Authors
- Sherer, Anita P. MSN, RN, PCCN
- Crane, Patricia B. PhD, RN, FAHA, FNAP
- Abel, Willie Mae PhD, RN, ACNS-BC
- Efird, Jimmy PhD, MSc
Abstract
Objectives: In this study, the effects of sociodemographic and clinical factors on heart failure (HF) readmission risk were examined.
Background: Hospitals now incur financial penalties for excessive HF readmission rates; therefore, identifying factors associated with risk is essential for designing risk-reduction strategies.
Methods: A retrospective cohort study using chart reviews compared HF inpatients (N = 245) who were readmitted with those who were not readmitted.
Results: The sample included mostly white (64%) elderly (mean [SD] age, 69.8 [15.1] years) men (49%) and women (51%). Using Cox regression, the number of comorbidities (3-4 or 5-8) and type of comorbidities, specifically renal insufficiency (readmission ratio [RR], 1.7; P = .003), atrial fibrillation (RR, 1.7; P = .005), cardiomyopathy (RR, 1.5; P = .020), followed by a history of myocardial infarction/coronary artery disease (RR, 1.4; P = .055), were the predictors of HF readmission.
Conclusions: Targeting those with high-risk comorbidities is important in designing measures to prevent or delay readmission of HF patients.
Article Content
Heart failure (HF) now affects 5 700 000 Americans and accounts for 990 000 hospital discharges annually.1 It ranks in the top 2 discharge diagnoses and is the number 1 discharge diagnosis in the elderly population. The average cost per admission is $31 730.2 Heart failure most often develops as a result of hypertension, coronary artery disease, myocardial infarction, diabetes, or cardiomyopathy.3 The chronic nature of HF, coupled with multiple complex cardiac and noncardiac comorbidities, predisposes persons with HF to frequent hospitalizations.4-6 Thus, while the clinical syndrome of HF is the end stage of cardiac disease, seldom is the patient's admission affected only by a HF diagnosis. Frequently, the patient presents with multiple comorbidities that complicate recovery, increase mortality risk, and require a comprehensive treatment plan.5 Among 11 855 702 Medicare beneficiaries discharged from hospital care, almost one-fifth are readmitted within 30 days, and the largest proportions of readmissions are attributed to an HF diagnosis.7
As a result of these alarming statistics, HF care has been targeted for quality improvement and cost reduction by both The Joint Commission and the Centers for Medicare and Medicaid Services. Both have created significant incentives for hospitals to improve adherence to evidence-based guidelines for HF care.8-12 In addition to using the HF Core Measures from The Joint Commission (including discharge instructions, evaluation of left ventricular systolic function, and pharmacological management), Centers for Medicaid Services is now seeking to reduce the number of HF patients who are readmitted for all causes within 30 days and levying financial penalties for hospitals exceeding national benchmarks for 30-day readmission rates. Current data indicate that two-thirds of US hospitals have readmission rates higher than these benchmarks and are therefore subject to financial penalties. Hospitals with large numbers of patients with complex illnesses, such as teaching centers and facilities with large uninsured populations, are at highest risk for not meeting the benchmarks.13 Furthermore, adherence to Core Measures8 does not necessarily translate to lower HF readmission rates.14 Thus, as financial pressures mount, hospitals need to identify factors that directly influence readmission of HF patients so they can develop strategies to reduce preventable hospital readmissions.
In a systematic review of 117 studies, only 5 presented models to predict risk of readmission.15 Of these 5, only 1 study predicted HF specific readmission; the other predicted all-cause readmissions or death. Efforts to date have yielded inconsistent findings and no clear predictive clinical model for HF readmission.15-20 These inconsistent findings may be related to factors such as the focus on either specific populations, total comorbidity scores, or biomarkers when examining risk for readmission. These important studies have provided evidence for readmission but are lacking consistent findings to assist in understanding the complex clinical picture of HF. Hence, research has not provided nurses with robust clinical indicators of those at highest risk for readmission. Because it is estimated that 40% of cases of HF readmissions are preventable,21 examining those at highest risk for HF readmissions could provide essential information in developing targeted nursing interventions to prevent or delay readmission and maximize outcomes after discharge from the acute care setting. Thus, the purposes of this study are to comprehensively examine factors associated with HF from the literature in a random sample of those admitted and those who were readmitted with HF in a specified time period and to develop a model to predict readmission risk.
Methods
Sample
A retrospective cohort study using medical record reviews at a large regional medical center in the Southeast was conducted with HF patients admitted between October 1, 2007, and October 1, 2009. Patients were identified based on qualifying diagnostic-related grouping codes indicating a primary diagnosis of HF at discharge. Any HF admission between October 1, 2007, and October 1, 2008, was considered the index admission for the study. A total of 1022 patients met the selection criteria. Of those discharged with a primary diagnosis of HF during the designated time period, 234 were readmitted for HF. Using a computerized program, we randomly selected 125 medical records of those readmitted and 125 of those not readmitted, for a total sample of 250. Because we were examining risk of readmission, we examined all charts for expiration during this time frame. Five of the 125 patients not readmitted died during that admission. Therefore, the final sample was 120 not readmitted and 125 readmitted (N = 245). This sample was derived from a priori power analyses to address the purpose of the study. Approval for the study was obtained from the appropriate institutional review boards.
Measures
A standardized data collection instrument was developed from the literature to capture sociodemographic and a multitude of HF-specific data. Face and content validities were confirmed by a panel of clinical experts including nurses, physicians and pharmacists. The tool included 37 variables for the index admission and an additional 7 variables for the readmission. Sociodemographic variables included age, sex, and race. We also collected the insurance type, time of admission, referrals, and other relevant data to describe this population. Clinical factors included items such as ejection fraction (EF), comorbidities (10, with an option to list other), serum B-natriuretic peptide (BNP) levels, serum creatinine levels, type of cardiac medications (15, with an option to list other), number of discharge medications, note if prescribed antianxiety or antidepressant, presence of biventricular pacemaker or implantable cardioverter defibrillator, home health follow-up, HF clinic follow-up, intensive care admission, education, and height and weight. The tool was converted, using TeleForm software, to a scanable form that exported data to an end database.
Nurses and clinical pharmacists collected data from the electronic medical record using the standardized scanable form (TeleForm), thus minimizing transcription errors. A hard copy of each medical record was accessed to collect data not found in the electronic format. The data collectors were trained to retrieve data elements from only the history and physical form. Reliability testing was completed using the tool before formal data collection, and training ensued until an interrater reliability of 95% was achieved. Each item on the data collection form was accessed using the medical record and recorded by the data collector on the TeleForm. The principal investigator met regularly with the data collectors to clarify processes and answer questions. The forms were then collected by the principal investigator and examined for completeness. Once complete, the forms were scanned and imported into a Microsoft Access database and imported into SAS.
Data Analyses
Initially, univariable analyses were conducted to examine the association of each variable with HF. Those that met the cutoff point for inclusion were then entered into the model, which included selected sociodemographic (age, sex, race) and clinical (EF, comorbidities, BNP, body mass index [BMI], creatinine, number of discharge medications) factors on hospital readmissions in those admitted with a primary diagnosis of HF.
Data were analyzed using SAS (version 9.3) software. Assumptions of normality and possible outliers were reviewed for all data. Initially, descriptive statistics were computed for both groups, and t tests were used to examine significant differences between those readmitted for HF and those not readmitted. The Kaplan-Meier product-limit method was used to compute time-to-event probabilities. Adjusted and unadjusted readmission ratios (RRs) and 95% confidence intervals were computed using a Cox regression model. Variables deemed clinically relevant were included in the multivariable models. The test statistic of Grambsch and Therneau was used to check for interactions by time. A Cox regression model was also used to examine the influence of selected variables on readmissions. A P value of <= .05 was considered significant for all analyses.
Results
Most of the sample (N = 245) was white (59%); the sample ranged in age from 21 to 98 years (mean [SD], 69.9 [15] years), with almost equal proportions of men and women (Table). Most were admitted through the emergency department (79%) and had an average of 4 comorbidities. An average of 11.3 medications was noted at discharge. Ejection fraction ranged from 10 to 79 (mean [SD], 41 [17]). Body mass index ranged from 15.6 to 65.5 kg/m2 (mean [SD], 30.1 [8.5] kg/m2), with 23% overweight and 34% obese. Mean (SD) serum BNP level at admission was 1047 (822), with 78% of the sample having BNP levels greater than 400 picograms per milliliter. Fewer than one-fifth (28%) had creatinine levels 2.0 milligrams/deciliter or above.
As noted in the Table, comparing those readmitted with those not readmitted, there were few differences in age, EF, BMI, number of cardiac medications, or serum creatinine levels. When the continuous variables of those who were readmitted and those not readmitted were compared using a t test, only the mean number of comorbidities differed. Those who were readmitted had more comorbid conditions (t = 5.54 [243]; P < .0001).
The only significant predictors of readmission were the number of comorbidities and the number of discharge medications. Comorbidities were categorized into no comorbidities (reference), 0 to 2 comorbidities, 3 to 4 comorbidities, and 5 to 8 comorbidities. Those with 3 to 4 comorbidities were 3.6-fold more likely to be readmitted than referents, and those with 5 to 8 comorbidities were 5.1-fold more likely to be readmitted than referents (P for trend = .0001).
Three comorbidities were significant variables predicting risk for HF readmission: renal insufficiency, atrial fibrillation, and cardiomyopathy, followed by history of myocardial infarction and/or coronary artery disease. Those with renal insufficiency were the most likely to be readmitted for HF (RR, 1.7; P = .003), followed by those with atrial fibrillation (RR, 1.7; P = .005) and cardiomyopathy (RR, 1.5; P = .02). A history of myocardial infarction/coronary artery disease followed (RR, 1.4; P = .055).
The number of discharge medications was categorized into 4 quartiles: fewer than 8 medications (referent), 9 to 11 medications, 12 to 14 medications, and more than 14 medications (see the Table). Those with 9 to 11 medications were 1.1-fold more likely to be readmitted, those with 12 to 14 medications were 1.3-fold more likely to be readmitted, and those with more than 14 medications were 1.7 fold more likely to be readmitted than referents (P for trend = .037).
Discussion
This study contributes to the science of understanding readmission risk in those with HF. The unique contribution is the comprehensive approach to understanding this risk. The final model indicated that not only the number of comorbidities but also the specific comorbid conditions of renal insufficiency, atrial fibrillation, and cardiac myopathy and the presence of polypharmacy at discharge predicted readmission risk.
The sample in this study represented the HF population in the literature. The demographics of the sample in this study are similar to HF population statistics: Those with HF are older, the prevalence of HF is higher in blacks than in whites, both men and women are affected by HF, and most individuals with HF have precedent hypertension.3 Systolic dysfunction resulting from myocardial ischemia or infarction is 1 of the most common causes of HF in the Western world, and 56% of the study sample had a history of myocardial infarction or coronary artery disease.3,22
Because HF is associated with cardiac function, assessment of EF is considered a hallmark of HF care. Alterations in left ventricular function, whether systolic or diastolic in origin, have been shown to increase the risk of developing overt HF.3,22 In patients with symptomatic HF, both systolic and diastolic dysfunctions contribute to the development of the clinical syndrome of HF, and both types of left ventricular dysfunction were equally represented in this sample.3
In this study, as the number of comorbidities increased, so did the risk. Our finding is also consistent with reports from other studies.6,23-26 In a large Canadian study (N = 42 399) that examined comorbidities and mortality in those with HF, the most prevalent comorbidities included chronic ischemic heart disease (7%), atrial fibrillation (6.5%), diabetes (6.4%), and hypertension (4.6%).27 These proportions were significantly lower than the proportions of these comorbid conditions in the current study and may be due to geographical differences. However, with the Canadian study, the number of comorbidities for those with a discharge diagnosis of HF was 3.9.27 Similar to this study, more than 50% of persons in our sample (N = 250) had 3 to 4 comorbid conditions.
Common comorbidities such as diabetes, hypertension, and chronic obstructive pulmonary disease were not high-risk predictors in this study. The current study identified 3 comorbidities that were associated with increased HF readmission risk: renal insufficiency, atrial fibrillation, and cardiomyopathy, and a history of myocardial infarction/coronary artery disease followed. Although these risk factors have also been identified in other studies,18,21,28,29 no study has noted the 3 specific comorbidities that predicted readmission risk in this study. Consequently, attention to not only the number but also the type of comorbidities in those with HF may indicate those at highest risk for HF readmission
Serum creatinine level, a clinical indicator of renal insufficiency, was not predictive of HF readmission risk in this study. Rather, just a history of renal disease (comorbid condition noted in medical record) was sufficient for predicting risk of readmission in this sample. Renal insufficiency as a predictor of HF readmissions is noted in the results of other studies.30-34 For example, a retrospective, descriptive study examining characteristics common to HF patients readmitted within 30 days found that 45% of those who were readmitted had underlying chronic renal insufficiency/failure.33 Also, the Efficacy of Vasopressin Antagonism in Heart Failure Outcome Study, which examined 1528 systolic HF patients at 1 week after hospital discharge, found that lower creatinine clearance was an independent predictor of 1-year cardiovascular rehospitalization and mortality.31 However, based on our findings, only a history of renal disease/renal insufficiency was more of an indicator of readmission risk than were serum creatinine levels during the admission.
Atrial fibrillation is also associated with HF readmission risk.35-38 In a retrospective study of 189 African American and Hispanic patients hospitalized for HF, univariable analysis identified that atrial fibrillation was 1 of the 11 significant predictors of death and/or readmission.35 Atrial fibrillation was also found as a significant predictor of readmission or death in a study by Fung and colleagues36 when examining data from 238 patients with HF and normal EF. Those with atrial fibrillation had a much higher HF readmission rate than did those who were in sinus rhythm (28.6% vs 10.6%; P < .01).36 These results coupled with our findings indicate that atrial fibrillation is a significant predictor of readmission risk in those with HF.
Most HF risk predictive models incorporate cardiovascular comorbidities, including cardiomyopathy, myocardial infarction, and coronary artery disease.16-18,21,28 Cardiomyopathy refers to a heterogeneous group of myocardial diseases that cause structural or electrical abnormalities of the heart and predispose affected individuals to develop the clinical syndrome of HF. A review of HF trends by Wong et al24 noted that nearly half of participants studied in the years 2003 to 2008 had a history of myocardial infarction. In our study, cardiomyopathy was a predictor for readmission, followed by a history of myocardial infarction/coronary artery disease.
The number of discharge medications was a significant risk factor in this study. As the number of medications increased, the risk for readmission also increased. Those who take fewer medications are more likely to be adherent to their medication regimen39,40 and thus may be less likely to be readmitted because of issues associated with medications. Because the number of medications may be associated with the number of comorbidities, it is not surprising that admission risk in this study was related to both variables.
This study did not find that sociodemographic factors such as age, sex, and race were predictors of HF readmission rates. Not finding aging as a predictor of readmission was unexpected because other studies have found an influence of aging on HF readmissions,18 with rates climbing by as much as 24% with every decade of life37,41 and those older than 80 years showing a 4-fold increase in 30-day readmission rates.42 Several studies found that age and race influence HF readmission,43-45 whereas mixed results exist on how the sex of a person is related to HF readmissions.37,41,45-47
Other important clinical factors noted in the literature (BNP, BMI, and EF) were not significant predictors of HF readmissions in this sample. In contrast, other studies showed that higher BNP values have been associated with HF readmission risk.31,48 In addition, Oreopoulos et al49 found that higher BMI values were associated with better clinical outcomes with HF. Although EF assessment is considered a gold standard in HF care, studies examining the effect of EF on readmission have produced inconsistent findings4,50; however, some studies have linked HF with preserved EF (diastolic HF) to higher readmission risk, particularly in women and those with more comorbidities.36,44,46,51 Given the results of other studies5,52-54 and the prevalence of these comorbidities in the HF population, the findings of this study were unexpected.
Heart failure is a complicated, multifactorial disease process. Targeting those with more comorbidities, especially those with renal insufficiency, atrial fibrillation, cardiomyopathy, and a history of myocardial infarction/coronary artery disease, could be important when designing preventive measures. Developing a tool that measures the frequency of readmission within the past year as well as the presence of more than 3 comorbidities and/or high-risk comorbidities and more than 9 medications may be beneficial in identifying those at highest risk. Also, simplifying clinical predictors may contribute to the development of an actionable HF readmission risk model. Nurses can then affect comorbidity and medication management through comprehensive discharge planning, high-quality patient education, effective care transitions, and timely follow-up care. Furthermore, findings from this study indicate that preconceived markers of risk for readmission, such as age, race, and EF, may not be as important in predicting readmission as specific comorbidities.
Whereas those with HF have a high prevalence of comorbid conditions, no studies were found noting the combination of comorbid conditions and polypharmacy predicting higher readmission risks. This study included a majority of those older than 65 years, and thus, the participants had more comorbidities. The 2013 American College of Cardiology Foundation and the American Heart Association Guideline for the Management of Heart Failure noted that most of the randomized clinical trials in those with HF excluded those with multiple comorbidities.55 Furthermore, these guidelines noted that successful risk modification strategies are needed to prevent or delay the progression of HF. Thus, identifying specific comorbidities and polypharmacy, as noted in this study, indicates those at highest risk for readmission and may assist clinicians to aggressively intervene. Additional research is needed to determine if interventions targeting these groups delay or prevent the progression and/or exacerbation of HF symptoms requiring readmission.
Limitations
Limitations of this study included data collection through retrospective medical chart reviews, which necessarily assumes that all relevant data were accurately recorded. In addition, because we randomly sampled the medical records of those who were readmitted with HF and those who were not, we could denote only readmission risk, not relative risk. Thus, the findings from this study may not be directly comparable with those studies reporting relative risks. Finally, while minimal, the occurrence of missing data in the medical record is another limitation of this study.
Summary
Predicting HF readmissions is complex and multifactorial. Research on this topic has shown that numerous factors influence readmission.4,5,15 The findings of this study add to this body of knowledge by indicating the importance of identifying the number and specific types (renal insufficiency, atrial fibrillation, cardiac myopathy/history of myocardial infarction/coronary artery disease) of comorbidities and number of medications at discharge when determining risk of readmission for those with HF. These simple noninvasive indicators can easily be added to clinical assessment to identify those at highest risk for readmission.
What's New and Important?
* While the numbers of comorbidities correlated with readmissions, this study modeled the specific comorbid conditions that significantly denoted risk: renal insufficiency, atrial fibrillation, cardiomyopathy, and history of myocardial infarction/coronary artery disease.
* As the number of medications at discharge increases, the risk of readmission increases. Polypharmacy coupled with specific comorbidities may be a better indicator of risk for readmission.
* Age, sex, nor race predicted HF readmissions.
* Typical clinical markers associated with HF outcomes (BNP, BMI, and EF) were not specific predictors in this study.
* Common comorbidities in those with HF, such as diabetes, hypertension, and chronic obstructive pulmonary disease, did not predict HF readmissions.
Acknowledgments
The authors acknowledge the Heart Failure Research Team at Cone Health for their efforts in data collection with special thanks to Lisa Curran, PharmD, BCPS, CPP, and Thresa Brown, DNP, RN, ACNS-BC.
References
1. American Heart Association. Cardiomyopathy and Heart Failure. Heart Disease and Stroke Statistics: 2013 Update. Dallas, TX: American Heart Association; 2013. [Context Link]
2. Barrett M, Wilson E, Whalen D 2007 HCUP Nationwide Inpatient Sample (NIS) Comparison Report. HCUP Methods Series Report #2010-03. Online September 9, 2010. Rockville, MD: U.S. Agency for Health Research and Quallity. http://www.hcup-us.ahrq.gov/reports/methods.jsp. [Context Link]
3. Go AS, Mozaffarian D, Roger VL, et al. Heart disease and stroke statistics-2013 update: a report from the American Heart Association. Circulation. 2013; 127: e6-e245. [Context Link]
4. Giamouzis G, Kalogeropoulos A, Georgiopoulou V, et al. Hospitalization epidemic in patients with heart failure: risk factors, risk prediction, knowledge gaps, and future directions. J Card Fail. 2011; 17(1): 54-75. [Context Link]
5. Page RL, Lindenfeld J. The comorbidity conundrum: a focus on the role of noncardiovascular chronic conditions in the heart failure patient. Curr Cardiol Rep. 2012; 14(3): 276-284. [Context Link]
6. Braunstein JB, Anderson GF, Gerstenblith G, et al. Noncardiac comorbidity increases preventable hospitalizations and mortality among Medicare beneficiaries with chronic heart failure. J Am Coll Cardiol. 2003; 42(7): 1226-1233. [Context Link]
7. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009; 360: 1418-1428. [Context Link]
8. Bonow RO, Ganiats TG, Beam CT, et al. ACCF/AHA/AMA-PCPI 2011 performance measures for adults with heart failure. Circulation; 2012: 125 http://circ.ahajournals.org/content/early/2012/04/23/CIR.0b013e3182507bec.citati. Accessed November 20, 2014. [Context Link]
9. Yancy CW, Jessup M, Bozkurt B, et al. 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Circulation. 2013; 128: e240-e327. [Context Link]
10. Core Measure Sets. 2013. http://www.jointcommission.org/core_measure_sets.aspx. Accessed June 10, 2013. [Context Link]
11. Centers for Medicare and Medicaid Services. Medicare Hospital Quality Chartbook 2011: Performance Report on Readmission Measures for Myocardial Infarction, Heart Failure, and Pneumonia. Washington, DC: Centers for Medicare & Medicaid Services; 2011. [Context Link]
12. Lindenfeld J, Albert NM, Boehmer JP, et al. HFSA 2010 comprehensive heart failure practice guideline. J Card Fail. 2010; 16(6): e1-e194. [Context Link]
13. Joynt KE, Jha AK. A path forward on Medicare readmissions. N Engl J Med. 2013; 368: 1175-1177. [Context Link]
14. Mazimba S, N. G, A. P, et al. Heart failure performance measures: do they have an impact on 30-day readmission rates? Am J Med Qual. 2013; 28(4): 324-329. [Context Link]
15. Ross JS, Mulvey GK, Stauffer B, et al. Statistical models and patient predictors of readmission for heart failure: a systematic review. Arch Intern Med. 2008; 168(13): 1371-1386.16. [Context Link]
16. Betihavas V, Davidson PM, Newton PJ, Frost SA, Macdonald PS, Stewart S. What are the factors in risk prediction models for rehospitalization for adults with chronic heart failure? Aust Crit Care. 2012; 25(1): 31-40. [Context Link]
17. Kansagara D, Englander H, Salanitro A, et al. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011; 306(15): 1688-1698. [Context Link]
18. Au AG, McAlister FA, Bakal JA, Ezekowitz J, Kaul P, van Walraven C. Predicting the risk of unplanned readmission or death within 30 days of discharge after a heart failure hospitalization. Am Heart J. 2012; 164(3): 365-372. [Context Link]
19. van Walraven C, Dhalla IA, Bell C, et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ. 2010; 182(6): 551-557. [Context Link]
20. Amarasingham R, Moore BJ, Tabak YP, et al. An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data. Med Care. 2010; 48(11): 981-988. [Context Link]
21. Anderson MA, Levsen J, Dusio ME, et al. Evidenced-based factors in readmission of patients with heart failure. J Nurs Care Qual. 2006; 21(2): 160-167. [Context Link]
22. Gheorghiade M, De Luca L, Fonarow GC, Filippatos G, Metra M, Francis GS. Pathophysiologic targets in the early phase of acute heart failure syndromes. Am J Cardiol. 2005; 19(96-6A): 11G-17G. [Context Link]
23. Sueta CA, Schenck A, Chowdhury M, Hall R, Simpson RJJ. Effect of angiotensin-converting enzyme inhibitor therapy on 30-day outcome in patient > or =65 years of age with chronic congestive heart failure. Am J Cardiol. 2000; 86(10): 1151-1153 A1159. [Context Link]
24. Wong CY, Chaudhry SI, Desai MM, Krumholz HM. Trends in comorbidity, disability, and polypharmacy in heart failure. Am J Med. 2011; 124(2): 136-143. [Context Link]
25. Foraker RE, Rose KM, Suchindran CM, Chang PP, McNeill AM, Rosamond WD. Socioeconomic status, Medicaid coverage, clinical comorbidity, and rehospitalization or death after an incident heart failure hospitalization: Atherosclerosis Risk in Communities cohort (1987 to 2004). Circ Heart Fail. 2011; 4(3): 308-316. [Context Link]
26. Muzzarelli S, G. L, T. MM, et al. Predictors of early readmission or death in elderly patients with heart failure. Am Heart J. 2010; 160(2): 308-314. [Context Link]
27. Dai S, Walsh P, Wielgosz A, Gurevich Y, Bancej C, Morrison H. Comorbidities and mortality associated with hospitalized heart failure in Canada. Can J Cardiol. 2012; 28(1): 74-79. [Context Link]
28. Hamner JB, Ellison KJ. Predictors of hospital readmission after discharge in patients with congestive heart failure. Heart Lung. 2005; 34(4): 231-239. [Context Link]
29. Mogensen UM, Ersboll M, Andersen M, et al. Clinical characteristics and major comorbidities in heart failure patients more than 85 years of age compared with younger age groups. Eur J Heart Fail. 2011; 13(11): 1216-1223. [Context Link]
30. Chae CU, Albert CM, Glynn RJ, Guralnik JM, Curhan GC. Mild renal insufficiency and risk of congestive heart failure in men and women > or =70 years of age. Am J Cardiol. 2003; 92(6): 682-686. [Context Link]
31. Dunlay SM, Gheorghiade M, Reid KJ, et al. Critical elements of clinical follow-up after hospital discharge for heart failure: insights from the EVEREST trial. Eur J Heart Fail. 2010; 12(4): 367-374. [Context Link]
32. McAlister FA, Ezekowitz J, Tonelli M, Armstrong PW. Renal insufficiency and heart failure: prognostic and therapeutic implications from a prospective cohort study. Circulation. 2004; 109(8): 1004-1009. [Context Link]
33. Hallerbach M, Francoeur A, Pomerantz SC, et al. Patterns and predictors of early hospital readmission in patients with congestive heart failure. Am J Med Qual. 2008; 23(1): 18-23. [Context Link]
34. Amsalem Y, Garty M, Schwartz R, et al. Prevalence and significance of unrecognized renal insufficiency in patients with heart failure. Eur Heart J. 2008; 29: 1029-1036. [Context Link]
35. Garg S, Baskar S, Blum S, Bhalodkar N. Predictors of death or readmission in African-Americans and Hispanics hospitalized for congestive heart failure in an inner city hospital. Internet J Cardiol. 2005; 3(1). http://ispub.com/IJC/3/1/11440. Accessed November 20, 2014. [Context Link]
36. Fung JW, Sanderson JE, Yip GW, Zhang Q, Yu CM. Impact of atrial fibrillation in heart failure with normal ejection fraction: a clinical and echocardiographic study. J Card Fail. 2007; 13(8): 649-655. [Context Link]
37. Koitabashi T, Inomata T, Niwano S, et al. Paroxysmal atrial fibrillation coincident with cardiac decompensation is a predictor of poor prognosis in chronic heart failure. Circ J. 2005; 69(7): 823-830. [Context Link]
38. Ahmed MI, White M, Ekundayo OJ, et al. A history of atrial fibrillation and outcomes in advanced systolic heart failure: a propensity-matched study. Eur Heart J. 2009; 30(16): 2029-2037. [Context Link]
39. Gradman AH, Basile JN, Carter BL, Bakris GL. Combination therapy in hypertension. J Am Soc Hypertens. 2010; 4(2): 90-98. [Context Link]
40. Munger MA, Van Tassell BW, LaFleur J. Medication nonadherence: an unrecognized cardiovascular risk factor. MedGenMed. 2007; 9(3):58. http://www.medscape.com/viewarticle/561319_4. Accessed April 28, 2010. [Context Link]
41. Blackledge HM, Newton J, Squire IB. Prognosis for South Asian and white patients newly admitted to hospital with heart failure in the United Kingdom: historical cohort study. BMJ Case Rep. 2003; 327(7414): 526-531. [Context Link]
42. Kossovsky MP, Sarasin FP, Perneger TV, Chopard P, Siqaud P, Gaspoz J. Unplanned readmissions of patients with congestive heart failure: do they reflect in-hospital quality of care or patient characteristics? Am J Med. 2000; 109(5): 386-390. [Context Link]
43. Joynt KE, Orav EJ, Jha AK. Patient race, site of care, and 30-day readmission rates among elderly Americans. J Am Med Assoc. 2011; 305(7): 675-681. [Context Link]
44. Berry C, Hogg K, Norrie J, Stevenson K, Brett M, McMurray J. Heart failure with preserved left ventricular systolic function: a hospital cohort study. Heart. 2005; 91(7): 907-913. [Context Link]
45. Howie-Esquivel J, Dracup K. Effect of gender, ethnicity, pulmonary disease, and symptom stability on rehospitalization in patients with heart failure. Am J Cardiol. 2007; 100(7): 1139-1144. [Context Link]
46. Alla F, Al-Hindi AY, Lee CR, Schwartz TA, Patterson JH, Adams KFJ. Relation of sex to morbidity and mortality in patients with heart failure and reduced or preserved left ventricular ejection fraction. Am Heart J. 2007; 153(6): 1074-1080. [Context Link]
47. Nieminen MS, Harjola VP, Hochadel M, et al. Gender related differences in patients presenting with acute heart failure: results from EuroHeart Failure Survey II. Eur J Heart Fail. 2008; 10(2): 140-148. [Context Link]
48. Jourdain P, Jondeau G, Funck F, et al. Plasma brain natriuretic peptide-guided therapy to improve outcome in heart failure: the STARS-BNP Multicenter Study. J Am Coll Cardiol. 2007; 49(16): 1733-1739. [Context Link]
49. Oreopoulos A, Padwal R, Kalantar-Zadeh K, Fonarow GC, Norris CM, McAlister FA. Body mass index and mortality in heart failure: a meta-analysis. Am Heart J. 2008; 156(1): 13-22. [Context Link]
50. Fonarow GC, Stough WG, Abraham WT, et al. Characteristics, treatments, and outcomes of patients with preserved systolic function hospitalized for heart failure: a report from the OPTIMIZE-HF Registry. J Am Coll Cardiol. 2007; 50(8): 768-777. [Context Link]
51. Wong DT, Clark RA, Dundon BK, Philpott A, Molaee P, Shakib S. Caveat anicula! Beware of quiet little old ladies: demographic features, pharmacotherapy, readmissions and survival in a 10-year cohort of patients with heart failure and preserved systolic function. Med J Aust. 2010; 192(1): 9-13. [Context Link]
52. Shindler DM, Kostis JB, Yusuf S, et al. Diabetes mellitus, a predictor of morbidity and mortality in the Studies of Left Ventricular Dysfunction (SOLVD) Trials and Registry. Am J Cardiol. 1996; 77(11): 1017-1020. [Context Link]
53. Domanski M, Krause-Steinrauf H, Deedwania P, et al. The effect of diabetes on outcomes of patients with advanced heart failure in the BEST trial. J Am Coll Cardiol. 2003; 42(5): 914-922. [Context Link]
54. Dries DL, Sweitzer NK, Drazner MH, Stevenson LW, Gersh BJ. Prognostic impact of diabetes mellitus in patients with heart failure according to the etiology of left ventricular systolic dysfunction. J Am Coll Cardiol. 2001; 38(2): 421-428. [Context Link]
55. Yancy CW, Mariell J, Bozkurt B, et al. 2013 ACCF/AHA guideline for the management of heart failure. J Am Coll Cardiol. 2013; 62(16): e147-e239. [Context Link]