Literature DB >> 33295794

Performance of Atrial Fibrillation Risk Prediction Models in Over 4 Million Individuals.

Shaan Khurshid1,2, Uri Kartoun3, Jeffrey M Ashburner1,4, Ludovic Trinquart5,6, Anthony Philippakis1, Amit V Khera1, Patrick T Ellinor1,7, Kenney Ng3, Steven A Lubitz1,7.   

Abstract

BACKGROUND: Atrial fibrillation (AF) is associated with increased risks of stroke and heart failure. Electronic health record (EHR)-based AF risk prediction may facilitate efficient deployment of interventions to diagnose or prevent AF altogether.
METHODS: We externally validated an electronic health record AF (EHR-AF) score in IBM Explorys Life Sciences, a multi-institutional dataset containing statistically deidentified EHR data for over 21 million individuals (Explorys Dataset). We included individuals with complete AF risk data, ≥2 office visits within 2 years, and no prevalent AF. We compared EHR-AF to existing scores including CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology Atrial Fibrillation), C2HEST (coronary artery disease or chronic obstructive pulmonary disease, hypertension, elderly, systolic heart failure, thyroid disease), and CHA2DS2-VASc. We assessed association between AF risk scores and 5-year incident AF, stroke, and heart failure using Cox proportional hazards modeling, 5-year AF discrimination using C indices, and calibration of predicted AF risk to observed AF incidence.
RESULTS: Of 21 825 853 individuals in the Explorys Dataset, 4 508 180 comprised the analysis (age 62.5, 56.3% female). AF risk scores were strongly associated with 5-year incident AF (hazard ratio per SD increase 1.85 using CHA2DS2-VASc to 2.88 using EHR-AF), stroke (1.61 using C2HEST to 1.92 using CHARGE-AF), and heart failure (1.91 using CHA2DS2-VASc to 2.58 using EHR-AF). EHR-AF (C index, 0.808 [95% CI, 0.807-0.809]) demonstrated favorable AF discrimination compared to CHARGE-AF (0.806 [95% CI, 0.805-0.807]), C2HEST (0.683 [95% CI, 0.682-0.684]), and CHA2DS2-VASc (0.720 [95% CI, 0.719-0.722]). Of the scores, EHR-AF demonstrated the best calibration to incident AF (calibration slope, 1.002 [95% CI, 0.997-1.007]). In subgroup analyses, AF discrimination using EHR-AF was lower in individuals with stroke (C index, 0.696 [95% CI, 0.692-0.700]) and heart failure (0.621 [95% CI, 0.617-0.625]).
CONCLUSIONS: EHR-AF demonstrates predictive accuracy for incident AF using readily ascertained EHR data. AF risk is associated with incident stroke and heart failure. Use of such risk scores may facilitate decision support and population health management efforts focused on minimizing AF-related morbidity.

Entities:  

Keywords:  atrial fibrillation; electronic health record; heart failure; incidence; risk

Mesh:

Year:  2020        PMID: 33295794      PMCID: PMC7856013          DOI: 10.1161/CIRCEP.120.008997

Source DB:  PubMed          Journal:  Circ Arrhythm Electrophysiol        ISSN: 1941-3084


  27 in total

1.  Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond.

Authors:  Michael J Pencina; Ralph B D'Agostino; Ralph B D'Agostino; Ramachandran S Vasan
Journal:  Stat Med       Date:  2008-01-30       Impact factor: 2.373

Review 2.  Atrial Fibrillation and Hypertension.

Authors:  Mikhail S Dzeshka; Alena Shantsila; Eduard Shantsila; Gregory Y H Lip
Journal:  Hypertension       Date:  2017-09-11       Impact factor: 10.190

3.  Atrial fibrillation incidence and risk factors in relation to race-ethnicity and the population attributable fraction of atrial fibrillation risk factors: the Multi-Ethnic Study of Atherosclerosis.

Authors:  Carlos J Rodriguez; Elsayed Z Soliman; Alvaro Alonso; Katrina Swett; Peter M Okin; David C Goff; Susan R Heckbert
Journal:  Ann Epidemiol       Date:  2014-11-28       Impact factor: 3.797

4.  PREVEntion and regReSsive Effect of weight-loss and risk factor modification on Atrial Fibrillation: the REVERSE-AF study.

Authors:  Melissa E Middeldorp; Rajeev K Pathak; Megan Meredith; Abhinav B Mehta; Adrian D Elliott; Rajiv Mahajan; Darragh Twomey; Celine Gallagher; Jeroen M L Hendriks; Dominik Linz; R Doug McEvoy; Walter P Abhayaratna; Jonathan M Kalman; Dennis H Lau; Prashanthan Sanders
Journal:  Europace       Date:  2018-12-01       Impact factor: 5.214

5.  Relationships between sinus rhythm, treatment, and survival in the Atrial Fibrillation Follow-Up Investigation of Rhythm Management (AFFIRM) Study.

Authors:  Scott D Corley; Andrew E Epstein; John P DiMarco; Michael J Domanski; Nancy Geller; H Leon Greene; Richard A Josephson; Joyce C Kellen; Richard C Klein; Andrew D Krahn; Mary Mickel; L Brent Mitchell; Joy Dalquist Nelson; Yves Rosenberg; Eleanor Schron; Lynn Shemanski; Albert L Waldo; D George Wyse
Journal:  Circulation       Date:  2004-03-08       Impact factor: 29.690

6.  Stroke Prevention in Atrial Fibrillation Study. Final results.

Authors: 
Journal:  Circulation       Date:  1991-08       Impact factor: 29.690

7.  Atrial fibrillation as a risk factor for stroke recurrence.

Authors:  Susana Penado; Marta Cano; Olga Acha; José L Hernández; José A Riancho
Journal:  Am J Med       Date:  2003-02-15       Impact factor: 4.965

8.  A Simple Clinical Risk Score (C2HEST) for Predicting Incident Atrial Fibrillation in Asian Subjects: Derivation in 471,446 Chinese Subjects, With Internal Validation and External Application in 451,199 Korean Subjects.

Authors:  Yan-Guang Li; Daniele Pastori; Alessio Farcomeni; Pil-Sung Yang; Eunsun Jang; Boyoung Joung; Yu-Tang Wang; Yu-Tao Guo; Gregory Y H Lip
Journal:  Chest       Date:  2018-10-04       Impact factor: 9.410

9.  Validation of the Framingham Heart Study and CHARGE-AF Risk Scores for Atrial Fibrillation in Hispanics, African-Americans, and Non-Hispanic Whites.

Authors:  Eric Shulman; Faraj Kargoli; Philip Aagaard; Ethan Hoch; Luigi Di Biase; John Fisher; Jay Gross; Soo Kim; Andrew Krumerman; Kevin J Ferrick
Journal:  Am J Cardiol       Date:  2015-10-19       Impact factor: 2.778

10.  A Simple and Portable Algorithm for Identifying Atrial Fibrillation in the Electronic Medical Record.

Authors:  Shaan Khurshid; John Keaney; Patrick T Ellinor; Steven A Lubitz
Journal:  Am J Cardiol       Date:  2015-11-06       Impact factor: 2.778

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  13 in total

1.  ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation.

Authors:  Shaan Khurshid; Samuel Friedman; Christopher Reeder; Paolo Di Achille; Nathaniel Diamant; Pulkit Singh; Lia X Harrington; Xin Wang; Mostafa A Al-Alusi; Gopal Sarma; Andrea S Foulkes; Patrick T Ellinor; Christopher D Anderson; Jennifer E Ho; Anthony A Philippakis; Puneet Batra; Steven A Lubitz
Journal:  Circulation       Date:  2021-11-08       Impact factor: 29.690

2.  Predictive Accuracy of a Clinical and Genetic Risk Model for Atrial Fibrillation.

Authors:  Shaan Khurshid; Nina Mars; Christopher M Haggerty; Qiuxi Huang; Lu-Chen Weng; Dustin N Hartzel; Kathryn L Lunetta; Jeffrey M Ashburner; Christopher D Anderson; Emelia J Benjamin; Veikko Salomaa; Patrick T Ellinor; Brandon K Fornwalt; Samuli Ripatti; Ludovic Trinquart; Steven A Lubitz
Journal:  Circ Genom Precis Med       Date:  2021-08-31

3.  Performance of an electronic health record-based predictive model to identify patients with atrial fibrillation across countries.

Authors:  Ruth Mokgokong; Renate Schnabel; Henning Witt; Robert Miller; Theodore C Lee
Journal:  PLoS One       Date:  2022-07-08       Impact factor: 3.752

Review 4.  Genetics of atrial fibrillation-practical applications for clinical management: if not now, when and how?

Authors:  Shinwan Kany; Bruno Reissmann; Andreas Metzner; Paulus Kirchhof; Dawood Darbar; Renate B Schnabel
Journal:  Cardiovasc Res       Date:  2021-06-16       Impact factor: 10.787

5.  GA4GH: International policies and standards for data sharing across genomic research and healthcare.

Authors:  Heidi L Rehm; Angela J H Page; Lindsay Smith; Jeremy B Adams; Gil Alterovitz; Lawrence J Babb; Maxmillian P Barkley; Michael Baudis; Michael J S Beauvais; Tim Beck; Jacques S Beckmann; Sergi Beltran; David Bernick; Alexander Bernier; James K Bonfield; Tiffany F Boughtwood; Guillaume Bourque; Sarion R Bowers; Anthony J Brookes; Michael Brudno; Matthew H Brush; David Bujold; Tony Burdett; Orion J Buske; Moran N Cabili; Daniel L Cameron; Robert J Carroll; Esmeralda Casas-Silva; Debyani Chakravarty; Bimal P Chaudhari; Shu Hui Chen; J Michael Cherry; Justina Chung; Melissa Cline; Hayley L Clissold; Robert M Cook-Deegan; Mélanie Courtot; Fiona Cunningham; Miro Cupak; Robert M Davies; Danielle Denisko; Megan J Doerr; Lena I Dolman; Edward S Dove; L Jonathan Dursi; Stephanie O M Dyke; James A Eddy; Karen Eilbeck; Kyle P Ellrott; Susan Fairley; Khalid A Fakhro; Helen V Firth; Michael S Fitzsimons; Marc Fiume; Paul Flicek; Ian M Fore; Mallory A Freeberg; Robert R Freimuth; Lauren A Fromont; Jonathan Fuerth; Clara L Gaff; Weiniu Gan; Elena M Ghanaim; David Glazer; Robert C Green; Malachi Griffith; Obi L Griffith; Robert L Grossman; Tudor Groza; Jaime M Guidry Auvil; Roderic Guigó; Dipayan Gupta; Melissa A Haendel; Ada Hamosh; David P Hansen; Reece K Hart; Dean Mitchell Hartley; David Haussler; Rachele M Hendricks-Sturrup; Calvin W L Ho; Ashley E Hobb; Michael M Hoffman; Oliver M Hofmann; Petr Holub; Jacob Shujui Hsu; Jean-Pierre Hubaux; Sarah E Hunt; Ammar Husami; Julius O Jacobsen; Saumya S Jamuar; Elizabeth L Janes; Francis Jeanson; Aina Jené; Amber L Johns; Yann Joly; Steven J M Jones; Alexander Kanitz; Kazuto Kato; Thomas M Keane; Kristina Kekesi-Lafrance; Jerome Kelleher; Giselle Kerry; Seik-Soon Khor; Bartha M Knoppers; Melissa A Konopko; Kenjiro Kosaki; Martin Kuba; Jonathan Lawson; Rasko Leinonen; Stephanie Li; Michael F Lin; Mikael Linden; Xianglin Liu; Isuru Udara Liyanage; Javier Lopez; Anneke M Lucassen; Michael Lukowski; Alice L Mann; John Marshall; Michele Mattioni; Alejandro Metke-Jimenez; Anna Middleton; Richard J Milne; Fruzsina Molnár-Gábor; Nicola Mulder; Monica C Munoz-Torres; Rishi Nag; Hidewaki Nakagawa; Jamal Nasir; Arcadi Navarro; Tristan H Nelson; Ania Niewielska; Amy Nisselle; Jeffrey Niu; Tommi H Nyrönen; Brian D O'Connor; Sabine Oesterle; Soichi Ogishima; Vivian Ota Wang; Laura A D Paglione; Emilio Palumbo; Helen E Parkinson; Anthony A Philippakis; Angel D Pizarro; Andreas Prlic; Jordi Rambla; Augusto Rendon; Renee A Rider; Peter N Robinson; Kurt W Rodarmer; Laura Lyman Rodriguez; Alan F Rubin; Manuel Rueda; Gregory A Rushton; Rosalyn S Ryan; Gary I Saunders; Helen Schuilenburg; Torsten Schwede; Serena Scollen; Alexander Senf; Nathan C Sheffield; Neerjah Skantharajah; Albert V Smith; Heidi J Sofia; Dylan Spalding; Amanda B Spurdle; Zornitza Stark; Lincoln D Stein; Makoto Suematsu; Patrick Tan; Jonathan A Tedds; Alastair A Thomson; Adrian Thorogood; Timothy L Tickle; Katsushi Tokunaga; Juha Törnroos; David Torrents; Sean Upchurch; Alfonso Valencia; Roman Valls Guimera; Jessica Vamathevan; Susheel Varma; Danya F Vears; Coby Viner; Craig Voisin; Alex H Wagner; Susan E Wallace; Brian P Walsh; Marc S Williams; Eva C Winkler; Barbara J Wold; Grant M Wood; J Patrick Woolley; Chisato Yamasaki; Andrew D Yates; Christina K Yung; Lyndon J Zass; Ksenia Zaytseva; Junjun Zhang; Peter Goodhand; Kathryn North; Ewan Birney
Journal:  Cell Genom       Date:  2021-11-10

Review 6.  Identifying Atrial Fibrillation Mechanisms for Personalized Medicine.

Authors:  Brototo Deb; Prasanth Ganesan; Ruibin Feng; Sanjiv M Narayan
Journal:  J Clin Med       Date:  2021-12-01       Impact factor: 4.241

Review 7.  C2HEST score for atrial fibrillation risk prediction models: a Diagnostic Accuracy Tests meta-analysis.

Authors:  Habib Haybar; Kimia Shirbandi; Fakher Rahim
Journal:  Egypt Heart J       Date:  2021-12-04

8.  Re-CHARGE-AF: Recalibration of the CHARGE-AF Model for Atrial Fibrillation Risk Prediction in Patients With Acute Stroke.

Authors:  Jeffrey M Ashburner; Xin Wang; Xinye Li; Shaan Khurshid; Darae Ko; Ana Trisini Lipsanopoulos; Priscilla R Lee; Taylor Carmichael; Ashby C Turner; Corban Jackson; Patrick T Ellinor; Emelia J Benjamin; Steven J Atlas; Daniel E Singer; Ludovic Trinquart; Steven A Lubitz; Christopher D Anderson
Journal:  J Am Heart Assoc       Date:  2021-10-20       Impact factor: 5.501

9.  Cohort design and natural language processing to reduce bias in electronic health records research.

Authors:  Shaan Khurshid; Christopher Reeder; Lia X Harrington; Pulkit Singh; Gopal Sarma; Samuel F Friedman; Paolo Di Achille; Nathaniel Diamant; Jonathan W Cunningham; Ashby C Turner; Emily S Lau; Julian S Haimovich; Mostafa A Al-Alusi; Xin Wang; Marcus D R Klarqvist; Jeffrey M Ashburner; Christian Diedrich; Mercedeh Ghadessi; Johanna Mielke; Hanna M Eilken; Alice McElhinney; Andrea Derix; Steven J Atlas; Patrick T Ellinor; Anthony A Philippakis; Christopher D Anderson; Jennifer E Ho; Puneet Batra; Steven A Lubitz
Journal:  NPJ Digit Med       Date:  2022-04-08

10.  The Liverpool Heart And bRain Project (L-HARP): Protocol for an Observational Cohort Study of Cardiovascular Risk and Outcomes Following Stroke.

Authors:  Stephanie L Harrison; Deirdre A Lane; Benjamin J R Buckley; Kausik Chatterjee; Muath Alobaida; Emily Shipley; Gregory Y H Lip
Journal:  Vasc Health Risk Manag       Date:  2022-04-26
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