Abstract
Aims
A longitudinal and comprehensive description of health-related quality of life (HRQOL) has not been performed during hospitalization for HF or soon after discharge.
Methods and Results
A post-hoc analysis was performed of the ASCEND-HF trial. The EuroQOL five dimensions questionnaire (EQ-5D) was administered to study participants at baseline, hour 24, discharge/day 10, and day 30. EQ-5D includes functional dimensions mapped to corresponding utility scores (i.e. 0 = death and 1 = perfect health), and a visual analogue scale (VAS) ranging from 0 (i.e. “worst imaginable health state”) to 100 (i.e. “best imaginable health state”). The association between baseline and discharge EQ-5D measurements and subsequent clinical outcomes including death and rehospitalization were assessed using multivariable logistic regression and Cox proportional hazards regression. A total of 6943 patients (97%) had complete EQ-5D data at baseline. Mapped utility and VAS scores (mean±SD) increased over time, respectively, from 0.56±0.23 and 45±22 at baseline to 0.67±0.26 and 58±22 at hour 24 and 0.79±0.20 and 68±22 at discharge and remained stable at day 30. Lower mapped utility scores at baseline (odds ratio per 0.1 decrease in utility score [OR] 1.03, 95% confidence interval [CI] 1.00–1.06) and discharge (OR 1.10, 95% CI 1.05–1.15) and VAS scores at baseline (OR per 10 point decrease 1.05, 95% CI 1.01–1.09) were significantly associated with increased risk of 30-day all-cause death or HF rehospitalization.
Conclusions
Patients hospitalized for HF had severely impaired health status at baseline, and although this improved substantially during admission, health status remained persistently abnormal at discharge.
Keywords: heart failure, hospitalized, quality of life, morbidity, mortality
INTRODUCTION
Although the management of ambulatory HF patients with reduced ejection fraction has improved with guideline-directed medical and device-based therapy1, there is increasing recognition that the ‘patient journey’ may nevertheless be characterized by functional limitations and impairments in health-related quality of life (HRQOL) that are not captured by hard clinical endpoints, i.e. hospitalizations and mortality.2–5 In this respect, there remains an unmet need to develop validated and reproducible patient-centered outcome measures in order to assess the quality of care in everyday practice and the efficacy of novel therapies and management strategies in clinical trials.6–8 Although HRQOL has been studied in patients with HF in the outpatient setting,9, 10 a longitudinal and comprehensive description of HRQOL has not been performed during hospitalization for HF or soon after discharge.11
The global Acute Study of Clinical Effectiveness of Nesiritide and Decompensated Heart Failure (ASCEND-HF) trial database provides a unique opportunity to characterize the clinical course of health status in patients hospitalized for HF, the relationship between early dyspnea relief and subsequent health status, and the association between health status and post-discharge morbidity and mortality.
METHODS
Overview
The study design12 and primary results13 of the ASCEND-HF trial have been previously reported. Briefly, ASCEND-HF was a global, prospective, randomized, double-blind, placebo-controlled trial designed to examine the short- and long-term efficacy and safety of nesiritide, a recombinant natriuretic peptide. A total of 7141 patients hospitalized for HF as evidenced by dyspnea at rest or with minimal activity, ≥1 accompanying sign, and ≥1 objective measure were randomized to nesiritide or placebo, in addition to standard therapy, within 24 hours of the first intravenous HF-related treatment. Relevant exclusion criteria included a high likelihood to be discharged from the hospital in ≤24 hours or a comorbid condition with an associated life expectancy of <6 months. The ASCEND-HF trial was conducted in accordance with the Declaration of Helsinki the protocol was independently approved by the institutional review board or ethics committee at each participating center, and written informed consent was obtained from all participants.
Health Status
The EuroQOL five dimensions (EQ-5D) questionnaire, a widely used, generic, and self-administered survey designed to assess health status in adults, was administered at baseline, hour 24, discharge/day 10, and day 30. The instrument is comprised of two components, a descriptive profile and a single-index visual analogue scale (VAS). The descriptive profile includes five dimensions (i.e. mobility, self-care, usual activity, pain/discomfort, and anxiety/depression) and allows for 3 possible response options for each domain (i.e. no problem, some/moderate problem, or severe/extreme problem). Each set of 5 responses was mapped to a corresponding utility weight based on valuation scores derived from a representative sample of the United States population with 1 designating perfect health, 0 denoting death, and <0 suggesting states considered worse than death. The VAS provides a global assessment of health status ranging from 0 (i.e. “worst imaginable health state”) to 100 (i.e. “best imaginable health state”).
Study Definitions and Endpoints
Dyspnea was measured with a self-reported 7-point categorical Likert scale (i.e. markedly better, moderately better, minimally better, no change, minimally worse, moderately worse, or markedly worse). Patients were classified as having early dyspnea relief if they experienced moderate or marked improvement in dyspnea at 6 hours. The primary outcome of the ASCEND-HF trial was 30-day all-cause death or HF rehospitalization. Additional outcomes of interest for the present analysis were 30-day cardiovascular (CV) death or rehospitalization and 180-day mortality. An independent and blinded adjudication committee determined the cause of all hospitalizations and deaths occurring within 30 days. Hospitalization for HF was defined as admission for worsening signs or symptoms of HF resulting in the new administration of intravenous therapies, mechanical or surgical intervention, or provision of ultrafiltration, hemofiltration, or dialysis specifically for the management of persistent or worsening HF.
Statistical Analysis
All continuous data were reported as a mean±standard deviation (SD) and/or median (25th, 75th) percentiles and as frequencies and percentages for categorical data. Baseline patient characteristics including demographics, medical history, laboratory values, and medication use, were compared by quintiles of baseline VAS scores. Continuous variables were evaluated using analysis of variance or Kruskal-Wallis testing, while categorical variables were assessed using Chi-square test or Fisher’s exact test, as appropriate. Linear regression assessed the relationship between early dyspnea relief and subsequent utility and VAS scores at discharge and at day 30. Patients who died prior to those assessments were excluded. For each comparison, three models were assessed: unadjusted, adjusted for baseline EQ-5D measures only, and adjusted for potential confounders. Multiple imputation was used for missing adjustment covariate data in regression analyses. Twenty-five imputations were used and results shown reflect the combined result. Backward stepwise selection was used to identify potential confounders with inclusion criteria p-value < 0.10, selected in at least 80% of imputations, to create a final adjusted linear regression model. Logistic regression assessed the relationship between baseline utility score and 30-day outcomes. Cox proportional hazards regression assessed the relationship for 180-day mortality. The relationship between discharge utility score and subsequent outcomes was assessed among patients discharged alive from the index hospitalization. Models were adjusted for covariates previously identified in these data as associated with outcomes.14 Similar models assessed the association of VAS scores at baseline and discharge with outcomes 30- and 180-day outcomes. For Cox regression analysis of 180-day all-cause death, the proportional hazards assumption was assessed for EQ-5D and VAS scores; no violations were detected.
Funding and Manuscript Preparation
Scios Inc. (Mountain View, CA) provided financial and material support for the ASCEND-HF trial. Database management and statistical analysis was performed by the Duke Clinical Research Institute. The authors take responsibility for the manuscript’s integrity, and had complete control and authority over its preparation and the decision to publish.
RESULTS
Patient Characteristics
A total of 6943 patients had complete EQ-5D data. Study participants had a mean age of 65±14 years and 66% were male (Table 1). More than 40% of patients were self-described as non-white and a similar proportion were enrolled at centers outside of the United States or Western Europe. Ischemic heart disease was reported in 60% of study participants and the mean±SD left ventricular ejection fraction was 30±13%.
Table 1.
Baseline Patient Characteristics by Baseline EQ-5D VAS
| EQ-5D VAS Quintile | |||||||
|---|---|---|---|---|---|---|---|
| Characteristic | All Patients (N=6943) |
Quintile 1 [0 to 25] (N=1403) |
Quintile 2 [26 to 40] (N=1607) |
Quintile 3 [41 to 50] (N=1506) |
Quintile 4 [51 to 65] (N=1187) |
Quintile 5 [66 to 100] (N=1240) |
P- Value |
| Demographics | |||||||
| Age, yrs | 65 ± 14.1 | 65 ± 14.0 | 65 ± 13.7 | 66 ± 13.9 | 65 ± 14.1 | 66 ± 15.1 | <.001 |
| Female Gender | 2373 (34.2%) |
508 (36.2%) |
566 (35.2%) |
545 (36.2%) |
375 (31.6%) |
379 (30.6%) |
0.002 |
| Race Groups | <.001 | ||||||
| White | 3860 (55.6%) |
787 (56.1%) |
911 (56.8%) |
838 (55.6%) |
624 (52.6%) |
700 (56.5%) |
|
| Black or African American |
1055 (15.2%) |
290 (20.7%) |
170 (10.6%) |
184 (12.2%) |
160 (13.5%) |
251 (20.2%) |
|
| Asian | 1724 (24.8%) |
271 (19.3%) |
455 (28.3%) |
414 (27.5%) |
354 (29.8%) |
230 (18.5%) |
|
| Other | 301 (4.3%) | 54 (3.9%) | 69 (4.3%) | 70 (4.6%) | 49 (4.1%) | 59 (4.8%) | |
| Baseline Weight, kg | 78 (64, 95) | 80 (65, 98) | 77 (63, 92) | 77 (63, 95) | 77 (64, 93) | 80 (66, 96) | <.001 |
| Baseline Height, cm | 168 (160, 175) |
168 (160, 175) |
167 (160, 174) |
168 (160, 175) |
168 (160, 175) |
170 (162, 178) |
<.001 |
| Region | <.001 | ||||||
| Asia-Pacific | 1723 (24.8%) |
269 (19.2%) |
463 (28.8%) |
411 (27.3%) |
358 (30.2%) |
222 (17.9%) |
|
| Central Europe | 941 (13.6%) |
250 (17.8%) |
322 (20.0%) |
169 (11.2%) |
115 (9.7%) | 85 (6.9%) | |
| Latin America | 647 (9.3%) | 114 (8.1%) | 121 (7.5%) | 137 (9.1%) | 122 (10.3%) |
153 (12.3%) |
|
| North America | 3169 (45.7%) |
670 (47.8%) |
609 (37.9%) |
676 (44.9%) |
505 42.5%) |
709 (57.2%) |
|
| Western Europe | 461 (6.6%) | 99 (7.1%) | 91 (5.7%) | 113 (7.5%) | 87 (7.3%) | 71 (5.7%) | |
| Medical History | |||||||
| NYHA Classification | <.001 | ||||||
| NYHA Class not assessed |
1202 (17.3%) |
255 (18.2%) |
255 (15.9%) |
262 (17.4%) |
188 (15.8%) |
242 (19.5%) |
|
| I | 249 (3.6%) | 39 (2.8%) | 30 (1.9%) | 48 (3.2%) | 58 (4.9%) | 74 (6.0%) | |
| II | 1073 (15.5%) |
156 (11.1%) |
208 (12.9%) |
280 (18.6%) |
214 (18.0%) |
215 (17.3%) |
|
| III | 2785 (40.1%) |
669 (47.7%) |
682 (42.4%) |
552 (36.7%) |
423 (35.6%) |
459 (37.0%) |
|
| IV | 1634 (23.5%) |
284 (20.2%) |
432 (26.9%) |
364 (24.2%) |
304 (25.6%) |
250 (20.2%) |
|
| Ischemic Heart Disease | 4166 (60.0%) |
816 (58.2%) |
994 (61.9%) |
928 (61.6%) |
731 (61.6%) |
697 (56.2%) |
0.006 |
| HF Hospitalization past year |
2709 (39.1%) |
612 (43.7%) |
655 (40.9%) |
557 (37.1%) |
426 (35.9%) |
459 (37.0%) |
<.001 |
| Left Ventricular Ejection Fraction, previous 12 months |
30 ± 12.9 | 30 ± 12.4 | 29 ± 12.3 | 31 ± 13.2 | 31 ± 12.9 | 31 ± 13.4 | <.001 |
| LVEF <40% past year | 1033 (19.7%) |
185 (17.7%) |
171 (14.1%) |
257 (22.9%) |
202 (22.1%) |
218 (23.0%) |
<.001 |
| History of Hypertension | 5004 (72.1%) |
1022 (72.9%) |
1115 (69.4%) |
1093 (72.6%) |
842 (70.9%) |
932 (75.2%) |
0.012 |
| History of Diabetes Mellitus | 2960 (42.6%) |
587 (41.9%) |
668 (41.6%) |
673 (44.7%) |
519 (43.7%) |
513 (41.4%) |
0.284 |
| History of Coronary Artery Disease |
3786 (54.6%) |
748 (53.4%) |
907 (56.5%) |
840 (55.8%) |
658 (55.4%) |
633 (51.1%) |
0.032 |
| History of Cerebrovascular Disease |
813 (11.7%) |
184 (13.1%) |
187 (11.6%) |
169 (11.2%) |
126 (10.6%) |
147 (11.9%) |
0.347 |
| History of Peripheral Arterial Vascular Disease |
716 (10.3%) |
164 (11.7%) |
149 (9.3%) |
148 (9.8%) |
119 (10.0%) |
136 (11.0%) |
0.212 |
| Baseline Chronic Respiratory Disease |
1146 (16.5%) |
257 (18.3%) |
253 (15.8%) |
257 (17.1%) |
172 (14.5%) |
207 (16.7%) |
0.094 |
| History of Atrial Fibrillation/Flutter |
2589 (37.3%) |
520 (37.1%) |
580 (36.1%) |
576 (38.2%) |
432 (36.4%) |
481 (38.8%) |
0.531 |
| History of ICD/CRT | 624 (9.0%) |
134 (9.6%) |
147 (9.2%) |
124 (8.2%) |
101 (8.5%) |
118 (9.5%) |
0.660 |
| Laboratory Values | |||||||
| Baseline Systolic BP, mmHg | 123 (110, 140) |
122 (110, 140) |
122 (110, 138) |
125 (110, 140) |
123 (110, 140) |
123 (110, 140) |
0.088 |
| Baseline Heart Rate, beats/min |
82 (72, 95) |
84 (72, 96) |
83 (72, 96) |
81 (71, 94) |
82 (72, 95) |
80 (70, 92) |
<.001 |
| Baseline Respiratory Rate, breaths/min |
23 (21, 26) |
24 (22, 26) |
24 (21, 26) |
23 (21, 26) |
23 (21, 25) |
22 (20, 24) |
<.001 |
| Baseline sodium, mmol/L | 139 (136, 141) |
139 (136, 141) |
139 (136, 141) |
139 (136, 141) |
139 (136, 141) |
139 (136, 141) |
0.051 |
| Baseline Potassium, mmol/L |
4.1 (3.7, 4.5) |
4.1 (3.7, 4.4) |
4.1 (3.8, 4.5) |
4.1 (3.7, 4.4) |
4.1 (3.7, 4.5) |
4.0 (3.7, 4.4) |
<.001 |
| Baseline BUN, mg/dL | 25 (18, 38) |
26 (18, 40) |
26 (18, 39) |
26 (18, 39) |
24 (17, 37) |
24 (17, 37) |
0.027 |
| Baseline Creatinine, mg/dL | 1.2 (1.0, 1.6) |
1.2 (1.0, 1.6) |
1.2 (1.0, 1.5) |
1.2 (1.0, 1.6) |
1.2 (1.0, 1.6) |
1.3 (1.0, 1.6) |
0.110 |
| Baseline Hemoglobin, g/dL | 13 (11, 14) |
13 (11, 14) |
13 (11, 14) |
13 (11, 14) |
13 (11, 14) |
13 (11, 14) |
0.008 |
| Baseline NT-proBNP, Pg/mL |
4492 (2090, 9128) |
4743 (2208, 9484) |
4935 (2413, 9128) |
4134 (2059, 9274) |
4110 (1812, 9174) |
4297 (2037, 8197) |
0.028 |
| Baseline BNP, pg/mL | 990 (543, 1860) |
970 (508, 1776) |
935 (464, 1805) |
1020 (585, 1917) |
994 (593, 1900) |
1047 (585, 1927) |
0.038 |
| Baseline RDW, % | 15 (14, 17) |
16 (14, 17) |
15 (14, 17) |
15 (14, 17) |
15 (14, 17) |
15 (14, 17) |
<.001 |
|
Medication at/before Baseline |
|||||||
| ACEI or ARB | 4218 (60.8%) |
841 (60.0%) |
1012 (63.1%) |
910 (60.4%) |
702 (59.1%) |
753 (60.7%) |
0.261 |
| Beta Blockers | 4049 (58.3%) |
795 (56.7%) |
908 (56.6%) |
895 (59.4%) |
667 (56.2%) |
784 (63.2%) |
<.001 |
| Aldosterone Antagonists | 1931 (27.8%) |
388 (27.7%) |
506 (31.5%) |
395 (26.2%) |
309 (26.0%) |
333 (26.9%) |
0.004 |
| Chronic Thiazide Diuretics | 463 (6.7%) |
77 (5.5%) |
111 (6.9%) |
105 (7.0%) |
83 (7.0%) |
87 (7.0%) |
0.409 |
| Nitrates | 1637 (23.6%) |
334 (23.8%) |
391 (24.3%) |
360 (23.9%) |
268 (22.6%) |
284 (22.9%) |
0.803 |
| Hydralazine | 520 (7.5%) |
92 (6.6%) |
82 (5.1%) |
102 (6.8%) |
99 (8.3%) |
145 (11.7%) |
<.001 |
| Digoxin | 1836 (26.5%) |
365 (26.0%) |
456 (28.4%) |
405 (26.9%) |
295 (24.9%) |
315 (25.4%) |
0.229 |
| Calcium Channel Blockers | 883 (12.7%) |
186 (13.3%) |
171 (10.6%) |
204 (13.5%) |
138 (11.6%) |
184 (14.8%) |
0.008 |
| Oral Anticoagulants | 1676 (24.1%) |
331 (23.6%) |
363 (22.6%) |
367 (24.4%) |
290 (24.4%) |
325 (26.2%) |
0.259 |
| Aspirin | 3408 (49.1%) |
665 (47.4%) |
806 (50.2%) |
735 (48.8%) |
577 (48.6%) |
625 (50.4%) |
0.504 |
| Clinical Profile | |||||||
| Baseline BMI | 27.6 (23.8, 32.6) |
28.2 (24.0, 33.1) |
27.1 (23.7, 32.2) |
27.6 (23.5, 32.9) |
27.5 (23.7, 32.0) |
27.7 (24.2, 32.6) |
0.027 |
| Baseline Diastolic BP | 74 (67, 83) |
74 (68, 82) |
75 (68, 84) |
74 (66, 83) |
74 (66, 83) |
75 (66, 84) |
0.423 |
| Orthopnea | 5342 (77.1%) |
1186 (84.7%) | 1218 (75.8%) |
1142 (76.0%) |
858 (72.4%) |
938 (75.8%) |
<.001 |
| Rales >1/3 lung fields | 3635 (52.4%) |
872 (62.2%) |
830 (51.6%) |
764 (50.7%) |
571 (48.1%) | 598 (48.2%) |
<.001 |
| JVP | 3906 (56.3%) |
916 (65.5%) |
882 (55.0%) |
808 (53.7%) |
593 (50.0%) | 707 (57.0%) |
<.001 |
| Peripheral Edema | 5197 (74.9%) |
1161 (82.8%) |
1176 (73.2%) |
1105 (73.4%) | 817 (68.8%) | 938 (75.7%) |
<.001 |
| History of Myocardial Infarction |
2415 (34.8%) |
518 (36.9%) |
612 (38.1%) |
506 (33.6%) |
396 (33.4%) | 383 (30.9%) |
<.001 |
| History of Hyperlipidemia | 2894 (41.7%) |
550 (39.2%) |
621 (38.7%) |
661 (43.9%) |
468 (39.4%) | 594 (47.9%) |
<.001 |
| Current Smoker | 936 (13.5%) |
197 (14.1%) |
215 (13.4%) |
189 (12.6%) |
173 (14.6%) | 162 (13.1%) |
0.561 |
| Clinical Course | |||||||
| Planned Treatment Group | 0.720 | ||||||
| Placebo | 3484 (50.2%) |
712 (50.7%) |
786 (48.9%) |
773 (51.3%) |
591 (49.8%) |
622 (50.2%) |
|
| Nesiritide | 3459 (49.8%) |
691 (49.3%) |
821 (51.1%) |
733 (48.7%) |
596 (50.2%) |
618 (49.8%) |
|
| Actual Treatment Group | 0.704 | ||||||
| Placebo | 3437 (50.1%) |
702 (50.6%) |
776 (48.9%) |
766 (51.4%) |
584 (50.0%) |
609 (49.8%) |
|
| Nesiritide | 3419 (49.9%) |
685 (49.4%) |
812 (51.1%) |
724 (48.6%) |
585 (50.0%) |
613 (50.2%) |
|
Patients reporting lower VAS scores at baseline tended to be younger, female, and were more likely to be enrolled in Eastern Europe. These patients were also more likely to have a prior history of hospitalization for HF and reduced systolic function and manifest signs and symptoms of congestion at baseline. The prevalence of cardiac and non-cardiac comorbidities was high irrespective of HRQOL at baseline and the relationships between B-type natriuretic peptide (BNP) and N-terminal pro-BNP (NT-proBNP) and HRQOL were discordant.
Clinical Course of EQ-5D
Mapped utility scores increased over time from 0.56±0.23 at baseline to 0.67±0.26 and 0.79±0.20, respectively, at hour 24 and discharge and remained stable at 0.78±0.20 at day 30. Figure 1 shows the distribution of responses for mobility, self-care, usual activities, pain/discomfort, and anxiety/depression over time. Similarly, VAS scores also increased over time from 45±22 at baseline to 58±22 and 68±22, respectively, at hour 24 and discharge and remained stable at 67±22 at day 30. Boxplots of mapped utility and VAS scores throughout hospitalization and post-discharge are shown in Figure 2.
Figure 1.
Distribution of functional dimension responses.
Note:
Mobility: 1 – I have no problems in walking about, 2 – I have some problems in walking about, 3 – I am confined to bed, or 4 – Question not answered
Self-Care: 1 – I have no problems with self-care, 2 – I have some problems washing or dressing myself, 3 – I am unable to wash or dress myself, or 4 – Question not answered
Usual Activities: 1 – I have no problems with performing my usual activities, 2 – I have some problems with performing my usual activities, 3 – I am unable to perform my usual activities, or 4 – Question not answered
Pain/Discomfort: 1 – I have no pain or discomfort, 2 – I have moderate pain or discomfort, 3 – I have extreme pain or discomfort, or 4 Question not answered
Anxiety/Depression: 1 – I am not anxious or depressed, 2 – I am moderately anxious or depressed, 3 – I am extremely anxious or depressed, or 4 – Question not answered
Figure 2.
Box plots of (A) mapped utility and (B) VAS scores.
Early Dyspnea Relief and EQ-5D
After multivariable adjustment, there was a significant positive correlation between early improvement in dyspnea and mapped utility and VAS scores at discharge (Table 2). Early dyspnea relief was associated with an increase in mean mapped utility score at discharge of 0.03 units (p<0.0001) and an increase in mean VAS score at discharge of 5.1 points (p<0.0001). Similarly, there was a significant positive association between early dyspnea improvement and mapped utility and VAS scores at day 30. Early dyspnea relief was associated with an increase in mean mapped utility score at day 30 of 0.02 units (p=0.0012) and an increase in mean VAS score at day 30 of 3.9 points (p<0.0001).
Table 2.
Relationship of early dyspnea relief to EQ-5D measures at discharge and day 30.
| Univariable | Baseline EQ-5D Adjusted | Multivariable | |||||||
|---|---|---|---|---|---|---|---|---|---|
| EQ-5D Measure | Estimate | 95% CI | P-Value | Estimate | 95% CI | P-Value | Estimate | 95% CI | P-Value |
| Discharge | |||||||||
| US Utility Scorea | 0.051 | 0.041 to 0.061 | <0.0001 | 0.03 | 0.021 to 0.039 | <0.0001 | 0.029 | 0.020 to 0.038 | <0.0001 |
| VASb | 8.1 | 7.0 to 9.2 | <0.0001 | 5.6 | 4.6 to 6.6 | <0.0001 | 5.1 | 4.1 to 6.0 | <0.0001 |
| Day 30 | |||||||||
| US Utility Scorec | 0.032 | 0.022 to 0.042 | <0.0001 | 0.018 | 0.008 to 0.028 | 0.0005 | 0.016 | 0.006 to 0.025 | 0.0012 |
| VASd | 6.3 | 5.1 to 7.4 | <0.0001 | 4.6 | 3.5 to 5.6 | <0.0001 | 3.9 | 2.8 to 4.9 | <0.0001 |
Multivariable model is adjusted for baseline utility score, age, gender, race, BMI, cerebrovascular disease, chronic renal disease, heart rate, peripheral vascular disease, hyperlipidemia, prior hospitalization in past year, log of BUN, NYHA classification, smoking, sodium, log of creatinine, and diabetes.
Multivariable model is adjusted for baseline vas, age, race, ACEI or ARB use, chronic renal disease, history of ICD/CRT, baseline diastolic blood pressure, peripheral vascular disease, respiratory rate, log of BUN, NYHA classification, pre-randomization oral anticoagulant use, jvp, smoking and BMI.
Multivariable model is adjusted for baseline utility score, age, gender, race, BMI, cerebrovascular disease, chronic renal disease, hemoglobin, heart rate, prior mi, sodium, peripheral vascular disease, respiratory rate, prior hospitalization in past year, log of BUN, NYHA classification, pre-randomization digoxin use, baseline weight.
Multivariable model is adjusted for baseline vas, age, race, chronic renal disease, baseline diastolic blood pressure, hemoglobin, prior mi, peripheral vascular disease, respiratory rate, prior hospitalization in past year, log of BUN, pre-randomization oral anticoagulant use, and jvp.
EQ-5D and Outcomes
There was an independent association between lower mapped utility scores and VAS scores at baseline and discharge with higher risk of 30-day all-cause death or HF rehospitalization (Table 3). Similarly, after adjusting for confounders, there was a significant relationship between mapped utility at baseline and 30-day CV death or rehospitalization. However, there was an inverse relationship between both mapped utility and VAS scores at discharge with 30-day CV death or rehospitalization resulting in decreases in mapped utility or VAS scores associated with increased risk. Finally, lower mapped utility score and VAS at baseline were not significantly associated 180-day mortality. After Multivariable adjustment, there was no relationship between VAS score at discharge and 180-day mortality, while lower mapped utility score at discharge was associated with increased risk of 180-day mortality.
Table 3.
Association between baseline and discharge EQ-5D utility and VAS scores and 30-day and 180-day outcomes.
| Model | Univariable HR/OR | 95% CI | P-Value | Multivariable HR/OR | 95% CI | P-Value |
|---|---|---|---|---|---|---|
| 30-Day All-Cause Death or Heart Failure Rehospitalization | ||||||
| Baseline (N=6770)) | ||||||
| Utility Score (per −0.1) | 1.05 | 1.03 to 1.09 | <0.0001 | 1.04 | 1.01 to 1.07 | 0.0077 |
| VAS (per −10) | 1.06 | 1.03 to 1.11 | 0.0003 | 1.05 | 1.01 to 1.09 | 0.0100 |
| Discharge | ||||||
| Utility Score (per −0.1) (N=5829) |
1.16 | 1.11 to 1.20 | <0.0001 | 1.10 | 1.05 to 1.15 | <0.0001 |
| VAS (per −10) (N=5788) | 1.06 | 1.02 to 1.11 | 0.0024 | 1.04 | 1.00 to 1.09 | 0.0438 |
| 30-Day Cardiac Death or Cardiac Rehospitalization | ||||||
| Baseline (N=6772) | ||||||
| Utility Score (per −0.1) | 1.04 | 1.01 to 1.06 | 0.0031 | 1.03 | 1.00 to 1.06 | 0.0491 |
| VAS (per −10) | 1.05 | 1.02 to 1.09 | 0.0039 | 1.03 | 1.00 to 1.07 | 0.0585 |
| Discharge | ||||||
| Utility Score (per −0.1) (N=5830) |
1.16 | 1.11 to 1.20 | <0.0001 | 1.10 | 1.06 to 1.15 | <0.0001 |
| VAS (per −10) (N=5789) | 1.08 | 1.04 to 1.12 | <0.0001 | 1.06 | 1.02 to 1.10 | 0.0033 |
| 180 Day Mortality | ||||||
| Baseline (N=6664) | ||||||
| Utility Score (per −0.1) | 1.04 | 1.01 to 1.08 | 0.0022 | 1.03 | 1.00 to 1.06 | 0.0900 |
| VAS (per −10) | 1.03 | 0.99 to 1.06 | 0.0994 | 1.00 | 0.96 to 1.04 | 0.8336 |
| Discharge | ||||||
| Utility Score (per −0.1) (N=5826) |
1.22 | 1.18 to 1.27 | <0.0001 | 1.13 | 1.09 to 1.18 | <0.0001 |
| VAS (per −10) (N=5786) | 1.09 | 1.05 to 1.14 | <0.0001 | 1.02 | 0.97 to 1.06 | 0.4687 |
adjusted for: all-cause rehospitalization at day 30, age, history of cerebrovascular disease, baseline sodium, baseline temperature, baseline weight, diastolic blood pressure at follow-up, hospitalization in prior year, baseline heart rate, baseline BUN, VAS score at day 30, and worsening heart failure at day 30.
adjusted for: age, baseline BUN, history of cerebrovascular disease, baseline creatinine, baseline depression, dyspnea, hospitalization in prior year, baseline sodium, JVP, baseline systolic blood pressure, baseline chronic respiratory disease.
DISCUSSION
This study found that patients admitted for a primary diagnosis of HF reported severe impairments in health status at baseline, which improved substantially during hospitalization, but persisted at discharge. Patients with worse health status tended to be younger and female and were more likely to have a prior history of HF hospitalization and reduced systolic function. In addition, there was a significant association between early dyspnea relief during hospitalization and better health status at both discharge and day 30. Finally, worse health status at baseline and discharge was generally associated with increased risk of morbidity and mortality.
Health Status in Acute Heart Failure
There are over 1 million primary admissions for HF annually in the United States representing 1–2% of all hospitalizations.15, 16 It is recognized that a hospital admission for HF is a critical event with a combined post-discharge morbidity and mortality rate as high as 45% within 60–90 days.17 This study further underscores the significance of a hospitalization to the natural history of this syndrome by extending these findings to health status. Although both EQ-5D mapped utility and VAS scores improved rapidly and dramatically during hospitalization, health status remained severely impaired throughout follow-up. This finding is best demonstrated by the mean VAS scores reported at discharge and day 30, which were comparable in magnitude to those measured in patients afflicted with a range of debilitating medical conditions including acute myocardial infarction,18 chronic obstructive pulmonary disease,19 chronic kidney disease,20 and a variety of common malignancies.21
Early Dyspnea Relief and Health Status
Although early dyspnea relief during hospitalization has been previously shown to be associated with a lower risk of post-discharge morbidity and mortality,22–25 this is the first study to find a significant relationship between early dyspnea relief and improved short-term health status. However, it remains unclear whether dyspnea relief is merely a short-term symptomatic goal or a surrogate marker and/or mediator of long-term outcomes. Of note, the association of early dyspnea relief on subsequent health status was greatest at discharge and somewhat diminished by day 30. Thus, additional research is required to establish the relationship between early dyspnea relief during index admission and HRQOL over a more prolonged duration of follow-up. Regardless, it does not necessarily follow from this study that early initiation of standard therapy or novel agents targeting dyspnea will have incremental benefit on HRQOL.
Health Status as a Quality Metric
Although quality improvement initiatives and clinical trials in the acute setting have traditionally focused on dyspnea and/or composites of cardiovascular morbidity and mortality, there are multiple reasons why HRQOL may be a desirable supplementary outcome. First, HRQOL is feasible to measure and responsive to short-term change in clinical status. Additionally, assessing HRQOL may be more reliable and reproducible than other non-fatal markers of morbidity such as unscheduled office/emergency room visits and urgent/emergent hospitalizations, which are fundamentally subjective and vary tremendously by geographic region.26, 27 Third, HRQOL can be measured longitudinally across clinical settings (i.e. inpatient vs. outpatient) and transitions of care. Fourth, in contrast to resting signs and symptoms of congestion,28 severe impairments in HRQOL commonly persist post-discharge and are not adequately addressed by the available therapeutic armamentarium. Finally, HRQOL at various time points were generally independently associated with increased risk of future morbidity and mortality.
Limitations
There are several limitations of the data that should be acknowledged. First, this study was conceived post-hoc and is therefore subject to the potential biases inherent to exploratory analyses of observational data, including unmeasured confounding. In addition, although early dyspnea relief was assessed at 6 hours, the median time from presentation to randomization was an additional 15 hours. Third, since events were only adjudicated through day 30, outcome analyses for 180-day events had to be restricted to mortality. Fourth, the EQ-5D questionnaire and other HRQOL surveys have primarily been validated in ambulatory patient populations. In addition, EQ-5D is a general health survey while disease-specific tools (i.e. Kansas City Cardiomyopathy Questionnaire and Minnesota Living with Heart Failure Questionnaire) do exist. Nonetheless, this study found EQ-5D to be responsive to changes in short-term clinical status and the use of a more generic assessment of HRQOL may permit more direct comparisons to other medical conditions. Finally, these data were collected in the context of a clinical trial with specific inclusion and exclusion criteria potentially restricting the generalizability of this study.
Conclusions
In conclusion, patients admitted for a primary diagnosis of HF experienced severe impairments in health status at baseline, which albeit improved by discharge, remained comparable in magnitude to other chronic disease states throughout the duration of follow-up. Importantly, health status, as assessed by EQ-5D, is feasible to measure, responsive to short-term changes in clinical status, and can be trended longitudinally. Thus, there are many favorable features that make HRQOL a desirable metric for assessing quality of care in routine practice and a supplementary endpoint for efficacy in clinical trials. Future research is required to validate EQ-5D and other HRQOL surveys as a reliable and reproducible tool across the continuum of care.
Acknowledgments
Dr. Hernandez reports consulting fees from Sanofi, Johnson and Johnson, AstraZeneca, and Corthera and research support from Amylin and Scios/Johnson and Johnson. Dr. Armstrong reports research support from Johnson & Johnson. Dr. Butler reports research support from the National Institutes of Health, European Union, and Health Resources Service Administration; and is a consultant to Amgen, Bayer, BG Medicine, Cardiocell, Celladon, Gambro, GE Healthcare, Medtronic, Novartis, Ono Pharma, Takeda, Trevena, and Zensun. Dr. Califf reports consulting fees from KOWA, Eli Lilly, Glaxo Smith-Kline, WebMD, Bristol-Myers-Squibb, Nitrox LLC, Bayer, Orexigen Therapeutics, Sanofi-Aventis, Medtronic, Boehringer Ingelheim, and Gilead and research support from BMS, Roche, Merck, Novartis, Scios/Johnson and Johnson, Amilyn, Bristol-Myers-Squibb, and Bayer. Dr. Ezekowitz reports consulting fees from Pfizer, Abbott Laboratories, Servier, and research support from Amgen and Johnson and Johnson. Dr. Felker reports research funding from Amgen, BG Medicine, Cytokinetics, Johnson & Johnson, Roche Diagnostic Corp, and Otsuka; consulting for Amgen, Cytokinetics, Roche, Otsuka, and Novartis. Dr. Metra has received consulting incomes from Bayer, Novartis, and Servier. Dr. O’Connor reports consulting fees from Novella and Amgen, ownership/partnership/principal in Biscardia, LLC, and research support from Otsuka, Roche Diagnostics, BG Medicine, Critical Diagnostics, Astellas, Gilead, GE Healthcare, and ResMed. Dr. Starling reports consulting fees from Novartis, BioControl, and Medtronic, ownership/partnership/principal in Cardiomems, research support from the National Institutes of Health, Medtronic, Biotronik, Novartis, and Thoratec, and receipt of benefits from the American Board of Internal Medicine. Dr. Tang receives research support from the US National Institutes of Health. Dr. Voors has received consultancy fees and/or research grants from: Alere, Amgen, Anexon, Bayer, Boehringer Ingelheim, Cardio3Biosciences, Celladon, Merck, Novartis, Servier, Torrent, and Vifor Pharma. Dr. Mentz receives research support from Amgen, AstraZeneca, BMS, GSK, Gilead, Novartis, Otsuka, and ResMed; honoraria from Thoratec.
Footnotes
Disclosures
All other authors declare no relevant financial disclosures.
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