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Long‐Term Exposure to Elevated Systolic Blood Pressure in Predicting Incident Cardiovascular Disease: Evidence From Large‐Scale Routine Electronic Health Records
BACKGROUND: How measures of long‐term exposure to elevated blood pressure might add to the performance of “current” blood pressure in predicting future cardiovascular disease is unclear. We compared incident cardiovascular disease risk prediction using past, current, and usual systolic blood pressur...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6645648/ https://www.ncbi.nlm.nih.gov/pubmed/31164039 http://dx.doi.org/10.1161/JAHA.119.012129 |
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author | Ayala Solares, Jose Roberto Canoy, Dexter Raimondi, Francesca Elisa Diletta Zhu, Yajie Hassaine, Abdelaali Salimi‐Khorshidi, Gholamreza Tran, Jenny Copland, Emma Zottoli, Mariagrazia Pinho‐Gomes, Ana‐Catarina Nazarzadeh, Milad Rahimi, Kazem |
author_facet | Ayala Solares, Jose Roberto Canoy, Dexter Raimondi, Francesca Elisa Diletta Zhu, Yajie Hassaine, Abdelaali Salimi‐Khorshidi, Gholamreza Tran, Jenny Copland, Emma Zottoli, Mariagrazia Pinho‐Gomes, Ana‐Catarina Nazarzadeh, Milad Rahimi, Kazem |
author_sort | Ayala Solares, Jose Roberto |
collection | PubMed |
description | BACKGROUND: How measures of long‐term exposure to elevated blood pressure might add to the performance of “current” blood pressure in predicting future cardiovascular disease is unclear. We compared incident cardiovascular disease risk prediction using past, current, and usual systolic blood pressure alone or in combination. METHODS AND RESULTS: Using data from UK primary care linked electronic health records, we applied a landmark cohort study design and identified 80 964 people, aged 50 years (derivation cohort=64 772; validation cohort=16 192), who, at study entry, had recorded blood pressure, no prior cardiovascular disease, and no previous antihypertensive or lipid‐lowering prescriptions. We used systolic blood pressure recorded up to 10 years before baseline to estimate past systolic blood pressure (mean, time‐weighted mean, and variability) and usual systolic blood pressure (correcting current values for past time‐dependent blood pressure fluctuations) and examined their prospective relation with incident cardiovascular disease (first hospitalization for or death from coronary heart disease or stroke/transient ischemic attack). We used Cox regression to estimate hazard ratios and applied Bayesian analysis within a machine learning framework in model development and validation. Predictive performance of models was assessed using discrimination (area under the receiver operating characteristic curve) and calibration metrics. We found that elevated past, current, and usual systolic blood pressure values were separately and independently associated with increased incident cardiovascular disease risk. When used alone, the hazard ratio (95% credible interval) per 20–mm Hg increase in current systolic blood pressure was 1.22 (1.18–1.30), but associations were stronger for past systolic blood pressure (mean and time‐weighted mean) and usual systolic blood pressure (hazard ratio ranging from 1.39–1.45). The area under the receiver operating characteristic curve for a model that included current systolic blood pressure, sex, smoking, deprivation, diabetes mellitus, and lipid profile was 0.747 (95% credible interval, 0.722–0.811). The addition of past systolic blood pressure mean, time‐weighted mean, or variability to this model increased the area under the receiver operating characteristic curve (95% credible interval) to 0.750 (0.727–0.811), 0.750 (0.726–0.811), and 0.748 (0.723–0.811), respectively, with all models showing good calibration. Similar small improvements in area under the receiver operating characteristic curve were observed when testing models on the validation cohort, in sex‐stratified analyses, or by using different landmark ages (40 or 60 years). CONCLUSIONS: Using multiple blood pressure recordings from patients’ electronic health records showed stronger associations with incident cardiovascular disease than a single blood pressure measurement, but their addition to multivariate risk prediction models had negligible effects on model performance. |
format | Online Article Text |
id | pubmed-6645648 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-66456482019-07-31 Long‐Term Exposure to Elevated Systolic Blood Pressure in Predicting Incident Cardiovascular Disease: Evidence From Large‐Scale Routine Electronic Health Records Ayala Solares, Jose Roberto Canoy, Dexter Raimondi, Francesca Elisa Diletta Zhu, Yajie Hassaine, Abdelaali Salimi‐Khorshidi, Gholamreza Tran, Jenny Copland, Emma Zottoli, Mariagrazia Pinho‐Gomes, Ana‐Catarina Nazarzadeh, Milad Rahimi, Kazem J Am Heart Assoc Original Research BACKGROUND: How measures of long‐term exposure to elevated blood pressure might add to the performance of “current” blood pressure in predicting future cardiovascular disease is unclear. We compared incident cardiovascular disease risk prediction using past, current, and usual systolic blood pressure alone or in combination. METHODS AND RESULTS: Using data from UK primary care linked electronic health records, we applied a landmark cohort study design and identified 80 964 people, aged 50 years (derivation cohort=64 772; validation cohort=16 192), who, at study entry, had recorded blood pressure, no prior cardiovascular disease, and no previous antihypertensive or lipid‐lowering prescriptions. We used systolic blood pressure recorded up to 10 years before baseline to estimate past systolic blood pressure (mean, time‐weighted mean, and variability) and usual systolic blood pressure (correcting current values for past time‐dependent blood pressure fluctuations) and examined their prospective relation with incident cardiovascular disease (first hospitalization for or death from coronary heart disease or stroke/transient ischemic attack). We used Cox regression to estimate hazard ratios and applied Bayesian analysis within a machine learning framework in model development and validation. Predictive performance of models was assessed using discrimination (area under the receiver operating characteristic curve) and calibration metrics. We found that elevated past, current, and usual systolic blood pressure values were separately and independently associated with increased incident cardiovascular disease risk. When used alone, the hazard ratio (95% credible interval) per 20–mm Hg increase in current systolic blood pressure was 1.22 (1.18–1.30), but associations were stronger for past systolic blood pressure (mean and time‐weighted mean) and usual systolic blood pressure (hazard ratio ranging from 1.39–1.45). The area under the receiver operating characteristic curve for a model that included current systolic blood pressure, sex, smoking, deprivation, diabetes mellitus, and lipid profile was 0.747 (95% credible interval, 0.722–0.811). The addition of past systolic blood pressure mean, time‐weighted mean, or variability to this model increased the area under the receiver operating characteristic curve (95% credible interval) to 0.750 (0.727–0.811), 0.750 (0.726–0.811), and 0.748 (0.723–0.811), respectively, with all models showing good calibration. Similar small improvements in area under the receiver operating characteristic curve were observed when testing models on the validation cohort, in sex‐stratified analyses, or by using different landmark ages (40 or 60 years). CONCLUSIONS: Using multiple blood pressure recordings from patients’ electronic health records showed stronger associations with incident cardiovascular disease than a single blood pressure measurement, but their addition to multivariate risk prediction models had negligible effects on model performance. John Wiley and Sons Inc. 2019-06-05 /pmc/articles/PMC6645648/ /pubmed/31164039 http://dx.doi.org/10.1161/JAHA.119.012129 Text en © 2019 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
spellingShingle | Original Research Ayala Solares, Jose Roberto Canoy, Dexter Raimondi, Francesca Elisa Diletta Zhu, Yajie Hassaine, Abdelaali Salimi‐Khorshidi, Gholamreza Tran, Jenny Copland, Emma Zottoli, Mariagrazia Pinho‐Gomes, Ana‐Catarina Nazarzadeh, Milad Rahimi, Kazem Long‐Term Exposure to Elevated Systolic Blood Pressure in Predicting Incident Cardiovascular Disease: Evidence From Large‐Scale Routine Electronic Health Records |
title | Long‐Term Exposure to Elevated Systolic Blood Pressure in Predicting Incident Cardiovascular Disease: Evidence From Large‐Scale Routine Electronic Health Records |
title_full | Long‐Term Exposure to Elevated Systolic Blood Pressure in Predicting Incident Cardiovascular Disease: Evidence From Large‐Scale Routine Electronic Health Records |
title_fullStr | Long‐Term Exposure to Elevated Systolic Blood Pressure in Predicting Incident Cardiovascular Disease: Evidence From Large‐Scale Routine Electronic Health Records |
title_full_unstemmed | Long‐Term Exposure to Elevated Systolic Blood Pressure in Predicting Incident Cardiovascular Disease: Evidence From Large‐Scale Routine Electronic Health Records |
title_short | Long‐Term Exposure to Elevated Systolic Blood Pressure in Predicting Incident Cardiovascular Disease: Evidence From Large‐Scale Routine Electronic Health Records |
title_sort | long‐term exposure to elevated systolic blood pressure in predicting incident cardiovascular disease: evidence from large‐scale routine electronic health records |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6645648/ https://www.ncbi.nlm.nih.gov/pubmed/31164039 http://dx.doi.org/10.1161/JAHA.119.012129 |
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