Sharing Data to Accelerate Medicine Development and Improve Neonatal Care: Data Standards and Harmonized Definitions

Costeloe, Kate; Turner, Mark A.; Padula, Michael A.; Shah, Prakesh; Modi, Neena; Soll, Roger F.; Haumont, Dominique; Kusuda, Satoshi; Göpel, Wolfgang; Chang, Yun Sil; Smith, P. Brian; Lui, Kei; Davis, Jonathan; Hudson, Lynn

doi:10.1016/j.jpeds.2018.07.082

Cited by 15 publications

(8 citation statements)

References 19 publications

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“…Data standardization and harmonized definitions are hereby crucial to generate “big data” data sets to accelerate drug development to improve neonatal care, and such initiatives are ongoing 45 . The UK Medicines for Neonates research program described the feasibility of developing routinely recorded operational clinical data from electronic patient records as a reliable resource to improve health care 46 .…”

Section: Discussionmentioning

confidence: 99%

Dose‐Related Adverse Drug Events in Neonates: Recognition and Assessment

Allegaert

Anker

2021

The Journal of Clinical Pharma

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The efficacy and safety of a drug is dose or exposure related, and both are used to assess the benefit‐risk balance of a given drug and ultimately to decide on the specific drug license, including its dose and indication(s). Unfortunately, both efficacy and safety are much more difficult to establish in neonates, resulting in very few drugs licensed for use in this vulnerable population. This review will focus on dose‐related adverse events in neonates. Besides the regulatory classification on seriousness, adverse event assessment includes aspects related to signal detection, causality, and severity. Disentangling confounders from truly dose‐related adverse drug events remains a major challenge, as illustrated for drug‐induced renal impairment, drug‐induced liver injury, and neurodevelopmental outcome. Causality assessment, using either routine tools (Naranjo algorithm, World Health Organization's Uppsala Monitoring Center causality tool) or a Naranjo algorithm tailored to neonates, still does not sufficiently and reliably document causality in neonates. Finally, very recently, a first neonatal severity‐grading tool for neonates has been developed. Following the development of advanced pharmacokinetic approaches and techniques to predict and assess drug exposure, additional efforts are needed to truly and fully assess dose adverse drug events. To further operationalize the recently developed tools on causality and severity, reference databases on a palette of biomarkers and outcome variables and their covariates are an obvious next step. These databases should subsequently be integrated in modeling efforts to truly explore safety outcome, including aspects associated with or caused by drug dose or exposure.

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Section: Discussionmentioning

confidence: 99%

Dose‐Related Adverse Drug Events in Neonates: Recognition and Assessment

Allegaert

Anker

2021

The Journal of Clinical Pharma

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“…5 Moi University School of Medicine, Eldoret, Kenya. 6 Lata Medical Research Foundation, Nagpur, India. 7 University Teaching Hospital, Lusaka, Zambia.…”

Section: About This Supplement Informationmentioning

confidence: 99%

“…The investigators found that the tools helped increase accuracy of data collection throughout the research project. With the increasing global emphasis on harmonization and data sharing in research, ensuring not only high quality of data but also comparability of data across diverse settings is critical to accurate interpretation of findings [6,7].…”

Section: Introductionmentioning

confidence: 99%

Looking beyond the numbers: quality assurance procedures in the Global Network for Women’s and Children’s Health Research Maternal Newborn Health Registry

et al. 2020

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Background Quality assurance (QA) is a process that should be an integral part of research to protect the rights and safety of study participants and to reduce the likelihood that the results are affected by bias in data collection. Most QA plans include processes related to study preparation and regulatory compliance, data collection, data analysis and publication of study results. However, little detailed information is available on the specific procedures associated with QA processes to ensure high-quality data in multi-site studies. Methods The Global Network for Women’s and Children’s Health Maternal Newborn Health Registy (MNHR) is a prospective population-based registry of pregnancies and deliveries that is carried out in 8 international sites. Since its inception, QA procedures have been utilized to ensure the quality of the data. More recently, a training and certification process was developed to ensure that standardized, scientifically accurate clinical definitions are used consistently across sites. Staff complete a web-based training module that reviews the MNHR study protocol, study forms and clinical definitions developed by MNHR investigators and are certified through a multiple choice examination prior to initiating study activities and every six months thereafter. A standardized procedure for supervision and evaluation of field staff is carried out to ensure that research activites are conducted according to the protocol across all the MNHR sites. Conclusions We developed standardized QA processes for training, certification and supervision of the MNHR, a multisite research registry. It is expected that these activities, together with ongoing QA processes, will help to further optimize data quality for this protocol.

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“…This will enable longterm infant follow-up into adulthood following interventions in pregnancy (42). We are involved in collaborative work to harmonise definitions and adhere to international standards for electronic nomenclatures (43). We also aim to incorporate physiological monitoring, imaging and genomics data into the NNRD.…”

Section: Future Developmentsmentioning

confidence: 99%

Information technology infrastructure, quality improvement and research: the UK National Neonatal Research Database

Modi¹

2019

Transl Pediatr

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Technological developments, coupled with strengthened governance and data security have led to increasing recognition of the potential of real-world health data to benefit patient care and health services. Real-world health data are those captured in the course of routine care. Here I describe a mature source of real-world health data, the UK National Neonatal Research Database and provide examples of the many types of uses it supports.

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Sharing Data to Accelerate Medicine Development and Improve Neonatal Care: Data Standards and Harmonized Definitions

Cited by 15 publications

References 19 publications

Dose‐Related Adverse Drug Events in Neonates: Recognition and Assessment

Dose‐Related Adverse Drug Events in Neonates: Recognition and Assessment

Looking beyond the numbers: quality assurance procedures in the Global Network for Women’s and Children’s Health Research Maternal Newborn Health Registry

Information technology infrastructure, quality improvement and research: the UK National Neonatal Research Database

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