scholarly journals Change of Health‐Related Quality of Life Over Time and Its Association With Patient Outcomes in Patients With Heart Failure

2020 ◽  
Vol 9 (17) ◽  
Author(s):  
Nariman Sepehrvand ◽  
Anamaria Savu ◽  
John A. Spertus ◽  
Jason R. B. Dyck ◽  
Todd Anderson ◽  
...  

Background Improving health‐related quality of life is an important goal in the management of patients with heart failure (HF). Defining health‐related quality of life changes over time in patients with HF with preserved (HFpEF) or reduced ejection fraction and showing their association with other important clinical events could support the use of health‐related quality of life as a measure of quantifying HF care. Methods and Results In the Alberta HEART (Heart Failure Aetiology and Analysis Team) cohort (n=621), patients were categorized into 4 subgroups: healthy controls (n=98), at risk (n=163), HFpEF (n=191), and HF with reduced ejection fraction (n=169). The change of the Kansas City Cardiomyopathy Questionnaire (KCCQ), EuroQOL 5 dimensions, and Functional Assessment of Cancer Therapy—Anemia over 12 months, and its association with a composite of death or rehospitalization within 3 years were assessed. At baseline, the KCCQ overall summary score was 73 (interquartile range, 53–86) in HFpEF and 78 (interquartile range, 56–90) in HF with reduced ejection fraction ( P =0.22). Overall, 30.5% of patients with HF experienced ≥5‐point improvements and 32.4% had ≥5‐point worsening in KCCQ overall summary score at 12 months, which did not differ between HFpEF and HF with reduced ejection fraction ( P =0.23). Clinical events were higher in patients with HF who had a decline in KCCQ over 12 months as compared with those with stable KCCQ scores (70.2% versus 52.0%, P =0.012). The results were similar for the Functional Assessment of Cancer Therapy—Anemia and EuroQOL 5 dimensions. Conclusions In patients with HF, the KCCQ quantified clinically meaningful changes over time, which were associated with important clinical outcomes in patients with HFpEF. Given the observed variability and prognostication in different patient trajectories, health‐related quality of life measures could be valuable for quantifying the quality of care in healthcare systems.

2019 ◽  
Vol 74 (25) ◽  
pp. 3176-3178
Author(s):  
Muthiah Vaduganathan ◽  
Gregg C. Fonarow ◽  
Stephen J. Greene ◽  
Adam D. DeVore ◽  
Nancy M. Albert ◽  
...  

2007 ◽  
Vol 9 (1) ◽  
pp. 83-91 ◽  
Author(s):  
Eldrin F. Lewis ◽  
Gervasio A. Lamas ◽  
Eileen O'Meara ◽  
Christopher B. Granger ◽  
Mark E. Dunlap ◽  
...  

2018 ◽  
Vol 24 (8) ◽  
pp. S98
Author(s):  
Jorge Conte ◽  
Jose Nativi-Nicolau ◽  
Mingyuan Zhang ◽  
Tom Greene ◽  
Joshua Biber ◽  
...  

2018 ◽  
Vol 6 (7) ◽  
pp. 552-560 ◽  
Author(s):  
Rebecca Napier ◽  
Steven E. McNulty ◽  
David T. Eton ◽  
Margaret M. Redfield ◽  
Omar AbouEzzeddine ◽  
...  

2019 ◽  
Vol 7 (10) ◽  
pp. 862-874 ◽  
Author(s):  
Alvin Chandra ◽  
Muthiah Vaduganathan ◽  
Eldrin F. Lewis ◽  
Brian L. Claggett ◽  
Adel R. Rizkala ◽  
...  

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
P McEwan ◽  
J.J.V McMurray ◽  
P.S Jhund ◽  
K.F Docherty ◽  
L Qin

Abstract Background The DAPA-HF trial demonstrated that dapagliflozin was superior to placebo at preventing cardiovascular death and hospitalisation for heart failure (hHF) events in patients with chronic heart failure with reduced ejection fraction (HFrEF). The trial also demonstrated a clinically important benefit of dapagliflozin on health-related quality of life (HRQoL). However, key predictors of HRQoL in HFrEF patients remain uncertain. The objective of this study was to determine, using DAPA-HF trial data, the patient characteristics and disease-related events associated with patient HRQoL, measured by health state utility values. Methods Mixed effects regression models were developed based on pooled individual patient data from DAPA-HF to determine patient utility estimated from responses to the EQ-5D-5L questionnaire, incorporating a subject specific random intercept. In line with NICE guidance, utility estimates were derived using UK-specific utility tariffs after mapping EQ-5D-5L data to EQ-5D-3L values. Univariable analysis was first undertaken to assess candidate predictors of utility; followed by a multivariable model including statistically significant predictors, e.g. Kansas City Cardiomyopathy Questionnaire Total Symptom Score (KCCQ-TSS) and the incidence hHF events, and controlling for differences in baseline characteristics. All variables were included in a single model to provide independent (adjusted) estimates for each covariable. Results 19,983 EQ-5D-5L questionnaires from 4,744 patients were included. Mean patient utility at baseline was 0.716 (95% CI: 0.711, 0.722). Univariable analysis demonstrated NYHA, KCCQ-TSS, T2DM, BMI, age, geographic location, non-ischaemic/unknown aetiology and atrial fibrillation were statistically significant in their association with patient utility while prior hHF, race, eGFR and left ventricular ejection fraction were not. Multivariable analysis results are summarised in Fig. 1. The baseline characteristic with the greatest impact on EQ-5D was KCCQ-TSS quartile, with EQ-5D increasing with KCCQ-TSS and the difference in utility between patients in quartile 1 (lowest score) and quartile 4 (highest score) estimated at 0.233 (0.226, 0.240). When controlled for baseline characteristics, being post-event was significantly associated with HRQoL; patients who experienced hospitalisation for HF had 0.036 (0.014, 0.058) lower utility on average within one month of the event and 0.025 (0.011, 0.039) lower utility up to one-year after the event. For patients who had stroke or myocardial infarction events there were reductions in utility of 0.206 (0.141, 0.272) and 0.108 (0.039, 0.177) respectively at 1 month. Conclusion HF symptoms, measured by the KCCQ, were strongly associated with patient health utility. Therapeutic interventions that can improve HF symptoms have the potential to improve HRQoL and reduce the burden of HF. FUNDunding Acknowledgement Type of funding sources: Private company. Main funding source(s): AstraZeneca


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