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Developing informatics infrastructure to curate datasets using electronic health record data from five hospitals for translational cardiovascular research
INTRODUCTION: It has been challenging for researchers to access granular electronic health record (EHR) data at scale. One emerging prospect is to use big data to traverse the translational spectrum from an early discovery phase to a later implementation phase. PURPOSE: To create a research-ready da...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9779771/ http://dx.doi.org/10.1093/ehjdh/ztac076.2794 |
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author | Kaura, A Sterne, J A C Trickey, A Mulla, A Glampson, B Davies, J Woods, K Panoulas, V Shah, A D Patel, R S Kharbanda, R Shah, A M Perera, D Channon, K M Mayet, J |
author_facet | Kaura, A Sterne, J A C Trickey, A Mulla, A Glampson, B Davies, J Woods, K Panoulas, V Shah, A D Patel, R S Kharbanda, R Shah, A M Perera, D Channon, K M Mayet, J |
author_sort | Kaura, A |
collection | PubMed |
description | INTRODUCTION: It has been challenging for researchers to access granular electronic health record (EHR) data at scale. One emerging prospect is to use big data to traverse the translational spectrum from an early discovery phase to a later implementation phase. PURPOSE: To create a research-ready dataset to support translational research in cardiovascular medicine, using routinely-collected EHR data from multiple hospitals. As an early discovery phase study, we estimated the effect of invasive versus non-invasive management on the survival of patients with non-ST elevation myocardial infarction (NSTEMI) aged 80 years or older (SENIOR-NSTEMI Study). As a later implementation phase study, we determined the relationship between the full spectrum of troponin level and mortality in patients in whom troponin testing was performed for clinical purposes (TROP-RISK Study). METHODS: Using Microsoft SQL we developed a dataset of 257948 consecutive patients who had a troponin measured between 2010 and 2017 at five hospitals. We extracted phenotypically detailed data, including demographics, blood tests, procedural data, and survival status. For the SENIOR-NSTEMI Study, eligible patients were 80 years or older who were diagnosed with NSTEMI. We estimated mortality hazard ratios comparing invasive with non-invasive management. For the TROP-RISK Study, we modelled the relation between peak troponin level and all-cause mortality using multivariable adjusted restricted cubic spline Cox regression analyses. RESULTS: For the SENIOR-NSTEMI Study, 1500 patients with NSTEMI were included who had a median age of 86 (interquartile range (IQR) 82–89) years of whom (845 [56%]) received non-invasive management. During a median follow-up of 3 (IQR 1.2–4.8) years, the adjusted cumulative five-year mortality was 36% in the invasive and 55% in the non-invasive group (hazard ratio 0.68, 95% confidence interval 0.55–0.84). For the TROP-RISK Study, during a median follow-up of 1198 days (IQR 514–1866 days), 55850 (21.7%) deaths occurred. There was an unexpected inverted U-shaped relation between troponin level and mortality in acute coronary syndrome (ACS) patients (n=120049) (Figure 1A). The paradoxical decline in mortality at very high troponin levels may be driven in part by the changing case mix as troponin levels increase; a higher proportion of patients with very high troponin levels received invasive management (Figure 1B). CONCLUSION: Routine EHR data can be aggregated across multiple sites to create highly granular datasets for research. The SENIOR-NSTEMI Study showed a survival advantage of invasive compared with non-invasive management of elderly patients with NSTEMI, who were underrepresented in previous trials. The inverted U-shaped relationship between troponin and mortality in ACS patients in the TROP-RISK Study demonstrates that assembling sufficiently large datasets can cast light on patterns of disease that are impossible to adequately define in single centre studies. FUNDING ACKNOWLEDGEMENT: Type of funding sources: Public grant(s) – National budget only. Main funding source(s): 1) NIHR Imperial Biomedical Research Centre, as part of the NIHR Health Informatics Collaborative, and 2) British Heart Foundation |
format | Online Article Text |
id | pubmed-9779771 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-97797712023-01-27 Developing informatics infrastructure to curate datasets using electronic health record data from five hospitals for translational cardiovascular research Kaura, A Sterne, J A C Trickey, A Mulla, A Glampson, B Davies, J Woods, K Panoulas, V Shah, A D Patel, R S Kharbanda, R Shah, A M Perera, D Channon, K M Mayet, J Eur Heart J Digit Health Abstracts INTRODUCTION: It has been challenging for researchers to access granular electronic health record (EHR) data at scale. One emerging prospect is to use big data to traverse the translational spectrum from an early discovery phase to a later implementation phase. PURPOSE: To create a research-ready dataset to support translational research in cardiovascular medicine, using routinely-collected EHR data from multiple hospitals. As an early discovery phase study, we estimated the effect of invasive versus non-invasive management on the survival of patients with non-ST elevation myocardial infarction (NSTEMI) aged 80 years or older (SENIOR-NSTEMI Study). As a later implementation phase study, we determined the relationship between the full spectrum of troponin level and mortality in patients in whom troponin testing was performed for clinical purposes (TROP-RISK Study). METHODS: Using Microsoft SQL we developed a dataset of 257948 consecutive patients who had a troponin measured between 2010 and 2017 at five hospitals. We extracted phenotypically detailed data, including demographics, blood tests, procedural data, and survival status. For the SENIOR-NSTEMI Study, eligible patients were 80 years or older who were diagnosed with NSTEMI. We estimated mortality hazard ratios comparing invasive with non-invasive management. For the TROP-RISK Study, we modelled the relation between peak troponin level and all-cause mortality using multivariable adjusted restricted cubic spline Cox regression analyses. RESULTS: For the SENIOR-NSTEMI Study, 1500 patients with NSTEMI were included who had a median age of 86 (interquartile range (IQR) 82–89) years of whom (845 [56%]) received non-invasive management. During a median follow-up of 3 (IQR 1.2–4.8) years, the adjusted cumulative five-year mortality was 36% in the invasive and 55% in the non-invasive group (hazard ratio 0.68, 95% confidence interval 0.55–0.84). For the TROP-RISK Study, during a median follow-up of 1198 days (IQR 514–1866 days), 55850 (21.7%) deaths occurred. There was an unexpected inverted U-shaped relation between troponin level and mortality in acute coronary syndrome (ACS) patients (n=120049) (Figure 1A). The paradoxical decline in mortality at very high troponin levels may be driven in part by the changing case mix as troponin levels increase; a higher proportion of patients with very high troponin levels received invasive management (Figure 1B). CONCLUSION: Routine EHR data can be aggregated across multiple sites to create highly granular datasets for research. The SENIOR-NSTEMI Study showed a survival advantage of invasive compared with non-invasive management of elderly patients with NSTEMI, who were underrepresented in previous trials. The inverted U-shaped relationship between troponin and mortality in ACS patients in the TROP-RISK Study demonstrates that assembling sufficiently large datasets can cast light on patterns of disease that are impossible to adequately define in single centre studies. FUNDING ACKNOWLEDGEMENT: Type of funding sources: Public grant(s) – National budget only. Main funding source(s): 1) NIHR Imperial Biomedical Research Centre, as part of the NIHR Health Informatics Collaborative, and 2) British Heart Foundation Oxford University Press 2022-12-22 /pmc/articles/PMC9779771/ http://dx.doi.org/10.1093/ehjdh/ztac076.2794 Text en Reproduced from: European Heart Journal, Volume 43, Issue Supplement_2, October 2022, ehac544.2794, https://doi.org/10.1093/eurheartj/ehac544.2794 by permission of Oxford University Press on behalf of the European Society of Cardiology. The opinions expressed in the Journal item reproduced as this reprint are those of the authors and contributors, and do not necessarily reflect those of the European Society of Cardiology, the editors, the editorial board, Oxford University Press or the organization to which the authors are affiliated. The mention of trade names, commercial products or organizations, and the inclusion of advertisements in this reprint do not imply endorsement by the Journal, the editors, the editorial board, Oxford University Press or the organization to which the authors are affiliated. The editors and publishers have taken all reasonable precautions to verify drug names and doses, the results of experimental work and clinical findings published in the Journal. The ultimate responsibility for the use and dosage of drugs mentioned in this reprint and in interpretation of published material lies with the medical practitioner, and the editors and publisher cannot accept liability for damages arising from any error or omissions in the Journal or in this reprint. Please inform the editors of any errors. © The Author(s) 2022. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Abstracts Kaura, A Sterne, J A C Trickey, A Mulla, A Glampson, B Davies, J Woods, K Panoulas, V Shah, A D Patel, R S Kharbanda, R Shah, A M Perera, D Channon, K M Mayet, J Developing informatics infrastructure to curate datasets using electronic health record data from five hospitals for translational cardiovascular research |
title | Developing informatics infrastructure to curate datasets using electronic health record data from five hospitals for translational cardiovascular research |
title_full | Developing informatics infrastructure to curate datasets using electronic health record data from five hospitals for translational cardiovascular research |
title_fullStr | Developing informatics infrastructure to curate datasets using electronic health record data from five hospitals for translational cardiovascular research |
title_full_unstemmed | Developing informatics infrastructure to curate datasets using electronic health record data from five hospitals for translational cardiovascular research |
title_short | Developing informatics infrastructure to curate datasets using electronic health record data from five hospitals for translational cardiovascular research |
title_sort | developing informatics infrastructure to curate datasets using electronic health record data from five hospitals for translational cardiovascular research |
topic | Abstracts |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9779771/ http://dx.doi.org/10.1093/ehjdh/ztac076.2794 |
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