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Original Investigation |

Factors Identified as Precipitating Hospital Admissions for Heart Failure and Clinical Outcomes:  Findings From OPTIMIZE-HF FREE

Gregg C. Fonarow, MD; William T. Abraham, MD; Nancy M. Albert, RN, PhD; Wendy Gattis Stough, PharmD; Mihai Gheorghiade, MD; Barry H. Greenberg, MD; Christopher M. O’Connor, MD; Karen Pieper, MS; Jie Lena Sun, MS; Clyde W. Yancy, MD; James B. Young, MD; OPTIMIZE-HF Investigators and Hospitals
[+] Author Affiliations

Author Affiliations: Department of Medicine, UCLA [University of California, Los Angeles] Medical Center (Dr Fonarow); Division of Cardiology, Ohio State University, Columbus (Dr Abraham); George M. and Linda H. Kaufman Center for Heart Failure (Dr Albert) and Department of Cardiovascular Medicine, Heart Failure Section (Dr Young), Cleveland Clinic Foundation, Cleveland, Ohio; Department of Medicine (Dr Stough) and Division of Cardiology (Dr O’Connor), Duke University Medical Center, Durham, North Carolina; Department of Clinical Research, Campbell University School of Pharmacy, Research Triangle Park, North Carolina (Dr Stough); Division of Cardiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois (Dr Gheorghiade); Department of Medicine, University of California San Diego Medical Center (Dr Greenberg); Duke Clinical Research Institute, Durham (Dr O’Connor and Mss Pieper and Sun); and Department of Medicine, The University of Texas Southwestern Medical Center, Dallas (Dr Yancy). Dr Yancy is now with Baylor Heart and Vascular Institute, Baylor University Medical Center, Dallas.Group Information: A list of the Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients With Heart Failure (OPTIMIZE-HF) hospitals and investigators was published in JAMA. 2007;297(1):61-70.


Arch Intern Med. 2008;168(8):847-854. doi:10.1001/archinte.168.8.847.
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Published online

Background  Few studies have examined factors identified as contributing to heart failure (HF) hospitalization, and, to our knowledge, none has explored their relationship to length of stay and mortality. This study evaluated the association between precipitating factors identified at the time of HF hospital admission and subsequent clinical outcomes.

Methods  During 2003 to 2004, 259 US hospitals in OPTIMIZE-HF submitted data on 48 612 patients, with a prespecified subgroup of at least 10% providing 60- to 90-day follow-up data. Identifiable factors contributing to HF hospitalization were captured at admission and included ischemia, arrhythmia, nonadherence to diet or medications, pneumonia/respiratory process, hypertension, and worsening renal function. Multivariate analyses were performed for length of stay, in-hospital mortality, 60- to 90-day follow-up mortality, and death/rehospitalization.

Results  Mean patient age was 73.1 years, 52% of patients were female, and mean ejection fraction was 39.0%. Of 48 612 patients, 29 814 (61.3%) had 1 or more precipitating factors identified, with pneumonia/respiratory process (15.3%), ischemia (14.7%), and arrhythmia (13.5%) being most frequent. Pneumonia (odds ratio, 1.60), ischemia (1.20), and worsening renal function (1.48) were independently associated with higher in-hospital mortality, whereas uncontrolled hypertension (0.74) was associated with lower in-hospital mortality. Ischemia (1.52) and worsening renal function (1.46) were associated with a higher risk of follow-up mortality. Uncontrolled hypertension as a precipitating factor was associated with lower postdischarge death/rehospitalization (hazard ratio, 0.71).

Conclusions  Precipitating factors are frequently identified in patients hospitalized for HF and are associated with clinical outcomes independent of other predictive variables. Increased attention to these factors, many of which are avoidable, is important in optimizing the management of HF.

Trial Registration  clinicaltrials.gov Identifier: NCT00344513

Figures in this Article

Heart failure (HF) is the leading cause of hospitalization among US adults older than 65 years, and these hospitalizations contribute substantially to the high costs of the disease. There are 3.6 million hospitalizations with HF as the primary or a secondary cause each year in the United States.1,2 Hospitalizations for HF are also associated with substantial morbidity and mortality; the likelihood of death and rehospitalization is considerably greater than for a comparable period of chronic but stable HF.3,4 Understanding precipitants that contribute to exacerbations of HF and lead to HF admissions, particularly those that are avoidable, is of great importance to clinicians and could favorably influence HF disease management.

A number of factors have been identified that may acutely exacerbate HF and contribute to hospitalization for it. These include arrhythmias, myocardial ischemia, respiratory infection, uncontrolled hypertension, and nonadherence to medications and diet.39 However, relatively few studies have examined the frequency at which these factors are present among patients hospitalized for HF.59 Most available data are limited by being obtained from relatively small numbers of patients hospitalized at a single center or derived from observations of patients enrolled in clinical trials, which have select enrollment criteria and closer monitoring than under usual care settings.59 Analyses of large representative patient populations from all regions of the country and all types of hospitals are critical in providing insight into the frequency of factors that precipitate HF hospitalizations. Furthermore, whether there is an association between factors identified at the time of hospital admission as contributing to HF exacerbation and subsequent clinical outcomes has not been previously studied, to our knowledge.

The Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients With Heart Failure (OPTIMIZE-HF) is a registry and performance-improvement program for patients hospitalized for HF.10 The objectives of this analysis of OPTIMIZE-HF data were to determine the frequency at which various factors contributing to HF hospitalization are identified and to improve the understanding of whether and to what extent these factors influence clinical outcomes, including hospital length of stay, in-hospital mortality, early postdischarge mortality, and death/rehospitalization.

OPTIMIZE-HF is a comprehensive hospital-based registry and process-of-case improvement program designed to provide optimal medical care and education to patients hospitalized for HF. The OPTIMIZE-HF program has been described in detail elsewhere1013 and will be briefly summarized.

PATIENT ELIGIBILITY

Hospital teams used HF case-ascertainment methods identical to those of The Joint Commission.14 Patients qualified for enrollment if they were hospitalized for episodes of new or worsening HF as the primary cause of admission or if significant HF symptoms developed during hospitalization for another primary diagnosis, with HF being the primary discharge diagnosis.1013 Consecutive patients were enrolled irrespective of their ventricular function, including systolic dysfunction documented by a left ventricular ejection fraction less than 40%, HF symptoms in the setting of preserved left ventricular systolic function (diastolic dysfunction HF), or HF without left ventricular function measurement.1013

From March 1, 2003, to December 31, 2004, 48 612 patients hospitalized at 259 centers in the United States were enrolled in the OPTIMIZE-HF registry. All regions of the United States were represented, and institutions from community hospitals to large tertiary medical centers participated.10,11 A prespecified patient subgroup (targeted to be ≥ 10% of the total number) was followed up for 60 to 90 days after discharge for the collection of outcomes data, as previously described.10 Sites had the option of participating in the follow-up data collection, and the protocol was approved by each participating center's institutional review board or through use of a central institutional review board. Ninety-one hospitals provided 60- to 90-day follow-up data, and this cohort was demographically similar to patients in the overall registry.1013 Automated electronic data checks were used to prevent out-of-range entry or duplicate patients. A database audit was performed, on the basis of predetermined criteria, of a random sample of 5% of the first 10 000 patients verified against source documents.10,11 Written informed consent was obtained before enrollment from patients who participated in the follow-up data collection. Participants were screened for inclusion before hospital discharge or identified from administrative databases subsequent to discharge.

The registry captured data on important characteristics (demographic, pathophysiologic, and clinical), treatment patterns, and outcomes of patients hospitalized for HF by means of the Web-based case report form. Data on factors contributing to exacerbation of the patients' HF were prespecified and collected as follows: ischemia/acute coronary syndromes (ACSs), uncontrolled hypertension, pneumonia/respiratory process, worsening renal function, arrhythmia, nonadherence–diet, nonadherence–medications, and other. More than 1 factor could be selected if applicable. Admission staff, medical staff, or both recorded race/ethnicity, usually as the patient was registered. Previous studies in patients hospitalized for HF have suggested differences in characteristics and outcomes based on race/ethnicity. The registry coordinating center was Outcome Sciences Inc (Cambridge, Massachusetts).

STATISTICAL ANALYSIS

All statistical analyses were performed independently at the Duke Clinical Research Institute. The data are reported as mean and standard deviation for continuous variables and as percentages of nonmissing values for categorical variables. Patient characteristics and evidence-based treatments at hospital discharge were compared by Pearson χ2 test for categorical variables and Wilcoxon rank sum test for continuous variables. Multivariate models of in-hospital death, length of hospital stay, postdischarge mortality, and postdischarge death or rehospitalization were developed to be used for consistent covariate adjustment across all studies as previously described.1013 The types of models were logistic for in-hospital mortality, general linear modeling for length of stay, Cox proportional hazards for postdischarge mortality, and logistic for postdischarge mortality and rehospitalization (date of rehospitalization was not available for survival modeling). The model-development process was similar for all 4 outcomes and used stepwise and backward variable selection methods. The linearity assumption for continuous measures was evaluated by means of restricted cubic spline transformations. When needed, appropriate transformations such as piecewise linear splines were applied. P < .05 was used for both entry and remaining in the model. The potential covariates were preselected, with 45 for in-hospital mortality, 39 for length of stay, 19 for postdischarge mortality, and 70 for postdischarge mortality or rehospitalization (posted at http://www.optimize-hf.org).1013 To test the association of precipitating factors and clinical outcomes in the final adjusted models, indicators for the presence or absence of each precipitating factor were added to the previously developed models. Additional models were constructed to compare risk-adjusted outcomes for patients with no vs 1 or more precipitating factors identified. SAS version 8.2 statistical software (SAS Institute Inc, Cary, North Carolina) was used for all statistical analyses.

CLINICAL CHARACTERISTICS OF PATIENTS HOSPITALIZED FOR HF

The OPTIMIZE-HF enrolled a total of 48 612 patients hospitalized for HF at 259 academic and community hospitals of varying size from all regions of the United States. Mean patient age was 73.1 years; 52% of patients were female and 74% were white (Table 1). Comorbidities were frequent and included hypertension in 71% of patients, diabetes mellitus in 42%, and chronic obstructive pulmonary disease in 28%. The cause of HF was ischemic in 46% of enrolled patients, and the mean left ventricular ejection fraction was 39.0%. Of patients assessed, 48.8% had documented left ventricular systolic dysfunction and 51.2% had HF with preserved systolic function. The follow-up cohort included 5791 patients, whose characteristics were similar to those of the overall registry (Table 1).

Table Graphic Jump LocationTable 1. Patient Characteristics and Comorbidities

One or more precipitating factors for HF admission were identified in 61.3% of patients. The frequencies of these individual factors are shown in Table 1 for the hospital and follow-up cohorts. The precipitating factors of pneumonia/respiratory processes (15.3%), ischemia/ACSs (14.7%), arrhythmia (13.5%), and uncontrolled hypertension (10.7%) were identified as the most common in the hospital cohort. Nonadherence to medications was identified in 8.9% and nonadherence to diet in 5.2% of patients. Two or more precipitating factors were identified in 9310 patients (19.2%) (Table 1). Specific precipitating factors were identified with greater frequency among the follow-up cohort.

IN-HOSPITAL OUTCOMES

There were 1834 in-hospital deaths reported among 48 612 enrolled patients (3.8%). The in-hospital mortality rates (unadjusted) by precipitating factors are shown in the Figure. Patients in whom no precipitating factor was identified were at modestly lower risk of in-hospital mortality than those with 1 or more precipitating factors risk (3.4% vs 4.0%; adjusted odds ratio, 0.88; 95% confidence interval, 0.78-1.00; P = .046). Risk-adjusted in-hospital mortality was significantly increased when admission was related to pneumonia/respiratory process, ischemia/ACSs, or worsening renal function (Table 2). Admission with uncontrolled hypertension or nonadherence to diet as a contributing factor was associated with a lower risk-adjusted in-hospital mortality. There were no significant interactions between the precipitating factors within the models. Other covariates that were independently associated with in-hospital mortality included patient age, admission systolic blood pressure and heart rate, serum sodium and creatinine levels, and comorbidities such as chronic obstructive pulmonary disease, liver disease, and peripheral vascular disease (data posted at http://www.optimize-hf.org). The median hospital length of stay was 4.0 days (25th-75th interquartile range, 3.0-7.0) and mean length of stay was 6.4 days (SD, 85.2 days). Mean length of stay was shortest (5.1 days) in patients with uncontrolled hypertension and nonadherence to medications and longest in patients with worsened renal function (6.9 days). In multivariate analysis, arrhythmia, pneumonia/respiratory process, and worsening renal function were associated with significantly longer risk-adjusted length of stay, whereas uncontrolled hypertension, nonadherence to diet, and nonadherence to medications were associated with shorter risk-adjusted length of stay (Table 2).

Place holder to copy figure label and caption
Figure.

Unadjusted in-hospital mortality rates by precipitating factors for heart failure admission. ACSs indicates acute coronary syndromes.

Graphic Jump Location
Table Graphic Jump LocationTable 2. Precipitating Factors and Multivariate Risk-Adjusted In-Hospital Clinical Outcomes
60- TO 90-DAY POSTDISCHARGE OUTCOMES

During the 60- to 90-day period after hospital discharge, the follow-up cohort experienced 465 deaths (8.3%), occurring a median of 42.0 days (25th-75th interquartile range, 24.0-66.0) after discharge. Rehospitalization within the follow-up period occurred in 1715 patients (29.6%). The combined end point of death/rehospitalization was met in 36.0% of patients. The rates of death and death/rehospitalization for patients with each of the precipitating factors are shown in Table 3. Patients without a precipitating factor identified had risk-adjusted postdischarge mortality similar to that of patients with 1 or more precipitating factors (6.9% vs 8.9%; adjusted odds ratio, 0.84; 95% confidence interval, 0.66-1.07; P = .15). Risk-adjusted postdischarge mortality was significantly increased in patients whose admission was precipitated by ischemia/ACSs or worsening renal function, independent of other predictive variables (Table 4; additional data posted at http://www.optimize-hf.org). Among patients with uncontrolled hypertension, there was significantly decreased risk-adjusted in-hospital mortality and significantly decreased mortality or readmission at 60 to 90 days after discharge. None of the other precipitating factors were associated with significantly higher or lower postdischarge death/rehospitalization.

Table Graphic Jump LocationTable 3. Precipitating Factors and 60- to 90-Day Unadjusted Postdischarge Outcomes
Table Graphic Jump LocationTable 4. Precipitating Factors and 60- to 90-Day Multivariate Risk-Adjusted Postdischarge Outcomes

The OPTIMIZE-HF has demonstrated that, among a large, representative population of patients admitted to the hospital for HF, 1 or more exacerbating factors contributing to HF hospitalization were identified in most patients. The contributing factors of pneumonia/respiratory processes (15.3%), ischemia (14.7%), arrhythmia (13.5%), and uncontrolled hypertension (10.7%) were identified the most frequently. Certain of these precipitating factors were independently associated with worse clinical outcomes. Pneumonia/respiratory processes and worsening renal function as precipitating factors for hospitalization identified patients at significantly increased risk of greater length of stay and in-hospital mortality. Ischemia and worsening renal function as precipitating factors for admission were associated with increased risk of mortality at 60 to 90 days after discharge. Nonadherence to medications, nonadherence to diet, and uncontrolled hypertension each were associated with shorter stay and lower in-hospital mortality. These findings provide important insights into the factors that contribute to admission for HF and their influence on subsequent outcomes.

Worsening of renal function during hospitalization for HF has previously been identified as being associated with worse outcomes.15,16 The OPTIMIZE-HF data further extend those findings and demonstrate that HF admission precipitated by worsening renal function is also associated with significantly worse patient outcomes, both in-hospital and after discharge. The finding that pneumonia or another respiratory process as an admission precipitant is associated with increased mortality is consistent with previous studies of administrative databases showing that patients hospitalized for HF and chronic obstructive pulmonary disease or pneumonia have higher mortality risk.17 Improved in-hospital and postdischarge prognosis among patients with HF admitted with highly elevated systolic blood pressure has been previously reported and parallels the finding that uncontrolled hypertension as a precipitant of HF admission is associated with better outcomes.11 Patients with decompensation of HF resulting from uncontrolled hypertension can usually be readily stabilized in the hospital with blood pressure control with relatively short length of stay and lower risk of adverse near-term outcomes.11

Patient adherence to dietary restrictions and evidence-based medications is a cornerstone of HF disease management, and nonadherence to medications has been associated with increased risk of hospitalization and mortality in outpatients with chronic HF.2,3,18 Patients with nonadherence to medications or diet are likely to be admitted with excessive sodium retention, which was the leading decompensation factor in 55% of 975 patients in a retrospective audit at 2 large midwestern medical centers.19 These patients may more readily achieve compensation in response to salt restriction, adjustment of diuretics, and provision of medications during the inpatient hospitalization. It should be noted that patients with nonadherence to medications or diet as an admission precipitant were at high adjusted risk of 60- to 90-day postdischarge mortality and death/rehospitalization similar to the overall HF population. Patients identified as nonadherent to medications would be expected to be counseled during the index hospitalization regarding the importance of adherence to their medical regimen and thus may be less likely, at least in the short term, to repeat the medication nonadherence that precipitated a recent HF hospitalization. It should also be emphasized that the use of evidence-based HF medications in eligible patients at discharge is strongly associated with improved postdischarge outcomes.12

Previous studies have assessed the frequency at which precipitating factors are present among hospitalized patients with HF.59 A single-center study using retrospective chart review reported on causal factors among 435 patients hospitalized for HF.5 The most commonly identified factors for HF exacerbations leading to hospitalization in that study were acute chest pain in 33% of patients, respiratory tract infection in 16%, uncontrolled hypertension in 15%, and nonadherence to medications in 15%. An analysis of 328 HF hospitalizations from 161 unique patients referred to a hospital HF service for transplantation in Italy showed the most common potential causative factors to be the presence of arrhythmias in 24% of hospitalizations, infections in 23%, poor adherence in 15%, and angina in 14%.6 Study of 179 consecutive patients admitted to a teaching hospital in Germany identified dietary sodium excess in 43% of patients, nonadherence to medications in 24%, ischemia in 13%, and uncontrolled hypertension in 8%.7 Another single-center study reported on precipitating factors leading to decompensation of HF in 101 patients of low socioeconomic status using systematic patient interviews and medical record review.8 The most common precipitating factors identified were lack of adherence to a low-sodium diet, medications, or both in 64% of patients. Uncontrolled hypertension was an identified cause in 44% and cardiac arrhythmias in 29% of patients. A multicenter study of HF precipitating factors involving 768 patients with systolic HF enrolled in the Randomized Evaluation of Strategies for Left Ventricular Dysfunction Pilot Study included a total of 323 episodes of worsening of HF in 180 patients, whether resulting in an HF hospitalization or not, during 43 weeks of follow-up.9 Factors implicated in worsening of HF status in that study included nonadherence to salt restriction (22% of patients); pulmonary infections (20%); use of antiarrhythmic agents (15%), arrhythmias (13%), and calcium-channel blockers (13%); and inappropriate reductions in HF therapy (10%).

Each of these studies had 1 or more major limitations, including retrospective nature of the data collection, limited number of patients studied, involvement of a single center, referral of patients to a specialty service or enrollment of select patients in a double-blind trial of systolic HF therapy, and inability to define whether the precipitant was a cause or an effect of HF exacerbation.9 Identifying precipitants of HF exacerbation within the context of a clinical trial raises the issue of patient selection bias because these patients are more likely to adhere to medical advice and receive closer follow-up than in a usual care setting. Thus, the patients in these previous studies may not represent the general hospitalized HF population, limiting the applicability of the findings.

The strengths of the present study include that it was performed with the use of a systematic approach to identify factors contributing to HF hospitalization and was conducted in 259 US hospitals from all regions of the country with a well-defined cohort of patients.1013 Another important feature of the present study is that it is the first, to our knowledge, that assessed the relationship between precipitating factors and clinical outcomes independent of other prognostic variables. Understanding whether and to what extent precipitants of HF hospitalization influence length of stay, mortality, and rehospitalization risk is important because this knowledge may help guide clinicians in designing more effective management strategies for hospitalized patients with HF and to prevent HF hospitalizations.3,20

National HF guidelines recommend that patients hospitalized for HF undergo evaluation for precipitating factors and suggest that proper detection and treatment of precipitating factors is an important part of the management of acute decompensated HF.3 These recommendations were level of evidence C, expert opinion only. These OPTIMIZE-HF data lend further support to these recommendations and provide data demonstrating that certain precipitating factors are associated with clinical outcomes independent of other established prognostic factors. Patients identified as being at higher risk of adverse outcomes may benefit from closer monitoring during hospitalization and more frequent follow-up after discharge. Several of these precipitating factors, including nonadherence to diet and medications, may be influenced by optimizing patient education techniques and disease management strategies.2,3,20 Because pneumonia/respiratory process was the most common precipitating factor and was associated with worse outcomes, every effort should be made to prevent pneumonia in patients with HF, including rigorous influenza and pneumococcal vaccination.3 Risk of ischemia and ACSs may be reduced with antiplatelet agents, statin therapy, and, possibly, revascularization in eligible patients.2,3 Disease management programs and treatment plans for patients with HF should include appropriate strategies for these concomitant conditions, and exacerbation of these conditions should be avoided to the extent possible.2,3 Future studies should be designed to prospectively test interventions targeting these contributing factors in this high-risk HF patient population.

This analysis of OPTIMIZE-HF data may be influenced by several limitations. Precipitating factors of HF were abstracted from documentation in the medical record and may have been underreported. It is known that patient interview or clinician impression may be insufficient to detect poor adherence to medications or diet.21 Follow-up data were obtained in a subset of patients and were limited to 60 to 90 days. Participation in OPTIMIZE-HF was voluntary, and sites received payment to defray costs associated with data collection. These findings may not apply to hospitals that differ from OPTIMIZE-HF hospitals in patient characteristics or care patterns, although a recent study22 suggests that Medicare patients enrolled in OPTIMIZE-HF had demographics similar to those of Medicare patients hospitalized for HF in the nation as a whole. Given the overall large number of patients observed, some differences, though statistically significant, may not be clinically relevant. Also, despite multivariate analyses, we cannot exclude that residual measured and unmeasured confounding accounts for some of these observations. Despite these limitations, this analysis provides new insights into the factors contributing to HF hospitalizations from a large representative data set of patients hospitalized for HF from all regions of the country and including patients with preserved systolic function and multiple comorbidities.

Deterioration of clinical status leading to HF hospitalizations is frequently accompanied by identifiable factors that contribute to decompensations beyond the underlying HF disease state. Among patients admitted for an HF hospitalization in OPTIMIZE-HF, close to two-thirds of patients had 1 or more precipitating factors identified. The most common factors identified as precipitants of HF exacerbations necessitating hospitalization include arrhythmia, ischemia, pneumonia/respiratory process, and nonadherence to diet and medications. Furthermore, factors contributing to HF admission identify patients at higher and lower risk of in-hospital and postdischarge adverse outcomes, independent of other predictive variables. Increased attention to these factors, many of which are avoidable, is important in optimizing the management of HF.

Correspondence: Gregg C. Fonarow, MD, Ahmanson-UCLA Cardiomyopathy Center, UCLA Medical Center, 10833 LeConte Ave, Room 47-123 CHS, Los Angeles, CA 90095-1679 (gfonarow@mednet.ucla.edu).

Accepted for Publication: November 11, 2007.

Author Contributions: Dr Fonarow had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Fonarow, Abraham, Albert, Stough, Greenberg, and O’Connor. Acquisition of data: Fonarow, Abraham, Albert, Greenberg, and Young. Analysis and interpretation of data: Fonarow, Abraham, Albert, Gheorghiade, Greenberg, O’Connor, Pieper, Sun, Yancy, and Young. Drafting of the manuscript: Fonarow and Greenberg. Critical revision of the manuscript for important intellectual content: Fonarow, Abraham, Albert, Stough, Gheorghiade, Greenberg, O’Connor, Pieper, Sun, Yancy, and Young. Statistical analysis: Fonarow, Pieper, and Sun. Obtained funding: Fonarow. Administrative, technical, and material support: Fonarow and Abraham. Study supervision: Fonarow, Greenberg, and O’Connor.

Financial Disclosure: Dr Fonarow has received research grants from Amgen, Biosite, Bristol-Myers Squibb, Boston Scientific/Guidant, GlaxoSmithKline, Medtronic, Merck & Co, Pfizer, Sanofi-Aventis, Scios Inc, and the National Institutes of Health (NIH). He is or has been on the speakers' bureau or has received honoraria in the past 5 years from Amgen, AstraZeneca, Biosite, Bristol-Myers Squibb, Boston Scientific/Guidant, GlaxoSmithKline, Kos, Medtronic, Merck & Co, NitroMed, Pfizer, Sanofi-Aventis, Schering-Plough, Scios Inc, St Jude Medical, Takeda, and Wyeth. He is or has been a consultant for Biosite, Bristol-Myers Squibb, Boston Scientific/Guidant, GlaxoSmithKline, Medtronic, Merck & Co, NitroMed, Orqis Medical, Pfizer, Sanofi-Aventis, Schering-Plough, Scios Inc, and Wyeth. Dr Abraham has received research grants from Amgen, Biotronik, CHF Solutions, GlaxoSmithKline, Heart Failure Society of America, Medtronic, Myogen, the NIH, Orqis Medical, Otsuka Maryland Research Institute, Paracor, and Scios Inc. He is or has been a consultant or on the speakers' bureau for Amgen, AstraZeneca, Boehringer-Ingelheim, CHF Solutions, GlaxoSmithKline, Guidant Corp, Medtronic, Merck & Co, Pfizer, ResMed, Respironics, Scios Inc, and St Jude Medical. He is on the advisory board of CardioKine, CardioKinetix Inc, CHF Solutions, Department of Veterans Affairs Cooperative Studies Program, Inovise, the NIH, and Savacor Inc. He has received honoraria from AstraZeneca, Boehringer-Ingelheim, GlaxoSmithKline, Guidant Corp, Medtronic, Merck & Co, Pfizer, ResMed, Respironics, Scios Inc, and St Jude Medical. Dr Albert is a consultant for GlaxoSmithKline and Medtronic. She is also on the speakers' bureau for GlaxoSmithKline, Medtronic, NitroMed, and Scios Inc and is employed by the Cleveland Clinic Foundation. Dr Stough has received research grants from Actelion, GlaxoSmithKline, Medtronic, Otsuka, and Pfizer. She is a consultant or on the speakers' bureau for Abbott, AstraZeneca, GlaxoSmithKline, Medtronic, Novacardia, Otsuka, Protein Design Labs, RenaMed, Sigma Tau, and Scios Inc. She has received honoraria from Abbott, AstraZeneca, GlaxoSmithKline, Medtronic, and Pfizer. Dr Gheorghiade has received research grants from the NIH, Otsuka, Sigma Tau, Merck & Co, and Scios Inc. He is or has been a consultant for Debbio Pharm, Errekappa Terapeutici, GlaxoSmithKline, PDL, and Medtronic. He has received honoraria from Abbott, AstraZeneca, GlaxoSmithKline, Medtronic, Otsuka, Protein Design Lab,Scios Inc, and Sigma Tau. Dr Greenberg has received research grant support from Amgen, Cardiodynamics, GlaxoSmithKline, Millennium, Novacardia, Otsuka, Pfizer, Sanofi-Aventis, and Titan. He is on the speaker's bureau/consultant for Amgen, AstraZeneca, GlaxoSmithKline, Guidant Corp, Medtronic, Merck & Co, NitroMed, Pfizer, Remon Medical Technologies, and Scios Inc. He is an advisory board member for CHF Solutions, GlaxoSmithKline, and NitroMed. He has received honoraria from AstraZeneca, GlaxoSmithKline, Medtronic, Merck & Co, NitroMed, Novartis, Pfizer, and Scios Inc. Dr O’Connor has received research grant support from the NIH. He is on the speakers' bureau and/or a consultant for Amgen, AstraZeneca, Bristol-Myers Squibb, GlaxoSmithKline, Guidant Corp, Medtronic, Merck & Co, NitroMed, Novartis, Otsuka, Pfizer, and Scios Inc. He has received honoraria from GlaxoSmithKline, Pfizer, and Otsuka. Mss Pieper and Sun are employees of the Duke Clinical Research Institute. Dr Yancy has received research grants from Cardiodynamics, GlaxoSmithKline, Scios Inc, Medtronic, and NitroMed. He is also a consultant or on the speakers' bureau for AstraZeneca, Cardiodynamics, GlaxoSmithKline, Medtronic, NitroMed, Novartis, and Scios Inc. He is on the advisory board for CHF Solutions, the Food and Drug Administration cardiovascular device panel, and the NIH. He has received honoraria from AstraZeneca, Cardiodynamics, GlaxoSmithKline, Medtronic, Novartis, and Scios Inc. Dr Young has received research grants from Abbott, Acorn, Amgen, Artesion Therapeutics, AstraZeneca, Biosite, GlaxoSmithKline, Guidant Corp, Medtronic, MicroMed, the NIH, Scios Inc, Vasogen, and World Heart. He is a consultant for Abbott, Acorn, Amgen, Biomax Canada, Biosite, Boehringer-Ingelheim, Bristol-Myers Squibb, Cotherix, Edwards Lifescience, GlaxoSmithKline, Guidant Corp, Medtronic, MicroMed, Novartis, Paracor, Proctor & Gamble, Protemix, Scios Inc, Sunshine, Thoratec, Transworld Medical Corp, Vasogen, Viacor, and World Heart.

Funding/Support: GlaxoSmithKline funded the OPTIMIZE-HF registry under the guidance of the OPTIMIZE-HF Steering Committee.

Role of the Sponsor: GlaxoSmithKline was involved in the design and conduct of the OPTIMIZE-HF registry and funded data collection and management through Outcome Inc, data management and statistical analyses through the Duke Clinical Research Institute, and administrative and material support by Accel Health. The sponsor was not involved in the management, analysis, or interpretation of data or the preparation of the manuscript. GlaxoSmithKline did review the manuscript before submission.

Additional Information: All data analyses were performed independently at the Duke Clinical Research Institute by Mss Pieper and Sun, with the input of the OPTIMIZE-HF Steering Committee.

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Ghali  JKKadakia  SCooper  RFerlinz  J Precipitating factors leading to decompensation of heart failure: traits among urban blacks. Arch Intern Med 1988;148 (9) 2013- 2016
PubMed
Tsuyuki  RT McKelvie  RSAmold  JMO  et al.  Acute precipitants of congestive heart failure exacerbations. Arch Intern Med 2001;161 (19) 2337- 2342
PubMed
Fonarow  GCAbraham  WTAlbert  NM  et al.  Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients with Heart Failure (OPTIMIZE-HF): rationale and design. Am Heart J 2004;148 (1) 43- 51
PubMed
Gheorghiade  MAbraham  WTAlbert  NM  et al. OPTIMIZE-HF Investigators and Coordinators, Systolic blood pressure at admission, clinical characteristics, and outcomes in patients hospitalized with acute heart failure. JAMA 2006;296 (18) 2217- 2226
PubMed
Fonarow  GCAbraham  WTAlbert  NM  et al. OPTIMIZE-HF Investigators and Hospitals, Association between performance measures and clinical outcomes for patients hospitalized with heart failure. JAMA 2007;297 (1) 61- 70
PubMed
Fonarow  GCAbraham  WTAlbert  NM  et al. OPTIMIZE-HF Investigators and Hospitals, Influence of a performance-improvement initiative on quality of care for patients hospitalized with heart failure: results of the Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients with Heart Failure (OPTIMIZE-HF). Arch Intern Med 2007;167 (14) 1493- 1502
PubMed
Joint Commission on Accreditation of Healthcare Organizations, Specification Manual for National Implementation of Hospital Core Measures.  Oakbrook Terrace, IL Joint Commission on Accreditation of Healthcare Organizations2004;
Forman  DEButler  JWang  Y  et al.  Incidence, predictors at admission, and impact of worsening renal function among patients hospitalized with heart failure. J Am Coll Cardiol 2004;43 (1) 61- 67
PubMed
Gottlieb  SSAbraham  WButler  J  et al.  The prognostic importance of different definitions of worsening renal function in congestive heart failure. J Card Fail 2002;8 (3) 136- 141
PubMed
Holguin  FFolch  ERedd  SCMannino  DM Comorbidity and mortality in COPD-related hospitalizations in the United States, 1979 to 2001. Chest 2005;128 (4) 2005- 2011
PubMed
Granger  BBSwedberg  KEkman  I  et al. CHARM investigators, Adherence to candesartan and placebo and outcomes in chronic heart failure in the CHARM programme: double-blind, randomized, controlled clinical trial. Lancet 2005;366 (9502) 2005- 2011
PubMed
Bennett  SJHuster  GABaker  SL  et al.  Characterization of the precipitants of hospitalization for heart failure decompensation. Am J Crit Care 1998;7 (3) 168- 174
PubMed
McAlister  FALawson  FMTeo  KKArmstrong  PW A systematic review of randomized trials of disease management programs in heart failure. Am J Med 2001;110 (5) 378- 384
PubMed
Haynes  RBTaylor  DWSackett  DLGibson  ESBernholz  CDMukherjee  J Can simple clinical measurements detect patient noncompliance? Hypertension 1980;2 (6) 757- 764
PubMed
Curtis  LHHernandez  AFGreiner  MA  et al.  Validity of a national heart failure quality of care registry: comparison of Medicare patients in OPTIMIZE-HF vs. non-OPTIMIZE-HF hospitals [abstract]. Circulation 2007;115 (21) 595

Figures

Place holder to copy figure label and caption
Figure.

Unadjusted in-hospital mortality rates by precipitating factors for heart failure admission. ACSs indicates acute coronary syndromes.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Patient Characteristics and Comorbidities
Table Graphic Jump LocationTable 2. Precipitating Factors and Multivariate Risk-Adjusted In-Hospital Clinical Outcomes
Table Graphic Jump LocationTable 3. Precipitating Factors and 60- to 90-Day Unadjusted Postdischarge Outcomes
Table Graphic Jump LocationTable 4. Precipitating Factors and 60- to 90-Day Multivariate Risk-Adjusted Postdischarge Outcomes

References

Rosamond  WFlegal  KFriday  G  et al. American Heart Association Statistics Committee and Stroke Statistics Subcommittee, Heart disease and stroke statistics—2007 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee [published correction appears in Circulation. 2007; February 6 115(5):e172]. Circulation 2007;115 (5) e69- e171
PubMed10.1161/CIRCULATIONAHA.106.179918
Hunt  SAAbraham  WTChin  MH  et al. American College of Cardiology/American Heart Association, ACC/AHA 2005 guideline update for the diagnosis and management of chronic heart failure in the adult: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Writing Committee to Update the 2001 Guidelines for the Evaluation and Management of Heart Failure). J Am Coll Cardiol 2001;38 (7) 2101- 2113
PubMed
Heart Failure Society of America, HFSA 2006 comprehensive heart failure practice guideline. J Card Fail 2006;12 (1) e1- e122
PubMed10.1016/j.cardfail.2005.11.005
Gheorghiade  MZannad  FSopko  G  et al. International Working Group on Acute Heart Failure Syndromes, Acute heart failure syndromes: current state and framework for future research. Circulation 2005;112 (25) 3958- 3968
PubMed
Chin  MHGoldman  L Factors contributing to the hospitalization of patients with congestive heart failure. Am J Public Health 1997;87 (4) 643- 648
PubMed
Opasich  CFebo  ORiccardi  G  et al.  Concomitant factors of decompensation in chronic heart failure. Am J Cardiol 1996;78 (3) 354- 357
PubMed
Michalsen  AKonig  GThimme  W Preventable causative factors leading to hospital admission with decompensated heart failure. Heart 1998;80 (5) 437- 441
PubMed
Ghali  JKKadakia  SCooper  RFerlinz  J Precipitating factors leading to decompensation of heart failure: traits among urban blacks. Arch Intern Med 1988;148 (9) 2013- 2016
PubMed
Tsuyuki  RT McKelvie  RSAmold  JMO  et al.  Acute precipitants of congestive heart failure exacerbations. Arch Intern Med 2001;161 (19) 2337- 2342
PubMed
Fonarow  GCAbraham  WTAlbert  NM  et al.  Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients with Heart Failure (OPTIMIZE-HF): rationale and design. Am Heart J 2004;148 (1) 43- 51
PubMed
Gheorghiade  MAbraham  WTAlbert  NM  et al. OPTIMIZE-HF Investigators and Coordinators, Systolic blood pressure at admission, clinical characteristics, and outcomes in patients hospitalized with acute heart failure. JAMA 2006;296 (18) 2217- 2226
PubMed
Fonarow  GCAbraham  WTAlbert  NM  et al. OPTIMIZE-HF Investigators and Hospitals, Association between performance measures and clinical outcomes for patients hospitalized with heart failure. JAMA 2007;297 (1) 61- 70
PubMed
Fonarow  GCAbraham  WTAlbert  NM  et al. OPTIMIZE-HF Investigators and Hospitals, Influence of a performance-improvement initiative on quality of care for patients hospitalized with heart failure: results of the Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients with Heart Failure (OPTIMIZE-HF). Arch Intern Med 2007;167 (14) 1493- 1502
PubMed
Joint Commission on Accreditation of Healthcare Organizations, Specification Manual for National Implementation of Hospital Core Measures.  Oakbrook Terrace, IL Joint Commission on Accreditation of Healthcare Organizations2004;
Forman  DEButler  JWang  Y  et al.  Incidence, predictors at admission, and impact of worsening renal function among patients hospitalized with heart failure. J Am Coll Cardiol 2004;43 (1) 61- 67
PubMed
Gottlieb  SSAbraham  WButler  J  et al.  The prognostic importance of different definitions of worsening renal function in congestive heart failure. J Card Fail 2002;8 (3) 136- 141
PubMed
Holguin  FFolch  ERedd  SCMannino  DM Comorbidity and mortality in COPD-related hospitalizations in the United States, 1979 to 2001. Chest 2005;128 (4) 2005- 2011
PubMed
Granger  BBSwedberg  KEkman  I  et al. CHARM investigators, Adherence to candesartan and placebo and outcomes in chronic heart failure in the CHARM programme: double-blind, randomized, controlled clinical trial. Lancet 2005;366 (9502) 2005- 2011
PubMed
Bennett  SJHuster  GABaker  SL  et al.  Characterization of the precipitants of hospitalization for heart failure decompensation. Am J Crit Care 1998;7 (3) 168- 174
PubMed
McAlister  FALawson  FMTeo  KKArmstrong  PW A systematic review of randomized trials of disease management programs in heart failure. Am J Med 2001;110 (5) 378- 384
PubMed
Haynes  RBTaylor  DWSackett  DLGibson  ESBernholz  CDMukherjee  J Can simple clinical measurements detect patient noncompliance? Hypertension 1980;2 (6) 757- 764
PubMed
Curtis  LHHernandez  AFGreiner  MA  et al.  Validity of a national heart failure quality of care registry: comparison of Medicare patients in OPTIMIZE-HF vs. non-OPTIMIZE-HF hospitals [abstract]. Circulation 2007;115 (21) 595

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