Abstract
Background
Hospitalization due to worsening heart failure (HF) is common and is associated with high mortality. However, the effect of incident HF hospitalization (compared to no HF hospitalization) on subsequent mortality has not been studied in a propensity-matched population of chronic HF patients.
Methods and Results
In the Digitalis Investigation Group trial, 5501 patients had no HF hospitalizations (4512 alive at two years after randomization) and 1732 had HF hospitalizations during the first two years (1091 alive at two years). Propensity scores for incident HF hospitalization during the first two years after randomization were calculated for each patient, and were used to match 1057 (97%) patients who had two-year HF hospitalization with 1057 patients who had no HF hospitalization. We used matched Cox regression analysis to estimate the effect of incident HF hospitalization during the first two years after randomization on post-two-year mortality. Compared with 153 deaths (rate, 420/10,000 person-years) in the no HF hospitalization group, 334 deaths (rate, 964/10,000 person-years) occurred in the HF hospitalization group (hazard ratio 2.49; 95% confidence interval 1.97–3.13; p<0.0001). Respective hazard ratios (95% confidence intervals) for cardiovascular and HF mortality were respectively 2.88 (2.23–3.74; p <0.0001) and 5.22 (3.34–8.15; p <0.0001).
Conclusions
Hospitalization due to worsening HF was associated with increased risk of subsequent mortality in ambulatory patients with chronic HF. These results highlight the importance of HF hospitalization as a marker of disease progression and poor outcomes in chronic HF, reinforcing the need for prevention of HF hospitalizations and strategies to improve post-discharge outcomes.
Keywords: Heart failure, hospitalization, mortality, propensity scores
Heart failure (HF) is common and is a main reason for hospitalizations among older adults.1 With the aging of the United States population, the prevalence of HF is projected to double over the next several decades, further increasing the burden of HF hospitalization. Data from hospitalized HF patients suggest that HF hospitalization is associated with high in-hospital, and short- and long-term post-discharge mortalities.2–6 However, little is known about the effect of incident HF hospitalization, compared with no HF hospitalization, on subsequent mortality in chronic HF. In this study, we test the hypothesis that incident HF hospitalization would be associated with increased subsequent mortality in a propensity score-matched cohort of ambulatory chronic HF patients.
METHODS
Study Design and Patients
This is a post-hoc propensity-matched study of the Digitalis Investigation Group (DIG) trial, conducted in 302 centers (186 in the US and 116 in Canada) during 1991–1993.7 Detailed description of the rationale, design, implementation, patient characteristics and results of the DIG trial have been reported elsewhere.7 Of the 7888 ambulatory chronic HF patients with normal sinus rhythm enrolled in the DIG trial, 6800 had ejection fraction ≤45%.
Heart Failure Hospitalization
During a mean follow up of 37 months, 5128 (66%) patients were hospitalized from all causes, of whom 2287 (45%) were due to worsening HF. Of the 2287 HF hospitalizations, 1732 (76%) occurred during the first two years after randomization. After excluding 641 (37%) patients who either died (n=629) or were lost to follow up (n=12) during the first two years, there were 1091 patients who had HF hospitalization during the first two years of follow up (Figure 1). Of the 5501 patients without any HF hospitalization, we excluded 989 (18%) patients who either died (n=941) or were lost to follow up (n=48) during the first two years after randomization. We matched 1057 (97%) of the 1091 patients who had HF hospitalizations during the first two years with 1057 patients who had no HF hospitalizations by their propensities to have HF hospitalizations during the first two years. Thus, our analysis focuses on a cohort of 1057 pairs of propensity score matched patients (Figure 1). DIG investigators ascertained incident hospitalizations and the primary diagnoses leading to hospitalizations by reviewing patients’ charts. There was no centralized adjudication of incident hospitalizations, including those due to worsening HF.
Figure 1.
Flow chart for the assembly of matched cohort
Primary and Secondary Outcomes
The primary outcome was all-cause mortality after the second year of follow up. In addition, we also studied mortalities due to cardiovascular causes, HF, due to non-HF cardiovascular causes, and non-cardiovascular causes. Study investigators, who were blinded to patients’ treatment assignment, ascertained causes of death. Vital status was collected up to December 31, 1995 and was ascertained for 99% of the patients.8 The median follow up in this analysis was 16 months from the end of first two years. As in randomized clinical trial, the current study was designed (i.e. a risk-adjusted balanced study cohort was assembled using propensity score matching) without access to the mortality data.
Estimation of Propensity Scores and Matching
Because there were significant differences in baseline patient characteristics between patients who had and did not have a HF hospitalization during the first two years of follow-up (Table 1, left hand panels), we used propensity score matching to achieve balance. The propensity score is the conditional probability of receiving an exposure (e.g. hospitalization for worsening HF) given a vector of measured covariates, and can be used to adjust for selection bias when assessing causal effects in observational studies.9–15 We estimated propensity scores for HF hospitalization during the first two years for all patients using a non-parsimonious multivariable logistic regression model. In that model, all baseline patient characteristics displayed in Table 1 (except those marked as derived variables) and clinically plausible interactions were included as covariates. The use of baseline covariates at randomization in the model allowed us to estimate prospectively propensity scores for incident HF hospitalizations.
Table 1.
Baseline patient characteristics by heart failure hospitalization (HFH) during first two years, before and after propensity score matching
| Before match | After match | |||||
|---|---|---|---|---|---|---|
| Number (%) or Mean (±SD) | No HFH (n =4512) |
HFH (n =1091) |
P value | No HFH (n =1057) |
HFH (n =1057) |
P value |
| Age, years | 63.0 (±11) | 64.2 (±11) | 0.001 | 64.5 (±11) | 64.2 (±11) | 0.562 |
| Age 65 years and older* | 2184 (48%) | 586 (54%) | 0.002 | 567 (54%) | 568 (54%) | >0.999 |
| Women | 1141 (25%) | 318 (29%) | 0.009 | 316 (30%) | 305 (29%) | 0.630 |
| Non-whites | 566 (13%) | 217 (20%) | <0.0001 | 199 (19%) | 199 (19%) | >0.999 |
| Body mass index, kg/m2 | 27.4 (±5) | 27.5 (±6) | 0.676 | 27.7 (±6) | 27.5 (±6) | 0.375 |
| Heart failure duration, mo | 29 (±36) | 30 (±38) | 0.217 | 28 (±35) | 30 (±37) | 0.369 |
| Ejection fraction, percent | 34 (±12) | 30 (±13) | <0.0001 | 30 (±12) | 30 (±13) | 0.956 |
| Ejection fraction >45% * | 701 (16%) | 106 (10%) | <0.0001 | 110 (10%) | 106 (10%) | 0.823 |
| Etiology of heart failure | ||||||
| Ischemic | 3132 (69%) | 701 (64%) | 0.002 | 688 (65%) | 685 (65%) | 0.816 |
| Hypertensive | 451 (10%) | 131 (12%) | 121 (11%) | 122 (12%) | ||
| Idiopathic | 656 (15%) | 168 (15%) | 167 (16%) | 164 (16%) | ||
| Others | 273 (6%) | 91 (8%) | 81 (8%) | 86 (8%) | ||
| Comorbid conditions | ||||||
| Prior myocardial infarction | 2898 (64%) | 639 (59%) | 0.001 | 627 (59%) | 623 (59%) | 0.891 |
| Current angina | 1193 (26%) | 306 (28%) | 0.282 | 289 (27%) | 293 (28%) | 0.886 |
| Hypertension | 2075 (46%) | 542 (50%) | 0.028 | 521 (49%) | 521 (49%) | >0.999 |
| Diabetes | 1041 (23%) | 392 (36%) | <0.0001 | 372 (35%) | 371 (35%) | >0.999 |
| Chronic kidney disease*‡ | 1826 (41%) | 539 (49%) | <0.0001 | 536 (51%) | 518 (49%) | 0.459 |
| Medications | ||||||
| Digoxin (pre-trial use) | 1707 (38%) | 555 (51%) | <0.0001 | 496 (47%) | 526 (50%) | 0.175 |
| Digoxin (by randomization) | 2356 (52%) | 448 (41%) | <0.0001 | 447 (42%) | 442 (42%) | 0.856 |
| ACE inhibitors | 4203 (93%) | 1029 (94%) | 0.165 | 1007 (95%) | 998 (94%) | 0.435 |
| Hydralazine & nitrates | 40 (1%) | 18 (2%) | 0.025 | 18 (2%) | 18 (2%) | >0.999 |
| Non-potassium-sparing diuretics | 3219 (71%) | 958 (88%) | <0.0001 | 928 (89%) | 924 (87%) | 0.816 |
| Potassium-sparing diuretics | 346 (8%) | 80 (7%) | 0.707 | 74 (7%) | 77 (7%) | 0.867 |
| Potassium supplements | 1043 (23%) | 413 (38%) | <0.0001 | 404 (38%) | 390 (37%) | 0.540 |
| Symptoms/signs of heart failure | ||||||
| Dyspnea at rest | 796 (18%) | 312 (29%) | <0.0001 | 299 (28%) | 289 (27%) | 0.649 |
| Dyspnea on exertion | 3238 (72%) | 879 (81%) | <0.0001 | 835 (79%) | 846 (80%) | 0.585 |
| Activity limitation | 3247 (72%) | 887 (81%) | <0.0001 | 856 (81%) | 854 (81%) | 0.954 |
| Jugular venous distension | 442 (10%) | 184 (17%) | <0.0001 | 168 (16%) | 169 (16%) | 0.953 |
| Third heart sound | 902 (20%) | 301 (28%) | <0.0001 | 308 (29%) | 291 (28%) | 0.446 |
| Pulmonary râles | 559 (12%) | 227 (21%) | <0.0001 | 205 (19%) | 207 (20%) | 0.956 |
| Lower extremity edema | 807 (18%) | 265 (24%) | <0.0001 | 251 (24%) | 248 (24%) | 0.917 |
| NYHA functional class | ||||||
| I | 787 (17%) | 118 (11%) | <0.0001 | 121 (11%) | 115 (11%) | 0.358 |
| II | 2629 (58%) | 551 (51%) | 546 (52%) | 545 (52%) | ||
| III | 1047 (23%) | 402 (37%) | 367 (35%) | 379 (36%) | ||
| IV | 49 (1%) | 20 (2%) | 23 (2%) | 18 (2%) | ||
| Heart rate, per minute | 77 (±13) | 81 (±13) | <0.0001 | 81 (±13) | 80 (±12) | 0.311 |
| Blood pressure, mm Hg | ||||||
| Systolic | 128 (±20) | 127 (±22) | 0.018 | 127 (±20) | 127 (±22) | 0.867 |
| Diastolic | 76 (±11) | 75 (±12) | 0.005 | 75 (±11) | 75 (±12) | 0.986 |
| Chest radiograph findings | ||||||
| Pulmonary congestion | 473 (11%) | 191 (18%) | <0.0001 | 186 (18%) | 176 (17%) | 0.598 |
| Cardiothoracic ratio > 0.5 | 2439 (54%) | 746 (68%) | <0.0001 | 708 (67%) | 715 (68%) | 0.771 |
| Serum concentrations | ||||||
| Creatinine, mg/dL | 1.2 (±0.3) | 1.3 (±0.4) | <0.0001 | 1.3 (±0.4) | 1.3 (±0.4) | 0.761 |
| Potassium, mEq/L | 4.3 (±0.4) | 4.3 (±0.5) | 0.416 | 4.3 (±0.4) | 4.3 (±0.5) | 0.956 |
| Estimated glomerular filtration rate, ml/min per1.73 m2 *† | 66 (±19) | 62 (±21) | <0.0001 | 61 (±19) | 62 (±21) | 0.253 |
These derived variables were not used in the multivariable regression model for propensity score
Chronic kidney disease defined as an estimated glomerular filtration rate <60 mL/min/1.73m2.
Based on Modification of Diet in Renal Disease Study formula
Our propensity score model discriminated well between patients with and without HF hospitalization (c statistic=0.73). We then used propensity score, to match each patient with HF hospitalization with another patient without HF hospitalization, but who had a very similar propensity score, thus matching 1057 (97% of the 1091 patients with HF hospitalization during the first two years) patients to 1057 patients without HF hospitalization.13–16 We used a greedy matching algorithm, which first looked for matches to five decimal places, and those matched were removed from the files.13, 14, 17 Then, the process was repeated to four, three, two and one decimal places. We assessed residual imbalances in baseline covariates between treatment groups after propensity score matching by estimating absolute standardized differences (Figure 2).13, 18, 19 Standardized differences quantify the bias in the means (or proportions) of covariates across the groups, expressed as a percentage of the pooled standard deviation.
Figure 2.
Absolute standardized differences of baseline covariates between patients with and without hospitalization for heart failure, before and after propensity score matching
Statistical Analysis
We used chi-square tests and independent sample t-tests, as appropriate, for descriptive analysis to compare baseline characteristics between pre-match patients with and without HF hospitalization. For descriptive analysis of post-match cohorts, McNemar tests and paired-sample t-tests were used as appropriate. We used Kaplan-Meier survival analyses, and matched Cox proportional hazards models to estimate the association between incident HF hospitalizations during the first two years of follow up and subsequent mortality. We confirmed the assumption of proportional hazards by a visual examination of the log (minus log) curves.
Although our propensity score match achieved excellent balance in all measured covariates between patients with and without HF hospitalization, we could not rule out bias due to unmeasured confounders. Like any non-randomized study, the conclusions of our study may be sensitive to potential hidden confounders. Therefore, we conducted formal sensitivity analyses to describe the weight of our evidence by quantifying the degree of hidden bias that would need to be present to invalidate our main conclusions.20, 21 We then examined the association of HF hospitalization and mortality in subgroups of patients in the pre-match cohort, adjusted for raw propensity scores. All statistical tests were evaluated using 2-tailed 95% confidence levels, and data analyses were performed using SPSS for Windows version 14.22
Results
The median age of the 2114 propensity score-matched patients was 65 years, (range 22–92), 621 (29%) were women and 398 (19%) were non-whites. Baseline patient characteristics by HF hospitalizations, before and after propensity score matching are displayed in Table 1 and Figure 2. Before matching, patients with incident HF hospitalizations were likely to be older and sicker with a higher burden of comorbidity. After matching, patients with and without HF hospitalization were balanced in all of measured baseline covariates (Table 1 and Figure 2). Our propensity score matching reduced absolute standardized differences for all observed covariates below 10% (most were below 5%), demonstrating substantial improvement in covariate balance across the groups (Figure 2).
Total Mortality
During a median follow-up of 16 months, 487 (23.0%) patients died from all causes, 411 (19.4%) due to cardiovascular causes, 202 (9.6%) died due to HF, 209 (9.9%) due to cardiovascular causes other than HF, and 76 (3.6%) died from non-cardiovascular causes. Kaplan-Meier plots for mortalities due to all causes, cardiovascular causes, and progressive HF are displayed in Figures 3 (a, b and c).
Figure 3.
Kaplan-Meier plots for cumulative risk of death due to (a) all causes, (b) cardiovascular causes, and (c) progressive heart failure (HF). HFH, heart failure hospitalization; HR, hazard ratio; CI, confidence interval.
Mortality due to all causes occurred in 153 patients without HF hospitalization during a total of 3,644 years of follow up (mortality rate, 420/10,000 person-yearss) and 334 patients with HF hospitalization during a total of 3,463 years of follow up (mortality rate, 964/10,000 person-yearss; hazard ratio [HR], when patients with HF hospitalization were compared with those without, 2.49, 95% confidence interval [CI], 1.97–3.13; p <0.0001; Table 2). Our sensitivity analysis suggests that an unmeasured binary covariate would need to increase the odds of HF hospitalization by >91% to explain away this association (z-statistic=7.02; 2-tailed p=0.0001), suggesting that these results are not sensitive to a hidden binary variable.
Table 2.
Association of mortality with heart failure hospitalization (HFH) during the first two years
| No HFH (N=1057) |
HFH (N=1057) |
Absolute rate difference* (per 10,000 person-years) |
Hazard ratio (95% confidence interval)† |
P value | |
|---|---|---|---|---|---|
| Number of deaths / total years of follow up (death rate, per 10,000 person-years) |
|||||
| All-cause | 153 / 3644 (420) |
334 / 3463 (964) |
+ 545 | 2.49 (1.97–3.13) |
<0.0001 |
| Cardiovascular | 115 / 3644 (316) |
296 / 3463 (855) |
+ 539 | 2.88 (2.23–3.74) |
<0.0001 |
| Heart failure‡ | 40 / 3644 (110) |
162 / 3463 (468) |
+ 358 | 5.22 (3.34–8.15) |
<0.0001 |
| Other cardiovascular§ | 75 / 3644 (206) |
134 / 3463 (387) |
+ 181 | 1.89 (1.36–2.63) |
<0.0001 |
| Non-cardiovascular | 38 / 3644 (104) |
38 / 3463 (110) |
+ 6 | 1.21 (0.70–2.08) |
0.493 |
Absolute differences were calculated by subtracting the percentage of deaths in the heart failure hospitalization group from the percentage of deaths in the non-heart failure hospitalization group (before values were rounded).
Hazard ratios and confidence intervals (CI) were estimated from matched Cox proportional-hazards models.
This category includes patients who died from worsening heart failure, even if the final event was an arrhythmia.
This category includes deaths presumed to result from arrhythmia without evidence of worsening heart failure and deaths due to atherosclerotic coronary disease, bradyarrhythmias, low-output states, and cardiac surgery. This category also includes deaths due to stroke, embolism, peripheral vascular disease, vascular surgery, and carotid endarterectomy.
In the full (pre-match) cohort (n=5,603), 11% and 32% of patients without and with HF hospitalizations subsequently died (unadjusted HR, 3.29, 95% CI, 2.87–3.77; p <0.0001). The association remained strong and significant when we adjusted for all baseline covariates (HR, 2.65, 95% CI, 2.30–3.05; p <0.0001), or propensity scores (HR, 2.61, 95% CI, 2.25–3.02; p <0.0001).
Cardiovascular Mortality
Mortality due to cardiovascular causes occurred in 296 (rate, 855/10,000 person-yearss) and 115 patients (rate, 316/10,000 person-yearss) with and without HF hospitalizations, respectively (HR, 2.88, 95% CI, 2.23–3.74; p <0.0001; Table 2). A hidden unmeasured covariate would need to increase the odds of HF hospitalization by >110% to explain away this association (z=6.79; p <0.0001).
HF Mortality
Mortality due to HF occurred in 162 patients (rate, 468/10,000 person-years) and 40 patients (rate, 110/10,000 person-years), respectively, with and without HF hospitalizations (HR, 5.22, 95% CI, 3.34–8.15; p <0.0001; Table 2). The odds of HF hospitalization must be increased by >140% by an unmeasured covariate to confound this association (z=6.79; p<0.0001).
Mortality due to Other Causes
Mortality due to cardiovascular causes other than HF occurred in 134 (rate, 387/10,000 person-years) and 75 patients (rate, 206/10,000 person-years) with and without and HF hospitalizations, respectively (HR, 1.89, 95% CI, 1.36–2.63; p <0.0001; Table 2).
HF hospitalization had no effect on mortality due to non-cardiovascular causes, which occurred in 38 patients in each group, respectively during 3,463 years (110/10,000 person-years) and 3,644 years (104/10,000 person-years) of follow up, respectively in patients with and without HF hospitalization (HR, 1.21, 95% CI, 0.70–2.08; p =0.493; Table 2).
Subgroup Analyses
HF hospitalization significantly increased subsequent total mortality in all the subgroups of HF patients studied (Figure 4), regardless of age, sex, race, HF etiology, left ventricular ejection fraction, New York Heart Association functional class, comorbidities and use of medications. There were no significant interactions between HF hospitalization and any of the subgroups, except for diabetes (p for interaction=0.050). This subgroup interaction, however, was no longer significant when adjusted for other covariates (adjusted p=0.150).
Figure 4.
Hazard ratios (HR) and 95% confidence intervals (CI) for post two-year all-cause mortality when heart failure hospitalization (HFH) during the first two years was compared with no HFH in subgroups of patients with chronic heart failure (ACE, angiotensin-converting enzyme; HFH, heart failure hospitalization; NYHA, New York Heart Association)
Discussion
The findings of the current analysis demonstrate that incident hospitalization due to worsening HF is associated with significant increase in subsequent mortality in ambulatory chronic HF patients. These findings are important, as worsening HF is the number one reason for hospitalization for HF patients. Further, HF is the number one reason for hospitalization among older adults. Most HF patients are 65 years and older, and with the aging of the population, the number of elderly HF patients is expected to double over the next several decades.
HF is a progressive disorder with poor prognosis.23–25 Common identifiable causes of HF hospitalizations include acute coronary syndrome, uncontrolled hypertension, arrhythmias and use of anti-arrhythmic drugs, pulmonary infections, and noncompliance with medications and diet.26–28 Our data suggest that HF hospitalization may be a marker of disease progression and poor prognosis in HF. There is cumulative evidence that serum troponin levels may be elevated in HF, which in turn may be associated with worsening HF, HF hospitalization, and mortality.29, 30 Elevated serum troponin levels in acute HF have been associated with increased risk of subsequent mortality and hospitalizations.31 Other explanations for poor post-discharge outcomes include bed rest and restricted mobility during hospitalizations.32, 33
As the number one reason for hospitalization among population ≥65 years, HF hospitalization is already a cause for significant burden to the health care system. HF hospitalizations also significantly impair quality of life of HF patients, most of whom are older adults. Our findings further highlight the negative consequence of HF hospitalizations and suggest that the prevention of HF hospitalizations should be a high priority for clinicians caring for HF patients. Clinicians should optimize the use of interventions proven to reduce HF hospitalizations, to improve quality of life and reduce burden on health care system. Whether use of such drugs would also reduce subsequent mortality in these patients is currently unknown. Clinicians should also counsel ambulatory chronic HF patients on the importance of compliance with medications and salt and fluid restrictions, use evidence-based HF therapies as appropriate, and treat comorbidities such as hypertension, coronary artery disease, hypertension and chronic kidney disease.
The findings of our study support the use of HF hospitalization as a hard endpoint in HF trials. In the SOLVD trial, the survival benefit of enalapril was observed only among the patients who were hospitalized at least once during the trial 34. Because treatment effects often depend on severity or stage of disease,35 a history of HF hospitalization may be used as an inclusion criteria in future HF trials. This is important as event rates in contemporary systolic HF patients receiving optimal therapy and in those with diastolic HF (clinical HF with normal or near normal ejection fraction) are expected to be low. Future studies are need to investigate whether cardiac resynchronization therapy during hospitalization and the prescription of beta-blockers at the time of hospital discharge might favorably reduce post-discharge mortality compared to patients without HF hospitalizations.36–38
A recent post hoc analysis of CHARM database demonstrated that post-baseline “discharge for first hospitalization for HF” was independently associated with increased mortality (HR 3.15; 95% CI, 2.83–3.50; p<0.0001).39 This is very similar to the association observed in the current study (HR, 2.49, 95%CI, 1.97–3.13; p<0.0001) and provide cumulative evidence of the deleterious effect of HF hospitalization on subsequent survival in chronic HF.
Our study has several limitations. Like any non-randomized study, propensity score analysis cannot account for confounding due to unmeasured covariates. However, our sensitivity analyses suggest that our findings were rather insensitive to hidden biases. We were able to find near-exact matching for about 97% of patients with HF hospitalization. This is in contrast to about 60% adequate matching in other studies 17, 19. The results of our study are based on predominantly white, male, and relatively younger HF patients with normal sinus rhythm. Therapy for systolic HF has evolved since the DIG trial was conducted. We also had no data on use of beta-blockers and aldosterone antagonists. Lack of data of diuretic dosage is another limitation of our study.40
Conclusions
Incident hospitalization due to worsening HF was associated with significant increase in all-cause and cardiovascular mortality in a wide spectrum of ambulatory patients with chronic mild to moderate systolic and diastolic HF. These findings highlight the importance of HF hospitalization as a marker of disease progression and poor outcomes in HF, and emphasize on the need for prevention of HF hospitalization, and treatment strategies for hospitalized HF patients to improve post-discharge outcomes.
Acknowledgments
“The Digitalis Investigation Group (DIG) study was conducted and supported by the NHLBI in collaboration with the DIG Investigators. This Manuscript was prepared using a limited access dataset obtained by the NHLBI and does not necessarily reflect the opinions or views of the DIG Study or the NHLBI.”
Funding/Support
Dr. Ahmed is supported by the National Institutes of Health (NIH) through grants from the National Heart, Lung, and Blood Institute (1-R01-HL085561-02 and P50-HL077100). Dr. Allman is supported by grants R01-AG15062 from the National Institute on Aging. Dr. Dell’Italia is supported by a Specialized Center for Clinically Oriented Research (SCCOR) in Cardiac Dysfunction grant P50HL077100 from the National Heart, Lung, and Blood Institute and the Office of Research and Development, Medical Service, Department of Veteran Affairs.
Footnotes
Author Contributions
Dr. Ahmed conceived the study hypothesis and design. Dr. Ahmed wrote the first and subsequent drafts of the manuscript incorporating important intellectual content from all authors. Dr. Ahmed had full access to the data and conducted the statistical analyses in consultation with Dr. Love. All authors interpreted the data, participated in critical revision of the paper and approved the final version of the article.
Contributor Information
Ali Ahmed, University of Alabama at Birmingham, and VA Medical Center, Birmingham, AL.
Richard M. Allman, Birmingham/Atlanta VA Geriatric Research, Education, and Clinical Center and the University of Alabama at Birmingham.
Gregg C. Fonarow, University of California in Los Angeles, Los Angeles, CA.
Thomas E. Love, Case Western Reserve University, Cleveland, OH.
Faiez Zannad, Université Henri Poincaré, Nancy, France.
Louis J. Dell’Italia, University of Alabama at Birmingham, and VA Medical Center, Birmingham, AL.
Michel White, Montreal Heart Institute and University of Montreal, Montreal, Canada.
Mihai Gheorghiade, Northwestern University, Chicago, IL.
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