Journal of Cardiac Failure Vol. 17 No. 1 2011

Review Articles

Hospitalization Epidemic in Patients With Heart Failure: RiskFactors, 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

From the 1Emossaly, Larissa, Gr

Manuscript recAugust 3, 2010; r

Reprint requestUniversity Hospit30322, Tel: 404-

emory.eduSupported in p

Board grant entitlin Heart Failure.’’All decisions reSee page 62 for1071-9164/$ - s� 2011 Elseviedoi:10.1016/j.ca

ABSTRACT

Patients with heart failure (HF) are hospitalized over a million times annually in the United States. Hos-pitalization marks a fundamental change in the natural history of HF, leading to frequent subsequentrehospitalizations and a significantly higher mortality compared with nonhospitalized patients. Three-fourths 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 hospitaliza-tion and rehospitalization is important to improve patient outcomes and curb health care costs. To imple-ment cost-effective strategies to contain the HF hospitalization epidemic, optimal schemes to identifyhigh-risk individuals are needed. In this review, we describe the risk factors that have been associatedwith hospitalization risk in HF and the various multimarker risk prediction schemes developed to predictHF rehospitalization. We comment on areas that represent gaps in our knowledge or difficulties in inter-pretation of the current literature, representing opportunities for future research. We also discuss issueswith 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 mil-lion individuals in the United States have HF, and morethan 550,000 are diagnosed annually.2 The complex arrayof physiologic, psychologic, social, and health care deliveryissues makes it a challenging chronic disease to manage.3,4

Over the past decade, the annual number of hospitalizationshas increased from 800,000 to O1 million for HF as a pri-mary, and from 2.4 to 3.6 million for HF as a primary or

ry University, Atlanta, Georgia and 2University of The-eece.eived December 12, 2009; revised manuscript receivedevised manuscript accepted August 16, 2010.s: Javed Butler, MD, MPH, Cardiology Division, Emoryal, 1365 Clifton Road, NE, Suite AT430, Atlanta, GA778-5273; Fax: 404-778-5285. E-mail: javed.butler@

art through an Emory University Heart and Vasculared ‘‘Novel Risk Markers and Prognosis Determination

garding this manuscript were made by a guest editor.disclosure information.ee front matterr Inc. All rights reserved.rdfail.2010.08.010

54

secondary diagnosis.5 Approximately 50% of HF patientsare rehospitalized within 6 months of discharge,6 and70% of rehospitalizations are related to worsening of previ-ously diagnosed HF.7 A recent analysis of all Medicare feesfor service readmission to hospitals for any cause showedHF to be the number 1 cause of rehospitalization.8 Heartfailure rehospitalization carries a significantly higher mor-tality risk compared with index hospitalization.9 Heart fail-ure is the primary reason for 12-15 million office visits and6.5 million hospital-days each year.10 With aging of thepopulation, HF rates and the associated rehospitalizationswill rise. By 2050, 1 in 5 persons in the United Stateswill be elderly11; 80% of patients hospitalized for HF areO65 years old.10 Furthermore, intense societal need for im-proving medical quality of care has shifted the focus from‘‘hard’’ outcome measures initially introduced (ie, all-causemortality) to ‘‘softer’’ outcomes; therefore, 30-day postdi-scharge HF readmission rates are now being consideredas quality measures.

To implement interventions to reduce HF rehospitaliza-tions cost-effectively, identification of high-risk individualsis essential. Numerous individual risk factors for HF

Risk Factors for Hospitalization in Heart Failure � Giamouzis et al 55

rehospitalization, assessed at various times related to indexhospitalization (admission, discharge, first follow-up visit,etc) have been reported. Importantly, identification oflow-risk individuals is also essential, because absence ofhigh risk does not necessarily indicate a low-risk patientwho can be safely discharged.12,13 Based on these factors,different multimarker risk-prediction models have been de-veloped to increase prediction accuracy and precision.14,15

However, gaps in our knowledge of the underlying patho-physiologic mechanisms pose difficulties in interpretingthe currently available excess of data. Furthermore, datain this respect are not always consistent. Given the hetero-geneous nature of the HF population, spanning from ische-mic to nonischemic, low to preserved ejection fraction,interfering with various comorbid conditions, a ‘‘one-size-fits-all’’ approach to risk stratification may not be appropri-ate and subpopulations may need to be targeted. Data basedonly on administrative records have been challenged,16,17

and addition of clinical information may improve riskprediction.18e22 Therefore, understanding the predictors,the timing of their appearance in the course of the syn-drome, their strength of association with certain outcomes,and in which specific subpopulations they predict risk is es-sential to devise effective prevention interventions to curbthe HF rehospitalization epidemic.In the present review, we provide a comprehensive over-

view of literature regarding individual predictors and themulti-marker risk models related to HF rehospitalizationrisk (Table 1). The details of individual risk factors are pre-sented in Supplementary Tables 2e4 (available online atwww.onlinejcf.com), where data are sorted by studies onindividuals with depressed, preserved, and unspecifiedejection fraction, respectively.

Literature Reviewed

In this review, we primarily list the studies and the riskfactors, and have created detailed tables to list more in-depth data, for references purposes. Considering the enor-mity of the topic, it was not possible to discuss the details,regarding either the risk factor’s strength of association oritss clinical and pathophysiologic significance, of eachand every individual risk factor. These would be topicsfor further focused reviews.Publications included in this review reported HF-specific

hospitalizations data as either primary or secondary out-come, or as part of a composite outcome with mortalityrate. To review data applicable to the current era of HFtherapy, we focused on studies published within the pastdecade. Ovid Medline, PubMed, and Scopus were searchedfrom January 1, 1999, to October 31, 2008, to identifyrelevant studies using the keywords: ‘‘heart failure,’’ ‘‘read-mission,’’ ‘‘rehospitalization,’’ ‘‘hospitalization,’’ ‘‘riskprediction,’’ ‘‘model,’’ ‘‘prognosis,’’ and ‘‘outcome.’’ Publi-cations eligible for inclusion reported on readmissionamong individual patients hospitalized for HF as a primary

outcome, secondary outcome, or part of a composite out-come. Only data from studies that included $100 patientswere included. NoneEnglish language studies, abstracts,pediatric studies, and publications without original data(reviews, letters, and editorials) were not included. Alsoexcluded were studies that reported results from caseseries, experimental studies, and those without quantifiedoutcomes.

Risk Factors for Hospitalization in Heart Failure

Sociodemographic

Heart failure rehospitalizations increase with age: A4-fold increase in 30-day readmission rate for elderlypatients $80 years23 and a 24% increase/10-year ageincrements in the annual readmission rate have been re-ported.24,25 Advanced age predicts readmission in multira-cial populations.26e30 Higher readmission rates have beenreported for both male4,24,25,31e33 and female34e37 gender;however, some studies have failed to replicate theseresults.38e41 Nonwhite race has been associated with a high-er risk,26,34 with higher rates in blacks and Latinos than inAsians and whites.42e45 Not all studies have confirmedthese findings.24,46e49 Risk is related to socioeconomic sta-tus50: A significant stepwise decrease in HF-related read-mission has been observed from the lowest to the highestincome quartile.51,52 Insurance status affects rehospitaliza-tion, with patients enrolled in a health maintenance organi-zation experiencing lower 6-month readmission rates thanMedicaid and Medicare patients.53 Lack of employmentis associated with readmissions,54,55 as is family statusand living alone.55,56 Smoking is associated with multiplereadmissions,56 although ‘‘smoker’s paradox’’ in hospital-ized HF patients has been reported, with current or recentsmoking being associated with a 23% lower risk-adjusted90-day rehospitalization risk.57 Current or past alcoholuse is an independent predictor of HF readmissions.55,56

Clinical

Several studies suggest higher readmission rates withischemic etiology.23,26,56,58e61 Some studies have reportedworse risk for patients with depressed ejectionfraction,58,62e64 whereas recent studies demonstrate similarrisk with preserved ejection fraction.47,65e70 Low systolicblood pressure is an independent predictor ofrehospitalization.60,71e73 A 2% increase in 60-day readmis-sion for every 1 mm Hg decrease has been reported.27,35,74

Similarly, 10 mm Hg decrease in diastolic pressure is asso-ciated with an 11% increase in cardiovascular mortality orHF rehospitalization.75 Increased heart rate has been shownto correlate with readmissions.76e79 Higher New YorkHeart Association functional class at discharge also predicts30-day and 1-year readmissions.25e27,56,80,81 Prior HF hos-pitalization is an independent risk factor for recurrentreadmissions.4,23,32,54,60,82e85 Prolonged length of stay

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

Study Year Study Type N Study Outcome Follow-Up Event Rate C-Index

Philbin and DiSalvo181 1999 Retrospective 42,731 HF rehospitalization 6.9 mo 21.3% 0.60

Independent Predictors HR 95% CI Independent Predictors HR 95% CI

Black race 1.28 1.16e1.41 Idiopathic cardiomyopathy 1.46 1.32e1.61Medicare insurance 1.66 1.38e2.00 Prior cardiac surgery 1.16 1.04e1.29Medicaid insurance 1.92 1.57e2.36 Use of telemetry monitoring 1.13 1.01e1.27Home health services needed 1.10 1.01e1.21 Treatment in a rural hospital 0.87 0.78e0.98Ischemic heart disease 1.25 1.16e1.34 Dischargedskilled nursing facility 0.68 0.59e0.79Valvular disease 1.19 1.09e1.29 Echocardiography performed 0.78 0.73e0.85Diabetes mellitus 1.45 1.33e1.58 Cardiac catheterization 0.60 0.49e0.73Renal disease 1.35 1.23e1.49 Chronic lung disease 1.10 1.02e1.20

Study Year Study Type N Study Outcome Follow-Up Event Rate C-Index

Krumholz et al3 2000 Retrospective 2,176 HF rehospitalization 6 mo 23.3% NA

Independent Predictors HR 95% CI Independent Predictors HR 95% CI

Prior admission within 1 y 1.25 1.05e1.48 Discharge creatinine O2.5 mg/dL 1.72 1.35e2.18Prior HF 1.23 1.02e1.48 Diabetes mellitus 1.17 0.99e1.39

Study Year Study Type N Study Outcome Follow-Up Event Rate C-Index

Felker et al73 2004 RCT 949 All-cause death 60 d 9.6% 0.77All-cause rehospitalization or death 60 d 35.2% 0.69

Independent Predictors HR 95% CI Independent Predictors HR 95% CI

HF hospitalization within 12 mo 1.14 1.06e1.23 Hemoglobin (per 1 g/dL) 0.89 0.82e0.97SBP (per 10 mmHg) 0.82 0.75e0.89 Past coronary intervention 1.46 1.00e2.12BUN (per 5 mg/dL) 1.26 1.14e1.41

Study Year Study Type N Study Outcome Follow-Up Event Rate C-Index

O’Connor et al182 2005 RCT and registry 908 All-cause rehospitalization or death 60 d 31.4% N/A

Independent Predictors HR 95% CI Independent Predictors HR 95% CI

Age (per 10 y) 1.26 1.11e1.44 SBP (!130 mm Hg) 0.79 0.69e0.91HF hospitalization within 12 mo 1.59 1.16e2.19 History of depression 0.58 0.35e0.97Nitrates at admission 1.73 1.22e2.45

Study Year Study Type N Study Outcome Follow-Up Event Rate C-Index

Pocock et al75 2006 RCT 7,599 All-cause death 38 mo 24.1% 0.75CV mortality or HF rehospitalization 38 mo 32.4% 0.75

Independent Predictors HR 95% CI Independent Predictors HR 95% CI

Dependent edema 1.23 1.12e1.35 Candesartan (vs placebo) 0.82 0.76e0.89Diabetes: insulin-treated 2.03 1.80e2.29 Age (per 10 y O60) 1.46 1.38e1.54

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Diabetes: other 1.58 1.43e1.74 Pulmonary crackles 1.25 1.13e1.38EF (per 5% lower) 1.13 1.11e1.16 Rest dyspnea 1.20 1.10e1.31HF hospitalization within 6 mo 1.73 1.55e1.93 Female 0.83 0.76e0.91HF hospitalization beyond 6 mo 1.22 1.09e1.37 Atrial fibrillation 1.16 1.07e1.27Diagnosis of HF O2 years ago 1.31 1.20e1.43 BMI (per 1 kg/m2 decrease !27.5) 1.03 1.01e1.04Cardiomegaly 1.35 1.23e1.47 Mitral regurgitation 1.16 1.05e1.28NYHA functional class III 1.32 1.20e1.45 Previous MI 1.11 1.02e1.21NYHA functional class IV 1.54 1.25e1.89 Pulmonary edema 1.26 1.03e1.54DBP (per 10 mm Hg) 1.11 1.07e1.16 Heart rate (per 10/min) 1.08 1.05e1.11Bundle branch block 1.26 1.15e1.38

Study Year Study Type N Study Outcome Follow-Up Event Rate C-Index

Yamokoski et al183 2007 RCT 373 Death 6 mo 19.3% N/AAll-cause rehospitalization 6 mo 49.3% 0.596

Independent Predictors HR 95% CI Independent Predictors HR 95% CI

BUN at discharge N/A N/A High-dose diuretics N/A N/A

Study Year Study Type N Study Outcome Follow-Up Event Rate C-Index

Keenan et al184 2008 Retrospective 283919 All-cause rehospitalization 30 d 6.5% 0.60

Independent Predictors HR 95% CI Independent Predictors HR 95% CI

Past CAB surgery 0.93 0.91e0.96 History of HF 1.09 1.07e1.12Asthma 1.06 1.03e1.10 Other GI disorders 1.06 1.04e1.08Cardiorespiratory failure/shock 1.08 1.06e1.11 Peptic ulcer, hemorrhage, other GI disorders 1.07 1.05e1.10Arrhythmias 1.06 1.04e1.08 Severe hematologic disorders 1.15 1.10e1.21ACS 1.12 1.10e1.15 Nephritis 1.08 1.03e1.12Valvular and rheumatic disease 1.08 1.06e1.10 Metastatic cancer and acute leukemia 1.14 1.07e1.21Vascular disease 1.07 1.05e1.09 Liver and biliary disease 1.06 1.02e1.09Chronic atherosclerosis 1.09 1.06e1.11 End-stage renal disease/dialysis 1.16 1.11e1.22Other/unspecified heart disease 1.05 1.03e1.08 Decubitus or chronic skin ulcer 1.10 1.07e1.13Hemi- or paraplegia, paralysis,

functional disability1.04 1.01e1.08 Iron deficiency and other anemias and blood disease 1.09 1.06e1.11

Stroke 1.03 1.00e1.07 Pneumonia 1.09 1.06e1.11Disorders of fluid

electrolyte/acid-base1.12 1.09e1.14 Drug/alcohol abuse/dependence/psychosis 1.07 1.04e1.10

Chronic pulmonary disease 1.17 1.14e1.19 Other psychiatric disorders (depression excluded) 1.08 1.05e1.12Diabetes with complications 1.08 1.06e1.11 Fibrosis of lung and other chronic lung disorders 1.05 1.02e1.08Renal failure 1.15 1.13e1.18 Protein-calorie malnutrition 1.05 1.01e1.09Other urinary tract disorders 1.12 1.10e1.15

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 frac-tion; GI, gastrointestinal; HR, hazard ratio; MI, myocardial infarction; NYHA, New York Heart Association; RCT, randomized control trial; SBP, systolic blood pressure.

RiskFactors

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HeartFailure

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

(O7 days) during index hospitalization portends higher6-month readmission rates4; when defined as O14 days,it confers a 3-fold increase risk.54 Physical signs of volumeoverload confer a 2-fold increased risk of 6-month readmis-sion in hospitalized HF patients.76 Also, signs of poor tissueperfusion, ie, ‘‘cold’’ state have a 2.5-fold higher risk.61 Inpatients with recent HF hospitalization, an objective clini-cal disease severity score was independently associatedwith annual risk for rehospitalization.29

Blood Tests

Lower admission hemoglobin level is associated witha higher risk.60,86 A 27% decrease in rehospitalization per2 g/dL increase in hemoglobin levels has been described.28

Similarly, a 2% higher risk of 1-year readmission for every1% lower admission hematocrit level has been reported.87

A 3 � 109/L increase in neutrophil count (approximately30% increase) is associated with a 25% increase in HF re-hospitalizations.28 The risk of HF rehospitalization afterhospital discharge increases by 8% per 3 mEq/L decreasein serum sodium levels.88 In another study, a 5 mEq/L in-crease conferred a 40% decrease in the 2-year HF rehospi-talization rate in patients with HF and preserved ejectionfraction.28 Total bilirubin levels predict risk in patientswith systolic dysfunction.27 A 2 mmol/L increase in admis-sion glucose confers a 6% increase in annual readmis-sions.89 A 25% increase in readmission per 1% increasein hemoglobin A1c levels has been reported.90 Both admis-sion84,85 and discharge36 serum creatinine levels have beencorrelated with readmission in patients with low84,85 orpreserved36 ejection fraction. A 39% increase in 6-monthreadmission rate per 1 mg/dL increase in serum creatininelevels has been reported.91 Similarly, increased blood ureanitrogen levels have been associated with higher readmis-sion rates28,92,93: A 2% risk per 1 mg/dL increase in admis-sion levels has been reported.32 Uric acid O7 mg/dL(420 mmol/L) in men and O6 mg/dL (360 mmol/L) inwomen with HF is associated with higher readmission ratesamong both hospitalized patients94 and outpatients.30

Several studies have reported the prognostic ability ofnatriuretic peptides.95e101 In a small single-center study,a B-type natriuretic peptide (BNP) level of O200 pg/mLprovided an optimal value in predicting HF readmission.102

Admission and discharge levels predict readmission in var-ious populations82,91,103e107 including those with systolicdysfunction,27,36 restrictive filling pattern,81 or chronic re-nal insufficiency.95 Although not consistently seen or uni-versally agreed upon, a reduction in natriuretic peptidelevels during hospitalization by O30%-40% has beenassociated with improved outcomes in somestudies.36,76,95,105,108e110 Postdischarge measurement alsoprovides risk stratification.29,111e113 Therefore, natriureticpeptideeguided therapy has been proposed,91,104,114,115

and several natriuretic peptideeguided trials have alreadybeen published. Even though their results are somewhat

conflicting, intense research interest in this fieldcontinues.114,116e118

Data on newer biomarkers are emerging. Elevated con-centration of cardiac troponin during HF hospitalization is astrong and consistent predictor of readmission.61,112,119e122

C-Reactive protein (CRP) levelsO0.9 mg/dL are associatedwith higher risk,123 and HF readmissions increase with in-creasing quartile of CRP124 or high-sensitivity CRP.120,125

Apolipoprotein A-I levels lower than !103 mg/dL areassociated with higher readmission rates independent ofhigh-density lipoprotein and BNP levels.126,127 Cystatin Cis also associated with higher risk irrespective of serumcreatinine.128 Other experimental biomarkers associatedwith HF rehospitalizations include advanced glycation end-products, 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 in-dependent predictor of HF morbidity.133,134 Increased car-diothoracic ratio on chest X-ray is associated with higherHF readmission rates.135 On echocardiography, severalstudies have reported lower readmission rates for patientswith higher ejection fraction26,29,30,35,81,120,136,137 A 2%fall in 6-month readmission rate per 1% increase in ejectionfraction has been reported.91 An increase of 1 mL/m2 in leftatrial volume index increases the risk of death or readmis-sion by 3%.138 Echocardiographically estimated systolicpulmonary arterial pressure has also been useful in predict-ing HF readmission.139 Right ventricular tissue Dopplerimaging predicts HF rehospitalization independently ofother Doppler diastolic function variables.140 Similarly, tis-sue Doppler imagingedetected mechanical dyssynchronypredicts HF readmission in patients with systolic dysfunc-tion and normal QRS duration.141 Finally, decreased heartrate variability,78,142e145 especially its very-low-frequencypower spectral components,146 is associated with higherrisk, and heart rate recovery after exercise provides addi-tional prognostic information.147

Comorbidity Burden

A large proportion of readmissions for HF are associatedwith comorbidities that precipitate, contribute to, or compli-cate HF admission,148 especially in the elderly.10 In a recentpopulation study, 39% of HF patients had $5 noncardiaccomorbidities and only 4% had HF alone.149

Cardiovascular

Presence of hypertension (admission systolic blood pres-sure O140 mm Hg) during HF hospitalization decreasesannual risk of HF readmission,24 especially in the elderly.59

In contrast, a history of hypertension (ie, documented anti-hypertensive 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 � Giamouzis et al 59

rehospitalization in black patients.136 Concomitantmyocardial infarction during HF hospitalization increasesannual readmission risk.24 Atrial fibrillation is common inHF and adversely affects hemodynamics.150,151 Severalstudies demonstrate that atrial fibrillation is associatedwith an increased risk of HF readmissions in patientswith low152 or preserved25,153 ejection fraction. Interest-ingly, rhythm control does not reduce risk for rehospitaliza-tion.154 Patients with valvular heart diseases, regardless ofsystolic function, have a 4-hold higher likelihood for HFrehospitalization.28

Noncardiovascular

Several studies have demonstrated a consistent associationbetween diabetes mellitus and increased rehospitalizationrates in HF with low66,155,156 or preserved66 ejection fractionor in multirace3,24,84,89,157 and in single-race26 populations.One study suggested an increased risk for black womenwith diabetes mellitus compared with men or nonblack pa-tients.136 Anemia is common in HF158: When defined as he-moglobin !12 g/dL, anemia is associated with higher HFreadmission.59,86,159e161 Hyponatremia, defined as serumsodium level !136 mEq/L, is a marker of increased 3-,83

6-,162 and 12-month122HF readmission. A history of renal in-sufficiency or the presence of elevated serum creatininelevels (O1.5 mg/dL) on admission are common in HF pa-tients163 and are associated with higher rates of HF rehospi-talization.24,55,164 Worsening renal function during HFhospitalization is also common,83 and regardless of the defi-nition used, it appears to predict independent risk for HF re-hospitalization.165,166 Cerebrovascular disease increases4-fold the likelihood of death or rehospitalization at 3monthsin patients hospitalizedwithHF.83History of stroke increasesthe annual mortality and readmission rate by 26% in HF pa-tients.24,167 Chronic obstructive pulmonary disease is associ-ated with higher risk,28,32,34,149 regardless of beta-blockeruse.168 Obstructive sleep apnea doubles169 and pulmonaryembolism quadruples83 the likelihood of HF readmission.Depression, present in almost one-half of chronic HF pa-tients,170,171 is associated with higher annual HF readmissionrates.55,170,172e174 Several scores have been used to quantifycumulative comorbidity burden: Higher HF readmissionrates have been reported for patients with higher Deyo co-morbidity score or Charlson comorbidity index.4,85

Quality of Life and Psychosocial Factors

Several self-assessment questionnaires quantify anindividual’s perception of their quality of life. A symptomstability score, based on either the Kansas City Cardiomy-opathy Questionnaire or the Minnesota Living withHeart Failure Questionnaire, correlates with the HFreadmission risk.34,175 Poor quality of life on the Notting-ham Health Profile is an independent predictor of readmis-sion among elderly HF patients.176 Higher readmissionrates have also been reported with worse 36-item Short

Form survey scores.84 Absence of emotional support or so-cial network among elderly hospitalized patients is a strongpredictor of increased readmission rate, especially amongwomen.85,177

Disease Management

Despite the fact that in the current therapeutic era morepatients are discharged on evidence-based medication, ad-herence to these therapies in the outpatient setting hasbeen shown to deteriorate over time.178 A targeted formaleducation and support intervention has been associatedwith a 44% decrease in the annual HF readmissionrate.179 A multidisciplinary team intervention, consistingof patient and family education, a prescribed diet, social-service consultation, medication review, and intensivefollow-up lead to improved quality of life and reduce HFreadmission rates.157 Poor follow-up is associated withhigher risk.54 A prepared follow-up plan with an appoint-ment scheduled in the HF clinic, provided to the patientupon discharge, decreases their likelihood for HF readmis-sion within 30 days after discharge.80 Health care providedby an HF-specialist reduces the risk for readmission com-pared with care provided by primary care physicians.56 Ahome-based telemanagement program has also been shownto reduce the annual HF readmission rate.180

Risk Prediction

Using administrative claims data, Philbin and DiSalvocreated a scoring system to quantify the annual HF-specific readmission risk among hospitalized HF patientsby using 16 variables (Table 1).181 This model had mar-ginal 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 independentpredictors of 6-month all-cause readmissions. Felkeret al. derived a model that predicted outcomes among pa-tients with decompensated HF.73 Among 41 variables eval-uated, 5 were predictive of death or readmission at 60 days.The discriminatory power of the model was better for themortality (C-index 0.77) but less for the composite endpoint including rehospitalization (C-index 0.69). O’Connoret al. combined data from a clinical trial and a registry tocreate a model to predict 60-day death or readmissionsamong hospitalized HF patients.182 Age, use of nitrates atadmission, and $1 admission for HF in the previous yearincreased risk, whereas systolic blood pressure !130mm Hg and, interestingly, a history of depression appearedto reduce risk. Pocock et al. developed a model from 7,599HF patients with preserved and decreased systolic func-tion.75 The final model included 21 predictor variablesfor cardiovascular death or HF rehospitalization. The 3most powerful predictors were age, diabetes mellitus, andejection fraction !45%. Yamokoski et al. created a modelto estimate the risk of death and readmission among

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.

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

hospitalized patients with severe HF.183 The prognosticmodel again showed modest discrimination. Finally,Keenan et al. developed an administrative claimsebasedmeasure for profiling hospital performance for 30-dayall-cause readmission rates.184 The model included 37 vari-ables, and the C-index was only 0.60.

Overall, these models had modest C-indexes ofw0.60 forreadmission risk prediction regardless of whether they wereadministrative data181,184 or medical records based.183 Thisraises the possibility that either important predictors of HF re-admission are not present in suchdatabases or nonmedical fac-tors play a major role in HF rehospitalization risk.

Knowledge Gap and Future Directions

It is apparent from the aforementioned discussion thatdespite a multitude of known risk factors, actual predictionof HF rehospitalization is difficult, with at best only modestresults seen in the previous literature. Considering the in-ability to both reduce the national burden of HF rehospital-ization rate and the inability to accurately predictrehospitalization risk as opposed to combined rehospitaliza-tion and mortality risk, 4 issues need close scrutiny: impor-tance of comorbid conditions; lack of therapeutic options;importance of nonclinical factors; and assessment and treat-ment of congestion.

Importance of Comorbid Conditions

Comorbid conditions play a major role in HF progres-sion 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 mayaffect the risk by causing, exacerbating, or maskingHF, or by affecting compliance or health careeseekingbehaviors (Fig. 1). It is simplistic to assume that a diseasemanagement, as opposed to a patient management, ap-proach ignoring the huge comorbidity burden will reducerehospitalization risk. To complicate matters further, alladministrative and most clinical databases are incrediblyill equipped to truly assess the importance of comorbidityburden. For example, the Medicare database is based onadministrative codes and does not have the clinical infor-mation to put the information in its correct perspective,eg, HF may represent low or preserved left ventricularejection fraction, or ischemic or nonischemic etiology.Moreover, the degree of functional abnormality is notavailable (eg, peak VO2, 6-minute walking distance ,oreven the NYHA functional class), and it is well knownthat the clinical relevance of a peak VO2 of 10 mLkg�1 min�1 is different than that of 20 mL kg�1

min�1, though both conditions may be labeled as ‘‘heartfailure.’’ Finally, the administrative codes are largely pro-vided by hospital administrative and not medical staff.Therefore, database-related comorbidity assessment likelyunderestimates the role of comorbidities by inaccuratelyassessing:

True prevalence (eg, sleep apnea or depression).Disease severity (eg, risk may vary with worsening degreeof pulmonary function derangement, but such details arenot available).

Risk Factors for Hospitalization in Heart Failure � Giamouzis et al 61

Adequacy of therapy (eg, it is possible that individuals withdiabetes mellitus have a different risk based on hemoglobinA1c levels).

These limitations may preclude accurate risk predictionand risk attenuation, underscoring the need for better as-sessment and treatment of comorbidities in future.

Assessment and Treatment of Congestion

Many hospitalized HF patients lose little or no weightduring hospitalization,185 although data from clinical trialpopulations differ from registry or observational data inthis respect.186 However, it may be inaccurately assumedthat all patients with decompensated HF have significantfluid overload, and in those who do have significant fluidoverload, that it will be possible to get rid of the excessfluid adequately, safely, and in a short time. Effective,safe, and timely diuresis is related to a complex interactionof hemodynamics and fluid compartment interactions(intra- vs extravascular volume and oncotic pressure). Cur-rent surrogates of volume assessment, eg, pulmonary arteryocclusive pressure or the natriuretic peptide levels, may notalways represent the volume status, and even if they did,when they can be safely optimized without complicationssuch as hypotension or renal failure is poorly understood.Although a recent consensus statement proposes a methodof assessment,187 real-life assessment of congestion con-tinues to be debated, and how to decongest patients effec-tively and safely is not well known.

Importance of Nonclinical Factors

It is possible, and likely, that nonclinical factors, espe-cially in the elderly, play a major role in HF worsening re-quiring hospitalization. Important risk factors may not beavailable in clinical or administrative databases unlessspecifically measured, eg, compliance with medications.Most risk scores do not account for the influence of patientself-care behavior or social vulnerabilities; withoutpaying specific attention to these as preventive interventiontargets, medical interventions per se may not reach their fullpotential.

Lack of Therapeutic Options

Data with therapies that have been proven to improve re-hospitalization risk are primarily for systolic dysfunctionand were almost exclusively generated in the chronic outpa-tient setting. Unfortunately, there continues to be a lack ofscientifically proven data supporting therapies that, whentargeted specifically at acute decompensated HF patients,prevent the risk for readmission. Furthermore, there is norandomized trial for the management of diastolic HF thatreduces readmission rate, although diastolic dysfunction ac-counts for O50% of HF, especially among the elderly pop-ulation.188 Data in this respect are largely either related todisease management programs189e191 or based on observa-tional data.192 The lack of successful therapy to date maybe partially driven by the lack of understanding of the

taxonomy of acute decompensated HFsyndromes,2,7,12,193e195 which likely represent several var-ied pathophysiologies necessitating different therapeuticapproaches. Perhaps no other area of major cardiovascularpublic health impact is less well understood than decom-pensated HF, underscoring the need for intense focused re-search in this area.

Other Issues

Several other issue regarding heart failure rehospitaliza-tion risk, prediction, and the use of these data merit high-light.

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 versusHF-specific rehospitalizations. Because the predictors ofmortality versus all-cause versus HF-specific rehospitaliza-tion may vary, synthesizing a summary conclusion based oncurrent literature, though important to implement specificstrategies for lowering this epidemic, is difficult. Neverthe-less, end points other than HF rehospitalization, such as all-cause rehospitalization, may provide extremely importantinformation regarding specific HF populations (eg, the el-derly HF patients with preserved ejection fraction, wheremore than one-half of rehospitalizations have been attrib-uted to noncardiovascular causes).

Time Period

Different studies have reported risk for 30-, 90-, 180-, or365-day outcomes. It is possible that short-term outcomesare related to in-hospital care, intermediate-term outcomesto postdischarge care, and long-term outcomes to other pa-tient- and provider-related factors. Data from these variousstudies therefore may not be directly comparable.

Timing of Risk Factor Measurement

When a particular risk factor is measured for risk predic-tion in the spectrum of illness and presentation merits atten-tion. This is especially important for the clinical (ie,congestion, body weight) and laboratory (admission or dis-charge values vs dynamic changes) parameters. There isa great variability in the timing of assessment of a particularrisk factor among different studies, and these risk factorsmay predict risk at one particular time point and not theother.

Diversity

Considerable data indicate that both biologic/diseasee andhealth care deliveryerelated disparities exist based on gen-der,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 groupsmay vary requiring inquiryof specific race/ethnicityerelated data.

Intention

Risk models for primary prevention are most optimal ifthey are parsimonious and specific. In contrast, models topredict mortality to allocate advanced therapies need tobe sensitive and complexity is less of a concern. BecauseHF readmission risk prediction can have various motivesand may differ for patients, providers, payers, or re-searchers, the need and quality of the prediction modelmay differ correspondingly.

Readmission and Quality of Care

There is intense societal focus on improving medicalquality of care.42,196e199 Initial quality measures were pro-cess related, and subsequently ‘‘hard’’ outcome measureswere introduced, eg, mortality. However, new measures,such as 30-day postdischarge HF readmission rates, are cur-rently used as quality measures. This consideration raisesseveral concerns. The published risk-adjusted HF readmis-sion predictions models are not optimal for accurate riskprediction for reasons stated above. Performance of othermodels that are currently unpublished in peer-reviewedjournals cannot be judged. In addition, there are no gold-standard rules for when a person should be admitted withHF, and patients may be hospitalized for borderline clinicalor nonclinical social reasons, eg, inadequate family support,assisted-living infrastructures, primary care physician avail-ability, etc. Finally, quality measures such as HF readmis-sion rate might precipitate provider behaviors regardingresisting admissions; this may be a safety concern.

Conclusion

Heart failure rehospitalization clearly marks a fundamen-tal change in the natural history of the syndrome, signifi-cantly increasing subsequent mortality and morbidity.Preventing HF rehospitalization is important to improve pa-tient outcomes and curb health care costs. However, to im-plement such cost-effective strategies, optimal schemes toidentify high-risk individuals, as well as low-risk patientsfor safe discharge, are needed. Numerous risk factorshave been associated with HF rehospitalization risk. Basedon these factors, different multimarker risk predictionmodels were developed to predict HF readmission risk.However, these data, for multiple reasons cited above, arelimited in their application. Further studies to assess the un-derlying pathophysiologic mechanisms and develop newtherapeutic options and better risk prediction schemes areneeded to curb this epidemic.

Disclosures

None.

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

Variable Source Year N

Age (y),mean 6 SD(Range)

Female(%)

Nonwhite(%) Study Outcome

Fo

SociodemographicAge Berry et al28 2005 315 70.5 6 12.9 45% N/A Death or HF rehospitalization 81

Pascual-Figal et al30 2007 212 69.6 6 11 29% N/A Death or HF rehospitalization 1

Shinagawa et al27 2008 183 65.4 6 14.2(58e75)

29% N/A Cardiac death or HFrehospitalization

29

Systolicdysfunction

Harjai et al63 1999 443 70 6 14 45% 36% HF rehospitalization 3

ClinicalSBP (admission) Filippatos et al74 2007 319 62 30% 35% Death or HF rehospitalization 6

Shinagawa et al27 2008 183 65.4 6 14.2(58e75)

29% N/A Cardiac death or HFrehospitalization

29

NYHA functionalclass (at discharge)

Shinagawa et al27 2008 183 65.4 6 14.2(58e75)

29% N/A Cardiac death or HFrehospitalization

29

ImagingQRS O120 ms Wang et al134 2008 2962 66.0 6 11.3 25.7% 14.9% CV death or HF rehospitalization 9LVEF Pascual-Figal et al30 2007 212 69.6 6 11 29% N/A Death or HF rehospitalization 1

LVEF !35% Xue et al120 2006 128 62 6 15 38% N/A Death or HF rehospitalization 1Systolic pulmonary

arterial pressureShalaby et al139 2008 270 66.5 6 11.6 13% 5% HF rehospitalization 19

Reduction in systolicpulmonary arterialpressure

Shalaby et al139 2008 270 66.5 6 11.6 13% 5% HF rehospitalization 19

Mechanicaldyssynchronyin TDI (Ts-diffO91 ms)

Cho et al141 2005 106 63 6 11 30% N/A Death or HF rehospitalization orcardiac transplantation

17

LaboratoryHemoglobin A1c Gerstein et al90 2008 2412 65.8 33% N/A HF rehospitalization 36

Hemoglobin Berry et al28 2005 315 70.5 6 12.9 45% N/A Death or HF rehospitalization

Neutrophils Berry et al28 2005 315 70.5 6 12.9 45% N/A Death or HF rehospitalization

BUN (admission) Berry et al28 2005 315 70.5 6 12.9 45% N/A Death or HF rehospitalization

Filippatos et al74 2007 319 62 30% 35% Death or HF rehospitalizationBNP (discharge) Shinagawa et al27 2008 183 65.4 6 14.2

(58e75)29% N/A Cardiac death or HF

rehospitalization29

Appendix 68

Journal

tion

llow-Up

EventRate HR 95% CI

4 d 53% 1.12 (per 5-yincrease)

1.04e1.21

y 38% 1.02 (per 1 yincrease)

1.01e1.04

.5 mo 44% 1.05 (per 1 yincrease)

1.03e1.07

0 d 16% 5.71 1.64e21.94

0 d 24.5% 0.98 0.96e0.99.5 mo 44% 0.986 (per mm Hg

increase)0.974e0.997

.5 mo 44% 1.645 1.055e2.516

.9 mo 41.6% 1.28 1.10e1.49y 38% 0.96 (per 1%

increase)0.94e0.99

y 32.8% 3.52 2.36e10.37.4 mo 23.7% 6.35 2.60e15.80

.4 mo 23.7% 0.29 0.12e0.76

mo 33% 4.98 2.13e11.65

.7 mo 36.2% 1.25 (per 1%increase)

1.19e1.31

814 d 53% 0.73 (per 2 g/dLincrease)

0.61e0.87

814 d 53% 1.25 (per 3 � 109/Lincrease)

1.12e1.40

814 d 53% 1.18 (per 5 mmol/Lincrease)

1.07e1.29

60 d 24.5% 1.02 1.01e1.03.5 mo 44% 1.002 (as a

continuousvariable)

1.001e1.003

ofCardiacFailure

Vol.17No.1January

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cTnT (O1.8 mg/L) Tang et al125 2008 136 57 6 14 24% N/A Death, cardiac transplantation,and HF rehospitalization

33 mo N/A 2.61 1.96e4.31

hsCRP (O3.2 mg/L) Tang et al125 2008 136 57 6 14 24% N/A Death, cardiac transplantation,and HF rehospitalization

33 mo N/A 3.81 2.14e9.35

Total bilirubin(admission levelsO1.2 mg/dL)

Shinagawa et al27 2008 183 65.4 6 14.2(58e75)

29% N/A Cardiac death or HFrehospitalization

29.5 mo 44% 1.90 1.32e2.72

ComorbidityDiabetes mellitus MacDonald et al66 2008 7599 66.0 6 11.4 32% 9% HF rehospitalization 37.7 mo 155 per 1,000

patient-y1.64 1.44e1.86

Valve disease Berry et al28 2005 315 70.5 6 12.9 45% N/A Death or HF rehospitalization 814 d 53% 3.72 1.68e8.24Hyperuricemia[discharge UAO7 mg/dL in menand 6 mg/dL inwomen]

Pascual-Figal et al30 2007 212 69.6 6 11 29% N/A Death or HF rehospitalization 1 y 38% 1.64 1.06e2.55

Depression Parissis et al170 2008 156 65 6 12 (35e76) 17% N/A Death or HF rehospitalization 6-mo 39.4% 1.074 (per 1 pointincrement inZung 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.

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

Variable Source Year NAge (y), mean 6

SD (IQR) Female Nonwhite Study OutcomeFollow-UpPeriod

EventRate HR 95% CI

ImagingQRS O120 ms Danciu et al133 2006 217 71 (63-80) 47% 57% Death or rehospitalization 6 mo 56% 1.15 N/ALaboratoryHemoglobin A1c Gerstein et al90 2008 2412 65.8 33% N/A CV death or HF rehospitalization 36.7 mo N/A 1.25 (per 1%

increase)1.20e1.31

Serum sodium Berry et al28 2005 130 72.6 6 12.4 62% N/A Death or HF rehospitalization 814 d 42% 0.60 (per 5 mmol/Lincrease)

0.43e0.84

BUN Berry et al28 2005 130 72.6 6 12.4 62% N/A Death or HF rehospitalization 814 d 42% 1.81, per 5 mmol/Lincrease

1.46e2.24

Serum Creatinine Bettencourt et al36 2007 224 74.6 6 10.5 67% N/A Death or rehospitalization 6 mo 43% 1.33 1.08e1.64NT-pro-BNP (admission-

discharge difference)Bettencourt et al36 2007 224 74.6 6 10.5 67% N/A Death or rehospitalization 6 mo 43% 7.79 2.03e29.86

ComorbidityDiabetes mellitus MacDonald et al66 2008 7599 66.0 6 11.4 32% 9% HF rehospitalization 37.7 mo 117 per 1,000

patient-y2.04 1.68e2.47

Angina pectoris Ofili et al136 1999 1200 64 6 16 (19e99) 51% 94% All-cause rehospitalization 12 mo 56% 1.87 1.06e3.32COPD Berry et al28 2005 130 72.6 6 12.4 62% N/A Death or HF rehospitalization 814 d 42% 9.86 4.48e21.7Valve disease Berry et al28 2005 130 72.6 6 12.4 62% N/A Death or HF rehospitalization 814 d 42% 3.96 1.67e9.38

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

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

Variable Source Year N Age (y) Female Nonwhite End Point Follow-UpEventRate HR 95% CI

SociodemographicAge Blackledge et al24 2003 5,789 77.5 50% 12.6% Death or rehospitalization 1 y 49% 1.24 (per 10-y

increase)1.20e1.28

Koitabashi et al25 2005 427 65.8 6 13.5 36% N/A HF rehospitalization 34 mo 34.9% 1.03 (per 1-yincrease)

1.02e1.05

Lee et al26 2008 668 66 6 12 32.6% 100% Death or HF rehospitalization 2 y 29.6% 1.02 (per 1-yincrease)

1.01e1.04

Ruiz-Ruiz et al132 2007 111 73.4 6 7.9 46.8% N/A HF rehospitalization 21 mo 48.6% 1.059 (per 1-yincrease)

1.006e1.115

Age ($80 y) Kossovsky et al23 2000 442 75.6 6 11.2 48% N/A HF rehospitalization 31 d N/A 3.90 1.30e12.00Gender (female) Blackledge et al24 2003 5,789 77.5 50% 12.6% Death or rehospitalization 1 y 49% 0.92 0.85e0.98

Mielniczuk et al35 2008 183 55.5 6 15 49% N/A Death or HF rehospitalization 1y 47% 2.43 1.00e4.40Gender (male) Harjai et al32 2001 443 70 6 14 45% 36% HF rehospitalization 30 d 15.8% 2.70 1.40e5.40

Ruiz-Ruiz et al132 2007 111 73.4 6 7.9 46.8% N/A HF rehospitalization 21 mo 48.6% 2.08 1.08e4.00Koitabashi et al25 2005 427 65.8 6 13.5 36% N/A HF rehospitalization 34 mo 34.9% 1.53 1.02e2.28

Race (nonwhite) Afzal et al43 1999 163 66.0 N/A 69% All-cause rehospitalization 6 mo 23.4% 1.83 N/ALee et al26 2008 668 66 6 12 32.6% 100% Death or HF rehospitalization 2 y 29.6% 1.65 1.04e2.63

Race (black) Alexander et al45 1999 90,316 73.2 53% 25% All-cause rehospitalization 1 y 60.1% 1.07 1.04e1.10Rathore et al42 2003 29,732 79.5 6 0.2 60% 11.6% All-cause rehospitalization 1 y 65% 1.09 1.06e1.13

Socioeconomicstatus (expressedas householdincome)

Philbin et al51 2001 41,776 74 6 13 57% 18% HF rehospitalization 6 mo 21.5% 1.18 (lowest v.highestquartile)

1.10e1.26

Socioeconomicstatus (residence-based)

Rathore et al52 2006 25,086 78.8 6 0.1 57.7% 15.5% All-causerehospitalization

1 y 68.0% 1.08 (lowest vshighestquartile)

1.03e1.12

Smoking (current) Evangelista et al56 2000 753 69 6 11.7(33e99)

2% 39.4% Multiple (O1) HF readmissions 1 y 29.2% 1.82 1.17e2.82

Fonarow et al57 2008 48,612 73.1 52% 26% All-cause rehospitalization 60 and 90 d 29% 0.77 0.63e0.94Alcohol use (current) Evangelista et al56 2000 753 69 6 11.7

(33e99)2% 39.4% Multiple (O1) HF readmissions 1 y 29.2% 5.92 3.83e9.13

Alcohol use (history) Faris et al55 2002 396 53 6 15(15e86)

26% 17% Rehospitalization 48 mo Rate, 1.7/patient-year

2.5 1.30e5.00

Living alone Evangelista et al56 2000 753 69 6 11.7(33e99)

2% 39.4% Multiple (O1) HF readmissions 1 y 29.2% 2.09 1.42e3.09

Faris et al55 2002 396 53 6 15(15e86)

26% 17% Rehospitalization 48 mo Rate, 1.7/patient-y

3.6 1.08e12.2

Social network(low vs high)

Rodriguez-Artalejoet al85

2006 371 77.2 6 6.7 58.2% N/A Rehospitalization 6 mo 36.4% 1.98 1.07e3.68

Lack of occupation Tsuchihashi et al54 2001 230 69 6 14 40% N/A HF rehospitalization 1 y 35.0% 2.59 1.22e5.48Faris et al55 2002 396 53 6 15

(15e86)26% 17% Rehospitalization 48 mo Rate, 1.7/

patient-year2.20 1.00e4.80

ClinicalIschemic etiology Babayan et al58 2003 493 N/A N/A N/A All-cause rehospitalization 1 year 56.6% 1.40 1.11e1.79

Evangelista et al56 2000 753 69 6 11.7(33e99)

2% 39.4% Multiple (O1) HF readmissions 1 y 29.2% 3.99 2.58e6.18

Ezekovitz et al59 2008 10,415 79.5 (73e85) 50.4% N/A All-cause rehospitalization 30 d 20.2% 1.36 1.12e1.67Lee et al26 2008 668 66 6 12 32.6% 100% Death or HF rehospitalization 2 y 29.6% 1.62 1.07e2.47

Previous myocardialrevascularization

Kossovsky et al23 2000 442 75.6 6 11.2 48% N/A HFrehospitalization

31 d N/A 3.00 1.50e6.10

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Previous MI Perna et al61 2005 184 64.5 6 13.2 39.7% N/A HF rehospitalization 3 y 75% 1.99 1.02e3.90Systolic dysfunction Babayan et al58 2003 493 N/A N/A N/A HF rehospitalization 12 mo N/A 2.44 1.46e4.08

Dauterman et al64 2001 782 78 54% 15% HF rehospitalization 1 y N/A 1.22 1.00e1.50Prior admission for

HF (anytime)Darze et al83 2007 198 69.4 6 13.5 44% N/A Death or rehospitalization 3 mo 46% 3.40 1.60e7.60

Prior admission forHF (last y)

Felker et al60 2003 906 68 30% 36% Death or rehospitalization 60 d 11.6% 1.14 1.06e1.23Rodriguez-Artalejo

et al842005 394 77.2 6 6.6 56.1% N/A HF rehospitalization 6 mo 35.0% 1.88 1.24e2.83

Tsuchihashi et al54 2001 230 69 6 14 40% N/A HF rehospitalization 1 y 35.0% 3.29 1.77e6.13Prior hospitalization

for any causeRodriguez-Artalejo

et al852006 371 77.2 6 6.7 58.2% N/A Rehospitalization 6 mo 36.4% 1.50 1.01e2.28

Prior hospitalizationfor any cause(last 6 mo)

Harjai et al32 2001 443 70 6 14 45% 36% HF rehospitalization 30 d 15.8% 1.30 1.20e1.40

Length of stay(O14 d)

Tsuchihashi et al54 2001 230 69 6 14 40% N/A HF rehospitalization 1 y 35.0% 3.21 1.22e8.46

Previous diagnosisof HF

Kossovsky et al23 2000 442 75.6 6 11.2 48% N/A HF rehospitalization 31 d N/A 4.50 2.30e8.80

History ofpercutaneouscoronaryintervention

Felker et al60 2003 906 68 30% 36% Death or rehospitalization 60 d 11.6% 1.46 1.00e2.12

SBP (admission level) Felker et al60 2003 906 68 30% 36% Death or rehospitalization 60 d 11.6% 0.88 (per10 mm Hgincrease)

0.82e0.97

SBP (clinical visit) Mielniczuk et al35 2008 183 55.5 6 15 49% N/A Death or HF rehospitalization 1 y 47% 0.95 (per1 mm Hgincrease)

0.92e0.98

Poor tissue perfusion Perna et al61 2005 184 64.5 6 13.2 39.7% N/A HF rehospitalization 3 y 75% 2.46 1.31e4.60Volume overload Bettencourt et al76 2004 182 73.0 6 11.0 53% N/A Death or rehospitalization 6 mo 43% 1.87 1.08e3.23Higher discharge

NYHAfunctional class

Armola and Topp.80 2001 179 70.2 N/A N/A HF rehospitalization 30 d 23.5% N/A N/AEvangelista et al56 2000 753 69 6 11.7

(33e99)2% 39.4% Multiple (O1) HF readmissions 1 y 29.2% 2.57 1.86e3.55

Feola et al81 2008 250 73 34% N/A Death or rehospitalization 6 mo 56.4% 1.50 1.06e2.11Koitabashi et al25 2005 427 65.8 6 13.5 36% N/A HF rehospitalization 34 mo 34.9% 1.63 1.11e2.39

NYHA functionalclass II vs I

Lee et al26 2008 668 66 6 12 32.6% 100% Death or HF rehospitalization 2 y 29.6% 2.20 1.24e3.89

NYHA functionalclass III-IV vs I

Lee et al26 2008 668 66 6 12 32.6% 100% Death or HF rehospitalization 2 y 29.6% 2.99 1.60e5.62

ImagingCardiothoracic ratio

(O0.50)Giamouzis et al135 2008 5,164 64 6 11 19% 9.5% HF rehospitalization 37 mo 41% 1.27 1.13e1.44

LVEF (discharge) Feola et al81 2008 250 73 34% N/A Death or rehospitalization 6 mo 56.4% 0.98 0.97e0.99LVEF (admission) Valle et al91 2008 315 77 6 9 53% N/A HF rehospitalization 6 mo 28.6% 0.983 (per 1%

increase)0.961e0.992

LVEF (clinical visit) Mielniczuk et al35 2008 183 55.5 6 15 49% N/A Death or HF rehospitalization 1 y 47% 0.95 (per 1%increase)

0.93e0.97

LAVi Dokainish et al140 2007 100 57 6 12 45% N/A Cardiac death or rehospitalizationfor worsening HF

527 6 47 d 46% N/A N/A

RVTDI Dokainish et al140 2007 100 57 6 12 45% N/A Cardiac death or rehospitalizationfor worsening HF

527 6 47 d 46% N/A N/A

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Table 4. (Continued)

Variable Source Year N Age (y) Female Nonwhite End Point Follow-UpEventRate HR 95% CI

Mitral peak earlydiastolic flowvelocity/TD earlydiastolic velocity

Dokainish et al140 2007 100 57 6 12 45% N/A Cardiac death or rehospitalizationfor worsening HF

527 6 47 d 46% N/A N/A

LaboratoryBNP (admission levelO200 pg/mL)

Niizeki et al102 2005 186 67 6 12 41% N/A Cardiac death or rehospitalizationfor worsening HF

18 mo 23.7% 2.41 1.02e5.73

NT-pro-BNP ($30%increase duringhospitalization)

Bettencourt et al76 2004 182 73.0 6 11.0 53% N/A Death orrehospitalization

6 mo 43% 5.96 3.23e11.01

NT-pro-BNP(in-hospitalvariation !30%)

Bettencourt et al76 2004 182 73.0 6 11.0 53% N/A Death or rehospitalization 6 mo 43% 2.03 1.14e3.64Pimenta et al95 2007 283 72.8 6 11.7 51.9% N/A Death or rehospitalization 6 mo 43.5% 2.68 1.54e4.68

NT-pro-BNP anddecreased renalfunction(in-hospitalvariation !30%)

Pimenta et al95 2007 283 72.8 6 11.7 51.9% N/A Death or rehospitalization 6 mo 43.5% 2.54 1.49e4.33

BNP (discharge) Feola et al81 2008 250 73 34% N/A Death or rehospitalization 6 mo 56.4% 1.006 1.004e1.009Ferreira et al105 2007 304 72.7 6 11.6 53.9% N/A Death or rehospitalization 6 mo 43% 2.02 1.28e3.20Koitabashi et al25 2005 427 65.8 6 13.5 36% N/A HF rehospitalization 34 mo 34.9% 1.002 1.001e1.003Logeart et al106 2004 114 69.4 6 14.4 30.7% N/A All-cause rehospitalization 6 mo 37% 1.25 (per

100 ng/Lincrease)

1.16e1.34

BNP (discharge level!250 pg/mL)

Valle et al91 2008 315 77 6 9 53% N/A HF rehospitalization 6 mo 28.6% 0.271 0.141e0.523

NT-pro-BNP anddecreased renalfunction (dischargevalue abovemedian)

Pimenta et al95 2007 283 72.8 6 11.7 51.9% N/A Death or rehospitalization 6 mo 43.5% 2.53 1.27e5.03

NT-proBNP(follow-up)

Pfister et al113 2008 290 64 (54e72) 20% N/A All-cause mortality,rehospitalizationfor ADHF and urgentcardiac transplantation

498 d 22.4% 1.9 (perincrease of1 SD of logNT-proBNP)

1.50e2.40

BNP (in-hospitaldecrease O30%vs no variation)

Ferreira et al105 2007 304 72.7 6 11.6 53.9% N/A Death or rehospitalization 6 mo 43% 2.24 1.37e3.66

BNP (in-hospitalincrease O30%vs decrease O30%)

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

BNP (predischargeBNP $360 pg/mLand decrease!50% duringhospitalization)

Cournot et al110 2008 157 83 6 6 49% N/A Death orrehospitalizationfor worsening HF

3, 12,and 18 mo

48%, 76%,84%

5.97 2.98e11.94

BNP (2 mo afterinitiation oftherapy)

Ishii et al112 2003 100 68 6 11 44% N/A Death or rehospitalization forworsening HF or MI

1 y 44% 3.11 (log BNPper 10-foldincrease)

1.61e6.01

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Vol.17No.1January

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BNP (O160 ng/L2 mo after initiationof therapy)

Ishii et al112 2003 100 68 6 11 44% N/A Death or rehospitalization forworsening HF or MI

1 y 44% 2.35 1.14e4.84

cTnT $0.10 ng/mL(within 24 h ofadmission)

Perna et al61 2005 184 64.5 6 13.2 39.7% N/A HF rehospitalization 3 y 75% 1.74 1.05e2.90

cTnT ($0.01 ng/mLduringhospitalizationfor ADHF)

Nishio et al119 2007 145 66.6 6 1.6 27% N/A Death or HF rehospitalization 1 y 19.3% 1.88 1.04e3.62

cTnT (2 mo afterinitiation oftherapy)

Ishii et al112 2003 100 68 6 11 44% N/A Death or hospitalization for worsening HF or MI

1 y 44% 2.07 (per 0.1mg/L increase)

1.43e3.01

cTnT (O0.001 mg/L2 mo after initiationof therapy)

Ishii et al112 2003 100 68 6 11 44% N/A Death or hospitalization forworsening HF or MI

1 y 44% 3.08 1.59e5.98

Hemoglobin(admission level)

Felker et al60 2003 906 68 30% 36% Death or rehospitalization 60 d 11.6% 0.88 (per 1 g/dLincrease)

0.82e0.97

Young et al86 2008 48,612 73.2 6 13.95 51.6% 25.9% Death or rehospitalization 60e90 d 36.2% 1.088 (per 1 g/dLdecrease)

1.044e1.134

Kosiborod et al87 2003 2,281 79 6 8 58% 10% All-cause rehospitalization 1 y N/A 1.02 (per 1%decrease)

1.01e1.03

Hemoglobin A1c Gerstein et al,90 2008 2,412 65.8 33.0% N/A Death or rehospitalizationfor worsening HF

36.7 mo N/A 1.25 (per 1%increase)

1.20e1.31

Serum sodium(admission level)

Gheorghiade et al88 2007 5,791 73 52% 26% Death or rehospitalization 60e90 d 36.4% 1.08 1.026e1.136

Glucose (admissionlevel, continuousvariable)

Berry et al89 2008 454 N/A 51% N/A Death or hospitalization forworsening HF

12 mo 36.3% 1.06, per2 mmol/Lincrease

1.02e1.10

Abnormal glucosetolerance

(admissionlevel $6 mmol/L)

Berry et al89 2008 454 N/A 51% N/A Death or hospitalization forworsening HF

12 mo 46% 1.61 1.07e2.41

Serum creatinine(admission level)

Rodriguez-Artalejo et al85

2006 371 77.2 6 6.7 58.2% N/A Rehospitalization 6 mo 36.4% 1.31 (per1 mg/dLincrease)

1.05e1.63

Rodriguez-Artalejo et al84

2005 394 77.2 6 6.6 56.1% N/A HF rehospitalization 6 mo 35.0% 1.33 (per1 mg/dLincrease)

1.09e1.64

Serum creatinine(discharge level)

Valle et al91 2008 315 77 6 9 53% N/A HF rehospitalization 6 mo 28.6% 1.39 (per1 mg/dLincrease)

1.09e1.77

\BUN (admissionlevel)

Felker et al60 2003 906 68 30% 36% Death or rehospitalization 60 d 11.6% 1.28 (per5 mg/dLincrease)

1.14e1.41

Harjai et al32 2001 443 70 6 14 45% 36% All-cause rehospitalization 30 d 26.2% 1.02 (per1 mg/dLincrease)

1.004e1.030

BUN (admission levelO28 mg/dL)

Shenkman et al92 2007 257 69 6 17 40% 32% Death or hospitalizationfor worsening HF

30 d 28.4% 1.83 1.03e3.24

CRP Anand et al124 2005 4,202 62.5 6 11 20% 9% First morbid eventa 12 mo 1.53 1.28e1.84Serum cystatin C Arimoto et al128 2005 140 66 6 13 38% N/A Cardiac death or HF

rehospitalization480 d 23% 1.94, per

increaseof one SD

1.29e6.64

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Table 4. (Continued)

Variable Source Year N Age (y) Female Nonwhite End Point Follow-UpEventRate HR 95% CI

Heart-type fatty acidebinding protein(O4.3 ng/mL)

Arimoto et al131 2005 179 67 6 12 39% N/A Cardiac death or HFrehospitalization

20 mo 25% N/A N/A

Niizeki et al102 2005 186 67 6 12 41% N/A Cardiac death or HFrehospitalization

18 mo 23.7% 5.42 2.20e13.32

Serum pentosidine(log)

Koyama et al129 2007 141 66 6 13 37.6% N/A Cardiac death or HFrehospitalization

479 d 22.7% 1.88 (per1 SDincrease)

1.23e2.69

Koyama et al130 2008 160 69 6 12 40.6% N/A Cardiac death or HFrehospitalization

872 d 30% 1.59 (per1 SDincrease)

1.11e1.29

sRAGE (log) Koyama et al130 2008 160 69 6 12 40.6% N/A Cardiac death or HFrehospitalization

872 d 30% 1.90 (per1 SDincrease)

1.16e3.09

Renin Nishio et al119 2007 145 66.6 6 1.6 27% N/A Death or HF rehospitalization 1 y 19.3% 2.38 1.22e5.05Serum PIPO124 ng/mL

Ruiz-Ruiz et al132 2007 111 73.4 6 7.9 46.8% N/A HF rehospitalization 21 mo 48.6% 1.015 1.006e1.024

ComorbidityHypertension(history)

Tsuchihashi et al54 2001 230 69 6 14 40% N/A HF rehospitalization 1 y 35.0% 1.99 1.06e3.72

Diabetes mellitus Berry et al89 2008 454 N/A 51% N/A Death or HF rehospitalization 12 mo 48% 1.88 1.22e2.88Blackledge et al24 2003 5,789 77.5 50% 12.6% Death or rehospitalization 1 y 49% 1.11 1.02e1.21Lee et al26 2008 668 66 6 12 32.6% 100% Death or HF rehospitalization 2 y 29.6% 1.80 1.25e2.60Rodriguez-

Artalejo et al842005 394 77.2 6 6.6 56.1% N/A HF rehospitalization 6 mo 35.0% 1.45 1.02e2.06

Anemia Berry et al159 2006 528 72 6 13 50% N/A Death or HF- rehospitalization 814 d 54% 1.40 1.04e1.89Ezekovitz et al59 2008 10,415 79.5 (73e85) 50.4% N/A All-cause rehospitalization 30 d 20.2% 1.30 1.00e1.69

Anemia (Htc #24%) Kosiborod et al161 2005 50,405 79.4 6 0.05 59% 15.6% HF rehospitalization 1 y N/A 1.21 (Htc#24% vs HtcO44%)

1.04e1.38

Anemia (admissionHb !12 g/dL)

Luthi et al160 2006 955 75.4 6 12.8 45.7% N/A All-cause rehospitalization 30 d 13.4% 1.60 1.00e2.58

Hyponatremia(admission Na!135 mEq/L)

Milo-Cotter et al162 2008 331 75.8 6 10.3 49% N/A Death or HF- rehospitalization 6 mo 46% N/A N/A

Concomitant AMI Blackledge et al24 2003 5,789 77.5 50% 12.6% Death or rehospitalization 1 y 49% 1.13 1.02e1.24Concomitantparoxysmal AF

Koitabashi et al25 2005 427 65.8 6 13.5 36% N/A HF rehospitalization 34 mo 34.9% 2.30 1.30e4.05

Hypertension Ezekovitz et al59 2008 10,415 79.5 (73e85) 50.4% N/A All-cause rehospitalization 30 d 20.2% 0.74 0.60e0.91Blackledge et al24 2003 5,789 77.5 50% 12.6% Death or rehospitalization 1 y 49% 0.88 0.82e0.94

Stroke (yes vs no) Blackledge et al24 2003 5,789 77.5 50% 12.6% Death or rehospitalization 1 y 49% 1.26 1.12e1.41Cerebrovasculardisease

Darze et al83 2007 198 69.4 6 13.5 44% N/A Death or rehospitalization 3 mo 46% 4.10 1.60e10.90

Hyponatremia(!136 mEq/L)

Darze et al83 2007 198 69.4 6 13.5 44% N/A Death or rehospitalization 3 mo 46% 3.70 1.60e8.50

Renal insufficiency Blackledge et al24 2003 5,789 77.5 50% 12.6% Death or rehospitalization 1 y 49% 1.57 1.44e1.72Faris et al55 2002 396 53 6 15

(15e86)26% 17% Rehospitalization 48 mo Rate, 1.7/

patient-year2.80 1.20e6.60

McCellan et al164 2004 755 75.7 6 10.9(30e100)

60.2% 29.7% Rehospitalization 30 d 32% 1.70 1.18e2.44

Acute renal failure Darze et al83 2007 198 69.4 6 13.5 44% N/A Death or rehospitalization 3 mo 46% 2.70 1.40e5.30

74

JournalofCardiacFailure

Vol.17No.1January

2011

WRF (both a $25%and a $0.3 mg/dLincrease in sCrfrom admission)

Metra et al165 2008 318 68 6 11 40% N/A CV death or HFrehospitalization

480 d 48% 1.47 1.13e1.81

Atrial dibrillation Blackledge et al24 2003 5,789 77.5 50% 12.6% Death or rehospitalization 1 y 49% 0.94 0.88e1.00Acute pulmonary

embolismDarze et al83 2007 198 69.4 6 13.5 44% N/A Death or rehospitalization 3 mo 46% 4.00 1.10e15.10

Depression Faris et al55 2002 396 53 6 15(15e86)

26% 17% Rehospitalization 48 mo Rate, 1.7/patient-year

1.25 1.07e1.90

Depression (major) Jiang et al174 2001 357 63 6 13 38% 30% HF rehospitalization 1 y 60.5% 2.98 1.17e7.59COPD Harjai et al32 2001 443 70 6 14 45% 36% HF rehospitalization 30 d 15.8% 2.20 1.10e4.50Charlson comorbidity

indexRodriguez-

Artalejo et al852006 371 77.2 6 6.7 58.2% N/A Rehospitalization 6 mo 36.4% 1.13 (per

1-pointincrease)

1.02e1.26

Social/PsychologicReadiness-for-

dischargecriteria

Kossovsky et al23 2000 442 75.6 6 11.2 48% N/A HF rehospitalization 31 d N/A 1.23 1.06e1.42

Follow-up plan Armola and Topp80 2001 179 70.2 N/A N/A HF rehospitalization 30 d 23.5% 0.33 N/APoor follow-up visits

(!1/mo or none)Tsuchihashi et al54 2001 230 69 6 14 40% N/A HF rehospitalization 1 y 35.0% 4.87 2.01e11.78

HF performancemeasures

Chung et al200 2008 400 N/A N/A N/A All-cause rehospitalization 6 mo N/A 0.74 0.57e0.97

Primary carephysician

Evangelista et al56 2000 753 69 6 11.7(33e99)

2% 39.4% Multiple (O1) HFreadmissions

1 y 29.2% 2.41 1.57e3.67

Home-basedtelemanagementprogram

Giordano et al180 2008 460 57 6 10 15% N/A HF rehospitalization 1 y 19% 0.50 0.34e0.73

SF-36 physicalsummary score

Rodriguez-Artalejo et al84

2005 394 77.2 6 6.6 56.1% N/A HF rehospitalization 6 mo 35.0% 1.59 1.12e2.26

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.

RiskFactors

forHospita

lizatio

nin

HeartFailure

�Giamouzis

etal

75