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Predictors of Heart Failure Readmission in a High-Risk Primarily Hispanic Population in a Rural Setting

Carlson, Beverly, PhD, RN, CNS, CCRN-K, FAHA; Hoyt, Helina, MS, RN, PHN; Gillespie, Kristi, MS, RN; Kunath, Julie, MS, APRN, ACCNS-AG, CCRN-CMC; Lewis, Dawn, BSN, RN; Bratzke, Lisa C., PhD, RN, ANP-BC, FAHA

Journal of Cardiovascular Nursing: May/June 2019 - Volume 34 - Issue 3 - p 267–274
doi: 10.1097/JCN.0000000000000567
ARTICLES: Heart Failure
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BACKGROUND High risk for readmission in patients with heart failure (HF) is associated with Hispanic ethnicity, multimorbidity, smaller hospitals, and hospitals serving low-socioeconomic or heavily Hispanic regions and those with limited cardiac services. Information for hospitals caring primarily for such high-risk patients is lacking.

OBJECTIVE The aim of this study was to identify factors associated with 30-day HF readmission after HF hospitalization in a rural, primarily Hispanic, low-socioeconomic, and underserved region.

METHODS Electronic medical records for all HF admissions within a 2-year period to a 107-bed hospital near the California-Mexico border were reviewed. Logistic regression was used to identify independent predictors of readmission.

RESULTS A total of 189 unique patients had 30-day follow-up data. Patients were primarily Hispanic (71%), male (58%), and overweight or obese (82.5%) with 4 or more chronic conditions (83%) and a mean age of 68 years. The 30-day HF readmission rate was 5.3%. Early readmission was associated with history of HF, more previous emergency department (ED) and hospital visits, higher diastolic blood pressure and hypokalemia at presentation, shorter length of stay, and higher heart rate, diastolic blood pressure, and atrial fibrillation (AF) at discharge. Using logistic regression, previous 6-month ED visits (odds ratio, 1.5; P = .009) and AF at discharge (odds ratio, 5.7; P = .039) were identified as independent predictors of 30-day HF readmission.

CONCLUSIONS Previous ED use and AF at discharge predicted early HF readmission in a high-risk, primarily Hispanic, rural population in a low-socioeconomic region.

Beverly Carlson, PhD, RN, CNS, CCRN-K, FAHA Assistant Professor, School of Nursing, San Diego State University, California.

Helina Hoyt, MS, RN, PHN Lecturer, School of Nursing, San Diego State University, California.

Kristi Gillespie, MS, RN Chief Nursing Officer, Pioneers Memorial Hospital, Brawley, California.

Julie Kunath, MS, APRN, ACCNS-AG, CCRN-CMC Clinical Nurse Specialist, Pioneers Memorial Hospital, Brawley, California.

Dawn Lewis, BSN, RN Staff Nurse, Pioneers Memorial Hospital, Brawley, California.

Lisa C. Bratzke, PhD, RN, ANP-BC, FAHA Assistant Professor, School of Nursing, University of Wisconsin – Madison, Wisconsin.

This study was supported by a research grant from the Sigma Theta Tau International Gamma Chapter.

Correspondence Beverly Carlson, PhD, RN, CNS, CCRN-K, FAHA, School of Nursing, San Diego State University, 5500 Campanile Dr, San Diego, CA 92182 (bcarlson@sdsu.edu).

Estimated at more than $30 billion annually, the staggering healthcare costs associated with heart failure (HF) are largely related to hospitalization.1 Consequently, in addition to providing the care needed to treat a new-onset or acutely decompensated HF state, hospitals must also provide adequate patient preparation for successful transition to outpatient care.2 Direction for targeting efforts to reduce HF hospitalizations is offered in many studies describing the characteristics of patients admitted with HF, as well as factors associated with readmission. However, consensus is lacking, and most of the results are not generalizable to smaller or rural hospitals and only highlight the disproportional burden that accompanies serving primarily high-risk patients. Alternative approaches are needed when most patients served meet the criteria for high risk reported in other settings.

Hospital-specific risk factors for higher readmission rate after HF hospitalization include smaller size, limited cardiac services, and patients from lower income or minority populations.3–5 Patient-specific risk factors for readmission include lower socioeconomic status,6 Medicaid versus commercial health insurance,7 and multiple chronic conditions.8,9 Ethnic disparities are evident at both the hospital level and the patient level with higher readmission rates in Hispanic-serving hospitals10 and in Hispanic patients.4,6,10 The risk is higher still in Hispanic patients whose primary language is Spanish and those born outside the United States.11 Yet, few studies have focused on inpatient settings that meet most or all of these criteria, specifically smaller facilities caring primarily for Hispanic patients of Mexican origin. Considering that population growth rate in the United States is highest for Hispanics compared with all other minority groups and that Mexico is the most common origin of Hispanics in the United States,12 this deficiency in the science is compelling. Information helpful to guide care and direct resources in this high-risk population is lacking. Therefore, we undertook an exploratory study to examine acute HF hospitalizations in a rural, underserved, low-socioeconomic region near the California-Mexico border. The specific aim of the analysis reported here was to identify factors associated with HF readmission within 30 days after HF hospitalization in rural, mostly Hispanic patients with HF in a low-socioeconomic region.

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Methods

The setting for this study was a 107-bed community hospital near the Mexican border. This facility serves a large rural and economically impoverished region in a county federally designated as a Health Professional Shortage Area with a population of more than 80% Hispanic, a poverty rate13 of 24.3%, and more than one-half of the residents insured by Medicaid or without insurance. Using International Classification of Diseases, Ninth Revision codes, we first identified all patients discharged with a principal diagnosis of HF within a 2-year period (from October 2013 through September 2015). The Center for Medicare and Medicaid Services (CMS) qualifying codes for National Hospital Inpatient Quality Reporting Measures for HF (428.x, 402.x1, 404.x1, and 404.x3) were used for patient identification. We also used CMS inclusion and exclusion criteria, limiting our search to principal discharge diagnosis only and only excluding maternity and neonatal encounters. We then conducted a retrospective chart review of the electronic medical records for all HF hospitalizations within the prescribed period. Variables included in the data collection form were based on previous research and clinical relevance to risk for readmission. Participants self-identified race and ethnicity, in accordance with US Census Bureau categories. Other data included self-reported demographic characteristics (age, gender, primary language, marital status, and living status), clinical characteristics (medical history, ejection fraction, previous 12-month emergency and hospital visits, body mass index (BMI), presenting signs and symptoms, vital signs, and laboratory values), medical therapy (respiratory and drug therapy before admission and during inpatient stay), and discharge status (length of stay [LOS], vital signs, and disposition). Patients were categorized as having HF with reduced ejection fraction or HF with preserved ejection fraction in accordance with definitions published in the most recent national guidelines.2 New York Heart Association class was not available in the records. Body mass index values were used to classify patients in accordance with Centers for Disease Control and Prevention standard weight status categories. The study was granted exempt status by the San Diego State University Institutional Review Board.

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Analysis

Demographic and clinical characteristics, medical therapies, and discharge status were analyzed using frequencies and measures of central tendency. To examine patient factors associated with 30-day HF readmission, we first excluded those patients who were transferred to another acute care facility (17), who were discharged to hospice (2), who died during hospitalization (2), or without 30 days of follow-up data (5). We then conducted univariate and bivariate analyses using Student t tests, Mann-Whitney, and χ2 with Fisher's exact test as appropriate for the level of measurement to test for differences between those patients readmitted for HF and those not readmitted for HF within 30 days of discharge. To identify predictors of HF readmission, we conducted logistic regression modeling. Those variables found to be significantly associated with the outcome in the preliminary analysis as well as key variables deemed clinically relevant (age, gender, comorbidity) were included in the initial model. When multicollinearity was indicated by predictor intercorrelation values of 0.7 or greater, we selected the variable to be included in the model based on clinical relevance. Significance of individual predictors, Nagelkerke R2 values and the Hosmer and Lemeshow test to assess goodness of fit, and predictive accuracy (sensitivity and specificity) were used to develop the most parsimonious model. Data were analyzed using SPSS version 23. We considered a 2-sided P value of less than .05 as statistically significant.

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Results

Patient Characteristics

A total of 215 patients experienced 288 HF hospitalizations during the 2-year period. Of these, 189 patients were eligible for the 30-day HF readmission analysis. Table 1 provides complete patient characteristics. Briefly, the final sample was primarily Hispanic (71.4%), male (57.7%), and overweight or obese (82.5%) with a mean age of 68 years and multiple chronic conditions. The incidence of depression was 10.6%. Forty (21.2%) reported current alcohol or drug abuse. Forty-five (23.8%) had a pacemaker and/or internal cardiac defibrillator in place. Only 18% (n = 34) had a history of revascularization. Ten (5.6%) were on chronic hemodialysis. Almost 30% of the patients had visited the emergency department (ED) a total of 78 times in the previous 30 days. A total of 240 ED visits were made in the previous 6 months. Approximately one-third of all previous ED visits and hospitalizations were for HF.

TABLE 1

TABLE 1

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Discharge Status

Length of stay ranged from less than 1 day to 15 days. During hospitalization, only 1 patient required mechanical ventilation and five (2.6%) required intravenous inotropic or vasopressor support. See Table 2 for patient status at discharge. Seventeen patients (9.0%) were discharged to an intermediate care facility, and 10 (5.3%) left the hospital against medical advice. Of the 162 patients (85.7%) discharged to home, 38 (23.5%) received home health services.

TABLE 2

TABLE 2

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Predictors of 30-Day Heart Failure Readmission

Ten patients (5.3%) were readmitted for HF within 30 days. Patient characteristics, discharge status, and statistical differences between those readmitted and those not readmitted for HF within 30 days of discharge are included in Tables 1 and 2. Patients who experienced early HF readmission were significantly more likely to speak English as their primary language and have a history of HF, a higher rate of ED use throughout the year before admission, more hospitalizations in the previous 6 months, and higher diastolic blood pressure (DBP) and lower serum potassium at presentation. Patients with early readmission also had a significantly shorter LOS than those not readmitted, as well as higher DBP, a higher heart rate (HR), and a higher incidence of atrial fibrillation at discharge. The final logistic regression model consisted of 6 predictors of early HF readmission. Inclusion of other variables cited in the literature as predictive of early readmission such as age, primary language spoken, and comorbidity worsened model fit indices and predictive accuracy, and these were not retained in the final model. The regression coefficient, odds ratio, and confidence interval for each of the predictors in the final model are provided in Table 3. The model was significant (χ2 = 27.033, P < .001, with df = 6). Prediction success was 95.8% overall. Goodness of fit was adequate with a Nagelkerke R2 value of 39.3% and a nonsignificant Hosmer and Lemeshow test (P = .95). In the final model, 2 variables were significant predictors of early readmission for HF: number of ED visits in the previous 6 months (P = .009) and atrial fibrillation at discharge (P = .039).

TABLE 3

TABLE 3

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Discussion

The aim of this study was to identify factors associated with HF readmission within 30 days of HF discharge in a rural, low-socioeconomic region near the California-Mexico border. Number of ED visits in the previous 6 months and atrial fibrillation at discharge were the 2 significant predictors of readmission in this sample. Ours is the first study to identify previous 6-month ED visits as predictive of early HF readmission, although ED or hospital visits have consistently been found to predict all-cause readmission.

In a large metropolitan region, high ED use in the 90 days before HF hospitalization predicted early all-cause readmission.14 In contrast, hospitalization rates in the 60 days or 12 months before HF hospitalization predicted all-cause readmission in several studies.15–18 Notably, Eapen et al16 found that hospitalization during the 6 months prior to HF hospitalization performed as well as a validated model using multiple clinical data points in the electronic health data record in predicting 30-day all-cause rehospitalization. In our sample, frequency of ED visits at every time interval and number of hospitalizations in the 6 months before the index HF hospitalization were all significantly higher in the readmitted patients. Previous ED visits may have been a better predictor in our study because ED use by our patients was higher than that reported by other investigators. In a sample of mostly white and Asian patients, Bradford et al14 found that 18% visited the ED in the 90 days before HF admission compared with our 44%. In a sample that was more than 60% black and 19% Hispanic, Amarasingham et al15 found a mean (SD) of 1.3 (5.1) ED visits in the 12 months before admission, whereas our mean was more than 2 visits. Our findings are congruent, though, with those of Hasegawa et al19 who identified Hispanic ethnicity, lower socioeconomic status, and comorbid chronic conditions as risk factors for more frequent ED visits in patients with HF. Emergency department use in the region we studied is known to be high, in large part due to a significant shortage of healthcare providers. In contrast, the incidence of previous hospitalization was lower in our sample compared with 22.7% in the previous 90 days reported by Bradford et al14 and a mean (SD) of 1.1 (1.7) reported by Amarasingham et al.15 Our unique finding of high ED use but low hospitalization before admission offers compelling evidence for the use of the ED as an alternative to primary care for patients with HF in this region. The higher incidence of seeking care in the ED in the patients who experienced early HF readmission may be indicative of lack of access to nonemergency care in this group.

Atrial fibrillation at discharge was another significant predictor of early HF readmission in our sample. This finding is consistent with findings reported in a Canadian sample of patients 75 years or older.9 Atrial fibrillation was also found to predict 30-day all-cause readmission in patients with HF in a large regional medical center in the southeast.20 Atrial fibrillation in HF may be indicative of declining ventricular function or increased neurohormonal activation. Alternatively, it may also be a causative factor of new or worsening HF. Regardless, the combination of HF and atrial fibrillation indicates a worse prognosis and the need for more diligent guideline-directed care.

Although not statistically significant predictors, LOS as well as DBP and HR at discharge contributed to model fit and predictive accuracy. Length of stay was lower and DBP and HR at discharge were higher in those patients with early subsequent readmission. These findings offer evidence that adequate time for medical intervention with restoration of physiologic stability is a necessary goal before discharge. The LOS in our sample was markedly low when compared with reports from other studies. Our median of 2 days is 1 to 5 days lower than the range of 3- to 7-day median values found in other reports,7,9,17,21 including Hispanic-only samples.6,22 Our mean value of 3.4 days was also far less than the 5.7 to 7.8 values reported in other studies7,21,23–25 and studies of only Hispanic patients with HF.10,26 This finding is likely at least partially due to the current emphasis on shortening hospital stays but is striking in this high-risk sample considering the prevalence of multiple chronic conditions. Only 1 previous multinational study also identified a shorter LOS as a risk factor for readmission.27 Despite this, our 30-day HF readmission rate was similar to other reports, including a large drug trial of patients with HF with reduced ejection fraction28 and a population study of Hispanic patients with HF in California.29

In contrast to previous evidence, being Spanish speaking was not predictive of readmission in our sample, and English as the primary language was actually higher in our readmitted group. This finding is likely influenced by the fact that, in the region studied, most Hispanics are born in the United States. Although more than 70% of our sample identified as Hispanic, less than half reported Spanish as their primary language. History of HF was also higher in the patients who experienced early readmission compared with those in the nonreadmitted group. This finding is consistent with those of Krumholz et al18 and is not unexpected considering the progressive nature of the chronic HF syndrome. It is also possible that newly diagnosed patients receive more preparation for discharge and follow-up care.

Key characteristics of our sample are important to note in interpreting our findings. Compared with patients in other HF studies, our sample was younger, heavier, and primarily Hispanic, with a higher prevalence of multiple chronic conditions. The mean age of our sample was younger than most,14,20,23,25,30 although similar to other Hispanic-only samples.26,29 These findings support previous evidence of HF occurring at a younger age in patients of Hispanic ethnicity.31 Our sample was also markedly overweight and hypertensive. More than 80% of our sample was overweight or obese, with a median weight of 84 kg, much heavier than samples from Fonarow et al30 and Eapen et al,16 who reported median body weights of 79 and 76 kg, respectively. Importantly, these previous reports were based on primarily non-Hispanic white samples and are not representative of ethnically diverse populations. Our sample's BMI of 30.9 was also higher than the 28.2 and 29.2 reported in 2 Hispanic-only samples with HF.22,26 Again, sampling differences are apparent in that these Hispanic-only samples were taken from patients living in various settings throughout the United States, whereas our sample is from a low-socioeconomic rural setting. The only report with a similar BMI was from a sample of inner-city African American and Hispanic patients with HF in the Bronx.32

The almost 90% rate of hypertension in our sample is also notably higher than that found in other HF literature. Prevalence rates in previous studies3,4,11,16,25,29,32 range from 46% to 78%, with those in Hispanic-only samples with HF6,10,22,26 ranging from 67% to 84%. A previous report of Hispanic patients of the same age and also living in California noted only a 35.3% prevalence of hypertension.29 Diabetes prevalence in our sample was high as well, with 61% compared with 36% to 51% reported by other investigators,4,11,16,25,30,32 but only slightly higher than 2 large registry Hispanic-only subsamples with rates of 56%20 and 58%.22 Our findings suggest that our sample is at an increased risk for negative outcomes given the adverse physiological effects and increased morbidity and mortality associated with obesity, hypertension, and diabetes.1,33–35 In addition, the presence of each of these conditions adds complexity to the medical treatment and self-management required for the patient with symptomatic HF. Finally, the level of comorbidity found in this sample is especially problematic in a region with an inadequate supply of health professionals. Notably, our sample was 71% Hispanic, yet we found higher prevalence rates for all 3 of these major health conditions than those reported for Hispanic-only patients with HF. Our findings provide further support of socioeconomic status and geographic location as social determinants of health disparities, in addition to ethnicity.

These findings also highlight the heterogeneity in patients with HF and the potential value for individual facilities to evaluate the characteristics of the population they serve. If the results of previous research identifying factors associated with early readmission were applied to our patients, most would be categorized as of high risk and in need of additional strategic interventions for successful transition to home. Providing such services to most patients with HF in a facility is not feasible, especially given that results of clinical trials vary greatly, generalizable evidence is lacking,36,37 and many suggested interventions for high-risk patients are resource intensive. Our results provide support for the high-level recommendation from CMS for all hospitals to analyze patient characteristics and identify those associated with readmission.38

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Limitations

The findings of our study are limited by the use of retrospective chart review to collect data using documentation in the Electronic Medical Record. This methodology is dependent on accurate documentation and precludes consideration of multiple other factors that may influence risk for early readmission after HF hospitalization. In particular, our analysis does not include behavioral factors such as adherence or follow-up appointments that have been identified as risk factors for readmission in previous research. Our findings are also limited by the use of International Classification of Diseases, Ninth Revision, codes to identify HF admissions. Although commonly used in readmission research, this method may have underestimated the number of true HF cases. In addition, our findings are from a single center and are therefore influenced by local practice patterns. The low number of patients readmitted within 30 days is also a limitation to be considered in interpreting our findings. Finally, we analyzed factors associated with 30-day readmission for HF only. Findings may differ significantly for all-cause readmission.

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Conclusons

This study was designed to inform potential strategies aimed at reducing hospitalizations among rural, mostly Hispanic patients with HF in a low-socioeconomic region. In our setting, ED use in the previous 6 months and atrial fibrillation at discharge predicted HF readmission within 30 days. These findings illustrate the need for clinicians at this hospital, and other hospitals serving similar high-risk populations, to ascertain previous healthcare utilization during their admission interviews. Furthermore, more intensive postdischarge follow-up may be indicated for those patients reporting ED use in the 6 months before admission, as well as for those with atrial fibrillation at discharge. This investigation informs programs aimed at reducing readmission among Hispanic, low-socioeconomic individuals with HF.

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What's New and Important

  • Among high-risk patients (primarily Hispanic with multiple chronic conditions, in a low-socioeconomic and underserved region), ED visits during the 6 months before an HF hospitalization and presence of atrial fibrillation at discharge predict 30-day HF readmission.
  • No other comorbidities were associated with 30-day HF readmission.
  • Spanish as the primary language did not predict readmission in a primarily Hispanic population.
  • Our findings highlight the value for facilities to evaluate the characteristics and risk factors of the population they serve.
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Keywords:

health disparities; heart failure; hispanic; hospitalization; readmission

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