Quantification of fibroblast growth factor 23 and N-terminal pro-B-type natriuretic peptide to identify patients with atrial fibrillation using a high-throughput platform: A validation study

Large-scale screening for atrial fibrillation (AF) requires reliable methods to identify at-risk populations. Using an experimental semi-quantitative biomarker assay, B-type natriuretic peptide (BNP) and fibroblast growth factor 23 (FGF23) were recently identified as the most suitable biomarkers for...

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Veröffentlicht in:PLoS medicine 2021-02, Vol.18 (2), p.e1003405-e1003405
Hauptverfasser: Chua, Winnie, Law, Jonathan P, Cardoso, Victor R, Purmah, Yanish, Neculau, Georgiana, Jawad-Ul-Qamar, Muhammad, Russell, Kalisha, Turner, Ashley, Tull, Samantha P, Nehaj, Frantisek, Brady, Paul, Kastner, Peter, Ziegler, André, Gkoutos, Georgios V, Pavlovic, Davor, Ferro, Charles J, Kirchhof, Paulus, Fabritz, Larissa
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container_title PLoS medicine
container_volume 18
creator Chua, Winnie
Law, Jonathan P
Cardoso, Victor R
Purmah, Yanish
Neculau, Georgiana
Jawad-Ul-Qamar, Muhammad
Russell, Kalisha
Turner, Ashley
Tull, Samantha P
Nehaj, Frantisek
Brady, Paul
Kastner, Peter
Ziegler, André
Gkoutos, Georgios V
Pavlovic, Davor
Ferro, Charles J
Kirchhof, Paulus
Fabritz, Larissa
description Large-scale screening for atrial fibrillation (AF) requires reliable methods to identify at-risk populations. Using an experimental semi-quantitative biomarker assay, B-type natriuretic peptide (BNP) and fibroblast growth factor 23 (FGF23) were recently identified as the most suitable biomarkers for detecting AF in combination with simple morphometric parameters (age, sex, and body mass index [BMI]). In this study, we validated the AF model using standardised, high-throughput, high-sensitivity biomarker assays. For this study, 1,625 consecutive patients with either (1) diagnosed AF or (2) sinus rhythm with CHA2DS2-VASc score of 2 or more were recruited from a large teaching hospital in Birmingham, West Midlands, UK, between September 2014 and February 2018. Seven-day ambulatory ECG monitoring excluded silent AF. Patients with tachyarrhythmias apart from AF and incomplete cases were excluded. AF was diagnosed according to current clinical guidelines and confirmed by ECG. We developed a high-throughput, high-sensitivity assay for FGF23, quantified plasma N-terminal pro-B-type natriuretic peptide (NT-proBNP) and FGF23, and compared results to the previously used multibiomarker research assay. Data were fitted to the previously derived model, adjusting for differences in measurement platforms and known confounders (heart failure and chronic kidney disease). In 1,084 patients (46% with AF; median [Q1, Q3] age 70 [60, 78] years, median [Q1, Q3] BMI 28.8 [25.1, 32.8] kg/m2, 59% males), patients with AF had higher concentrations of NT-proBNP (median [Q1, Q3] per 100 pg/ml: with AF 12.00 [4.19, 30.15], without AF 4.25 [1.17, 15.70]; p < 0.001) and FGF23 (median [Q1, Q3] per 100 pg/ml: with AF 1.93 [1.30, 4.16], without AF 1.55 [1.04, 2.62]; p < 0.001). Univariate associations remained after adjusting for heart failure and estimated glomerular filtration rate, known confounders of NT-proBNP and FGF23. The fitted model yielded a C-statistic of 0.688 (95% CI 0.656, 0.719), almost identical to that of the derived model (C-statistic 0.691; 95% CI 0.638, 0.744). The key limitation is that this validation was performed in a cohort that is very similar demographically to the one used in model development, calling for further external validation. Age, sex, and BMI combined with elevated NT-proBNP and elevated FGF23, quantified on a high-throughput platform, reliably identify patients with AF. Registry IRAS ID 97753 Health Research Authority (HRA), United Kingdom.
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Using an experimental semi-quantitative biomarker assay, B-type natriuretic peptide (BNP) and fibroblast growth factor 23 (FGF23) were recently identified as the most suitable biomarkers for detecting AF in combination with simple morphometric parameters (age, sex, and body mass index [BMI]). In this study, we validated the AF model using standardised, high-throughput, high-sensitivity biomarker assays. For this study, 1,625 consecutive patients with either (1) diagnosed AF or (2) sinus rhythm with CHA2DS2-VASc score of 2 or more were recruited from a large teaching hospital in Birmingham, West Midlands, UK, between September 2014 and February 2018. Seven-day ambulatory ECG monitoring excluded silent AF. Patients with tachyarrhythmias apart from AF and incomplete cases were excluded. AF was diagnosed according to current clinical guidelines and confirmed by ECG. We developed a high-throughput, high-sensitivity assay for FGF23, quantified plasma N-terminal pro-B-type natriuretic peptide (NT-proBNP) and FGF23, and compared results to the previously used multibiomarker research assay. Data were fitted to the previously derived model, adjusting for differences in measurement platforms and known confounders (heart failure and chronic kidney disease). In 1,084 patients (46% with AF; median [Q1, Q3] age 70 [60, 78] years, median [Q1, Q3] BMI 28.8 [25.1, 32.8] kg/m2, 59% males), patients with AF had higher concentrations of NT-proBNP (median [Q1, Q3] per 100 pg/ml: with AF 12.00 [4.19, 30.15], without AF 4.25 [1.17, 15.70]; p &lt; 0.001) and FGF23 (median [Q1, Q3] per 100 pg/ml: with AF 1.93 [1.30, 4.16], without AF 1.55 [1.04, 2.62]; p &lt; 0.001). Univariate associations remained after adjusting for heart failure and estimated glomerular filtration rate, known confounders of NT-proBNP and FGF23. The fitted model yielded a C-statistic of 0.688 (95% CI 0.656, 0.719), almost identical to that of the derived model (C-statistic 0.691; 95% CI 0.638, 0.744). The key limitation is that this validation was performed in a cohort that is very similar demographically to the one used in model development, calling for further external validation. Age, sex, and BMI combined with elevated NT-proBNP and elevated FGF23, quantified on a high-throughput platform, reliably identify patients with AF. 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This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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Using an experimental semi-quantitative biomarker assay, B-type natriuretic peptide (BNP) and fibroblast growth factor 23 (FGF23) were recently identified as the most suitable biomarkers for detecting AF in combination with simple morphometric parameters (age, sex, and body mass index [BMI]). In this study, we validated the AF model using standardised, high-throughput, high-sensitivity biomarker assays. For this study, 1,625 consecutive patients with either (1) diagnosed AF or (2) sinus rhythm with CHA2DS2-VASc score of 2 or more were recruited from a large teaching hospital in Birmingham, West Midlands, UK, between September 2014 and February 2018. Seven-day ambulatory ECG monitoring excluded silent AF. Patients with tachyarrhythmias apart from AF and incomplete cases were excluded. AF was diagnosed according to current clinical guidelines and confirmed by ECG. We developed a high-throughput, high-sensitivity assay for FGF23, quantified plasma N-terminal pro-B-type natriuretic peptide (NT-proBNP) and FGF23, and compared results to the previously used multibiomarker research assay. Data were fitted to the previously derived model, adjusting for differences in measurement platforms and known confounders (heart failure and chronic kidney disease). In 1,084 patients (46% with AF; median [Q1, Q3] age 70 [60, 78] years, median [Q1, Q3] BMI 28.8 [25.1, 32.8] kg/m2, 59% males), patients with AF had higher concentrations of NT-proBNP (median [Q1, Q3] per 100 pg/ml: with AF 12.00 [4.19, 30.15], without AF 4.25 [1.17, 15.70]; p &lt; 0.001) and FGF23 (median [Q1, Q3] per 100 pg/ml: with AF 1.93 [1.30, 4.16], without AF 1.55 [1.04, 2.62]; p &lt; 0.001). Univariate associations remained after adjusting for heart failure and estimated glomerular filtration rate, known confounders of NT-proBNP and FGF23. The fitted model yielded a C-statistic of 0.688 (95% CI 0.656, 0.719), almost identical to that of the derived model (C-statistic 0.691; 95% CI 0.638, 0.744). The key limitation is that this validation was performed in a cohort that is very similar demographically to the one used in model development, calling for further external validation. Age, sex, and BMI combined with elevated NT-proBNP and elevated FGF23, quantified on a high-throughput platform, reliably identify patients with AF. Registry IRAS ID 97753 Health Research Authority (HRA), United Kingdom.</description><subject>Age</subject><subject>Aged</subject><subject>Atrial fibrillation</subject><subject>Atrial Fibrillation - blood</subject><subject>Atrial Fibrillation - diagnosis</subject><subject>Biological markers</subject><subject>Biology and Life Sciences</subject><subject>Biomarkers</subject><subject>Biomarkers - blood</subject><subject>Body mass index</subject><subject>Brain natriuretic peptide</subject><subject>Cardiac arrhythmia</subject><subject>Cohort Studies</subject><subject>Confidence intervals</subject><subject>Dementia disorders</subject><subject>Development and progression</subject><subject>Diagnosis</subject><subject>EKG</subject><subject>Female</subject><subject>Fibrillation</subject><subject>Fibroblast Growth Factor-23</subject><subject>Fibroblast growth factors</subject><subject>Fibroblast Growth Factors - blood</subject><subject>Fibroblasts</subject><subject>Growth factors</subject><subject>Health aspects</subject><subject>Heart failure</subject><subject>Heart Failure - blood</subject><subject>Heart Failure - diagnosis</subject><subject>Humans</subject><subject>Male</subject><subject>Measurement</subject><subject>Medicine and Health Sciences</subject><subject>Middle Aged</subject><subject>Natriuretic Peptide, Brain - blood</subject><subject>Natriuretic peptides</subject><subject>Patients</subject><subject>Peptides</subject><subject>Prognosis</subject><subject>Research and Analysis Methods</subject><subject>Risk Factors</subject><subject>Statistical analysis</subject><subject>Validation studies</subject><issn>1549-1676</issn><issn>1549-1277</issn><issn>1549-1676</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>DOA</sourceid><recordid>eNqVk91u0zAUxyMEYmPwBggsISG4SLEbO052gVTGgKGxwWDcWk5sJ57SONjORl-OZ-OUdtOKegHyhS37d_7nyydJHhM8IRknrz6enp-dzI4nnz8dvp0QjDOK2Z1klzBapiTn-d1b553kQQgXGE9LXOL7yU6WsYwWU7ab_Poyyj5aY2sZreuRM8jYyruqkyGixrur2CIj6-g8mmZI9gqdpFH7ue1lhwbv0jdpXAwa9TJ6O3odbY0GPUSrNIoOwbaUX6AB9OEY0JUFxSUM9ktXtutWrsdg-wZJ1NqmTWPr3di0wxjRAO_G-fk-mqFL2Vm1wkMc1eJhcs_ILuhH630vOX93-O3gQ3p8-v7oYHac1jynMaVVnmupeYELyLvAjMm65JRzrApcKUxIVZIMm0wRqaA2mvKskDXHuiQ4NzLbS56udIfOBXHhRg_pBzFlmBSclywH4mhFKCcvxODtXPqFcNKKPxfON0J6KE6nhcKMZGTKDcYFZRSXVV4VyhBdaYwpMaD1eu1trOZa1VA3L7sN0c2X3raicZeCF4xziH8vebEW8O7HqEMUcxtqDZXutRshblrkNGe85IA--wvdnt2aaiQkYHvjwG-9FBWznNGclJxToNItVKN7DUG6XhsL1xv8ZAsPS-m5rbcavNwwACbqn7GRYwji6OvZf7An_86eft9kn99iWy272AbXjctPGTZBugJr70Lw2tw0kGCxHOHrSosBWinWIwxmT243_8boemaz3yL-QZs</recordid><startdate>20210203</startdate><enddate>20210203</enddate><creator>Chua, Winnie</creator><creator>Law, Jonathan P</creator><creator>Cardoso, Victor R</creator><creator>Purmah, Yanish</creator><creator>Neculau, Georgiana</creator><creator>Jawad-Ul-Qamar, Muhammad</creator><creator>Russell, Kalisha</creator><creator>Turner, Ashley</creator><creator>Tull, Samantha P</creator><creator>Nehaj, Frantisek</creator><creator>Brady, Paul</creator><creator>Kastner, Peter</creator><creator>Ziegler, André</creator><creator>Gkoutos, Georgios V</creator><creator>Pavlovic, Davor</creator><creator>Ferro, Charles J</creator><creator>Kirchhof, Paulus</creator><creator>Fabritz, Larissa</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISN</scope><scope>ISR</scope><scope>3V.</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><scope>CZK</scope><orcidid>https://orcid.org/0000-0001-8744-7152</orcidid><orcidid>https://orcid.org/0000-0002-9241-1733</orcidid><orcidid>https://orcid.org/0000-0001-6336-0327</orcidid><orcidid>https://orcid.org/0000-0002-5204-8554</orcidid><orcidid>https://orcid.org/0000-0002-3171-3551</orcidid><orcidid>https://orcid.org/0000-0002-6747-8813</orcidid><orcidid>https://orcid.org/0000-0003-0577-7081</orcidid><orcidid>https://orcid.org/0000-0002-4900-9618</orcidid><orcidid>https://orcid.org/0000-0002-2061-091X</orcidid><orcidid>https://orcid.org/0000-0002-1881-0197</orcidid></search><sort><creationdate>20210203</creationdate><title>Quantification of fibroblast growth factor 23 and N-terminal pro-B-type natriuretic peptide to identify patients with atrial fibrillation using a high-throughput platform: A validation study</title><author>Chua, Winnie ; Law, Jonathan P ; Cardoso, Victor R ; Purmah, Yanish ; Neculau, Georgiana ; Jawad-Ul-Qamar, Muhammad ; Russell, Kalisha ; Turner, Ashley ; Tull, Samantha P ; Nehaj, Frantisek ; Brady, Paul ; Kastner, Peter ; Ziegler, André ; Gkoutos, Georgios V ; Pavlovic, Davor ; Ferro, Charles J ; Kirchhof, Paulus ; Fabritz, Larissa</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c764t-4b66eae78083488055ac974770d80bd011b9130f3d1ad353e4738ac70e9106fa3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Age</topic><topic>Aged</topic><topic>Atrial fibrillation</topic><topic>Atrial Fibrillation - blood</topic><topic>Atrial Fibrillation - diagnosis</topic><topic>Biological markers</topic><topic>Biology and Life Sciences</topic><topic>Biomarkers</topic><topic>Biomarkers - blood</topic><topic>Body mass index</topic><topic>Brain natriuretic peptide</topic><topic>Cardiac arrhythmia</topic><topic>Cohort Studies</topic><topic>Confidence intervals</topic><topic>Dementia disorders</topic><topic>Development and progression</topic><topic>Diagnosis</topic><topic>EKG</topic><topic>Female</topic><topic>Fibrillation</topic><topic>Fibroblast Growth Factor-23</topic><topic>Fibroblast growth factors</topic><topic>Fibroblast Growth Factors - blood</topic><topic>Fibroblasts</topic><topic>Growth factors</topic><topic>Health aspects</topic><topic>Heart failure</topic><topic>Heart Failure - blood</topic><topic>Heart Failure - diagnosis</topic><topic>Humans</topic><topic>Male</topic><topic>Measurement</topic><topic>Medicine and Health Sciences</topic><topic>Middle Aged</topic><topic>Natriuretic Peptide, Brain - blood</topic><topic>Natriuretic peptides</topic><topic>Patients</topic><topic>Peptides</topic><topic>Prognosis</topic><topic>Research and Analysis Methods</topic><topic>Risk Factors</topic><topic>Statistical analysis</topic><topic>Validation studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chua, Winnie</creatorcontrib><creatorcontrib>Law, Jonathan P</creatorcontrib><creatorcontrib>Cardoso, Victor R</creatorcontrib><creatorcontrib>Purmah, Yanish</creatorcontrib><creatorcontrib>Neculau, Georgiana</creatorcontrib><creatorcontrib>Jawad-Ul-Qamar, Muhammad</creatorcontrib><creatorcontrib>Russell, Kalisha</creatorcontrib><creatorcontrib>Turner, Ashley</creatorcontrib><creatorcontrib>Tull, Samantha P</creatorcontrib><creatorcontrib>Nehaj, Frantisek</creatorcontrib><creatorcontrib>Brady, Paul</creatorcontrib><creatorcontrib>Kastner, Peter</creatorcontrib><creatorcontrib>Ziegler, André</creatorcontrib><creatorcontrib>Gkoutos, Georgios V</creatorcontrib><creatorcontrib>Pavlovic, Davor</creatorcontrib><creatorcontrib>Ferro, Charles J</creatorcontrib><creatorcontrib>Kirchhof, Paulus</creatorcontrib><creatorcontrib>Fabritz, Larissa</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Canada</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Neurosciences Abstracts</collection><collection>Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><collection>PLoS Medicine</collection><jtitle>PLoS medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chua, Winnie</au><au>Law, Jonathan P</au><au>Cardoso, Victor R</au><au>Purmah, Yanish</au><au>Neculau, Georgiana</au><au>Jawad-Ul-Qamar, Muhammad</au><au>Russell, Kalisha</au><au>Turner, Ashley</au><au>Tull, Samantha P</au><au>Nehaj, Frantisek</au><au>Brady, Paul</au><au>Kastner, Peter</au><au>Ziegler, André</au><au>Gkoutos, Georgios V</au><au>Pavlovic, Davor</au><au>Ferro, Charles J</au><au>Kirchhof, Paulus</au><au>Fabritz, Larissa</au><au>Sulzgruber, Patrick</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Quantification of fibroblast growth factor 23 and N-terminal pro-B-type natriuretic peptide to identify patients with atrial fibrillation using a high-throughput platform: A validation study</atitle><jtitle>PLoS medicine</jtitle><addtitle>PLoS Med</addtitle><date>2021-02-03</date><risdate>2021</risdate><volume>18</volume><issue>2</issue><spage>e1003405</spage><epage>e1003405</epage><pages>e1003405-e1003405</pages><issn>1549-1676</issn><issn>1549-1277</issn><eissn>1549-1676</eissn><abstract>Large-scale screening for atrial fibrillation (AF) requires reliable methods to identify at-risk populations. Using an experimental semi-quantitative biomarker assay, B-type natriuretic peptide (BNP) and fibroblast growth factor 23 (FGF23) were recently identified as the most suitable biomarkers for detecting AF in combination with simple morphometric parameters (age, sex, and body mass index [BMI]). In this study, we validated the AF model using standardised, high-throughput, high-sensitivity biomarker assays. For this study, 1,625 consecutive patients with either (1) diagnosed AF or (2) sinus rhythm with CHA2DS2-VASc score of 2 or more were recruited from a large teaching hospital in Birmingham, West Midlands, UK, between September 2014 and February 2018. Seven-day ambulatory ECG monitoring excluded silent AF. Patients with tachyarrhythmias apart from AF and incomplete cases were excluded. AF was diagnosed according to current clinical guidelines and confirmed by ECG. We developed a high-throughput, high-sensitivity assay for FGF23, quantified plasma N-terminal pro-B-type natriuretic peptide (NT-proBNP) and FGF23, and compared results to the previously used multibiomarker research assay. Data were fitted to the previously derived model, adjusting for differences in measurement platforms and known confounders (heart failure and chronic kidney disease). In 1,084 patients (46% with AF; median [Q1, Q3] age 70 [60, 78] years, median [Q1, Q3] BMI 28.8 [25.1, 32.8] kg/m2, 59% males), patients with AF had higher concentrations of NT-proBNP (median [Q1, Q3] per 100 pg/ml: with AF 12.00 [4.19, 30.15], without AF 4.25 [1.17, 15.70]; p &lt; 0.001) and FGF23 (median [Q1, Q3] per 100 pg/ml: with AF 1.93 [1.30, 4.16], without AF 1.55 [1.04, 2.62]; p &lt; 0.001). Univariate associations remained after adjusting for heart failure and estimated glomerular filtration rate, known confounders of NT-proBNP and FGF23. The fitted model yielded a C-statistic of 0.688 (95% CI 0.656, 0.719), almost identical to that of the derived model (C-statistic 0.691; 95% CI 0.638, 0.744). The key limitation is that this validation was performed in a cohort that is very similar demographically to the one used in model development, calling for further external validation. Age, sex, and BMI combined with elevated NT-proBNP and elevated FGF23, quantified on a high-throughput platform, reliably identify patients with AF. Registry IRAS ID 97753 Health Research Authority (HRA), United Kingdom.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>33534825</pmid><doi>10.1371/JOURNAL.PMED.1003405</doi><orcidid>https://orcid.org/0000-0001-8744-7152</orcidid><orcidid>https://orcid.org/0000-0002-9241-1733</orcidid><orcidid>https://orcid.org/0000-0001-6336-0327</orcidid><orcidid>https://orcid.org/0000-0002-5204-8554</orcidid><orcidid>https://orcid.org/0000-0002-3171-3551</orcidid><orcidid>https://orcid.org/0000-0002-6747-8813</orcidid><orcidid>https://orcid.org/0000-0003-0577-7081</orcidid><orcidid>https://orcid.org/0000-0002-4900-9618</orcidid><orcidid>https://orcid.org/0000-0002-2061-091X</orcidid><orcidid>https://orcid.org/0000-0002-1881-0197</orcidid><oa>free_for_read</oa></addata></record>
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subjects Age
Aged
Atrial fibrillation
Atrial Fibrillation - blood
Atrial Fibrillation - diagnosis
Biological markers
Biology and Life Sciences
Biomarkers
Biomarkers - blood
Body mass index
Brain natriuretic peptide
Cardiac arrhythmia
Cohort Studies
Confidence intervals
Dementia disorders
Development and progression
Diagnosis
EKG
Female
Fibrillation
Fibroblast Growth Factor-23
Fibroblast growth factors
Fibroblast Growth Factors - blood
Fibroblasts
Growth factors
Health aspects
Heart failure
Heart Failure - blood
Heart Failure - diagnosis
Humans
Male
Measurement
Medicine and Health Sciences
Middle Aged
Natriuretic Peptide, Brain - blood
Natriuretic peptides
Patients
Peptides
Prognosis
Research and Analysis Methods
Risk Factors
Statistical analysis
Validation studies
title Quantification of fibroblast growth factor 23 and N-terminal pro-B-type natriuretic peptide to identify patients with atrial fibrillation using a high-throughput platform: A validation study
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