Hospitalization Epidemic in Patients With Heart Failure: Risk Factors, Risk Prediction, Knowledge Gaps, and Future Directions

Journal of Cardiac Failure Vol. 17 No. 1 2011

Review Articles

Hospitalization Epidemic in Patients With Heart Failure: Risk Factors, Risk Prediction, Knowledge Gaps, and Future Directions GREGORY GIAMOUZIS, MD,1,2 ANDREAS KALOGEROPOULOS, MD,1 VASILIKI GEORGIOPOULOU, MD,1 SONJOY LASKAR, MD,1 ANDREW L. SMITH, MD,1 SANDRA DUNBAR, RN, DSN,1 FILIPPOS TRIPOSKIADIS, MD,2 AND JAVED BUTLER, MD, MPH1 Atlanta, Georgia; and Larissa, Greece

ABSTRACT Patients with heart failure (HF) are hospitalized over a million times annually in the United States. Hospitalization marks a fundamental change in the natural history of HF, leading to frequent subsequent rehospitalizations and a significantly higher mortality compared with nonhospitalized patients. Threefourths of all HF hospitalizations are due to exacerbation of symptoms in patients with known HF. One-half of hospitalized HF patients experience readmission within 6 months. Preventing HF hospitalization and rehospitalization is important to improve patient outcomes and curb health care costs. To implement cost-effective strategies to contain the HF hospitalization epidemic, optimal schemes to identify high-risk individuals are needed. In this review, we describe the risk factors that have been associated with hospitalization risk in HF and the various multimarker risk prediction schemes developed to predict HF rehospitalization. We comment on areas that represent gaps in our knowledge or difficulties in interpretation of the current literature, representing opportunities for future research. We also discuss issues with using HF readmission rate as a quality indicator. (J Cardiac Fail 2011;17:54e75) Key Words: Acute heart failure, risk factor, prognosis, risk prediction, outcome, model, hospitalization, rehospitalization.

Heart failure (HF) is a growing epidemic.1 Over 5 million individuals in the United States have HF, and more than 550,000 are diagnosed annually.2 The complex array of physiologic, psychologic, social, and health care delivery issues makes it a challenging chronic disease to manage.3,4 Over the past decade, the annual number of hospitalizations has increased from 800,000 to O1 million for HF as a primary, and from 2.4 to 3.6 million for HF as a primary or

secondary diagnosis.5 Approximately 50% of HF patients are rehospitalized within 6 months of discharge,6 and 70% of rehospitalizations are related to worsening of previously diagnosed HF.7 A recent analysis of all Medicare fees for service readmission to hospitals for any cause showed HF to be the number 1 cause of rehospitalization.8 Heart failure rehospitalization carries a significantly higher mortality risk compared with index hospitalization.9 Heart failure is the primary reason for 12-15 million office visits and 6.5 million hospital-days each year.10 With aging of the population, HF rates and the associated rehospitalizations will rise. By 2050, 1 in 5 persons in the United States will be elderly11; 80% of patients hospitalized for HF are O65 years old.10 Furthermore, intense societal need for improving medical quality of care has shifted the focus from ‘‘hard’’ outcome measures initially introduced (ie, all-cause mortality) to ‘‘softer’’ outcomes; therefore, 30-day postdischarge HF readmission rates are now being considered as quality measures. To implement interventions to reduce HF rehospitalizations cost-effectively, identification of high-risk individuals is essential. Numerous individual risk factors for HF

From the 1Emory University, Atlanta, Georgia and 2University of Thessaly, Larissa, Greece. Manuscript received December 12, 2009; revised manuscript received August 3, 2010; revised manuscript accepted August 16, 2010. Reprint requests: Javed Butler, MD, MPH, Cardiology Division, Emory University Hospital, 1365 Clifton Road, NE, Suite AT430, Atlanta, GA 30322, Tel: 404-778-5273; Fax: 404-778-5285. E-mail: javed.butler@ emory.edu Supported in part through an Emory University Heart and Vascular Board grant entitled ‘‘Novel Risk Markers and Prognosis Determination in Heart Failure.’’ All decisions regarding this manuscript were made by a guest editor. See page 62 for disclosure information. 1071-9164/$ - see front matter Ó 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.cardfail.2010.08.010

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Risk Factors for Hospitalization in Heart Failure

rehospitalization, assessed at various times related to index hospitalization (admission, discharge, first follow-up visit, etc) have been reported. Importantly, identification of low-risk individuals is also essential, because absence of high risk does not necessarily indicate a low-risk patient who can be safely discharged.12,13 Based on these factors, different multimarker risk-prediction models have been developed to increase prediction accuracy and precision.14,15 However, gaps in our knowledge of the underlying pathophysiologic mechanisms pose difficulties in interpreting the currently available excess of data. Furthermore, data in this respect are not always consistent. Given the heterogeneous nature of the HF population, spanning from ischemic to nonischemic, low to preserved ejection fraction, interfering with various comorbid conditions, a ‘‘one-sizefits-all’’ approach to risk stratification may not be appropriate and subpopulations may need to be targeted. Data based only on administrative records have been challenged,16,17 and addition of clinical information may improve risk prediction.18e22 Therefore, understanding the predictors, the timing of their appearance in the course of the syndrome, their strength of association with certain outcomes, and in which specific subpopulations they predict risk is essential to devise effective prevention interventions to curb the HF rehospitalization epidemic. In the present review, we provide a comprehensive overview of literature regarding individual predictors and the multi-marker risk models related to HF rehospitalization risk (Table 1). The details of individual risk factors are presented in Supplementary Tables 2e4 (available online at www.onlinejcf.com), where data are sorted by studies on individuals with depressed, preserved, and unspecified ejection fraction, respectively.

Literature Reviewed In this review, we primarily list the studies and the risk factors, and have created detailed tables to list more indepth data, for references purposes. Considering the enormity of the topic, it was not possible to discuss the details, regarding either the risk factor’s strength of association or itss clinical and pathophysiologic significance, of each and every individual risk factor. These would be topics for further focused reviews. Publications included in this review reported HF-specific hospitalizations data as either primary or secondary outcome, or as part of a composite outcome with mortality rate. To review data applicable to the current era of HF therapy, we focused on studies published within the past decade. Ovid Medline, PubMed, and Scopus were searched from January 1, 1999, to October 31, 2008, to identify relevant studies using the keywords: ‘‘heart failure,’’ ‘‘readmission,’’ ‘‘rehospitalization,’’ ‘‘hospitalization,’’ ‘‘risk prediction,’’ ‘‘model,’’ ‘‘prognosis,’’ and ‘‘outcome.’’ Publications eligible for inclusion reported on readmission among individual patients hospitalized for HF as a primary



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outcome, secondary outcome, or part of a composite outcome. Only data from studies that included $100 patients were included. NoneEnglish language studies, abstracts, pediatric studies, and publications without original data (reviews, letters, and editorials) were not included. Also excluded were studies that reported results from case series, experimental studies, and those without quantified outcomes.

Risk Factors for Hospitalization in Heart Failure Sociodemographic

Heart failure rehospitalizations increase with age: A 4-fold increase in 30-day readmission rate for elderly patients $80 years23 and a 24% increase/10-year age increments in the annual readmission rate have been reported.24,25 Advanced age predicts readmission in multiracial populations.26e30 Higher readmission rates have been reported for both male4,24,25,31e33 and female34e37 gender; however, some studies have failed to replicate these results.38e41 Nonwhite race has been associated with a higher risk,26,34 with higher rates in blacks and Latinos than in Asians and whites.42e45 Not all studies have confirmed these findings.24,46e49 Risk is related to socioeconomic status50: A significant stepwise decrease in HF-related readmission has been observed from the lowest to the highest income quartile.51,52 Insurance status affects rehospitalization, with patients enrolled in a health maintenance organization experiencing lower 6-month readmission rates than Medicaid and Medicare patients.53 Lack of employment is associated with readmissions,54,55 as is family status and living alone.55,56 Smoking is associated with multiple readmissions,56 although ‘‘smoker’s paradox’’ in hospitalized HF patients has been reported, with current or recent smoking being associated with a 23% lower risk-adjusted 90-day rehospitalization risk.57 Current or past alcohol use is an independent predictor of HF readmissions.55,56 Clinical

Several studies suggest higher readmission rates with ischemic etiology.23,26,56,58e61 Some studies have reported worse risk for patients with depressed ejection fraction,58,62e64 whereas recent studies demonstrate similar risk with preserved ejection fraction.47,65e70 Low systolic blood pressure is an independent predictor of rehospitalization.60,71e73 A 2% increase in 60-day readmission for every 1 mm Hg decrease has been reported.27,35,74 Similarly, 10 mm Hg decrease in diastolic pressure is associated with an 11% increase in cardiovascular mortality or HF rehospitalization.75 Increased heart rate has been shown to correlate with readmissions.76e79 Higher New York Heart Association functional class at discharge also predicts 30-day and 1-year readmissions.25e27,56,80,81 Prior HF hospitalization is an independent risk factor for recurrent readmissions.4,23,32,54,60,82e85 Prolonged length of stay

Study

Year 181

Philbin and DiSalvo

Independent Predictors Black race Medicare insurance Medicaid insurance Home health services needed Ischemic heart disease Valvular disease Diabetes mellitus Renal disease Study Krumholz et al3

1999

Study Type Retrospective

N 42,731

Study Outcome HF rehospitalization

Follow-Up

Event Rate

C-Index

6.9 mo

21.3%

0.60

HR

95% CI

Independent Predictors

HR

95% CI

1.28 1.66 1.92 1.10 1.25 1.19 1.45 1.35

1.16e1.41 1.38e2.00 1.57e2.36 1.01e1.21 1.16e1.34 1.09e1.29 1.33e1.58 1.23e1.49

Idiopathic cardiomyopathy Prior cardiac surgery Use of telemetry monitoring Treatment in a rural hospital Dischargedskilled nursing facility Echocardiography performed Cardiac catheterization Chronic lung disease

1.46 1.16 1.13 0.87 0.68 0.78 0.60 1.10

1.32e1.61 1.04e1.29 1.01e1.27 0.78e0.98 0.59e0.79 0.73e0.85 0.49e0.73 1.02e1.20

Follow-Up

Event Rate

C-Index

6 mo

23.3%

NA

Year

Study Type

N

2000

Retrospective

2,176

Study Outcome HF rehospitalization

Independent Predictors

HR

95% CI

Independent Predictors

HR

95% CI

Prior admission within 1 y Prior HF

1.25 1.23

1.05e1.48 1.02e1.48

Discharge creatinine O2.5 mg/dL Diabetes mellitus

1.72 1.17

1.35e2.18 0.99e1.39

Follow-Up

Event Rate

C-Index

60 d 60 d

9.6% 35.2%

0.77 0.69

Study Felker et al73 Independent Predictors HF hospitalization within 12 mo SBP (per 10 mmHg) BUN (per 5 mg/dL) Study 182

O’Connor et al

Independent Predictors Age (per 10 y) HF hospitalization within 12 mo Nitrates at admission Study Pocock et al75 Independent Predictors Dependent edema Diabetes: insulin-treated

Year

Study Type

N

Study Outcome

2004

RCT

949

All-cause death All-cause rehospitalization or death

HR

95% CI

Independent Predictors

HR

95% CI

1.14 0.82 1.26

1.06e1.23 0.75e0.89 1.14e1.41

Hemoglobin (per 1 g/dL) Past coronary intervention

0.89 1.46

0.82e0.97 1.00e2.12

Follow-Up

Event Rate

C-Index

60 d

31.4%

N/A

Year

Study Type

N

Study Outcome

2005

RCT and registry

908

All-cause rehospitalization or death

HR

95% CI

Independent Predictors

HR

95% CI

1.26 1.59 1.73

1.11e1.44 1.16e2.19 1.22e2.45

SBP (!130 mm Hg) History of depression

0.79 0.58

0.69e0.91 0.35e0.97

Follow-Up

Event Rate

C-Index

38 mo 38 mo

24.1% 32.4%

0.75 0.75

Year

Study Type

2006

RCT

N 7,599

Study Outcome All-cause death CV mortality or HF rehospitalization

HR

95% CI

Independent Predictors

HR

95% CI

1.23 2.03

1.12e1.35 1.80e2.29

Candesartan (vs placebo) Age (per 10 y O60)

0.82 1.46

0.76e0.89 1.38e1.54

56 Journal of Cardiac Failure Vol. 17 No. 1 January 2011

Table 1. Multimarker Risk Prediction Models for Heart Failure (HF) Rehospitalization Risk

Diabetes: other EF (per 5% lower) HF hospitalization within 6 mo HF hospitalization beyond 6 mo Diagnosis of HF O2 years ago Cardiomegaly NYHA functional class III NYHA functional class IV DBP (per 10 mm Hg) Bundle branch block Study Yamokoski et al183 Independent Predictors BUN at discharge Study

Independent Predictors Past CAB surgery Asthma Cardiorespiratory failure/shock Arrhythmias ACS Valvular and rheumatic disease Vascular disease Chronic atherosclerosis Other/unspecified heart disease Hemi- or paraplegia, paralysis, functional disability Stroke Disorders of fluid electrolyte/acid-base Chronic pulmonary disease Diabetes with complications Renal failure Other urinary tract disorders

1.43e1.74 1.11e1.16 1.55e1.93 1.09e1.37 1.20e1.43 1.23e1.47 1.20e1.45 1.25e1.89 1.07e1.16 1.15e1.38

Year

Study Type

2007

RCT

Pulmonary crackles Rest dyspnea Female Atrial fibrillation BMI (per 1 kg/m2 decrease !27.5) Mitral regurgitation Previous MI Pulmonary edema Heart rate (per 10/min) N 373

Study Outcome

1.25 1.20 0.83 1.16 1.03 1.16 1.11 1.26 1.08

1.13e1.38 1.10e1.31 0.76e0.91 1.07e1.27 1.01e1.04 1.05e1.28 1.02e1.21 1.03e1.54 1.05e1.11

Follow-Up

Event Rate

C-Index

6 mo 6 mo

19.3% 49.3%

N/A 0.596

Death All-cause rehospitalization

HR

95% CI

Independent Predictors

HR

95% CI

N/A

N/A

High-dose diuretics

N/A

N/A

Follow-Up

Event Rate

C-Index

30 d

6.5%

0.60

Year

Study Type

N

2008

Retrospective

283919

Study Outcome All-cause rehospitalization

HR

95% CI

Independent Predictors

HR

95% CI

0.93 1.06 1.08 1.06 1.12 1.08 1.07 1.09 1.05 1.04

0.91e0.96 1.03e1.10 1.06e1.11 1.04e1.08 1.10e1.15 1.06e1.10 1.05e1.09 1.06e1.11 1.03e1.08 1.01e1.08

History of HF Other GI disorders Peptic ulcer, hemorrhage, other GI disorders Severe hematologic disorders Nephritis Metastatic cancer and acute leukemia Liver and biliary disease End-stage renal disease/dialysis Decubitus or chronic skin ulcer Iron deficiency and other anemias and blood disease

1.09 1.06 1.07 1.15 1.08 1.14 1.06 1.16 1.10 1.09

1.07e1.12 1.04e1.08 1.05e1.10 1.10e1.21 1.03e1.12 1.07e1.21 1.02e1.09 1.11e1.22 1.07e1.13 1.06e1.11

1.03 1.12

1.00e1.07 1.09e1.14

Pneumonia Drug/alcohol abuse/dependence/psychosis

1.09 1.07

1.06e1.11 1.04e1.10

1.17 1.08 1.15 1.12

1.14e1.19 1.06e1.11 1.13e1.18 1.10e1.15

Other psychiatric disorders (depression excluded) Fibrosis of lung and other chronic lung disorders Protein-calorie malnutrition

1.08 1.05 1.05

1.05e1.12 1.02e1.08 1.01e1.09

 Giamouzis et al

ACS, acute coronary syndrome; BMI, body mass index; BUN, blood urea nitrogen; CAB, coronary artery bypass; CI, confidence interval; CV, cardiovascular; DBP, diastolic blood pressure; EF, ejection fraction; GI, gastrointestinal; HR, hazard ratio; MI, myocardial infarction; NYHA, New York Heart Association; RCT, randomized control trial; SBP, systolic blood pressure.

Risk Factors for Hospitalization in Heart Failure

Keenan et al184

1.58 1.13 1.73 1.22 1.31 1.35 1.32 1.54 1.11 1.26

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58 Journal of Cardiac Failure Vol. 17 No. 1 January 2011 (O7 days) during index hospitalization portends higher 6-month readmission rates4; when defined as O14 days, it confers a 3-fold increase risk.54 Physical signs of volume overload confer a 2-fold increased risk of 6-month readmission in hospitalized HF patients.76 Also, signs of poor tissue perfusion, ie, ‘‘cold’’ state have a 2.5-fold higher risk.61 In patients with recent HF hospitalization, an objective clinical disease severity score was independently associated with annual risk for rehospitalization.29

Blood Tests

Lower admission hemoglobin level is associated with a higher risk.60,86 A 27% decrease in rehospitalization per 2 g/dL increase in hemoglobin levels has been described.28 Similarly, a 2% higher risk of 1-year readmission for every 1% lower admission hematocrit level has been reported.87 A 3  109/L increase in neutrophil count (approximately 30% increase) is associated with a 25% increase in HF rehospitalizations.28 The risk of HF rehospitalization after hospital discharge increases by 8% per 3 mEq/L decrease in serum sodium levels.88 In another study, a 5 mEq/L increase conferred a 40% decrease in the 2-year HF rehospitalization rate in patients with HF and preserved ejection fraction.28 Total bilirubin levels predict risk in patients with systolic dysfunction.27 A 2 mmol/L increase in admission glucose confers a 6% increase in annual readmissions.89 A 25% increase in readmission per 1% increase in hemoglobin A1c levels has been reported.90 Both admission84,85 and discharge36 serum creatinine levels have been correlated with readmission in patients with low84,85 or preserved36 ejection fraction. A 39% increase in 6-month readmission rate per 1 mg/dL increase in serum creatinine levels has been reported.91 Similarly, increased blood urea nitrogen levels have been associated with higher readmission rates28,92,93: A 2% risk per 1 mg/dL increase in admission levels has been reported.32 Uric acid O7 mg/dL (420 mmol/L) in men and O6 mg/dL (360 mmol/L) in women with HF is associated with higher readmission rates among both hospitalized patients94 and outpatients.30 Several studies have reported the prognostic ability of natriuretic peptides.95e101 In a small single-center study, a B-type natriuretic peptide (BNP) level of O200 pg/mL provided an optimal value in predicting HF readmission.102 Admission and discharge levels predict readmission in various populations82,91,103e107 including those with systolic dysfunction,27,36 restrictive filling pattern,81 or chronic renal insufficiency.95 Although not consistently seen or universally agreed upon, a reduction in natriuretic peptide levels during hospitalization by O30%-40% has been associated with improved outcomes in some studies.36,76,95,105,108e110 Postdischarge measurement also provides risk stratification.29,111e113 Therefore, natriuretic peptideeguided therapy has been proposed,91,104,114,115 and several natriuretic peptideeguided trials have already been published. Even though their results are somewhat

conflicting, intense research interest in this field continues.114,116e118 Data on newer biomarkers are emerging. Elevated concentration of cardiac troponin during HF hospitalization is a strong and consistent predictor of readmission.61,112,119e122 C-Reactive protein (CRP) levels O0.9 mg/dL are associated with higher risk,123 and HF readmissions increase with increasing quartile of CRP124 or high-sensitivity CRP.120,125 Apolipoprotein A-I levels lower than !103 mg/dL are associated with higher readmission rates independent of high-density lipoprotein and BNP levels.126,127 Cystatin C is also associated with higher risk irrespective of serum creatinine.128 Other experimental biomarkers associated with HF rehospitalizations include advanced glycation endproducts, such as pentosidine129 and soluble receptor,130 heart-type fatty acidebinding protein,102,131 serum renin,119 and serum procollagen type I levels.132 Other Tests

Prolonged QRS duration on electrocardiogram is an independent predictor of HF morbidity.133,134 Increased cardiothoracic ratio on chest X-ray is associated with higher HF readmission rates.135 On echocardiography, several studies have reported lower readmission rates for patients with higher ejection fraction26,29,30,35,81,120,136,137 A 2% fall in 6-month readmission rate per 1% increase in ejection fraction has been reported.91 An increase of 1 mL/m2 in left atrial volume index increases the risk of death or readmission by 3%.138 Echocardiographically estimated systolic pulmonary arterial pressure has also been useful in predicting HF readmission.139 Right ventricular tissue Doppler imaging predicts HF rehospitalization independently of other Doppler diastolic function variables.140 Similarly, tissue Doppler imagingedetected mechanical dyssynchrony predicts HF readmission in patients with systolic dysfunction and normal QRS duration.141 Finally, decreased heart rate variability,78,142e145 especially its very-low-frequency power spectral components,146 is associated with higher risk, and heart rate recovery after exercise provides additional prognostic information.147 Comorbidity Burden

A large proportion of readmissions for HF are associated with comorbidities that precipitate, contribute to, or complicate HF admission,148 especially in the elderly.10 In a recent population study, 39% of HF patients had $5 noncardiac comorbidities and only 4% had HF alone.149 Cardiovascular

Presence of hypertension (admission systolic blood pressure O140 mm Hg) during HF hospitalization decreases annual risk of HF readmission,24 especially in the elderly.59 In contrast, a history of hypertension (ie, documented antihypertensive treatment) is related to an increased risk.54 Angina pectoris is associated with a higher annual risk of

Risk Factors for Hospitalization in Heart Failure

rehospitalization in black patients.136 Concomitant myocardial infarction during HF hospitalization increases annual readmission risk.24 Atrial fibrillation is common in HF and adversely affects hemodynamics.150,151 Several studies demonstrate that atrial fibrillation is associated with an increased risk of HF readmissions in patients with low152 or preserved25,153 ejection fraction. Interestingly, rhythm control does not reduce risk for rehospitalization.154 Patients with valvular heart diseases, regardless of systolic function, have a 4-hold higher likelihood for HF rehospitalization.28 Noncardiovascular

Several studies have demonstrated a consistent association between diabetes mellitus and increased rehospitalization rates in HF with low66,155,156 or preserved66 ejection fraction or in multirace3,24,84,89,157 and in single-race26 populations. One study suggested an increased risk for black women with diabetes mellitus compared with men or nonblack patients.136 Anemia is common in HF158: When defined as hemoglobin !12 g/dL, anemia is associated with higher HF readmission.59,86,159e161 Hyponatremia, defined as serum sodium level !136 mEq/L, is a marker of increased 3-,83 6-,162 and 12-month122 HF readmission. A history of renal insufficiency or the presence of elevated serum creatinine levels (O1.5 mg/dL) on admission are common in HF patients163 and are associated with higher rates of HF rehospitalization.24,55,164 Worsening renal function during HF hospitalization is also common,83 and regardless of the definition used, it appears to predict independent risk for HF rehospitalization.165,166 Cerebrovascular disease increases 4-fold the likelihood of death or rehospitalization at 3 months in patients hospitalized with HF.83 History of stroke increases the annual mortality and readmission rate by 26% in HF patients.24,167 Chronic obstructive pulmonary disease is associated with higher risk,28,32,34,149 regardless of beta-blocker use.168 Obstructive sleep apnea doubles169 and pulmonary embolism quadruples83 the likelihood of HF readmission. Depression, present in almost one-half of chronic HF patients,170,171 is associated with higher annual HF readmission rates.55,170,172e174 Several scores have been used to quantify cumulative comorbidity burden: Higher HF readmission rates have been reported for patients with higher Deyo comorbidity score or Charlson comorbidity index.4,85 Quality of Life and Psychosocial Factors

Several self-assessment questionnaires quantify an individual’s perception of their quality of life. A symptom stability score, based on either the Kansas City Cardiomyopathy Questionnaire or the Minnesota Living with Heart Failure Questionnaire, correlates with the HF readmission risk.34,175 Poor quality of life on the Nottingham Health Profile is an independent predictor of readmission among elderly HF patients.176 Higher readmission rates have also been reported with worse 36-item Short



Giamouzis et al

59

Form survey scores.84 Absence of emotional support or social network among elderly hospitalized patients is a strong predictor of increased readmission rate, especially among women.85,177 Disease Management

Despite the fact that in the current therapeutic era more patients are discharged on evidence-based medication, adherence to these therapies in the outpatient setting has been shown to deteriorate over time.178 A targeted formal education and support intervention has been associated with a 44% decrease in the annual HF readmission rate.179 A multidisciplinary team intervention, consisting of patient and family education, a prescribed diet, socialservice consultation, medication review, and intensive follow-up lead to improved quality of life and reduce HF readmission rates.157 Poor follow-up is associated with higher risk.54 A prepared follow-up plan with an appointment scheduled in the HF clinic, provided to the patient upon discharge, decreases their likelihood for HF readmission within 30 days after discharge.80 Health care provided by an HF-specialist reduces the risk for readmission compared with care provided by primary care physicians.56 A home-based telemanagement program has also been shown to reduce the annual HF readmission rate.180

Risk Prediction Using administrative claims data, Philbin and DiSalvo created a scoring system to quantify the annual HFspecific readmission risk among hospitalized HF patients by using 16 variables (Table 1).181 This model had marginal discriminative ability, with a C-index of 0.60. Krumholz et al. developed a model for readmission risk3: Among 32 variables examined, 4 emerged as independent predictors of 6-month all-cause readmissions. Felker et al. derived a model that predicted outcomes among patients with decompensated HF.73 Among 41 variables evaluated, 5 were predictive of death or readmission at 60 days. The discriminatory power of the model was better for the mortality (C-index 0.77) but less for the composite end point including rehospitalization (C-index 0.69). O’Connor et al. combined data from a clinical trial and a registry to create a model to predict 60-day death or readmissions among hospitalized HF patients.182 Age, use of nitrates at admission, and $1 admission for HF in the previous year increased risk, whereas systolic blood pressure !130 mm Hg and, interestingly, a history of depression appeared to reduce risk. Pocock et al. developed a model from 7,599 HF patients with preserved and decreased systolic function.75 The final model included 21 predictor variables for cardiovascular death or HF rehospitalization. The 3 most powerful predictors were age, diabetes mellitus, and ejection fraction !45%. Yamokoski et al. created a model to estimate the risk of death and readmission among

60 Journal of Cardiac Failure Vol. 17 No. 1 January 2011

Fig. 1. The complex relationship between comorbidities and heart failure. Comorbid conditions may affect heart failure by causing it, exacerbating decompensation, masking symptoms, or affecting compliance with evidence-based medication.

hospitalized patients with severe HF.183 The prognostic model again showed modest discrimination. Finally, Keenan et al. developed an administrative claimsebased measure for profiling hospital performance for 30-day all-cause readmission rates.184 The model included 37 variables, and the C-index was only 0.60. Overall, these models had modest C-indexes of w0.60 for readmission risk prediction regardless of whether they were administrative data181,184 or medical records based.183 This raises the possibility that either important predictors of HF readmission are not present in such databases or nonmedical factors play a major role in HF rehospitalization risk. Knowledge Gap and Future Directions It is apparent from the aforementioned discussion that despite a multitude of known risk factors, actual prediction of HF rehospitalization is difficult, with at best only modest results seen in the previous literature. Considering the inability to both reduce the national burden of HF rehospitalization rate and the inability to accurately predict rehospitalization risk as opposed to combined rehospitalization and mortality risk, 4 issues need close scrutiny: importance of comorbid conditions; lack of therapeutic options; importance of nonclinical factors; and assessment and treatment of congestion. Importance of Comorbid Conditions

Comorbid conditions play a major role in HF progression and risk for rehospitalization. Many comorbidities,

eg, diabetes mellitus or renal failure, may worsen HF, and HF may vice versa worsen the comorbid condition. Regarding HF rehospitalization risk, comorbidities may affect the risk by causing, exacerbating, or masking HF, or by affecting compliance or health careeseeking behaviors (Fig. 1). It is simplistic to assume that a disease management, as opposed to a patient management, approach ignoring the huge comorbidity burden will reduce rehospitalization risk. To complicate matters further, all administrative and most clinical databases are incredibly ill equipped to truly assess the importance of comorbidity burden. For example, the Medicare database is based on administrative codes and does not have the clinical information to put the information in its correct perspective, eg, HF may represent low or preserved left ventricular ejection fraction, or ischemic or nonischemic etiology. Moreover, the degree of functional abnormality is not available (eg, peak VO2, 6-minute walking distance ,or even the NYHA functional class), and it is well known that the clinical relevance of a peak VO2 of 10 mL kg 1 min 1 is different than that of 20 mL kg 1 min 1, though both conditions may be labeled as ‘‘heart failure.’’ Finally, the administrative codes are largely provided by hospital administrative and not medical staff. Therefore, database-related comorbidity assessment likely underestimates the role of comorbidities by inaccurately assessing: True prevalence (eg, sleep apnea or depression). Disease severity (eg, risk may vary with worsening degree of pulmonary function derangement, but such details are not available).

Risk Factors for Hospitalization in Heart Failure

Adequacy of therapy (eg, it is possible that individuals with diabetes mellitus have a different risk based on hemoglobin A1c levels). These limitations may preclude accurate risk prediction and risk attenuation, underscoring the need for better assessment and treatment of comorbidities in future.



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taxonomy of acute decompensated HF syndromes,2,7,12,193e195 which likely represent several varied pathophysiologies necessitating different therapeutic approaches. Perhaps no other area of major cardiovascular public health impact is less well understood than decompensated HF, underscoring the need for intense focused research in this area.

Assessment and Treatment of Congestion

Many hospitalized HF patients lose little or no weight during hospitalization,185 although data from clinical trial populations differ from registry or observational data in this respect.186 However, it may be inaccurately assumed that all patients with decompensated HF have significant fluid overload, and in those who do have significant fluid overload, that it will be possible to get rid of the excess fluid adequately, safely, and in a short time. Effective, safe, and timely diuresis is related to a complex interaction of hemodynamics and fluid compartment interactions (intra- vs extravascular volume and oncotic pressure). Current surrogates of volume assessment, eg, pulmonary artery occlusive pressure or the natriuretic peptide levels, may not always represent the volume status, and even if they did, when they can be safely optimized without complications such as hypotension or renal failure is poorly understood. Although a recent consensus statement proposes a method of assessment,187 real-life assessment of congestion continues to be debated, and how to decongest patients effectively and safely is not well known. Importance of Nonclinical Factors

It is possible, and likely, that nonclinical factors, especially in the elderly, play a major role in HF worsening requiring hospitalization. Important risk factors may not be available in clinical or administrative databases unless specifically measured, eg, compliance with medications. Most risk scores do not account for the influence of patient self-care behavior or social vulnerabilities; without paying specific attention to these as preventive intervention targets, medical interventions per se may not reach their full potential. Lack of Therapeutic Options

Data with therapies that have been proven to improve rehospitalization risk are primarily for systolic dysfunction and were almost exclusively generated in the chronic outpatient setting. Unfortunately, there continues to be a lack of scientifically proven data supporting therapies that, when targeted specifically at acute decompensated HF patients, prevent the risk for readmission. Furthermore, there is no randomized trial for the management of diastolic HF that reduces readmission rate, although diastolic dysfunction accounts for O50% of HF, especially among the elderly population.188 Data in this respect are largely either related to disease management programs189e191 or based on observational data.192 The lack of successful therapy to date may be partially driven by the lack of understanding of the

Other Issues Several other issue regarding heart failure rehospitalization risk, prediction, and the use of these data merit highlight. Outcome

Different investigators have assessed varying outcomes, eg, prediction of hospitalization versus rehospitalization, rehospitalization alone versus combined with mortality, and prediction of all-cause versus cardiovascular versus HF-specific rehospitalizations. Because the predictors of mortality versus all-cause versus HF-specific rehospitalization may vary, synthesizing a summary conclusion based on current literature, though important to implement specific strategies for lowering this epidemic, is difficult. Nevertheless, end points other than HF rehospitalization, such as allcause rehospitalization, may provide extremely important information regarding specific HF populations (eg, the elderly HF patients with preserved ejection fraction, where more than one-half of rehospitalizations have been attributed to noncardiovascular causes). Time Period

Different studies have reported risk for 30-, 90-, 180-, or 365-day outcomes. It is possible that short-term outcomes are related to in-hospital care, intermediate-term outcomes to postdischarge care, and long-term outcomes to other patient- and provider-related factors. Data from these various studies therefore may not be directly comparable. Timing of Risk Factor Measurement

When a particular risk factor is measured for risk prediction in the spectrum of illness and presentation merits attention. This is especially important for the clinical (ie, congestion, body weight) and laboratory (admission or discharge values vs dynamic changes) parameters. There is a great variability in the timing of assessment of a particular risk factor among different studies, and these risk factors may predict risk at one particular time point and not the other. Diversity

Considerable data indicate that both biologic/diseasee and health care deliveryerelated disparities exist based on gender,24,25,32,35,132 race/ethnicity,26,42,43,45 and age.23e28,30,132 This may be particular important for nonclinical risk factors, including patient perception, belief system, and compliance.

62 Journal of Cardiac Failure Vol. 17 No. 1 January 2011 Predictors in these various groups may vary requiring inquiry of specific race/ethnicityerelated data. Intention

Risk models for primary prevention are most optimal if they are parsimonious and specific. In contrast, models to predict mortality to allocate advanced therapies need to be sensitive and complexity is less of a concern. Because HF readmission risk prediction can have various motives and may differ for patients, providers, payers, or researchers, the need and quality of the prediction model may differ correspondingly. Readmission and Quality of Care

There is intense societal focus on improving medical quality of care.42,196e199 Initial quality measures were process related, and subsequently ‘‘hard’’ outcome measures were introduced, eg, mortality. However, new measures, such as 30-day postdischarge HF readmission rates, are currently used as quality measures. This consideration raises several concerns. The published risk-adjusted HF readmission predictions models are not optimal for accurate risk prediction for reasons stated above. Performance of other models that are currently unpublished in peer-reviewed journals cannot be judged. In addition, there are no goldstandard rules for when a person should be admitted with HF, and patients may be hospitalized for borderline clinical or nonclinical social reasons, eg, inadequate family support, assisted-living infrastructures, primary care physician availability, etc. Finally, quality measures such as HF readmission rate might precipitate provider behaviors regarding resisting admissions; this may be a safety concern. Conclusion Heart failure rehospitalization clearly marks a fundamental change in the natural history of the syndrome, significantly increasing subsequent mortality and morbidity. Preventing HF rehospitalization is important to improve patient outcomes and curb health care costs. However, to implement such cost-effective strategies, optimal schemes to identify high-risk individuals, as well as low-risk patients for safe discharge, are needed. Numerous risk factors have been associated with HF rehospitalization risk. Based on these factors, different multimarker risk prediction models were developed to predict HF readmission risk. However, these data, for multiple reasons cited above, are limited in their application. Further studies to assess the underlying pathophysiologic mechanisms and develop new therapeutic options and better risk prediction schemes are needed to curb this epidemic. Disclosures None.

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Appendix

Variable Sociodemographic Age

Source

Nonwhite (%)

315 70.5 6 12.9

45%

N/A

Death or HF rehospitalization

814 d

53%

2007

212 69.6 6 11

29%

N/A

Death or HF rehospitalization

1y

38%

2008

183 65.4 6 14.2 (58e75) 443 70 6 14

29%

N/A

29.5 mo

44%

45%

36%

Cardiac death or HF rehospitalization HF rehospitalization

30 d

16%

319 62 183 65.4 6 14.2 (58e75) 183 65.4 6 14.2 (58e75)

30% 29%

35% N/A

60 d 29.5 mo

24.5% 44%

29%

N/A

Death or HF rehospitalization Cardiac death or HF rehospitalization Cardiac death or HF rehospitalization

29.5 mo

44%

25.7% 29%

14.9% N/A

9.9 mo 1y

41.6% 38%

N

2005

Pascual-Figal et al Shinagawa et al27

30

Systolic dysfunction Clinical SBP (admission)

Female (%)

Year

Berry et al28

63

Age (y), mean 6 SD (Range)

Harjai et al

1999

Filippatos et al74 Shinagawa et al27

2007 2008

2008 NYHA functional Shinagawa et al27 class (at discharge) Imaging 2008 2962 66.0 6 11.3 QRS O120 ms Wang et al134 LVEF Pascual-Figal et al30 2007 212 69.6 6 11 LVEF !35% Systolic pulmonary arterial pressure Reduction in systolic pulmonary arterial pressure Mechanical dyssynchrony in TDI (Ts-diff O91 ms) Laboratory Hemoglobin A1c

Study Outcome

Event Rate

HR 1.12 (per 5-y increase) 1.02 (per 1 y increase) 1.05 (per 1 y increase) 5.71

95% CI 1.04e1.21 1.01e1.04 1.03e1.07 1.64e21.94

0.98 0.986 (per mm Hg increase) 1.645

0.96e0.99 0.974e0.997

1.10e1.49 0.94e0.99 2.36e10.37 2.60e15.80

1.055e2.516

Xue et al120 Shalaby et al139

2006 2008

128 62 6 15 270 66.5 6 11.6

38% 13%

N/A 5%

Death or HF rehospitalization HF rehospitalization

1y 19.4 mo

32.8% 23.7%

1.28 0.96 (per 1% increase) 3.52 6.35

Shalaby et al139

2008

270 66.5 6 11.6

13%

5%

HF rehospitalization

19.4 mo

23.7%

0.29

0.12e0.76

Cho et al141

2005

106

33%

4.98

2.13e11.65

Gerstein et al90

2008 2412 65.8

1.25 (per 1% increase) 0.73 (per 2 g/dL increase) 1.25 (per 3  109/L increase) 1.18 (per 5 mmol/L increase) 1.02 1.002 (as a continuous variable)

1.19e1.31

63 6 11

CV death or HF rehospitalization Death or HF rehospitalization

FollowUp

30%

N/A

Death or HF rehospitalization or cardiac transplantation

17 mo

33%

N/A

HF rehospitalization

36.7 mo

36.2%

Hemoglobin

28

Berry et al

2005

315 70.5 6 12.9

45%

N/A

Death or HF rehospitalization

814 d

53%

Neutrophils

Berry et al28

2005

315 70.5 6 12.9

45%

N/A

Death or HF rehospitalization

814 d

53%

28

53%

BUN (admission)

Berry et al

2005

315 70.5 6 12.9

45%

N/A

Death or HF rehospitalization

814 d

BNP (discharge)

Filippatos et al74 Shinagawa et al27

2007 2008

319 62 183 65.4 6 14.2 (58e75)

30% 29%

35% N/A

Death or HF rehospitalization Cardiac death or HF rehospitalization

60 d 29.5 mo

24.5% 44%

0.61e0.87 1.12e1.40 1.07e1.29 1.01e1.03 1.001e1.003

68 Journal of Cardiac Failure Vol. 17 No. 1 January 2011

Table 2. Predictors of Heart Failure (HF) Rehospitalization in Individuals With Low Ejection Fraction

cTnT (O1.8 mg/L) hsCRP (O3.2 mg/L) Total bilirubin (admission levels O1.2 mg/dL) Comorbidity Diabetes mellitus

Tang et al125

2008

136

57 6 14

24%

N/A

125

2008

136

57 6 14

24%

N/A

Shinagawa et al27

2008

183 65.4 6 14.2 (58e75)

29%

N/A

MacDonald et al66

2008 7599 66.0 6 11.4

32%

315 70.5 6 12.9 212 69.6 6 11

45% 29%

N/A N/A

Death or HF rehospitalization Death or HF rehospitalization

17%

N/A

Death or HF rehospitalization

Tang et al

28

2005 Valve disease Berry et al Hyperuricemia Pascual-Figal et al30 2007 [discharge UA O7 mg/dL in men and 6 mg/dL in women] Depression Parissis et al170 2008

156

65 6 12 (35e76)

9%

Death, cardiac transplantation, and HF rehospitalization Death, cardiac transplantation, and HF rehospitalization Cardiac death or HF rehospitalization HF rehospitalization

33 mo

N/A

2.61

1.96e4.31

33 mo

N/A

3.81

2.14e9.35

29.5 mo

44%

1.90

1.32e2.72

155 per 1,000 1.64 patient-y 53% 3.72 38% 1.64

1.44e1.86

37.7 mo 814 d 1y

6-mo

39.4%

1.68e8.24 1.06e2.55

1.074 (per 1 point increment in Zung scale)

1.009e1.142

BNP, V-type natriuretic peptide; cTnT, cardiac troponin T; hsCRP, high-sensitivity C-reactive protein; LVEF, left ventricular ejection fraction; TDI, tissue Doppler imaging; other abbreviations as in Table 1.

Variable Imaging QRS O120 ms Laboratory Hemoglobin A1c

Source

Year

N

Danciu et al133

2006

217

Gerstein et al90

2008 2412

Age (y), mean 6 SD (IQR) Female Nonwhite

Study Outcome

6 mo

56%

1.15

36.7 mo

N/A

1.25 (per 1% increase) 0.60 (per 5 mmol/L increase) 1.81, per 5 mmol/L increase 1.33 7.79

1.08e1.64 2.03e29.86

2.04

1.68e2.47

1.87 9.86 3.96

1.06e3.32 4.48e21.7 1.67e9.38

71 (63-80)

47%

57%

Death or rehospitalization

65.8

33%

N/A

CV death or HF rehospitalization

2005

130 72.6 6 12.4

62%

N/A

Death or HF rehospitalization

814 d

42%

2005

130 72.6 6 12.4

62%

N/A

Death or HF rehospitalization

814 d

42%

2007 224 74.6 6 10.5 Serum Creatinine Bettencourt et al NT-pro-BNP (admission- Bettencourt et al36 2007 224 74.6 6 10.5 discharge difference) Comorbidity Diabetes mellitus MacDonald et al66 2008 7599 66.0 6 11.4

67% 67%

N/A N/A

Death or rehospitalization Death or rehospitalization

32%

9%

51% 62% 62%

94% N/A N/A

28

Serum sodium

Berry et al

BUN

Berry et al28 36

Angina pectoris COPD Valve disease

Ofili et al136 Berry et al28 Berry et al28

1999 1200 64 6 16 (19e99) 2005 130 72.6 6 12.4 2005 130 72.6 6 12.4

HF rehospitalization All-cause rehospitalization Death or HF rehospitalization Death or HF rehospitalization

6 mo 6 mo

43% 43%

37.7 mo

117 per 1,000 patient-y 56% 42% 42%

12 mo 814 d 814 d

95% CI N/A 1.20e1.31 0.43e0.84 1.46e2.24

69

COPD, chronic obstructive pulmonary disease; IQR, interquartile range; NT-pro-BNP, N-terminal proeB-type natriuretic peptide; other abbreviations as in Table 1.

HR

Giamouzis et al

Event Rate



Follow-Up Period

Risk Factors for Hospitalization in Heart Failure

Table 3. Predictors of Heart Failure (HF) Rehospitalization in Individuals With Preserved Ejection Fraction

Variable Sociodemographic Age

Source

Year

N

2003

5,789

Koitabashi et al

2005

Lee et al26

Blackledge et al24 25

Age ($80 y) Gender (female) Gender (male) Race (nonwhite) Race (black) Socioeconomic status (expressed as household income) Socioeconomic status (residencebased) Smoking (current)

Age (y)

Female Nonwhite

Event Rate

1y

49%

34 mo

34.9%

2y

29.6%

HF rehospitalization

21 mo

48.6%

HF rehospitalization Death or rehospitalization Death or HF rehospitalization HF rehospitalization HF rehospitalization HF rehospitalization All-cause rehospitalization Death or HF rehospitalization All-cause rehospitalization All-cause rehospitalization HF rehospitalization

31 d 1y 1y 30 d 21 mo 34 mo 6 mo 2y 1y 1y 6 mo

N/A 49% 47% 15.8% 48.6% 34.9% 23.4% 29.6% 60.1% 65% 21.5%

77.5

50%

12.6%

Death or rehospitalization

427

65.8 6 13.5

36%

N/A

HF rehospitalization

2008

668

66 6 12

32.6%

100%

132

Ruiz-Ruiz et al

2007

111

73.4 6 7.9

46.8%

N/A

Kossovsky et al23 Blackledge et al24 Mielniczuk et al35 Harjai et al32 Ruiz-Ruiz et al132 Koitabashi et al25 Afzal et al43 Lee et al26 Alexander et al45 Rathore et al42 Philbin et al51

2000 2003 2008 2001 2007 2005 1999 2008 1999 2003 2001

442 5,789 183 443 111 427 163 668 90,316 29,732 41,776

75.6 77.5 55.5 70 73.4 65.8 66.0 66 73.2 79.5 74

Rathore et al52

2006

25,086

Evangelista et al56

2000

753

57

Follow-Up

End Point

6 11.2

Death or HF rehospitalization

6 0.2 6 13

48% 50% 49% 45% 46.8% 36% N/A 32.6% 53% 60% 57%

N/A 12.6% N/A 36% N/A N/A 69% 100% 25% 11.6% 18%

78.8 6 0.1

57.7%

15.5%

All-cause rehospitalization

1y

68.0%

69 6 11.7 (33e99) 73.1 69 6 11.7 (33e99) 53 6 15 (15e86) 69 6 11.7 (33e99) 53 6 15 (15e86) 77.2 6 6.7

2%

39.4%

Multiple (O1) HF readmissions

1y

52% 2%

26% 39.4%

All-cause rehospitalization Multiple (O1) HF readmissions

60 and 90 d 1y

26%

17%

Rehospitalization

39.4%

Multiple (O1) HF readmissions

26%

17%

Rehospitalization

48 mo

58.2%

N/A

Rehospitalization

6 mo

6 6 6 6

15 14 7.9 13.5

6 12

HR 1.24 (per 10-y increase) 1.03 (per 1-y increase) 1.02 (per 1-y increase) 1.059 (per 1-y increase) 3.90 0.92 2.43 2.70 2.08 1.53 1.83 1.65 1.07 1.09 1.18 (lowest v. highest quartile)

95% CI 1.20e1.28 1.02e1.05 1.01e1.04 1.006e1.115 1.30e12.00 0.85e0.98 1.00e4.40 1.40e5.40 1.08e4.00 1.02e2.28 N/A 1.04e2.63 1.04e1.10 1.06e1.13 1.10e1.26

29.2%

1.08 (lowest vs highest quartile) 1.82

1.03e1.12 1.17e2.82

29% 29.2%

0.77 5.92

0.63e0.94 3.83e9.13

2.5

1.30e5.00

2.09

1.42e3.09

Rate, 1.7/ patient-y 36.4%

3.6

1.08e12.2

1.98

1.07e3.68

35.0% Rate, 1.7/ patient-year

2.59 2.20

1.22e5.48 1.00e4.80

Alcohol use (current)

Fonarow et al Evangelista et al56

2008 2000

48,612 753

Alcohol use (history)

Faris et al55

2002

396

Living alone

Evangelista et al56

2000

753

Faris et al55

2002

396

Rodriguez-Artalejo et al85 Tsuchihashi et al54 Faris et al55

2006

371

2001 2002

230 396

69 6 14 53 6 15 (15e86)

40% 26%

N/A 17%

HF rehospitalization Rehospitalization

1y 48 mo

Babayan et al58 Evangelista et al56

2003 2000

493 753

N/A 2%

N/A 39.4%

All-cause rehospitalization Multiple (O1) HF readmissions

1 year 1y

56.6% 29.2%

1.40 3.99

1.11e1.79 2.58e6.18

Ezekovitz et al59 Lee et al26 Kossovsky et al23

2008 2008 2000

10,415 668 442

N/A 69 6 11.7 (33e99) 79.5 (73e85) 66 6 12 75.6 6 11.2

30 d 2y 31 d

20.2% 29.6% N/A

1.36 1.62 3.00

1.12e1.67 1.07e2.47 1.50e6.10

Social network (low vs high) Lack of occupation Clinical Ischemic etiology

Previous myocardial revascularization

2%

50.4% 32.6% 48%

N/A 100% N/A

All-cause rehospitalization Death or HF rehospitalization HF rehospitalization

48 mo 1y

Rate, 1.7/ patient-year 29.2%

70 Journal of Cardiac Failure Vol. 17 No. 1 January 2011

Table 4. Predictors of Heart Failure (HF) RehospitalizationdEjection Fraction Unspecified

Previous MI Systolic dysfunction Prior admission for HF (anytime) Prior admission for HF (last y)

184 493 782 198

64.5 6 13.2 N/A 78 69.4 6 13.5

39.7% N/A 54% 44%

N/A N/A 15% N/A

HF rehospitalization HF rehospitalization HF rehospitalization Death or rehospitalization

3y 12 mo 1y 3 mo

75% N/A N/A 46%

1.99 2.44 1.22 3.40

1.02e3.90 1.46e4.08 1.00e1.50 1.60e7.60

Felker et al60 Rodriguez-Artalejo et al84 Tsuchihashi et al54 Rodriguez-Artalejo et al85 Harjai et al32

2003 2005

906 394

68 77.2 6 6.6

30% 56.1%

36% N/A

Death or rehospitalization HF rehospitalization

60 d 6 mo

11.6% 35.0%

1.14 1.88

1.06e1.23 1.24e2.83

2001 2006

230 371

69 6 14 77.2 6 6.7

40% 58.2%

N/A N/A

HF rehospitalization Rehospitalization

1y 6 mo

35.0% 36.4%

3.29 1.50

1.77e6.13 1.01e2.28

2001

443

70 6 14

45%

36%

HF rehospitalization

30 d

15.8%

1.30

1.20e1.40

Tsuchihashi et al54

2001

230

69 6 14

40%

N/A

HF rehospitalization

1y

35.0%

3.21

1.22e8.46

Kossovsky et al23

442

75.6 6 11.2

48%

N/A

HF rehospitalization

31 d

N/A

4.50

2.30e8.80

Felker et al

2003

906

68

30%

36%

Death or rehospitalization

60 d

11.6%

1.46

1.00e2.12

Felker et al60

2003

906

68

30%

36%

Death or rehospitalization

60 d

11.6%

0.82e0.97

SBP (clinical visit)

Mielniczuk et al35

2008

183

55.5 6 15

49%

N/A

Death or HF rehospitalization

1y

47%

Poor tissue perfusion Volume overload Higher discharge NYHA functional class

Perna et al61 Bettencourt et al76 Armola and Topp.80 Evangelista et al56

2005 2004 2001 2000

184 182 179 753

39.7% 53% N/A 2%

HF rehospitalization Death or rehospitalization HF rehospitalization Multiple (O1) HF readmissions

3y 6 mo 30 d 1y

75% 43% 23.5% 29.2%

1.31e4.60 1.08e3.23 N/A 1.86e3.55

Feola et al81 Koitabashi et al25 Lee et al26

2008 2005 2008

250 427 668

64.5 6 13.2 73.0 6 11.0 70.2 69 6 11.7 (33e99) 73 65.8 6 13.5 66 6 12

0.88 (per 10 mm Hg increase) 0.95 (per 1 mm Hg increase) 2.46 1.87 N/A 2.57

34% 36% 32.6%

N/A N/A 100%

Death or rehospitalization HF rehospitalization Death or HF rehospitalization

6 mo 34 mo 2y

56.4% 34.9% 29.6%

1.50 1.63 2.20

1.06e2.11 1.11e2.39 1.24e3.89

Lee et al26

2008

668

66 6 12

32.6%

100%

Death or HF rehospitalization

2y

29.6%

2.99

1.60e5.62

Giamouzis et al135

2008

5,164

64 6 11

19%

HF rehospitalization

37 mo

41%

1.27

1.13e1.44

Feola et al81 Valle et al91

2008 2008

250 315

73 77 6 9

34% 53%

N/A N/A

Death or rehospitalization HF rehospitalization

6 mo 6 mo

56.4% 28.6%

0.97e0.99 0.961e0.992

Mielniczuk et al35

2008

183

55.5 6 15

49%

N/A

Death or HF rehospitalization

1y

47%

0.93e0.97

LAVi

140

Dokainish et al

2007

100

57 6 12

45%

N/A

527 6 47 d

46%

RVTDI

Dokainish et al140

2007

100

57 6 12

45%

N/A

Cardiac death or rehospitalization for worsening HF Cardiac death or rehospitalization for worsening HF

0.98 0.983 (per 1% increase) 0.95 (per 1% increase) N/A

527 6 47 d

46%

N/A

NYHA functional class II vs I NYHA functional class III-IV vs I Imaging Cardiothoracic ratio (O0.50) LVEF (discharge) LVEF (admission) LVEF (clinical visit)

N/A N/A N/A 39.4%

9.5%

0.92e0.98

N/A N/A

(continued on next page)

Giamouzis et al

2000

60



2005 2003 2001 2007

Risk Factors for Hospitalization in Heart Failure

Prior hospitalization for any cause Prior hospitalization for any cause (last 6 mo) Length of stay (O14 d) Previous diagnosis of HF History of percutaneous coronary intervention SBP (admission level)

Perna et al61 Babayan et al58 Dauterman et al64 Darze et al83

71

Variable Mitral peak early diastolic flow velocity/TD early diastolic velocity Laboratory BNP (admission level O200 pg/mL) NT-pro-BNP ($30% increase during hospitalization) NT-pro-BNP (in-hospital variation !30%) NT-pro-BNP and decreased renal function (in-hospital variation !30%) BNP (discharge)

BNP (discharge level !250 pg/mL) NT-pro-BNP and decreased renal function (discharge value above median) NT-proBNP (follow-up) BNP (in-hospital decrease O30% vs no variation) BNP (in-hospital increase O30% vs decrease O30%) BNP (predischarge BNP $360 pg/mL and decrease !50% during hospitalization) BNP (2 mo after initiation of therapy)

End Point

Follow-Up

Event Rate

N/A

Cardiac death or rehospitalization for worsening HF

527 6 47 d

46%

N/A

41%

N/A

18 mo

23.7%

2.41

1.02e5.73

73.0 6 11.0

53%

N/A

Cardiac death or rehospitalization for worsening HF Death or rehospitalization

6 mo

43%

5.96

3.23e11.01

182 283

73.0 6 11.0 72.8 6 11.7

53% 51.9%

N/A N/A

Death or rehospitalization Death or rehospitalization

6 mo 6 mo

43% 43.5%

2.03 2.68

1.14e3.64 1.54e4.68

2007

283

72.8 6 11.7

51.9%

N/A

Death or rehospitalization

6 mo

43.5%

2.54

1.49e4.33

Feola et al81 Ferreira et al105 Koitabashi et al25 Logeart et al106

2008 2007 2005 2004

250 304 427 114

73 72.7 6 11.6 65.8 6 13.5 69.4 6 14.4

34% 53.9% 36% 30.7%

N/A N/A N/A N/A

Death or rehospitalization Death or rehospitalization HF rehospitalization All-cause rehospitalization

6 mo 6 mo 34 mo 6 mo

56.4% 43% 34.9% 37%

1.004e1.009 1.28e3.20 1.001e1.003 1.16e1.34

Valle et al91

2008

315

77 6 9

53%

N/A

HF rehospitalization

6 mo

28.6%

1.006 2.02 1.002 1.25 (per 100 ng/L increase) 0.271

Pimenta et al

2007

283

72.8 6 11.7

51.9%

N/A

Death or rehospitalization

6 mo

43.5%

2.53

1.27e5.03

Pfister et al113

2008

290

64 (54e72)

20%

N/A

498 d

22.4%

2007

304

72.7 6 11.6

53.9%

N/A

6 mo

43%

1.9 (per increase of 1 SD of log NT-proBNP) 2.24

1.50e2.40

Ferreira et al105

All-cause mortality, rehospitalization for ADHF and urgent cardiac transplantation Death or rehospitalization

Ferreira et al105

2007

304

72.7 6 11.6

53.9%

N/A

Death or rehospitalization

6 mo

43%

3.85

2.24e6.63

Cournot et al110

2008

157

83 6 6

49%

N/A

Death or rehospitalization for worsening HF

3, 12, and 18 mo

48%, 76%, 84%

5.97

2.98e11.94

Ishii et al112

2003

100

68 6 11

44%

N/A

Death or rehospitalization for worsening HF or MI

1y

44%

3.11 (log BNP per 10-fold increase)

1.61e6.01

Source

Year

N

Age (y)

Female Nonwhite

Dokainish et al

2007

100

57 6 12

45%

Niizeki et al102

2005

186

67 6 12

Bettencourt et al76

2004

182

Bettencourt et al76 Pimenta et al95

2004 2007

Pimenta et al95

140

95

HR

95% CI N/A

0.141e0.523

1.37e3.66

72 Journal of Cardiac Failure Vol. 17 No. 1 January 2011

Table 4. (Continued)

BNP (O160 ng/L 2 mo after initiation of therapy) cTnT $0.10 ng/mL (within 24 h of admission) cTnT ($0.01 ng/mL during hospitalization for ADHF) cTnT (2 mo after initiation of therapy) cTnT (O0.001 mg/L 2 mo after initiation of therapy) Hemoglobin (admission level)

Ishii et al112

2003

100

68 6 11

Perna et al61

2005

184

Nishio et al119

2007

Ishii et al112

N/A

Death or rehospitalization for worsening HF or MI

1y

44%

2.35

1.14e4.84

64.5 6 13.2

39.7%

N/A

HF rehospitalization

3y

75%

1.74

1.05e2.90

145

66.6 6 1.6

27%

N/A

Death or HF rehospitalization

1y

19.3%

1.88

1.04e3.62

2003

100

68 6 11

44%

N/A

Death or hospitalizati on for worsening HF or MI

1y

44%

2.07 (per 0.1 mg/L increase)

1.43e3.01

Ishii et al112

2003

100

68 6 11

44%

N/A

Death or hospitalization for worsening HF or MI

1y

44%

3.08

1.59e5.98

Felker et al60

2003

906

30%

36%

Death or rehospitalization

60 d

11.6%

86

2008

51.6%

25.9%

Death or rehospitalization

60e90 d

36.2%

58%

10%

All-cause rehospitalization

1y

N/A

33.0%

N/A

36.7 mo

N/A

52%

26%

Death or rehospitalization for worsening HF Death or rehospitalization

60e90 d

36.4%

Young et al

Kosiborod et al87 90

68

48,612 73.2 6 13.95

2003

2,281

79 6 8 65.8

2008

2,412

Serum sodium (admission level) Glucose (admission level, continuous variable) Abnormal glucose tolerance (admission level $6 mmol/L) Serum creatinine (admission level)

Gheorghiade et al88

2007

5,791

Berry et al89

2008

454

N/A

51%

N/A

Death or hospitalization for worsening HF

12 mo

36.3%

Berry et al89

2008

454

N/A

51%

N/A

Death or hospitalization for worsening HF

12 mo

46%

RodriguezArtalejo et al85

2006

371

77.2 6 6.7

58.2%

N/A

Rehospitalization

6 mo

36.4%

RodriguezArtalejo et al84

2005

394

77.2 6 6.6

56.1%

N/A

HF rehospitalization

6 mo

35.0%

Serum creatinine (discharge level)

Valle et al91

2008

315

77 6 9

53%

N/A

HF rehospitalization

6 mo

28.6%

\BUN (admission level)

Felker et al60

2003

906

30%

36%

Death or rehospitalization

60 d

11.6%

Harjai et al32

2001

443

70 6 14

45%

36%

All-cause rehospitalization

30 d

26.2%

Shenkman et al92

2007

257

69 6 17

40%

32%

30 d

28.4%

Anand et al124 Arimoto et al128

2005 2005

4,202 140

62.5 6 11 66 6 13

20% 38%

9% N/A

Death or hospitalization for worsening HF First morbid eventa Cardiac death or HF rehospitalization

12 mo 480 d

23%

BUN (admission level O28 mg/dL) CRP Serum cystatin C

73

68

1.06, per 2 mmol/L increase 1.61

1.31 (per 1 mg/dL increase) 1.33 (per 1 mg/dL increase) 1.39 (per 1 mg/dL increase) 1.28 (per 5 mg/dL increase) 1.02 (per 1 mg/dL increase) 1.83

1.02e1.10 1.07e2.41

1.05e1.63 1.09e1.64 1.09e1.77 1.14e1.41 1.004e1.030 1.03e3.24

73

1.53 1.28e1.84 1.94, per 1.29e6.64 increase of one SD (continued on next page)

Giamouzis et al

Gerstein et al,



Hemoglobin A1c

0.88 (per 1 g/dL 0.82e0.97 increase) 1.088 (per 1 g/dL 1.044e1.134 decrease) 1.02 (per 1% 1.01e1.03 decrease) 1.25 (per 1% 1.20e1.31 increase) 1.08 1.026e1.136

Risk Factors for Hospitalization in Heart Failure

44%

Variable

Source 131

Heart-type fatty acide Arimoto et al binding protein (O4.3 ng/mL) Niizeki et al102 Serum pentosidine (log)

sRAGE (log) Renin Serum PIP O124 ng/mL Comorbidity Hypertension (history) Diabetes mellitus

Anemia Anemia (Htc #24%) Anemia (admission Hb !12 g/dL) Hyponatremia (admission Na !135 mEq/L) Concomitant AMI Concomitant paroxysmal AF Hypertension Stroke (yes vs no) Cerebrovascular disease Hyponatremia (!136 mEq/L) Renal insufficiency

Acute renal failure

Year

N

Age (y)

Female Nonwhite

2005

179

67 6 12

39%

N/A

Follow-Up

Event Rate

Cardiac death or HF rehospitalization Cardiac death or HF rehospitalization Cardiac death or HF rehospitalization

20 mo

25%

N/A

N/A

18 mo

23.7%

5.42

2.20e13.32

479 d

22.7%

1.23e2.69

1.22e5.05 1.006e1.024

End Point

HR

95% CI

2005

186

67 6 12

41%

N/A

129

Koyama et al

2007

141

66 6 13

37.6%

N/A

Koyama et al130

2008

160

69 6 12

40.6%

N/A

Cardiac death or HF rehospitalization

872 d

30%

Koyama et al130

2008

160

69 6 12

40.6%

N/A

Cardiac death or HF rehospitalization

872 d

30%

Nishio et al119 Ruiz-Ruiz et al132

2007 2007

145 111

66.6 6 1.6 73.4 6 7.9

27% 46.8%

N/A N/A

Death or HF rehospitalization HF rehospitalization

1y 21 mo

19.3% 48.6%

1.88 (per 1 SD increase) 1.59 (per 1 SD increase) 1.90 (per 1 SD increase) 2.38 1.015

Tsuchihashi et al54

2001

230

69 6 14

40%

N/A

HF rehospitalization

1y

35.0%

1.99

1.06e3.72

Berry et al89 Blackledge et al24 Lee et al26 RodriguezArtalejo et al84 Berry et al159 Ezekovitz et al59 Kosiborod et al161

2008 2003 2008 2005

454 5,789 668 394

N/A 77.5 66 6 12 77.2 6 6.6

51% 50% 32.6% 56.1%

N/A 12.6% 100% N/A

Death or HF rehospitalization Death or rehospitalization Death or HF rehospitalization HF rehospitalization

12 mo 1y 2y 6 mo

48% 49% 29.6% 35.0%

1.88 1.11 1.80 1.45

1.22e2.88 1.02e1.21 1.25e2.60 1.02e2.06

2006 2008 2005

528 10,415 50,405

72 6 13 79.5 (73e85) 79.4 6 0.05

50% 50.4% 59%

N/A N/A 15.6%

Death or HF- rehospitalization All-cause rehospitalization HF rehospitalization

814 d 30 d 1y

54% 20.2% N/A

1.04e1.89 1.00e1.69 1.04e1.38

Luthi et al160

2006

955

75.4 6 12.8

45.7%

N/A

All-cause rehospitalization

30 d

13.4%

1.40 1.30 1.21 (Htc #24% vs Htc O44%) 1.60

Milo-Cotter et al162

2008

331

75.8 6 10.3

49%

N/A

Death or HF- rehospitalization

6 mo

46%

N/A

Blackledge et al24 Koitabashi et al25

2003 2005

5,789 427

77.5 65.8 6 13.5

50% 36%

12.6% N/A

Death or rehospitalization HF rehospitalization

1y 34 mo

49% 34.9%

1.13 2.30

1.02e1.24 1.30e4.05

Ezekovitz et al59 Blackledge et al24 Blackledge et al24 Darze et al83

2008 2003 2003 2007

10,415 5,789 5,789 198

79.5 (73e85) 77.5 77.5 69.4 6 13.5

50.4% 50% 50% 44%

N/A 12.6% 12.6% N/A

All-cause rehospitalization Death or rehospitalization Death or rehospitalization Death or rehospitalization

30 d 1y 1y 3 mo

20.2% 49% 49% 46%

0.74 0.88 1.26 4.10

0.60e0.91 0.82e0.94 1.12e1.41 1.60e10.90

Darze et al83

2007

198

69.4 6 13.5

44%

N/A

Death or rehospitalization

3 mo

46%

3.70

1.60e8.50

Blackledge et al24 Faris et al55

2003 2002

5,789 396

50% 26%

12.6% 17%

Death or rehospitalization Rehospitalization

1y 48 mo

1.57 2.80

1.44e1.72 1.20e6.60

McCellan et al164

2004

755

60.2%

29.7%

Rehospitalization

30 d

1.70

1.18e2.44

Darze et al83

2007

198

77.5 53 6 15 (15e86) 75.7 6 10.9 (30e100) 69.4 6 13.5

44%

N/A

Death or rehospitalization

3 mo

2.70

1.40e5.30

49% Rate, 1.7/ patient-year 32% 46%

1.11e1.29 1.16e3.09

1.00e2.58 N/A

74 Journal of Cardiac Failure Vol. 17 No. 1 January 2011

Table 4. (Continued)

WRF (both a $25% and a $0.3 mg/dL increase in sCr from admission) Atrial dibrillation Acute pulmonary embolism Depression Depression (major) COPD Charlson comorbidity index

40%

N/A

CV death or HF rehospitalization

480 d

48%

1.47

1.13e1.81

5,789 198

77.5 69.4 6 13.5

50% 44%

12.6% N/A

Death or rehospitalization Death or rehospitalization

1y 3 mo

49% 46%

0.94 4.00

0.88e1.00 1.10e15.10

2002

396

26%

17%

Rehospitalization

48 mo

1.25

1.07e1.90

Jiang et al174 Harjai et al32 RodriguezArtalejo et al85

2001 2001 2006

357 443 371

53 6 15 (15e86) 63 6 13 70 6 14 77.2 6 6.7

38% 45% 58.2%

30% 36% N/A

HF rehospitalization HF rehospitalization Rehospitalization

1y 30 d 6 mo

2.98 2.20 1.13 (per 1-point increase)

1.17e7.59 1.10e4.50 1.02e1.26

Kossovsky et al23

2000

442

75.6 6 11.2

48%

N/A

HF rehospitalization

31 d

N/A

1.23

1.06e1.42

Armola and Topp80 Tsuchihashi et al54

2001 2001

179 230

70.2 69 6 14

N/A 40%

N/A N/A

HF rehospitalization HF rehospitalization

30 d 1y

23.5% 35.0%

0.33 4.87

N/A 2.01e11.78

Chung et al200

2008

400

N/A

N/A

N/A

All-cause rehospitalization

6 mo

N/A

0.74

0.57e0.97

Evangelista et al

2000

753

39.4%

29.2%

2.41

1.57e3.67

2008

460

N/A

Multiple (O1) HF readmissions HF rehospitalization

1y

Giordano et al180

69 6 11.7 (33e99) 57 6 10

1y

19%

0.50

0.34e0.73

RodriguezArtalejo et al84

2005

394

N/A

HF rehospitalization

6 mo

35.0%

1.59

1.12e2.26

2008

318

Blackledge et al24 Darze et al83

2003 2007

Faris et al55

56

77.2 6 6.6

2% 15% 56.1%

Rate, 1.7/ patient-year 60.5% 15.8% 36.4%

AF, atrial fibrillation; CRP, C-reactive protein; Htc, hematocrit; PIP, propeptide of procollagen type I; sCr, serum creatinine; LAVi, left atrial volume index; RVTDI, right ventricular tissue Doppler imaging; sRAGE, soluble receptor for advanced glycation end products; TD, tissue Doppler; WRF, worsening of renal function;other abbreviations as in Tables 1-3.

Risk Factors for Hospitalization in Heart Failure

Social/Psychologic Readiness-fordischarge criteria Follow-up plan Poor follow-up visits (!1/mo or none) HF performance measures Primary care physician Home-based telemanagement program SF-36 physical summary score

68 6 11

Metra et al165

 Giamouzis et al

75