Sophie Relph1, Maria Elstad2, Bolaji Coker3,4, Matias C Vieira5,6, Natalie Moitt5, Walter Muruet Gutierrez5, Asma Khalil7,8, Jane Sandall5, Andrew Copas9, Deborah A Lawlor10,11,12, Dharmintra Pasupathy5,13. 1. Department of Women and Children's Health, School of Life Course Sciences, Faculty of Life Sciences and Medicine, King's College London, 10th Floor North Wing, St. Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK. sophie.relph@kcl.ac.uk. 2. School of Population Health and Environmental Sciences, Faculty of Life Sciences and Medicine, King's College London, 4th Floor, Addison House, Guy's Campus, London, SE1 1UL, UK. 3. Division of Health and Social Care Research, King's College London, London, UK. 4. NIHR Biomedical Research Centre at Guy's and St Thomas' NHS Foundation Trust and King's College London, Guy's Hospital, London, UK. 5. Department of Women and Children's Health, School of Life Course Sciences, Faculty of Life Sciences and Medicine, King's College London, 10th Floor North Wing, St. Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK. 6. Department of Obstetrics and Gynaecology, University of Campinas (UNICAMP), School of Medical Sciences, 101 Alexander Fleming St, Cidade Universitaria, Campinas, SP, Brazil. 7. Fetal Medicine Unit, St George's University Hospitals NHS Foundation Trust, Blackshaw Road, London, SW17 0QT, UK. 8. Molecular & Clinical Sciences Research Institute, St George's, University of London, Cranmer Terrace, London, SW17 0RE, UK. 9. Centre for Pragmatic Global Health Trials, Institute for Global Health, University College London, Gower Street, London, WC1E 6BT, UK. 10. Population Health Science, Bristol Medical School, University of Bristol, Bristol, BS8 2BL, UK. 11. Bristol NIHR Biomedical Research Centre, Bristol, BS8 2BL, UK. 12. MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, BS8 2BL, UK. 13. Speciality of Obstetrics, Gynaecology and Neonatology, Westmead Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, Australia.
Abstract
BACKGROUND: The use of electronic patient records for assessing outcomes in clinical trials is a methodological strategy intended to drive faster and more cost-efficient acquisition of results. The aim of this manuscript was to outline the data collection and management considerations of a maternity and perinatal clinical trial using data from electronic patient records, exemplifying the DESiGN Trial as a case study. METHODS: The DESiGN Trial is a cluster randomised control trial assessing the effect of a complex intervention versus standard care for identifying small for gestational age foetuses. Data on maternal/perinatal characteristics and outcomes including infants admitted to neonatal care, parameters from foetal ultrasound and details of hospital activity for health-economic evaluation were collected at two time points from four types of electronic patient records held in 22 different electronic record systems at the 13 research clusters. Data were pseudonymised on site using a bespoke Microsoft Excel macro and securely transferred to the central data store. Data quality checks were undertaken. Rules for data harmonisation of the raw data were developed and a data dictionary produced, along with rules and assumptions for data linkage of the datasets. The dictionary included descriptions of the rationale and assumptions for data harmonisation and quality checks. RESULTS: Data were collected on 182,052 babies from 178,350 pregnancies in 165,397 unique women. Data availability and completeness varied across research sites; each of eight variables which were key to calculation of the primary outcome were completely missing in median 3 (range 1-4) clusters at the time of the first data download. This improved by the second data download following clarification of instructions to the research sites (each of the eight key variables were completely missing in median 1 (range 0-1) cluster at the second time point). Common data management challenges were harmonising a single variable from multiple sources and categorising free-text data, solutions were developed for this trial. CONCLUSIONS: Conduct of clinical trials which use electronic patient records for the assessment of outcomes can be time and cost-effective but still requires appropriate time and resources to maximise data quality. A difficulty for pregnancy and perinatal research in the UK is the wide variety of different systems used to collect patient data across maternity units. In this manuscript, we describe how we managed this and provide a detailed data dictionary covering the harmonisation of variable names and values that will be helpful for other researchers working with these data. TRIAL REGISTRATION: Primary registry and trial identifying number: ISRCTN 67698474 . Registered on 02/11/16.
BACKGROUND: The use of electronic patient records for assessing outcomes in clinical trials is a methodological strategy intended to drive faster and more cost-efficient acquisition of results. The aim of this manuscript was to outline the data collection and management considerations of a maternity and perinatal clinical trial using data from electronic patient records, exemplifying the DESiGN Trial as a case study. METHODS: The DESiGN Trial is a cluster randomised control trial assessing the effect of a complex intervention versus standard care for identifying small for gestational age foetuses. Data on maternal/perinatal characteristics and outcomes including infants admitted to neonatal care, parameters from foetal ultrasound and details of hospital activity for health-economic evaluation were collected at two time points from four types of electronic patient records held in 22 different electronic record systems at the 13 research clusters. Data were pseudonymised on site using a bespoke Microsoft Excel macro and securely transferred to the central data store. Data quality checks were undertaken. Rules for data harmonisation of the raw data were developed and a data dictionary produced, along with rules and assumptions for data linkage of the datasets. The dictionary included descriptions of the rationale and assumptions for data harmonisation and quality checks. RESULTS: Data were collected on 182,052 babies from 178,350 pregnancies in 165,397 unique women. Data availability and completeness varied across research sites; each of eight variables which were key to calculation of the primary outcome were completely missing in median 3 (range 1-4) clusters at the time of the first data download. This improved by the second data download following clarification of instructions to the research sites (each of the eight key variables were completely missing in median 1 (range 0-1) cluster at the second time point). Common data management challenges were harmonising a single variable from multiple sources and categorising free-text data, solutions were developed for this trial. CONCLUSIONS: Conduct of clinical trials which use electronic patient records for the assessment of outcomes can be time and cost-effective but still requires appropriate time and resources to maximise data quality. A difficulty for pregnancy and perinatal research in the UK is the wide variety of different systems used to collect patient data across maternity units. In this manuscript, we describe how we managed this and provide a detailed data dictionary covering the harmonisation of variable names and values that will be helpful for other researchers working with these data. TRIAL REGISTRATION: Primary registry and trial identifying number: ISRCTN 67698474 . Registered on 02/11/16.
Entities:
Keywords:
Cluster randomised trial; Data linkage; Data management; Electronic patient records; Maternal; Methodology; Perinatal
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Authors: Matias C Vieira; Sophie Relph; Walter Muruet-Gutierrez; Maria Elstad; Bolaji Coker; Natalie Moitt; Louisa Delaney; Chivon Winsloe; Andrew Healey; Kirstie Coxon; Alessandro Alagna; Annette Briley; Mark Johnson; Louise M Page; Donald Peebles; Andrew Shennan; Baskaran Thilaganathan; Neil Marlow; Lesley McCowan; Christoph Lees; Deborah A Lawlor; Asma Khalil; Jane Sandall; Andrew Copas; Dharmintra Pasupathy Journal: PLoS Med Date: 2022-06-21 Impact factor: 11.613