Racial Differences in Heart Failure Outcomes: Evidence From the Tele-HF Trial (Telemonitoring to Improve Heart Failure Outcomes)
Mini-Focus Issue: Special Populations in Heart Failure
Abstract
Objectives:
The purpose of this study was to determine whether there are racial differences in patient-reported health status as well as mortality and rehospitalization after hospitalization for heart failure (HF).
Background:
Little is known about whether racial differences exist in patient-reported outcomes after HF hospitalization.
Methods:
We analyzed data from 1,427 patients (636 non-Hispanic African Americans [45%]; 791 non-Hispanic whites [55%]) enrolled in the Tele-HF (Telemonitoring to Improve Heart Failure Outcomes) trial. Health status was measured with the Kansas City Cardiomyopathy Questionnaire (KCCQ) at baseline and then at 3 and 6 months. Generalized linear mixed models and propensity score methods were used to adjust for clustering within sites and differences between races.
Results:
Although black patients reported better adjusted health status at baseline (black vs. white difference in KCCQ summary scores was 6.22; 95% confidence interval [CI]: 2.98 to 9.46; p < 0.001), after adjusting for patient demographics, comorbidities, clinical laboratory values, and baseline KCCQ score, we detected no significant racial differences in patient-reported health status at 3 months (black vs. white difference in KCCQ score: 2.28; 95% CI: −0.84 to 5.41; p = 0.15) or 6 months (black vs. white difference in KCCQ score: 1.91; 95% CI: −1.31 to 5.13; p = 0.24).
Conclusions:
Compared with white patients, black patients with HF had better patient-reported health status shortly after HF admission but not at 3 or 6 months. Our study failed to show that black patients were disadvantaged with regard to health status after HF hospitalization. (Tele-HF: Yale Heart Failure Telemonitoring Study; NCT00303212)
Introduction
Racial disparities in cardiovascular disease have long been recognized. However, little is known about whether racial differences exist in patient-reported outcomes after a heart failure (HF) hospitalization. Despite pharmacologic and technical advances in the care of patients with HF, racial disparities in HF care and outcomes remain an enormous public health concern (1–4). Although HF is disproportionately present among African Americans (5–9), it is unclear whether there are racial differences in patient-reported health status outcomes after HF hospitalization.
Prior studies using Medicare data of older patients discharged after a hospitalization for HF have indicated that, compared with white patients, African American patients have higher readmission rates (1,2) and lower mortality (1). However, these studies did not include younger patients and focused on mortality and readmission, although health status (patients’ symptoms, function, and quality of life) is a primary concern for patients. Illuminating racial disparities in outcomes is important because HF affects black individuals at a younger age than white individuals. It thus remains unknown whether racial differences exist in mortality and readmission in a younger HF population and whether there are racial differences in patient-reported health status after HF hospitalization.
To address these gaps in knowledge, we sought to compare a comprehensive set of HF outcomes including health status, readmission, and mortality in non-Hispanic white and non-Hispanic black patients enrolled in the multicenter, randomized controlled Tele-HF (Telemonitoring to Improve Heart Failure Outcomes) trial. A previous study reported that telemonitoring did not improve health outcomes among patients recently hospitalized for HF using the Tele-HF dataset (10). Our main goal was to determine whether there were short-term and long-term racial differences with respect to white and black patients’ health status, mortality, and readmission rates.
Methods
Study population
The primary data source was the Tele-HF trial, which has been previously described (10,11). Briefly, the Tele-HF trial was funded by National Heart, Lung, and Blood Institute (grant 5 R01HL080228) as a multicenter, randomized, controlled trial to assess whether daily, remote telemonitoring of symptoms and body weight would improve HF outcomes. The Yale University School of Medicine and each participating site (see the Online Appendix) approved the trial protocol.
An independent Data and Safety Monitoring Board was appointed to monitor adherence to the protocol, to evaluate the recruitment and retention of patients, and to assess the quality of the data and safety of the telemonitoring intervention.
Patients were recruited from 2006 through 2009 at 33 participating sites across the United States. Candidates considered for inclusion in the trial were patients hospitalized for HF in the previous 30 days. Exclusion criteria were residence in a nursing home, low expected probability of survival for the next 6 months, inability to stand on a scale, severe cognitive impairment (12), and a planned hospitalization for a procedure. There were no significant differences between the telemonitoring group and the usual care group in mortality and readmission rates (10). After excluding 226 patients who self-reported Hispanic or “other” race from the study, our analyses included 1,427 patients with HF of the 1,653 Tele-HF patients.
Variables and outcomes
Self-reported race information was recorded when a patient was enrolled. The primary outcome measurements for this study were the Kansas City Cardiomyopathy Questionnaire (KCCQ) overall summary scores (which are derived from the physical function, symptom, social function, and quality-of-life domains; range: 0 to 100 with higher scores reflecting better health status) (13) at baseline and at 3 and 6 months). Scores range from 0 to 100, where higher scores indicate better health status (e.g., fewer symptoms, less function impairment, and higher quality of life). A centralized call center was used to administer the KCCQ shortly after discharge (baseline) and again at 3 and 6 months. Patient readmission and mortality within 180 days of enrollment were analyzed as secondary outcomes.
Statistical analyses
We compared patient demographics, socioeconomic status, and medical history as well as clinical characteristics by race using means and standard deviations or medians and interquartile range for skewed data and percentages for categorical variables. Wilcoxon’s rank sum test for continuous variables and the Pearson chi-square test or Fisher exact test for categorical variables were used to examine statistical significance of observed differences.
Multivariate linear regression analyses using generalized linear mixed models (GLMM) were performed to examine the relationship between race (black vs. white) and the overall summary score of KCCQ across time (baseline and 3 and 6 months) between these 2 racial groups. The GLMM models included a random intercept to account for clustering within a site and to adjust the covariance structure to account for repeated observations on subjects. Missing data at follow-up were assumed to be missing at random after empirical data checking. Maximum likelihood estimation method was used in all GLMM models. We first identified an overall model predicting KCCQ summary scores across all 3 time intervals by identifying those variables that were either clinically important or statistically significant (p < 0.05). Because black and white patients differ in numerous demographic, socioeconomic, and clinical characteristics, we sought to balance the groups by creating a propensity score to be black, as has been done in prior analyses (14). The propensity to be black was derived from a nonparsimonious logistic regression on all available variables at each sequential step. We estimated the propensity score and then included it as a covariate along with other covariates related to the outcome in the final fully adjusted model predicting black versus white differences in KCCQ’s overall summary scores across time (15). The model was estimated sequentially. First the unadjusted model was fitted, and then we sequentially adjusted for demographics, insurance payer, medical history, and lab values each time fitting a new model. It is suggested that regression adjustment for propensity score has great balancing properties (16).
Likewise, multivariate logistic regression analyses using GLMMs were conducted to evaluate the association between race (black vs. white) and readmission and mortality rates (180 days). Models accounted for the clustering of patients within a site through the use of random intercepts. Similar model-building techniques, including sequential propensity score method and regression method of adjusting for propensity score, were used to test for racial differences for each outcome.
In addition, to ensure that racial differences detected in any outcome measure did not depend on the group assignment (telemonitoring vs. usual care), an interaction term between race (black vs. white) and group (telemonitoring vs. usual care) was added to each full model for readmission and mortality measurements, and a 3-way interaction term (i.e., race, group, and time) was added to the KCCQ model. Treatment assignment was not found to be statistically significant in any of these models and therefore was not included in the final models.
All statistical analyses were performed using SAS version 9.3 software (SAS Institute, Cary, North Carolina). A 2-tailed significance level of 0.05 was used for all tests.
Results
There were 1,427 white and black patients with HF (636 non-Hispanic African Americans [45%]; 791 non-Hispanic whites [55%]) from 33 participating hospitals in the Tele-HF trial. The mean age was 61.5 ± 15.2 years, and 42.5% were women. There were significant racial differences in patient demographics, socioeconomic status, medical history, risk factors, and laboratory values (Table 1).
| Non-Hispanic White (n = 791) | Non-Hispanic Black (n = 636) | p Value | |
|---|---|---|---|
| Demographics | |||
| Age, yrs | |||
| Mean ± SD | 67.2 ± 13.7 | 54.3 ± 13.9 | <0.001 |
| Median (IQR) | 68 (20.0) | 54 (18.0) | |
| Female | 327 (41.3) | 280 (44.0) | 0.31 |
| Payer | |||
| Commercial/PPO | 265 (33.5) | 147 (23.1) | <0.001 |
| Medicare | 495 (62.6) | 211 (33.2) | <0.001 |
| Medicaid | 72 (9.1) | 160 (25.2) | <0.001 |
| HMO | 49 (6.2) | 43 (6.8) | 0.67 |
| VA | 11 (1.4) | 6 (0.9) | 0.44 |
| Self-pay | 66 (8.3) | 117 (18.4) | <0.001 |
| Other | 32 (4.1) | 16 (2.5) | 0.11 |
| Unknown | 21 (2.7) | 16 (2.5) | 0.87 |
| Socioeconomic status | |||
| Annual household income of <$10,000 | 89 (11.3) | 184 (28.9) | <0.001 |
| High school graduate | 567 (71.7) | 382 (60.1) | <0.001 |
| Medical history | |||
| CAD/MI/IC | 500 (63.2) | 237 (37.3) | <0.001 |
| Hypercholesterolemia | 466 (58.9) | 301 (47.3) | <0.001 |
| Hypertension | 568 (71.8) | 542 (85.2) | <0.001 |
| Liver disease | 9 (1.1) | 20 (3.1) | 0.008 |
| Renal failure | 194 (24.5) | 181 (28.5) | 0.09 |
| Cardiac resynchronization therapy | 56 (7.1) | 26 (4.1) | 0.02 |
| Peripheral vascular disease | 104 (13.2) | 46 (7.2) | <0.001 |
| Permanent pacemaker | 132 (16.7) | 57 (9.0) | <0.0001 |
| Chronic pulmonary disease | 201 (25.4) | 117 (18.4) | 0.002 |
| Diabetes | 383 (48.4) | 284 (44.7) | 0.16 |
| Cerebrovascular disease | 76 (9.6) | 56 (8.8) | 0.60 |
| Risk factors | |||
| Current smoker | 47 (5.9) | 81 (12.7) | <0.001 |
| BMI, kg/m2 | 0.16 | ||
| Mean ± SD | 26.2 ± 10.4 | 27.4 ± 12.1 | |
| Median (IQR) | 26.1 (13.3) | 26.6 (17.2) | |
| Systolic blood pressure, mm Hg | <0.001 | ||
| Mean ± SD | 117.2 ± 19.8 | 126 ± 24.6 | |
| Median (IQR) | 116 (28.0) | 124 (35.0) | |
| Diastolic blood pressure, mm Hg | |||
| Mean ± SD | 66.7 ± 11.9 | 75.7 ± 14.7 | <0.001 |
| Median (IQR) | 66 (15.0) | 75 (21.0) | <0.001 |
| NYHA functional classification | 0.13 | ||
| I | 46 (5.8) | 37 (5.8) | |
| II | 309 (39.1) | 211 (33.2) | |
| III | 389 (49.2) | 342 (53.8) | |
| IV | 47 (5.9) | 46 (7.2) | |
| Low cognition: Folstein score ≤24 (12) | 50 (6.3) | 75 (11.8) | <0.001 |
| Laboratory values at enrollment | |||
| Low LVEF (<40%) | 486 (61.4) | 499 (78.5) | <0.001 |
| Baseline KCCQ Summary Score | |||
| Mean ± SD | 59 ± 24 | 60 ± 25 | 0.69 |
Compared with white patients, black patients were much younger, more likely to be in the Medicaid and self-pay categories, had lower annual household incomes and lower high school graduation rates, and were less likely to have a medical history of coronary artery disease, myocardial infarction, and ischemic cardiomyopathy. Blacks were more likely to have a medical history of hypertension, higher systolic and diastolic blood pressure, cognitive impairment (Folstein score of ≤24) (12), and reduced left ventricular ejection fraction (LVEF of <40%).
Primary outcomes
After accounting for clustering within site and correlation among repeated measurements, black patients with HF reported better health status at baseline, as measured by the KCCQ overall summary score (black vs. white score difference: 3.97; 95% confidence interval [CI]: 1.00 to 6.95; p = 0.009). However, differences in unadjusted KCCQ summary scores were not observed at 3 months (black vs. white score difference: 1.17; 95% CI: −1.93 to 4.27; p = 0.46) or 6 months (black vs. white score differences: 0.71; 95% CI: −2.48 to 3.91; p = 0.66). After further adjustment for all other patient-level factors, together with the propensity score, the racial differences in overall summary scores of KCCQ at baseline still persisted (black vs. white score differences: 6.22; 95% CI: 2.98 to 9.46; p < 0.001) (Table 2). To further understand this difference, we performed a post-hoc analysis of propensity score adjusted KCCQ baseline subscores to identify which domains might be most influential. We found that there were significant racial differences in almost all domains of the baseline KCCQ scores, except for self-efficacy and symptom stability (Table 3). By contrast, no significant racial differences were detected in fully adjusted models at 3 months (black vs. white score difference: 2.28; 95% CI: −0.84 to 5.41; p = 0.15) or 6 months (adjusted black vs. white score difference: 1.91; 95% CI: −1.31 to 5.13; p = 0.24). Importantly, both racial groups experienced notable improvement in mean quality of life scores during the follow-up period (Table 2, Online Table 1, Figure 1) ranging from 58.4 to 68.9 points for whites and 64.6 to 70.9 points for blacks (p < 0.01 for interaction of race and time).
| Models | Estimate | 95% CI | p Value |
|---|---|---|---|
| Time: at enrollment | |||
| Unadjusted model | 3.97 | 1.00 to 6.95 | 0.009 |
| Model 1: adjusted for demographics | 5.82 | 2.73 to 8.92 | <0.001 |
| Model 2: model 1 + adjusted for payer | 6.07 | 2.94 to 9.19 | <0.001 |
| Model 3: model 2 + adjusted for medical history | 5.93 | 2.73 to 9.14 | <0.001 |
| Model 4: model 3 + adjusted for laboratory values | 6.22 | 2.98 to 9.46 | <0.001 |
| Time: 3 months after enrollment | |||
| Unadjusted model | 1.17 | –1.93 to 4.27 | 0.46 |
| Model 1: adjusted for demographics | 2.98 | –0.21 to 6.17 | 0.07 |
| Model 2: model 1 + adjusted for payer | 2.86 | –0.33 to 6.04 | 0.08 |
| Model 3: model 2 + adjusted for medical history | 2.29 | –0.84 to 5.41 | 0.15 |
| Model 4: model 3 + adjusted for laboratory values | 2.28 | –0.84 to 5.41 | 0.15 |
| Time: 6 months after enrollment | |||
| Unadjusted model | 0.71 | –2.48 to 3.91 | 0.66 |
| Model 1: adjusted for demographics | 2.54 | –0.74 to 5.83 | 0.13 |
| Model 2: model 1 + adjusted for payer | 2.42 | –0.86 to 5.70 | 0.15 |
| Model 3: model 2 + adjusted for medical history | 1.88 | –1.34 to 5.09 | 0.25 |
| Model 4: model 3 + adjusted for laboratory values | 1.91 | –1.31 to 5.13 | 0.24 |
| KCCQ Subscore | Non-Hispanic White (n = 791) | Non-Hispanic Black (n = 636) | p Value |
|---|---|---|---|
| Physical Limitation Subscore | 67.7 ± 1.6 | 73.0 ± 1.8 | 0.01 |
| Symptom Stability Subscore | 73.7 ± 1.1 | 77.0 ± 1.4 | 0.06 |
| Symptom Frequency Subscore | 55.8 ± 1.6 | 62.4 ± 1.8 | 0.001 |
| Symptom Burden Subscore | 64.3 ± 1.6 | 71.8 ± 1.8 | <0.001 |
| Total Symptom Score Subscore | 60.1 ± 1.5 | 67.2 ± 1.8 | <0.001 |
| Self-Efficacy Subscore | 84.5 ± 1.1 | 83.8 ± 1.3 | 0.65 |
| Quality of Life Subscore | 52.5 ± 1.4 | 58.4 ± 1.6 | 0.002 |
| Social Limitation Subscore | 53.8 ± 2.2 | 59.9 ± 2.5 | 0.02 |
Estimated Differences in KCCQ Summary Score
A plot for estimated racial differences in the Kansas City Cardiomyopathy Questionnaire (KCCQ) summary scores from the final model (adjusted for patient demographics, payer, medical history, and laboratory values).
Secondary outcomes
We observed no significant racial differences in unadjusted 30-day (white: 18.3%, 95% CI: 15.7% to 21.2%; black: 16.4%, 95% CI: 13.6% to 19.5%; p = 0.33) or 180-day (white: 50.4%, 95% CI: 46.9% to 54.0%; black: 47.8%, 95% CI: 43.9% to 51.8%; p = 0.32) all-cause readmission and 30-day mortality (white: 2.3%, 95% CI: 1.4% to 3.6%; black: 1.3%, 95% CI: 0.5% to 2.5%; p = 0.15). Significant racial differences occurred in unadjusted 180-day mortality rates (white: 13.4%, 95% CI: 11.1% to 16.0%; black: 9.0%, 95% CI: 6.9% to 11.5%; p = 0.01) using the chi-square test. However, such racial differences were no longer statistically significant after fully adjusting for propensity score to be black, patient demographics, payer, socioeconomic status, medical history, risk factors, and laboratory values (adjusted odds ratio [AOR]: 0.85; 95% CI: 0.52 to 1.37; p = 0.49). Likewise, fully adjusted 180-day all-cause readmission rates were similar (AOR: 0.92; 95% CI: 0.70 to 1.21; p = 0.53) (Table 4). Due to a low number of events, we were unable to calculate propensity score adjusted models for 30-day mortality and readmission outcomes.
| Models | AOR | 95% CI | p Value |
|---|---|---|---|
| 180-day readmission | |||
| Unadjusted model | 0.87 | 0.70–1.09 | 0.23 |
| Model 1: adjusted for demographics | 0.88 | 0.69–1.11 | 0.28 |
| Model 2: model 1 + adjusted for payer | 0.89 | 0.70–1.14 | 0.36 |
| Model 3: model 2 + adjusted for socioeconomic status | 0.83 | 0.64–1.06 | 0.14 |
| Model 4: model 3 + adjusted for medical history | 0.86 | 0.66–1.13 | 0.27 |
| Model 5: model 4 + adjusted for laboratory values | 0.92 | 0.70–1.21 | 0.53 |
| 180-day mortality | |||
| Unadjusted model | 0.63 | 0.43–0.91 | 0.01 |
| Model 1: adjusted for demographics | 0.84 | 0.56–1.25 | 0.39 |
| Model 2: model 1 + adjusted for payer | 0.79 | 0.53–1.20 | 0.27 |
| Model 3: model 2 + adjusted for socioeconomic status | 0.71 | 0.47–1.09 | 0.12 |
| Model 4: model 3 + adjusted for medical history | 0.81 | 0.51–1.29 | 0.38 |
| Model 5: model 4 + adjusted for laboratory values | 0.85 | 0.52–1.37 | 0.49 |
Discussion
We found that, compared with white patients, black patients with HF had better self-reported health status overall and in several domains (including physical limitation, symptom frequency, symptom burden, total symptom score, quality of life, and social limitation) early after discharge. However, such racial advantages were attenuated and no longer statistically significant at 3 or 6 months, although the health status of both racial groups improved during follow-up. Moreover, we did not detect significant racial differences in 180-day readmission and mortality rates from the fully adjusted models.
Although there is a pressing need to report patient-centered outcomes, including patient’s health status (17–21), to the best of our knowledge, our study is one of the first studies to describe patients’ health status (e.g., symptoms, function, and quality of life) by race for patients recently discharged from the hospital after HF exacerbation. To quantify patients’ health status, we used the KCCQ questionnaire, which is a valid, sensitive, disease-specific health status measure for patients with HF. Compared with other health-related quality-of-life instruments for HF, the KCCQ questionnaire is more sensitive to clinical changes and thus is an ideal tool to demonstrate improvements in health status (22). Furthermore, unlike previous research using large administrative datasets to examine racial disparities in HF (1,2), our analyses were based on data collected from a multicenter randomized controlled trial (Tele-HF) with detailed clinical information (10,11).
Our finding that black patients with HF had higher KCCQ summary scores at baseline differed from that of another study recently reported (23). Using the HF-ACTION (Heart Failure—A Controlled Trial Investigating Outcomes in Exercise TraiNing) trial database (details of the trial design were published previously [24,25]), which included 2,175 patients with HF (black: n = 749 [34%]; white: n = 1426 [66%]), investigators showed that the unadjusted baseline KCCQ overall score was higher in white patients with HF than in black patients (white vs. black: 69 vs. 66; p < 0.001) (23). There are several possible explanations for the different findings between the 2 studies. First and foremost, we focused on hospitalized HF patients enrolled in the Tele-HF trial, whereas the HF-ACTION trial examined stable outpatients with diagnosis of HF. As expected, white patients in our study were older (67 vs. 62, respectively), tended to have had a history of myocardial infarction (63% vs. 51%, respectively), and were more likely to have hypertension (72% vs. 51%, respectively). In addition, the percentage of black patients was much higher in our study (45% vs. 34%). A recent study using the GWTH-HF (American Heart Association Get With The Guidelines) national registry reported an even lower percentage of non-Hispanic black patients with HF (23%) (26).
Moreover, we further limited the study to non-Hispanic white and non-Hispanic black patients to obtain a more homogeneous study population. Finally, we cannot rule out the possibility that black patients with HF admitted to hospitals might have lower admission severity than whites (e.g., risk factors that were not captured by the Tele-HF trail may have contributed to differential severity), which has been reported previously (27–29). Nonclinical factors such as being less well insured and less educated, inadequate outpatient follow-up, poor social support, and nonadherence with medications or diet are possible explanations for blacks being hospitalized at an earlier stage of HF (27). Also, this might reflect the racial differences in response to the HF treatment due to phenotype differences across racial groups (30,31).
We also found that there were no significant racial differences in readmission and mortality rates. These findings contrasted with the results from 2 prior studies using a national Medicare fee-for-service sample. Rathore et al. (1) reported that black Medicare patients hospitalized with HF had slightly higher risk-adjusted rates of 1-year readmission (relative risk [RR]: 1.09; 95% CI: 1.06 to 1.13) but had lower mortality rates up to 1 year (30-day mortality RR: 0.78; 95% CI: 0.68 to 0.91; 1-year mortality RR: 0.93; 95% CI: 0.88 to 0.98) (1). Joynt et al. showed that black Medicare patients with HF were more likely to get readmitted within 30 days (AOR: 1.04; 95% CI: 1.03 to 1.06) (2). However, the 95% confidence intervals in our study covered the point estimates reported from these prior papers; therefore, our findings are not significantly inconsistent. Importantly, the effect size in the studies by Joynt et al. (2,3) was quite small, indicating that the odds of readmission were only 4% greater for blacks versus whites. In addition, our study differed from these 2 studies with respect to the patient population and study design. Although prior studies used national Medicare fee-for-service recipients (which were elderly patients) with HF to study racial differences in mortality and readmission, our analyses was based on a randomized controlled trial (our patients were overall much younger) to study racial disparities in HF outcomes. As shown previously, using different types of datasets (large administrative datasets vs. randomized controlled trial database with detailed clinical and health status information) could arrive at different conclusions (32). Furthermore, we examined 180-day mortality and readmission after hospitalization for HF, whereas Rathore et al. (1) examined 1-year mortality and 1-year readmission, and Joynt et al. (2,3) reported 30-day readmission. It is likely that studying different outcome measurements and adjusting for sufficient clinical risks (e.g., LVEF) in our study might lead to dissimilar findings. This also suggests that future studies are needed to confirm whether the length of follow-up period can play a critically important role in evaluating racial differences in HF outcomes.
There are several clinical implications of our findings from this study. First, our results highlight how race is strongly associated with many features of patients with HF (e.g., demographics, baseline characteristics, clinical profiles, responses to treatment, and others), although we did not observe significant racial differences in the outcomes during the follow-up. This suggests the importance of clinicians being aware of the ways that race is linked to the manifestation of HF and the need for them to provide personalized HF treatment for different subgroups. Because the Tele-HF database did not include detailed clinical information for the recent hospitalization with HF, future studies are needed to improve our understanding of the mechanism of the race in the presentation and the outcomes of HF. Second, our findings of higher KCCQ scores at baseline and very similar KCCQ scores during follow-up supports the use of this disease-specific, valid and reliable, self-administered KCCQ questionnaire at different times to monitor clinical changes and better predict medium- and long-term outcomes in HF (33). Third, we cannot rule out the possibility of “self-report bias” (i.e., selective revealing or suppression of information by specific groups) in the Tele-HF database. Literature has consistently demonstrated that self-reports are at risk of reporting bias when assessing quality of care (34) and health status (35). In addition, a prior study reported that there were black versus white disparities in health literacy among patients with HF (36).
Study limitations
Our study should be interpreted in the context of the following potential limitations. First, the Tele-HF trial had specific inclusion and exclusion criteria so that our findings might not be generalizable to the actual patient population. We excluded patients with HF who resided in a nursing home, had low expected probability of survival for the next 6 months, were unable to stand on a scale, had severe cognitive impairment, or had a planned hospitalization for a procedure. In other words, the patients included in the study might be systematically less severe. Moreover, black patients were much younger than their white counterparts in the study. Given that quality of life and outcomes for patients with HF could vary across age groups (37), it is possible that our regression models did not account for residual confounding due to age or other factors. Although we adjusted for many patient-level variables of prognostic importance, residual confounding may still have influenced our findings. Second, Tele-HF’s follow-up period was 180 days after enrollment. Therefore, we were unable to evaluate the racial differences in longer terms. Third, it is possible that this study was unable to detect very modest racial differences in these outcomes. Although we had non significant findings, we found that the confidence intervals encompassed ranges that included clinically meaningful differences. It is possible that modest racial differences could be detected with a high-powered study. Fourth, there might be racial differences with regard to the prior hospitalization for HF (e.g., number of complications, length of stay, and others), and such differences might explain some findings in this study. Unfortunately, we did not have detailed clinical information about the prior HF hospitalization and thus were unable to account for such differences in our analyses. Fifth, because we performed multiple statistical tests between black and white patients with HF, we cannot completely rule out the possibility of a false positive finding. However, the extremely low p value (<0.001) from testing racial difference in the KCCQ summary score at enrollment suggests that this is unlikely.
Conclusions
We found no differences in patient-reported health status, mortality or readmission outcomes by race in a multicenter randomized controlled trial of HF patients. We found that, compared with whites, black HF patients had higher self-reported health status early after hospitalization, but we failed to detect racial differences in health status at 3 and 6 months. No significant racial differences in 180-day readmission and mortality rates were detected after adjusting for clustering at sites and patient factors. These findings suggest that racial disparities in patients with HF are less likely to be evident after they are hospitalized for treatment.
COMPETENCY IN MEDICAL KNOWLEDGE: Compared with white patients, black patients with HF had better patient-reported health status shortly after HF admission but not at 3 or 6 months.
TRANSLATIONAL OUTLOOK: Future studies are needed to investigate the role of recent hospitalization with HF and thus improve our understanding of the mechanisms of race in the presentation and patient-reported outcomes of HF.
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Abbreviations and Acronyms
| AOR | adjusted odds ratio |
| CI | confidence interval |
| GLMM | generalized linear mixed models |
| HF | heart failure |
| KCCQ | Kansas City Cardiomyopathy Questionnaire |
| LVEF | left ventricular ejection fraction |
| RR | relative risk |
Footnotes
This study was funded by grant
