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Age, Clinical Characteristics and Outcomes of Patients With Acute Decompensated Heart Failure: Insights from the PROTECT Trial
S76 Journal of Cardiac Failure Vol. 18 No. 8S August 2012 and reduced LVEF (LVEF!45%) were noted in 32% and 68% of patients. Characteristics and outcome of CPR in patients with AHFS by pre-arrest LVEF are depicted in Table 1. The primary arrest mode(PAM) was identified to be cardiac in 91% of cases and respiratory in 9% of cases. The first pulseless rhythm before initiating CPR included VF/VT (18.1%), Asystole (29%), and PEA (52.9%). CPR was successfully performed in 24.8% of attempts but only 13% of patients requiring CPR survived to discharge. Conclusions: Left ventricular systolic dysfunction was associated with lower rates of survival to hospital discharge compared with preserved systolic function, although both groups had similar rates of initially successful resuscitation.
244 Heart Rate Variability Trend Predict 12- Month Cardiac Event-Free Survival and Health Related Quality of Life in Patients With Heart Failure Tsuey-Yuan Huang1, Hui-Chu Yu2, Mei-Ling Yeh3, Ming-Fen Tsai1, Shiow-Li Hwang3; 1Nursing, Chang Gung University of Science and Technology, Taoyuan, Taiwan; 2Nursing, National Taiwan University Hospital, Taipei, Taiwan; 3Nursing, National Taipei University of Nursing & Health Sciences, Taipei, Taiwan Introduction: Heart rate variability (HRV) is a noninvasive method for assessing autonomic nervous system tone. Increased sympathetic nerve system activity decreases HRV, on the contrary, increased parasympathetic activity increases it. Heart failure (HF) patients typically exhibit decreased HRV. Decrease in HRV reflects increased risk for poor outcome from HF. Specific Aims: To determine the effect of HRV trend on HF patients’ cardiac event-free survival and health related quality of life (HRQOL). Method: Longitudinal study design was used to examine the individual HRV trajectory effect on disease outcome. Clinical characteristics, HRV (using CardioCard PC Based Holter System and both time domain and frequency domains), and disease outcomes (by monthly phone interviews) were measured at the baseline assessment, three months, and 12 months later at outpatient clinics. Latent class growth model (LCGM) was used to examine the relationship of functional trajectories. Individuals were classified into 3 subgroups based on their categorized HRV trajectory over three time points. Based on the Bayesian information criteria, three-class model was used to determine the effect of HRV trends on HF patients’ cardiac event-free survival over 12 months. Kaplan-Meier survival analysis revealed the 12-month follow-up disease outcomes. Results: One hundred and seventeen HF patients were recruited (mean age 62.8 6 13.0 yrs; 79% male; 39% NYHA III/IV; 48% preserved systolic function HF). Group labeled “constant good,” (n588) was characterized by a good HRV status at the baseline and remained stable in 12 months (B50.00765); group labeled “recovery,” (n522) was characterized by a poor HRV status at the baseline with a significantly positive linear slope (B5 0.38722); group labeled “getting worse,” (n57) was characterized by a good HRV status at the baseline with a significantly negative linear slope (B5 -0.18087). The cumulative incidence of first event in HF patients with getting worse HRV trajectory were statistically higher than that in patients with constant good and recovery HRV trajectory (log-rank test, pvalue50.046). Patients in getting worse HRV trajectory group also had worst HRQOL compared with the other two groups. Conclusion: Cardiac event-free survival can be predicted by HRV trajectory in HF patients.
disease, paralysis, myocardial infarction, peripheral vascular disease, neurological disorders, depression and dementia than any other profile (all p!1x10-7). Relative to patients fitting the “common variations” profile, patients within the latter profiles all had higher inpatient costs: “metabolic syndrome” 5 10.2% greater costs, “endocrine/hemotologic” 5 16.7% greater costs and “vascular/ischemic” 5 21.4% greater costs (all p!1x10-7). Conclusions: Recognizing comorbid illness profiles among HF patients may be a helpful mechanism to identify subgroups at higher risk for accruing greater inpatient costs.
246 Age, Clinical Characteristics and Outcomes of Patients With Acute Decompensated Heart Failure: Insights from the PROTECT Trial M. Metra1, K. Chiswell2, M. Fiuzat2, V. Lazzarini2, J. Horton2, B. Davison3, J. Cleland4, P. Ponikowski5, J. Teerlink6, A. Voors7, M. Givertz8, G. Mansoor9, B. Massie6, G. Cotter3, C. O’Connor2; 1University of Brescia, Italy; 2Duke Clinical Research Institute, Durham, NC; 3Momentum Research, Durham, NC; 4University of Hull, United Kingdom; 5Clinical Military Hospital, Wroclaw, Poland; 6 University of California, San Francisco, CA; 7University Medical Center Groningen, The Netherlands; 8Brigham and Women’s Hospital, Boston, MA; 9 Merck Research Laboratories, Rahway, NJ Background: Most patients with heart failure (HF) are elderly but under-represented in clinical trials. We assessed the association between age and the clinical characteristics and outcomes of the 2033 patients with acute decompensated HF and mild to moderate renal dysfunction enrolled in the PROTECT trial. Methods: The population was divided into five age groups (!60 years, n5387; 60-65 years, n5246; 66-74 years, n5565; 75-79 years, n5373; O79 years, n5462). The association of age with outcomes was analyzed by multivariable Cox regression analysis. Results: Median age was 72 years (IQR, 62-79 years). Increasing age was associated with a greater proportion of white patients, women, patients with a history of hypertension and atrial fibrillation, as well as with a higher systolic blood pressure, left ventricular ejection fraction and BUN, and lower eGFR and hemoglobin at baseline (all p ! 0.0001). Older patients were less likely to receive beta-blockers and aldosterone antagonists either before admission (p!0.05) or at discharge (p!0.0001). In multivariable analysis, after adjustment for all the previous variables, age was an independent predictor of worse outcomes with hazard ratios (HRs) for each 5 years of increasing age of 1.14 [95% confidence interval (CI), 1.04-1.26, p 50.007] for Day-30 mortality, 1.12 [95%CI, 1.05-1.20, p50.001] for Day-180 mortality, and 1.17 [95%CI, 1.11-1.24, p ! 0.0001], for Day-30 mortality/rehospitalisations for cardiovascular or renal causes. Conclusion: Older patients have different clinical characteristics and a lower use of neurohormonal antagonists, compared to younger patients with HF. Age was independently associated with worse outcomes with a 12-14% increase in the risk of death and 17% increase in the risk of death or rehospitalization for each 5 years increment.These data may suggest that clinical trials and therapies may be targeted differently in older versus younger patients.
247 245 Comorbid Illness Profiles Predict Greater Costs: An Analysis of 417,477 Heart Failure Admissions Christopher S. Lee1, Julie T. Bidwell1, Quin E. Denfeld1, Ruth Masterson Creber2, Jill M. Gelow1, James O. Mudd1, Harleah G. Buck3; 1Oregon Health & Science University, Portland, OR; 2University of Pennsylvania, Philadelphia, PA; 3The Pennsylvania State University, University Park, PA Introduction: There is significant heterogeneity in inpatient costs associated with heart failure (HF) hospitalizations, and HF patients are known to have multiple complicating comorbidities. Hypothesis 1: Among patients admitted for HF, several comorbid illness profiles can be identified. Hypothesis 2: Specific comorbid illness profiles will be associated with a gradient in inpatient costs. Methods: We used a nationally-representative sample from the 2009 Agency for Healthcare Research and Quality (AHRQ) Health Care Utilization Project for this study of 417,477 adult HF hospitalizations. Latent class mixture modeling was used to identify common and distinct profiles among 32 complementary AHRQ and Deyo-Charlson comorbidity measures, with observed profiles labeled according to dominant characteristics. To account for sampling weights and nesting of patients under hospitals, two-level generalized linear mixed modeling was used to quantify differences in inpatient cost by profile. Results: Four distinct comorbid illness profiles were identified (model entropy 5 0.69; LMRT 5 34,817, p!1x10-7). The largest profile, “common variations” (46.6%) included patients with few and diffuse comorbidities. The “metabolic syndrome” profile (19%) was characterized by a greater percentage of patients with diabetes without complications, hypertension, obesity and chronic pulmonary disorders than any other profile (all p!1x10-7). The “endocrine/hematologic” profile (29.3%) had a greater percentage of patients with renal disease, anemia, fluid and electrolyte imbalances, diabetes with complications and coagulopathy than any other profile (all p!1x10-7). The “vascular/ischemic” profile (5.1%) had a greater percentage of patients with cerebrovascular
Allograft Left Ventricular Hypertrophy after Cardiac Transplantation Kimberly Parks1, Cristina Castrillo2, Angela Canteli-Alvarez2, Malissa Wood1, Asma Mahmud1, Rajeev Malhotra1, Marc J. Semigran1; 1Massachusetts General Hospital, Boston, MA; 2Instituto de Formacion e Investigacion, University Hospital Marques de Valdecilla, Santander, Spain Background: Left ventricular hypertrophy (LVH) is common after cardiac transplantation and its etiology is poorly understood. LVH after transplant is associated with worse outcomes. Risk factors for development of LVH in native hearts has been well studied, but no definitive risk factors in transplanted hearts have been identified. The aim of the study was to assess risk factors associated with development of LVH in transplanted hearts. Methods: 62 patients (13 female, 49 male, age 52 6 19 who underwent cardiac transplantation between 2002 and 2007 at a single center were retrospectively reviewed LVH was defined as a left ventricular posterior wall or septal wall thickness of greater than 11mm by linear measurements. We reviewed baseline allograft wall thickness prior to procurement and again at year 1, 2 and 3 post transplantation (PT). Using logistic regression, an analysis was performed to assess the relationship of the following variables with the presence of LVH: presence of rejection, mean arterial pressure, allograft vasculopathy, type of antihypertensive agent used (ACE-I, ARB, BB, CCB and alpha blockers), type of immunosuppressive agents used, recipient and donor age and sex, creatinine, GFR, BSA and presence of diabetes mellitus. Results: 31 (50%) patients had LVH at post transplant year 1 (IVS 13.3 6 1.8 mm, PWT 12.9 6 1.4) and 2 (IVS 13.3 6 1.4 mm, PWT 12.9 6 0.9). By year three, 39 (63%) patients had LVH (IVS 13.5 6 1.7, PWT 12.9 6 1.1). Serum Creatinine at year 1 (1.66 6 0.14 in those with LVH vs 1.41 6 0.2 in those without LVH) and use of ACE-I in year 1 predicted LVH in year 3. There was no association between any other variable and development of LVH. 4 of the 62 donor hearts assessed had LVH prior to implantation into the recipient. Of those, 2 had normal wall thickness at three years post transplant and 2 had persistent LVH. Conclusion: The development of LVH is common after cardiac transplantation. Higher serum creatinine and ACE-I use at PT year 1 was associated with the presence of LVH at PT year 3.
244 Heart Rate Variability Trend Predict 12- Month Cardiac Event-Free Survival and Health Related Quality of Life in Patients With Heart Failure Tsuey-Yuan Huang1, Hui-Chu Yu2, Mei-Ling Yeh3, Ming-Fen Tsai1, Shiow-Li Hwang3; 1Nursing, Chang Gung University of Science and Technology, Taoyuan, Taiwan; 2Nursing, National Taiwan University Hospital, Taipei, Taiwan; 3Nursing, National Taipei University of Nursing & Health Sciences, Taipei, Taiwan Introduction: Heart rate variability (HRV) is a noninvasive method for assessing autonomic nervous system tone. Increased sympathetic nerve system activity decreases HRV, on the contrary, increased parasympathetic activity increases it. Heart failure (HF) patients typically exhibit decreased HRV. Decrease in HRV reflects increased risk for poor outcome from HF. Specific Aims: To determine the effect of HRV trend on HF patients’ cardiac event-free survival and health related quality of life (HRQOL). Method: Longitudinal study design was used to examine the individual HRV trajectory effect on disease outcome. Clinical characteristics, HRV (using CardioCard PC Based Holter System and both time domain and frequency domains), and disease outcomes (by monthly phone interviews) were measured at the baseline assessment, three months, and 12 months later at outpatient clinics. Latent class growth model (LCGM) was used to examine the relationship of functional trajectories. Individuals were classified into 3 subgroups based on their categorized HRV trajectory over three time points. Based on the Bayesian information criteria, three-class model was used to determine the effect of HRV trends on HF patients’ cardiac event-free survival over 12 months. Kaplan-Meier survival analysis revealed the 12-month follow-up disease outcomes. Results: One hundred and seventeen HF patients were recruited (mean age 62.8 6 13.0 yrs; 79% male; 39% NYHA III/IV; 48% preserved systolic function HF). Group labeled “constant good,” (n588) was characterized by a good HRV status at the baseline and remained stable in 12 months (B50.00765); group labeled “recovery,” (n522) was characterized by a poor HRV status at the baseline with a significantly positive linear slope (B5 0.38722); group labeled “getting worse,” (n57) was characterized by a good HRV status at the baseline with a significantly negative linear slope (B5 -0.18087). The cumulative incidence of first event in HF patients with getting worse HRV trajectory were statistically higher than that in patients with constant good and recovery HRV trajectory (log-rank test, pvalue50.046). Patients in getting worse HRV trajectory group also had worst HRQOL compared with the other two groups. Conclusion: Cardiac event-free survival can be predicted by HRV trajectory in HF patients.
disease, paralysis, myocardial infarction, peripheral vascular disease, neurological disorders, depression and dementia than any other profile (all p!1x10-7). Relative to patients fitting the “common variations” profile, patients within the latter profiles all had higher inpatient costs: “metabolic syndrome” 5 10.2% greater costs, “endocrine/hemotologic” 5 16.7% greater costs and “vascular/ischemic” 5 21.4% greater costs (all p!1x10-7). Conclusions: Recognizing comorbid illness profiles among HF patients may be a helpful mechanism to identify subgroups at higher risk for accruing greater inpatient costs.
246 Age, Clinical Characteristics and Outcomes of Patients With Acute Decompensated Heart Failure: Insights from the PROTECT Trial M. Metra1, K. Chiswell2, M. Fiuzat2, V. Lazzarini2, J. Horton2, B. Davison3, J. Cleland4, P. Ponikowski5, J. Teerlink6, A. Voors7, M. Givertz8, G. Mansoor9, B. Massie6, G. Cotter3, C. O’Connor2; 1University of Brescia, Italy; 2Duke Clinical Research Institute, Durham, NC; 3Momentum Research, Durham, NC; 4University of Hull, United Kingdom; 5Clinical Military Hospital, Wroclaw, Poland; 6 University of California, San Francisco, CA; 7University Medical Center Groningen, The Netherlands; 8Brigham and Women’s Hospital, Boston, MA; 9 Merck Research Laboratories, Rahway, NJ Background: Most patients with heart failure (HF) are elderly but under-represented in clinical trials. We assessed the association between age and the clinical characteristics and outcomes of the 2033 patients with acute decompensated HF and mild to moderate renal dysfunction enrolled in the PROTECT trial. Methods: The population was divided into five age groups (!60 years, n5387; 60-65 years, n5246; 66-74 years, n5565; 75-79 years, n5373; O79 years, n5462). The association of age with outcomes was analyzed by multivariable Cox regression analysis. Results: Median age was 72 years (IQR, 62-79 years). Increasing age was associated with a greater proportion of white patients, women, patients with a history of hypertension and atrial fibrillation, as well as with a higher systolic blood pressure, left ventricular ejection fraction and BUN, and lower eGFR and hemoglobin at baseline (all p ! 0.0001). Older patients were less likely to receive beta-blockers and aldosterone antagonists either before admission (p!0.05) or at discharge (p!0.0001). In multivariable analysis, after adjustment for all the previous variables, age was an independent predictor of worse outcomes with hazard ratios (HRs) for each 5 years of increasing age of 1.14 [95% confidence interval (CI), 1.04-1.26, p 50.007] for Day-30 mortality, 1.12 [95%CI, 1.05-1.20, p50.001] for Day-180 mortality, and 1.17 [95%CI, 1.11-1.24, p ! 0.0001], for Day-30 mortality/rehospitalisations for cardiovascular or renal causes. Conclusion: Older patients have different clinical characteristics and a lower use of neurohormonal antagonists, compared to younger patients with HF. Age was independently associated with worse outcomes with a 12-14% increase in the risk of death and 17% increase in the risk of death or rehospitalization for each 5 years increment.These data may suggest that clinical trials and therapies may be targeted differently in older versus younger patients.
247 245 Comorbid Illness Profiles Predict Greater Costs: An Analysis of 417,477 Heart Failure Admissions Christopher S. Lee1, Julie T. Bidwell1, Quin E. Denfeld1, Ruth Masterson Creber2, Jill M. Gelow1, James O. Mudd1, Harleah G. Buck3; 1Oregon Health & Science University, Portland, OR; 2University of Pennsylvania, Philadelphia, PA; 3The Pennsylvania State University, University Park, PA Introduction: There is significant heterogeneity in inpatient costs associated with heart failure (HF) hospitalizations, and HF patients are known to have multiple complicating comorbidities. Hypothesis 1: Among patients admitted for HF, several comorbid illness profiles can be identified. Hypothesis 2: Specific comorbid illness profiles will be associated with a gradient in inpatient costs. Methods: We used a nationally-representative sample from the 2009 Agency for Healthcare Research and Quality (AHRQ) Health Care Utilization Project for this study of 417,477 adult HF hospitalizations. Latent class mixture modeling was used to identify common and distinct profiles among 32 complementary AHRQ and Deyo-Charlson comorbidity measures, with observed profiles labeled according to dominant characteristics. To account for sampling weights and nesting of patients under hospitals, two-level generalized linear mixed modeling was used to quantify differences in inpatient cost by profile. Results: Four distinct comorbid illness profiles were identified (model entropy 5 0.69; LMRT 5 34,817, p!1x10-7). The largest profile, “common variations” (46.6%) included patients with few and diffuse comorbidities. The “metabolic syndrome” profile (19%) was characterized by a greater percentage of patients with diabetes without complications, hypertension, obesity and chronic pulmonary disorders than any other profile (all p!1x10-7). The “endocrine/hematologic” profile (29.3%) had a greater percentage of patients with renal disease, anemia, fluid and electrolyte imbalances, diabetes with complications and coagulopathy than any other profile (all p!1x10-7). The “vascular/ischemic” profile (5.1%) had a greater percentage of patients with cerebrovascular
Allograft Left Ventricular Hypertrophy after Cardiac Transplantation Kimberly Parks1, Cristina Castrillo2, Angela Canteli-Alvarez2, Malissa Wood1, Asma Mahmud1, Rajeev Malhotra1, Marc J. Semigran1; 1Massachusetts General Hospital, Boston, MA; 2Instituto de Formacion e Investigacion, University Hospital Marques de Valdecilla, Santander, Spain Background: Left ventricular hypertrophy (LVH) is common after cardiac transplantation and its etiology is poorly understood. LVH after transplant is associated with worse outcomes. Risk factors for development of LVH in native hearts has been well studied, but no definitive risk factors in transplanted hearts have been identified. The aim of the study was to assess risk factors associated with development of LVH in transplanted hearts. Methods: 62 patients (13 female, 49 male, age 52 6 19 who underwent cardiac transplantation between 2002 and 2007 at a single center were retrospectively reviewed LVH was defined as a left ventricular posterior wall or septal wall thickness of greater than 11mm by linear measurements. We reviewed baseline allograft wall thickness prior to procurement and again at year 1, 2 and 3 post transplantation (PT). Using logistic regression, an analysis was performed to assess the relationship of the following variables with the presence of LVH: presence of rejection, mean arterial pressure, allograft vasculopathy, type of antihypertensive agent used (ACE-I, ARB, BB, CCB and alpha blockers), type of immunosuppressive agents used, recipient and donor age and sex, creatinine, GFR, BSA and presence of diabetes mellitus. Results: 31 (50%) patients had LVH at post transplant year 1 (IVS 13.3 6 1.8 mm, PWT 12.9 6 1.4) and 2 (IVS 13.3 6 1.4 mm, PWT 12.9 6 0.9). By year three, 39 (63%) patients had LVH (IVS 13.5 6 1.7, PWT 12.9 6 1.1). Serum Creatinine at year 1 (1.66 6 0.14 in those with LVH vs 1.41 6 0.2 in those without LVH) and use of ACE-I in year 1 predicted LVH in year 3. There was no association between any other variable and development of LVH. 4 of the 62 donor hearts assessed had LVH prior to implantation into the recipient. Of those, 2 had normal wall thickness at three years post transplant and 2 had persistent LVH. Conclusion: The development of LVH is common after cardiac transplantation. Higher serum creatinine and ACE-I use at PT year 1 was associated with the presence of LVH at PT year 3.