Dataset Information

Next Generation Sequencing in Newborn Screening in the United Kingdom National Health Service.

ABSTRACT: Next generation DNA sequencing (NGS) has the potential to improve the diagnostic and prognostic utility of newborn screening programmes. This study assesses the feasibility of automating NGS on dried blood spot (DBS) DNA in a United Kingdom National Health Service (UK NHS) laboratory. An NGS panel targeting the entire coding sequence of five genes relevant to disorders currently screened for in newborns in the UK was validated on DBS DNA. An automated process for DNA extraction, NGS and bioinformatics analysis was developed. The process was tested on DBS to determine feasibility, turnaround time and cost. The analytical sensitivity of the assay was 100% and analytical specificity was 99.96%, with a mean 99.5% concordance of variant calls between DBS and venous blood samples in regions with ?30× coverage (96.8% across all regions; all variant calls were single nucleotide variants (SNVs), with indel performance not assessed). The pipeline enabled processing of up to 1000 samples a week with a turnaround time of four days from receipt of sample to reporting. This study concluded that it is feasible to automate targeted NGS on routine DBS samples in a UK NHS laboratory setting, but it may not currently be cost effective as a first line test.

SUBMITTER: van Campen JC

PROVIDER: S-EPMC6914376 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Next Generation Sequencing in Newborn Screening in the United Kingdom National Health Service.

van Campen Julia C JC Sollars Elizabeth S A ESA Thomas Rebecca C RC Bartlett Clare M CM Milano Antonio A Parker Matthew D MD Dawe Jennifer J Winship Peter R PR Peck Gerrard G Grafham Darren D Kirk Richard J RJ Bonham James R JR Goodeve Anne C AC Dalton Ann A

International journal of neonatal screening 20191105 4

Next generation DNA sequencing (NGS) has the potential to improve the diagnostic and prognostic utility of newborn screening programmes. This study assesses the feasibility of automating NGS on dried blood spot (DBS) DNA in a United Kingdom National Health Service (UK NHS) laboratory. An NGS panel targeting the entire coding sequence of five genes relevant to disorders currently screened for in newborns in the UK was validated on DBS DNA. An automated process for DNA extraction, NGS and bioinfor ...[more]

PMID: 31844782

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Project description:IntroductionNon-medical prescribing was introduced into the United Kingdom (UK) to improve patient care, through extending healthcare professionals' roles. More recent government health service policy focuses on the increased demand and the need for efficiency. This systematic policy review aimed to describe any changes in government policy position and the role that non-medical prescribing plays in healthcare provision.MethodThe systematic policy review included policy and consultation documents that describe independent non-medical prescribing. A pre-defined protocol was registered with PROSPERO (CRD42015019786). Professional body websites, other relevant websites and the following databases were searched to identify relevant documents: HMIC, Lexis Nexis, UK Government Web Archive, UKOP, UK Parliamentary Papers and Web of Science. Documents published between 2006 and February 2018 were included.Results and discussionFollowing exclusions, 45 documents were selected for review; 23 relating to policy or strategy and 22 to consultations. Of the former, 13/23 were published 2006-2010 and the remainder since 2013. Two main themes were identified: chronological aspects and healthcare provision. In the former, a publication gap for policy documents resulted from a change in government and associated major healthcare service reorganisation. In the later, the role of non-medical prescribing was found to have evolved to support efficient service delivery, and cost reduction. For many professions, prescribing appears embedded into practice; however, the pharmacy profession continues to produce policy documents, suggesting that prescribing is not yet perceived as normal practice.ConclusionPrescribing appears to be more easily adopted into practice where it can form part of the overall care of the patient. Where new roles are required to be established, then prescribing takes longer to be universally adopted. While this review concerns policy and practice in the UK, the aspect of role adoption has wider potential implications.

Project description:BackgroundOver the last decade, increasing numbers of emergency department attendances and an even greater increase in emergency admissions have placed severe strain on the bed capacity of the National Health Service (NHS) of the United Kingdom. The result has been overcrowded emergency departments with patients experiencing long wait times for admission to an appropriate hospital bed. Nevertheless, scheduling issues can still result in significant underutilization of bed capacity. Bed occupancy rates may not correlate well with bed availability. More accurate and reliable long-term prediction of bed requirements will help anticipate the future needs of a hospital's catchment population, thus resulting in greater efficiencies and better patient care.ObjectiveThis study aimed to evaluate widely used automated time-series forecasting techniques to predict short-term daily nonelective bed occupancy at all trusts in the NHS. These techniques were used to develop a simple yet accurate national health system-level forecasting framework that can be utilized at a low cost and by health care administrators who do not have statistical modeling expertise.MethodsBed occupancy models that accounted for patterns in occupancy were created for each trust in the NHS. Daily nonelective midnight trust occupancy data from April 2011 to March 2017 for 121 NHS trusts were utilized to generate these models. Forecasts were generated using the three most widely used automated forecasting techniques: exponential smoothing; Seasonal Autoregressive Integrated Moving Average; and Trigonometric, Box-Cox transform, autoregressive moving average errors, and Trend and Seasonal components. The NHS Modernisation Agency's recommended forecasting method prior to 2020 was also replicated.ResultsThe accuracy of the models varied on the basis of the season during which occupancy was forecasted. For the summer season, percent root-mean-square error values for each model remained relatively stable across the 6 forecasted weeks. However, only the trend and seasonal components model (median error=2.45% for 6 weeks) outperformed the NHS Modernisation Agency's recommended method (median error=2.63% for 6 weeks). In contrast, during the winter season, the percent root-mean-square error values increased as we forecasted further into the future. Exponential smoothing generated the most accurate forecasts (median error=4.91% over 4 weeks), but all models outperformed the NHS Modernisation Agency's recommended method prior to 2020 (median error=8.5% over 4 weeks).ConclusionsIt is possible to create automated models, similar to those recently published by the NHS, which can be used at a hospital level for a large national health care system to predict nonelective bed admissions and thus schedule elective procedures.

Dataset Information

Next Generation Sequencing in Newborn Screening in the United Kingdom National Health Service.

Publications

Next Generation Sequencing in Newborn Screening in the United Kingdom National Health Service.

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