Nakao, Y., Gale, C. orcid.org/0000-0003-4732-382X, Nadarajah, R. orcid.org/0000-0001-9895-9356 et al. (1 more author) (2024) Predicting incident heart failure from population-based nationwide electronic health records: protocol for a model development and validation study. BMJ: British Medical Journal, 14 (1). e073455. ISSN 0959-535X
Abstract
Introduction Heart failure (HF) is increasingly common and associated with excess morbidity, mortality, and healthcare costs. Treatment of HF can alter the disease trajectory and reduce clinical events in HF. However, many cases of HF remain undetected until presentation with more advanced symptoms, often requiring hospitalisation. Predicting incident HF is challenging and statistical models are limited by performance and scalability in routine clinical practice. An HF prediction model implementable in nationwide electronic health records (EHRs) could enable targeted diagnostics to enable earlier identification of HF.
Methods and analysis We will investigate a range of development techniques (including logistic regression and supervised machine learning methods) on routinely collected primary care EHRs to predict risk of new-onset HF over 1, 5 and 10 years prediction horizons. The Clinical Practice Research Datalink (CPRD)-GOLD dataset will be used for derivation (training and testing) and the CPRD-AURUM dataset for external validation. Both comprise large cohorts of patients, representative of the population of England in terms of age, sex and ethnicity. Primary care records are linked at patient level to secondary care and mortality data. The performance of the prediction model will be assessed by discrimination, calibration and clinical utility. We will only use variables routinely accessible in primary care.
Ethics and dissemination Permissions for CPRD-GOLD and CPRD-AURUM datasets were obtained from CPRD (ref no: 21_000324). The CPRD ethical approval committee approved the study. The results will be submitted as a research paper for publication to a peer-reviewed journal and presented at peer-reviewed conferences.
Trial registration details The study was registered on Clinical Trials.gov (NCT 05756127). A systematic review for the project was registered on PROSPERO (registration number: CRD42022380892).
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/ This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
Keywords: | Humans; Calibration; England; Heart Failure; Electronic Health Records; Systematic Reviews as Topic; Ethnicity |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM) > Clinical & Population Science Dept (Leeds) The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Dentistry (Leeds) > Applied Health and Clinical Translation (Leeds) |
Funding Information: | Funder Grant number British Heart Foundation PG/23/11366 |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Feb 2024 14:07 |
Last Modified: | 16 May 2024 14:17 |
Published Version: | http://dx.doi.org/10.1136/bmjopen-2023-073455 |
Status: | Published |
Publisher: | British Medical Association |
Identification Number: | 10.1136/bmjopen-2023-073455 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:208839 |