Using electronic patient records to assess the effect of a complex antenatal intervention in a cluster randomised controlled trial—data management experience from the DESiGN Trial team

on behalf of the DESIGN Trial team

doi:10.1186/s13063-021-05141-8

Using electronic patient records to assess the effect of a complex antenatal intervention in a cluster randomised controlled trial—data management experience from the DESiGN Trial team

on behalf of the DESIGN Trial team

Research output: Contribution to journal › Article › peer-review

11 Citations (Scopus)

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.

Original language	English
Article number	195
Journal	Trials
Volume	22
Issue number	1
DOIs	https://doi.org/10.1186/s13063-021-05141-8
Publication status	Published - Dec 2021

Keywords

Cluster randomised trial
Data linkage
Data management
Electronic patient records
Maternal
Methodology
Perinatal

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1186/s13063-021-05141-8

Cite this

@article{e8e5a40a4955446eb162a7181b29bf9b,

title = "Using electronic patient records to assess the effect of a complex antenatal intervention in a cluster randomised controlled trial—data management experience from the DESiGN Trial team",

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.",

keywords = "Cluster randomised trial, Data linkage, Data management, Electronic patient records, Maternal, Methodology, Perinatal",

author = "{on behalf of the DESIGN Trial team} and Sophie Relph and Maria Elstad and Bolaji Coker and Vieira, {Matias C.} and Natalie Moitt and Gutierrez, {Walter Muruet} and Asma Khalil and Jane Sandall and Andrew Copas and Lawlor, {Deborah A.} and Dharmintra Pasupathy and Kirstie Coxon and Andrew Healey and Alessandro Alagna and Annette Briley and Mark Johnson and Christoph Lees and Neil Marlow and Lesley McCowan and Louise Page and Donald Peebles and Andrew Shennan and Basky Thilaganathan",

note = "Funding Information: Dharmintra Pasupathy was funded by Tommy{\textquoteright}s Charity. Andrew Healey and Jane Sandall are supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care South London at King{\textquoteright}s College Hospital NHS Foundation Trust. Deborah A Lawlor{\textquoteright}s contribution is supported by the NIHR Biomedical Research Centre at University Hospitals Bristol NHS Foundation Trust and the University of Bristol and she is an NIHR Senior Investigator (NF-SI-0611-10196). MCV was supported by a Science Without Borders Fellowship from CAPES (BEX: 9571/13-2). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. Funding Information: The DESiGN trial was funded by the Guy{\textquoteright}s and St Thomas{\textquoteright} Charity, the Stillbirth and Neonatal Death charity (Sands) and Tommy{\textquoteright}s Charity. Funding Information: We wish to thank the members of the DESiGN Trial team for their collaboration on this trial: Kirstie Coxon (Faculty of Health, Social Care and Education, Kingston and St. George?s University), Andrew Healey (Department of Health Service and Population Research, King?s College London), Alessandro Alagna (Lay representative, Guy?s and St Thomas? Charity), Annette Briley (Department of Women and Children?s Health, School of Life Course Sciences, Faculty of Life Sciences and Medicine, King?s College London), Mark Johnson (Department of Surgery and Cancer, Imperial College London), Christoph Lees (Department of Surgery and Cancer, Imperial College London), Neil Marlow (UCL Institute for Women?s Health, University College London), Lesley McCowan (Faculty of Medical and Health Sciences, University of Auckland), Louise Page (West Middlesex University Hospital, Chelsea & Westminster Hospital NHS Foundation Trust), Donald Peebles (UCL Institute for Women?s Health, University College London), Andrew Shennan (Department of Women and Children?s Health, School of Life Course Sciences, Faculty of Life Sciences and Medicine, King?s College London), Basky Thilaganathan (Molecular & Clinical Sciences Research Institute, St George?s, University of London). Publisher Copyright: {\textcopyright} 2021, The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",

year = "2021",

month = dec,

doi = "10.1186/s13063-021-05141-8",

language = "English",

volume = "22",

journal = "Trials",

issn = "1745-6215",

publisher = "BioMed Central",

number = "1",

}

TY - JOUR

T1 - Using electronic patient records to assess the effect of a complex antenatal intervention in a cluster randomised controlled trial—data management experience from the DESiGN Trial team

AU - on behalf of the DESIGN Trial team

AU - Relph, Sophie

AU - Elstad, Maria

AU - Coker, Bolaji

AU - Vieira, Matias C.

AU - Moitt, Natalie

AU - Gutierrez, Walter Muruet

AU - Khalil, Asma

AU - Sandall, Jane

AU - Copas, Andrew

AU - Lawlor, Deborah A.

AU - Pasupathy, Dharmintra

AU - Coxon, Kirstie

AU - Healey, Andrew

AU - Alagna, Alessandro

AU - Briley, Annette

AU - Johnson, Mark

AU - Lees, Christoph

AU - Marlow, Neil

AU - McCowan, Lesley

AU - Page, Louise

AU - Peebles, Donald

AU - Shennan, Andrew

AU - Thilaganathan, Basky

N1 - Funding Information: Dharmintra Pasupathy was funded by Tommy’s Charity. Andrew Healey and Jane Sandall are supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care South London at King’s College Hospital NHS Foundation Trust. Deborah A Lawlor’s contribution is supported by the NIHR Biomedical Research Centre at University Hospitals Bristol NHS Foundation Trust and the University of Bristol and she is an NIHR Senior Investigator (NF-SI-0611-10196). MCV was supported by a Science Without Borders Fellowship from CAPES (BEX: 9571/13-2). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. Funding Information: The DESiGN trial was funded by the Guy’s and St Thomas’ Charity, the Stillbirth and Neonatal Death charity (Sands) and Tommy’s Charity. Funding Information: We wish to thank the members of the DESiGN Trial team for their collaboration on this trial: Kirstie Coxon (Faculty of Health, Social Care and Education, Kingston and St. George?s University), Andrew Healey (Department of Health Service and Population Research, King?s College London), Alessandro Alagna (Lay representative, Guy?s and St Thomas? Charity), Annette Briley (Department of Women and Children?s Health, School of Life Course Sciences, Faculty of Life Sciences and Medicine, King?s College London), Mark Johnson (Department of Surgery and Cancer, Imperial College London), Christoph Lees (Department of Surgery and Cancer, Imperial College London), Neil Marlow (UCL Institute for Women?s Health, University College London), Lesley McCowan (Faculty of Medical and Health Sciences, University of Auckland), Louise Page (West Middlesex University Hospital, Chelsea & Westminster Hospital NHS Foundation Trust), Donald Peebles (UCL Institute for Women?s Health, University College London), Andrew Shennan (Department of Women and Children?s Health, School of Life Course Sciences, Faculty of Life Sciences and Medicine, King?s College London), Basky Thilaganathan (Molecular & Clinical Sciences Research Institute, St George?s, University of London). Publisher Copyright: © 2021, The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/12

Y1 - 2021/12

N2 - 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.

AB - 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.

KW - Cluster randomised trial

KW - Data linkage

KW - Data management

KW - Electronic patient records

KW - Maternal

KW - Methodology

KW - Perinatal

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U2 - 10.1186/s13063-021-05141-8

DO - 10.1186/s13063-021-05141-8

M3 - Article

AN - SCOPUS:85102167109

SN - 1745-6215

VL - 22

JO - Trials

JF - Trials

IS - 1

M1 - 195

ER -

Using electronic patient records to assess the effect of a complex antenatal intervention in a cluster randomised controlled trial—data management experience from the DESiGN Trial team

Abstract

Keywords

UN SDGs

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