Jae-Yung Kwon1,2, Richard Sawatzky3,4,5, Jennifer Baumbusch6, Pamela A Ratner7. 1. School of Nursing, University of British Columbia, Vancouver, Canada. jae-yung.kwon@twu.ca. 2. School of Nursing, Trinity Western University, 22500 University Drive, Langley, BC, V2Y 1Y1, Canada. jae-yung.kwon@twu.ca. 3. School of Nursing, Trinity Western University, 22500 University Drive, Langley, BC, V2Y 1Y1, Canada. 4. Evaluation and Outcome Sciences, Providence Health Care Research Institute, Vancouver, Canada. 5. Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. 6. School of Nursing, University of British Columbia, Vancouver, Canada. 7. Department of Educational and Counselling Psychology, and Special Education, Faculty of Education, University of British Columbia, Vancouver, BC, Canada.
Abstract
PURPOSE: Previous research about the health and quality of life of people with atrial fibrillation has typically identified a single health trajectory. Our study aimed to examine variability in health trajectories and patient characteristics associated with such variability. METHODS: We conducted a retrospective analysis of data collected between 2008 and 2016 for a cardiac registry in British Columbia (Canada) linked with administrative health data. The Atrial Fibrillation Effect on Quality of Life Questionnaire was used to measure health status at up to 10 clinic visits. Growth mixture models were used and a three-step multinomial logistic regression was conducted to identify predictors of subgroups with different trajectories. RESULTS: The patients (N = 7439) were primarily men (61.1%) over 60 years of age (72.9%). Three subgroups of health status trajectories were identified: "poor but improving", "good and stable", and "excellent and stable" health. Compared with the other two groups, patients in the "poor but improving group" were more likely to (1) be less than 60 years of age; (2) be women; (3) have greater risk of stroke; (4) have had ablation therapy within 6 months to 1 year or more than 2 years after their initial consultation; and (5) have had anticoagulation therapy within 6 months. CONCLUSION: Using growth mixture models, we found that not all health trajectories are the same. These models can help to understand variability in trajectories with different patient characteristics that could inform tailored interventions and patient education strategies.
PURPOSE: Previous research about the health and quality of life of people with atrial fibrillation has typically identified a single health trajectory. Our study aimed to examine variability in health trajectories and patient characteristics associated with such variability. METHODS: We conducted a retrospective analysis of data collected between 2008 and 2016 for a cardiac registry in British Columbia (Canada) linked with administrative health data. The Atrial Fibrillation Effect on Quality of Life Questionnaire was used to measure health status at up to 10 clinic visits. Growth mixture models were used and a three-step multinomial logistic regression was conducted to identify predictors of subgroups with different trajectories. RESULTS: The patients (N = 7439) were primarily men (61.1%) over 60 years of age (72.9%). Three subgroups of health status trajectories were identified: "poor but improving", "good and stable", and "excellent and stable" health. Compared with the other two groups, patients in the "poor but improving group" were more likely to (1) be less than 60 years of age; (2) be women; (3) have greater risk of stroke; (4) have had ablation therapy within 6 months to 1 year or more than 2 years after their initial consultation; and (5) have had anticoagulation therapy within 6 months. CONCLUSION: Using growth mixture models, we found that not all health trajectories are the same. These models can help to understand variability in trajectories with different patient characteristics that could inform tailored interventions and patient education strategies.
Authors: Michiel Rienstra; Steven A Lubitz; Saagar Mahida; Jared W Magnani; João D Fontes; Moritz F Sinner; Isabelle C Van Gelder; Patrick T Ellinor; Emelia J Benjamin Journal: Circulation Date: 2012-06-12 Impact factor: 29.690
Authors: Deepak L Bhatt; Joseph P Drozda; David M Shahian; Paul S Chan; Gregg C Fonarow; Paul A Heidenreich; Jeffrey P Jacobs; Frederick A Masoudi; Eric D Peterson; Karl F Welke Journal: J Am Coll Cardiol Date: 2015-10-02 Impact factor: 24.094
Authors: Hugh Calkins; Richard E Gliklich; Michelle B Leavy; Jonathan P Piccini; Jonathan C Hsu; Sanghamitra Mohanty; William Lewis; Saman Nazarian; Mintu P Turakhia Journal: Heart Rhythm Date: 2018-11-16 Impact factor: 6.343
Authors: Sandra B Lauck; Richard Sawatzky; Joy L Johnson; Karin Humphries; Matthew T Bennett; Santabhanu Chakrabarti; Charles R Kerr; Stanley Tung; John A Yeung-Lai-Wah; Pamela A Ratner Journal: Circ Cardiovasc Qual Outcomes Date: 2015-02-24
Authors: Samantha Cruz Rivera; Derek G Kyte; Olalekan Lee Aiyegbusi; Anita L Slade; Christel McMullan; Melanie J Calvert Journal: Health Qual Life Outcomes Date: 2019-10-16 Impact factor: 3.186
Authors: Dan Atar; Eivind Berge; Jean-Yves Le Heuzey; Saverio Virdone; A John Camm; Jan Steffel; Harry Gibbs; Samuel Z Goldhaber; Shinya Goto; Gloria Kayani; Frank Misselwitz; Janina Stepinska; Alexander G G Turpie; Jean-Pierre Bassand; Ajay K Kakkar Journal: Europace Date: 2020-02-01 Impact factor: 5.214
Authors: Jae-Yung Kwon; Richard Sawatzky; Jennifer Baumbusch; Sandra Lauck; Pamela A Ratner Journal: BMC Med Res Methodol Date: 2021-04-21 Impact factor: 4.615
Authors: Luc J H J Theunissen; Henricus-Paul Cremers; Dennis van Veghel; Pepijn H van der Voort; Peter E Polak; Sylvie F A M S de Jong; Geert Smits; Jos Dijkmans; Hareld M C Kemps; Lukas R C Dekker; Jeroen A A van de Pol Journal: J Arrhythm Date: 2022-01-10