014 (2008: 88.0%; 2014: 91.3%; adjusted APC 0.46% (95% CI 0.30 to 0.62) p+0–23+6 weeks (APC 6.03% (95% CI 2.47 to 3.53) p=0.002). Improvement largely occurred in London and South of England (APC: London 1.26% (95% CI 0.60 to 1.96); South of England 1.09% (95% CI 0.36 to 1.82); Midlands and East of England 0.15% (95% CI −0.56 to 0.86); and North of England 0.26% (95% CI −0.54 to 1.07)). Survival at the earliest gestations improved at a similar rate over 1995–2014 (22+0–25+6 weeks, APC 2.73% (95% CI 2.35 to 3.12), p value for change=0.25).ConclusionsContinued national improvement in the survival of very preterm admissions masks important regional variation. Timely assessment of preterm survival is feasible using electronic records.

Objectives High quality information, increasingly captured in clinical databases, is a useful resource for evaluating and improving newborn care. We conducted a systematic review to identify neonatal databases, and define their characteristics. Methods We followed a preregistered protocol using MesH terms to search MEDLINE, EMBASE, CINAHL, Web of Science and OVID Maternity and Infant Care Databases for articles identifying patient level databases covering more than one neonatal unit. Full-text articles were reviewed and information extracted on geographical coverage, criteria for inclusion, data source, and maternal and infant characteristics. Results We identified 82 databases from 2037 publications. Of the country-specific databases there were 39 regional and 39 national. Sixty databases restricted entries to neonatal unit admissions by birth characteristic or insurance cover; 22 had no restrictions. Data were captured specifically for 53 databases; 21 administrative sources; 8 clinical sources. Two clinical databases hold the largest range of data on patient characteristics, USA9s Pediatrix BabySteps Clinical Data Warehouse and UK9s National Neonatal Research Database. Conclusions A number of neonatal databases exist that have potential to contribute to evaluating neonatal care. The majority is created by entering data specifically for the database, duplicating information likely already captured in other administrative and clinical patient records. This repetitive data entry represents an unnecessary burden in an environment where electronic patient records are increasingly used. Standardisation of data items is necessary to facilitate linkage within and between countries.

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.

Background Following a Department of Health review in 2003, specialist neonatal services in England were re-organised into managed clinical networks with the aim of delivering high quality co-ordinated care.1 Maternity services were not included in this re-organisation. Effective network performance should result in reduced acute, and increased in-utero and back transfers among extremely preterm babies. Aims To test the hypothesis that since establishment of managed networks there has been an increase in the proportion of preterm babies (1) Delivered at units with the greatest intensive care experience, (2) Transferred between age 24 h and 28 days and (3) A reduction in the proportion transferred at age Design Caldicott Guardian approval has been obtained to receive routinely collected data from neonatal units for service evaluation. We compared postnatal transfers in infants born at 27 0 –28 6 weeks days gestational age before (epoch one 1 September 1998–31 August 2000) and after re-organisation (epoch two 1 January 2009–31 December 2009). Information from epoch one was obtained from the Confidential Enquiry into Stillbirths and Deaths in Infancy report on preterm infants2 and for epoch two from the 121 neonatal units contributing routinely collected data for that period, and differences tested for significance (χ 2 test and logistic regression). Results There were no significant between epoch differences in gender, delivery mode or multiple births. In epoch two, a higher proportion of babies were born in units with greater neonatal intensive care experience (p Conclusions Since reorganisation, a greater proportion of extremely preterm babies are born in the most experienced neonatal units, and a greater proportion undergo both acute and later postnatal transfer. The increase in delivery at experienced units would be expected to improve outcomes. The increase in acute postnatal transfers may suggest that neonatal and maternity services are not functioning effectively in facilitating in-utero transfers. We suggest that focus should be placed on organisation of perinatal networks, and the commissioning disparity between neonatal and maternity services should be rectified. Preterm babies are highly likely to undergo postnatal transfer; this should be explained to parents.

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.