Jenny Berg1, Peter Lindgren2, Märit Mejhert3, Magnus Edner4, Ulf Dahlström5, Thomas Kahan6. 1. Division of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Solna, Sweden; OptumInsight, Stockholm, Sweden. Electronic address: jenny.berg@ki.se. 2. Medical Management Center, Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Solna, Sweden; IVBAR, Stockholm, Sweden. 3. Department of Medicine, Ersta Hospital, Stockholm, Sweden; Division of Cardiovascular Medicine, Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden. 4. Karolinska Institutet, Heart Research Unit, Karolinska University Hospital, Solna, Sweden. 5. Departments of Cardiology and Medical and Health Sciences, Linköping University, Linköping, Sweden. 6. Division of Cardiovascular Medicine, Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden.
Abstract
BACKGROUND: There is limited information on drivers of utilities in patients with chronic heart failure (CHF). OBJECTIVES: To analyze determinants of utility in CHF and drivers of change over 1 year in a large sample from clinical practice. METHODS: We included 5334 patients from the Swedish Heart Failure Registry with EuroQol five-dimensional questionnaire information available following inpatient or outpatient care during 2008 to 2010; 3495 had 1-year follow-up data. Utilities based on Swedish and UK value sets were derived. We applied ordinary least squares (OLS) and two-part models for utility at inclusion and OLS regression for change over 1 year, all with robust standard errors. We assessed the predictive accuracy of both models using cross-validation. RESULTS: Patients' mean age was 73 years, 65% were men, 19% had a left ventricular ejection fraction of 50% or more, 23% had 40% to 49%, 27% had 30% to 39%, and 31% had less than 30%. For both models and value sets, utility at inclusion was affected by sex, age, New York Heart Association class, ejection fraction, hemoglobin, blood pressure, lung disease, diabetes, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, nitrates, antiplatelets, and diuretics. The OLS model performed slightly better than did the two-part model on a population level and for capturing utility ranges. Change in utility over 1 year was influenced by age, sex, and (measured at inclusion) disease duration, New York Heart Association class, blood pressure, ischemic heart disease, lung disease, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, and antiplatelets. CONCLUSIONS: Utilities in CHF and their change over time are influenced by diverse demographic and clinical factors. Our findings can be used to target clinical interventions and for economic evaluations of new therapies.
BACKGROUND: There is limited information on drivers of utilities in patients with chronic heart failure (CHF). OBJECTIVES: To analyze determinants of utility in CHF and drivers of change over 1 year in a large sample from clinical practice. METHODS: We included 5334 patients from the Swedish Heart Failure Registry with EuroQol five-dimensional questionnaire information available following inpatient or outpatient care during 2008 to 2010; 3495 had 1-year follow-up data. Utilities based on Swedish and UK value sets were derived. We applied ordinary least squares (OLS) and two-part models for utility at inclusion and OLS regression for change over 1 year, all with robust standard errors. We assessed the predictive accuracy of both models using cross-validation. RESULTS:Patients' mean age was 73 years, 65% were men, 19% had a left ventricular ejection fraction of 50% or more, 23% had 40% to 49%, 27% had 30% to 39%, and 31% had less than 30%. For both models and value sets, utility at inclusion was affected by sex, age, New York Heart Association class, ejection fraction, hemoglobin, blood pressure, lung disease, diabetes, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, nitrates, antiplatelets, and diuretics. The OLS model performed slightly better than did the two-part model on a population level and for capturing utility ranges. Change in utility over 1 year was influenced by age, sex, and (measured at inclusion) disease duration, New York Heart Association class, blood pressure, ischemic heart disease, lung disease, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, and antiplatelets. CONCLUSIONS: Utilities in CHF and their change over time are influenced by diverse demographic and clinical factors. Our findings can be used to target clinical interventions and for economic evaluations of new therapies.
Authors: Justin T Parizo; Jeremy D Goldhaber-Fiebert; Joshua A Salomon; Kiran K Khush; John A Spertus; Paul A Heidenreich; Alexander T Sandhu Journal: JAMA Cardiol Date: 2021-08-01 Impact factor: 30.154
Authors: John J V McMurray; David Trueman; Elizabeth Hancock; Martin R Cowie; Andrew Briggs; Matthew Taylor; Juliet Mumby-Croft; Fionn Woodcock; Michael Lacey; Rola Haroun; Celine Deschaseaux Journal: Heart Date: 2017-12-21 Impact factor: 5.994
Authors: Milos Slepecky; Antonia Kotianova; Jan Prasko; Ivan Majercak; Michal Kotian; Erika Gyorgyova; Marta Zatkova; Michaela Chupacova; Marie Ociskova; Tomas Sollar Journal: Psychol Res Behav Manag Date: 2019-07-04
Authors: Gian Luca Di Tanna; Michael Urbich; Heidi S Wirtz; Barbara Potrata; Marieke Heisen; Craig Bennison; John Brazier; Gary Globe Journal: Pharmacoeconomics Date: 2020-11-30 Impact factor: 4.981
Authors: Adam Witkowski; Dariusz Dudek; Stanisław Bartuś; Wojciech Wojakowski; Andrzej Gackowski; Marek Grygier; Mariusz Kuśmierczyk; Miłosz J Jaguszewski; Ewa Kowalik; Katarzyna Bondaryk; Maciej Niewada; Piotr Przygodzki; Michał Jakubczyk Journal: Cardiol J Date: 2021-10-21 Impact factor: 3.487