Steve Harris1, Sinan Shi2, David Brealey3, Niall S MacCallum3, Spiros Denaxas4, David Perez-Suarez2, Ari Ercole5, Peter Watkinson6, Andrew Jones7, Simon Ashworth8, Richard Beale9, Duncan Young6, Stephen Brett8, Mervyn Singer10. 1. Bloomsbury Institute of Intensive Care Medicine, University College Hospital, London, UK; Critical Care, University College London Hospitals NHS Foundation Trust, London, UK. Electronic address: s.harris8@nhs.net. 2. Research Software Engineering, University College London, London, UK. 3. Bloomsbury Institute of Intensive Care Medicine, University College Hospital, London, UK; Critical Care, University College London Hospitals NHS Foundation Trust, London, UK. 4. Institute of Health Informatics, University College London, Gower Street, London, WC1E 6BT, UK. 5. Division of Anaesthesia, Department of Medicine, Cambridge University, UK. 6. Critical Care Research Group (Kadoorie Centre), Nuffield Department of Clinical Neurosciences, Medical Sciences Division, Oxford University, UK. 7. Critical Care, Guy's and St. Thomas' NHS Foundation Trust, London, UK. 8. Critical Care, St. Mary's Hospital, Imperial College Healthcare NHS Trust, London, UK; Critical Care, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, UK. 9. Critical Care, Guy's and St. Thomas' NHS Foundation Trust, London, UK; Division of Asthma, Allergy and Lung Biology, King's College, London, UK. 10. Bloomsbury Institute of Intensive Care Medicine, University College Hospital, London, UK.
Abstract
OBJECTIVE: To build and curate a linkable multi-centre database of high resolution longitudinal electronic health records (EHR) from adult Intensive Care Units (ICU). To develop a set of open-source tools to make these data 'research ready' while protecting patient's privacy with a particular focus on anonymisation. MATERIALS AND METHODS: We developed a scalable EHR processing pipeline for extracting, linking, normalising and curating and anonymising EHR data. Patient and public involvement was sought from the outset, and approval to hold these data was granted by the NHS Health Research Authority's Confidentiality Advisory Group (CAG). The data are held in a certified Data Safe Haven. We followed sustainable software development principles throughout, and defined and populated a common data model that links to other clinical areas. RESULTS: Longitudinal EHR data were loaded into the CCHIC database from eleven adult ICUs at 5 UK teaching hospitals. From January 2014 to January 2017, this amounted to 21,930 and admissions (18,074 unique patients). Typical admissions have 70 data-items pertaining to admission and discharge, and a median of 1030 (IQR 481-2335) time-varying measures. Training datasets were made available through virtual machine images emulating the data processing environment. An open source R package, cleanEHR, was developed and released that transforms the data into a square table readily analysable by most statistical packages. A simple language agnostic configuration file will allow the user to select and clean variables, and impute missing data. An audit trail makes clear the provenance of the data at all times. DISCUSSION: Making health care data available for research is problematic. CCHIC is a unique multi-centre longitudinal and linkable resource that prioritises patient privacy through the highest standards of data security, but also provides tools to clean, organise, and anonymise the data. We believe the development of such tools are essential if we are to meet the twin requirements of respecting patient privacy and working for patient benefit. CONCLUSION: The CCHIC database is now in use by health care researchers from academia and industry. The 'research ready' suite of data preparation tools have facilitated access, and linkage to national databases of secondary care is underway.
OBJECTIVE: To build and curate a linkable multi-centre database of high resolution longitudinal electronic health records (EHR) from adult Intensive Care Units (ICU). To develop a set of open-source tools to make these data 'research ready' while protecting patient's privacy with a particular focus on anonymisation. MATERIALS AND METHODS: We developed a scalable EHR processing pipeline for extracting, linking, normalising and curating and anonymising EHR data. Patient and public involvement was sought from the outset, and approval to hold these data was granted by the NHS Health Research Authority's Confidentiality Advisory Group (CAG). The data are held in a certified Data Safe Haven. We followed sustainable software development principles throughout, and defined and populated a common data model that links to other clinical areas. RESULTS: Longitudinal EHR data were loaded into the CCHIC database from eleven adult ICUs at 5 UK teaching hospitals. From January 2014 to January 2017, this amounted to 21,930 and admissions (18,074 unique patients). Typical admissions have 70 data-items pertaining to admission and discharge, and a median of 1030 (IQR 481-2335) time-varying measures. Training datasets were made available through virtual machine images emulating the data processing environment. An open source R package, cleanEHR, was developed and released that transforms the data into a square table readily analysable by most statistical packages. A simple language agnostic configuration file will allow the user to select and clean variables, and impute missing data. An audit trail makes clear the provenance of the data at all times. DISCUSSION: Making health care data available for research is problematic. CCHIC is a unique multi-centre longitudinal and linkable resource that prioritises patient privacy through the highest standards of data security, but also provides tools to clean, organise, and anonymise the data. We believe the development of such tools are essential if we are to meet the twin requirements of respecting patient privacy and working for patient benefit. CONCLUSION: The CCHIC database is now in use by health care researchers from academia and industry. The 'research ready' suite of data preparation tools have facilitated access, and linkage to national databases of secondary care is underway.
Authors: Christopher Meiring; Abhishek Dixit; Steve Harris; Niall S MacCallum; David A Brealey; Peter J Watkinson; Andrew Jones; Simon Ashworth; Richard Beale; Stephen J Brett; Mervyn Singer; Ari Ercole Journal: PLoS One Date: 2018-11-14 Impact factor: 3.240
Authors: Christopher J McWilliams; Daniel J Lawson; Raul Santos-Rodriguez; Iain D Gilchrist; Alan Champneys; Timothy H Gould; Mathew Jc Thomas; Christopher P Bourdeaux Journal: BMJ Open Date: 2019-03-07 Impact factor: 2.692
Authors: Patrick J Thoral; Jan M Peppink; Ronald H Driessen; Eric J G Sijbrands; Erwin J O Kompanje; Lewis Kaplan; Heatherlee Bailey; Jozef Kesecioglu; Maurizio Cecconi; Matthew Churpek; Gilles Clermont; Mihaela van der Schaar; Ari Ercole; Armand R J Girbes; Paul W G Elbers Journal: Crit Care Med Date: 2021-06-01 Impact factor: 9.296