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Risk stratification for in-hospital mortality in heart failure using classification and regression tree (CART) methodology: analysis of 33,046 patients in the ADHERE registry
The 7th Annual Scientific Meeting
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HFSA
S79
288
289
Superiority of Big Endothelin-1 and Endothelin-1 over Natriuretic Peptides To Predict Survival in Severe Congestive Heart Failure Michel F. Rousseau,1 Ronald Van Beneden,2 Olivier Gurne,1 Philippe L. Selvais,1 Annie R. Robert,3 Sylvie A. Ahn,1 Hubert G. Pouleur,1 Jean Marie Ketelslegers2—1Division of Cardiology, University of Louvain, Brussels, Belgium; 2Diabetes and Nutrition Unit, University of Louvain, Brussels, Belgium; 3School of Public Health, University of Louvain, Brussels, Belgium
Prognostic Importance of Diabetes Mellitus in Patients with Acute Decompensated Heart Failure Andrew J. Burger,1 Darlene P. Horton,2 Doron Aronson3—1Cardiology Department, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA; 2, Scios Inc, Sunnyvale, CA; 3Cardiology Department, Rambam Medical Center, Haifa, Israel
Plasma concentrations of atrial and brain natriuretic peptides (ANP, BNP), of their N-terminal pro-peptides, of endothelin-1 (ET-1) and big ET-1 have prognostic significance in congestive heart failure (CHF) but their respective predictive value remains controversial and had never been directly compared in a group of patients with severe CHF. Methods: We analyzed the predictive value of plasma N-ANP 1-25, N-ANP 68-98, BNP, N-BNP, ET-1 and big ET-1 in 47 patients with severe CHF (NYHA III-IV, mean ejection fraction: 20 ⫾ 6%, mean age: 66 ⫾ 8 years, etiology:ischemic n ⫽ 38, non ischemic n ⫽ 9). Thirty healthy subjects served as controls. Thirty-four patients (72%) died during the first 34 months of follow-up and 1 had heart transplant. The mean follow-up for the 12 remaining alive patients was 81 ⫾ 15 months. Results: Geometric mean (and range) baseline values were: N-ANP 1-25:1903 pg/mL (6237148), N-ANP 68-98:2507 pg/mL (709-9075), BNP by radioimmunoassay (RIA):75 pg/mL (15-192), N-BNP:1521 fMol/mL (598-5491), ET-1:10.0 pg/mL (8.7-11.6) and big ET-1 by RIA:12.0 pg/mL (5.8-22.9). In patients with severe CHF, all neurohormones were significantly higher than in controls (p ⬍ 0.001). There were no significant differences in ejection fraction (20% vs 20%), in incidence of ischemic etiology (83% vs 79%) and in age (64 years vs 68 years) between survivors and non survivors. Using Cox univariate analysis, all neurohormones but BNP (p ⫽ 0.14) significantly predicted survival. By multivariate analysis, big ET-1 and ET-1 independently predicted survival (improvement χ2: 7.5 and 4.6, p ⬍ 0.01 and ⬍0.05). Using upper tertiles as cutpoints (big ET-1 ⱖ 12 pg/mL and/or ET-1 ⬎ 9 pg/mL), we defined 2 subgroups of CHF patients with a different prognosis (5 years survival : 55% vs 18%, p ⬍ 0.01). Conclusions: Big ET-1 and ET-1 are strong independent predictors of survival in patients with severe CHF. Further, their predicting value is better than natriuretic peptides or their pro-peptides. These markers identify a subgroup of patients with a very high mortality risk, which could improve the selection process for more aggressive therapies.
Background: The risk of congestive heart failure (CHF) and idiopathic cardiomyopathy is strongly increased in diabetes mellitus. Natural history studies on patients with CHF have suggested that the presence of diabetes mellitus adversely affects prognosis. However, it is not known whether the effect of diabetes mellitus extends to patients with severe decompensated heart failure. Methods: We studied 498 patients (mean age 62 ⫾ 14 years) enrolled in the VMAC (Vasodilation in the Management of Acute CHF) trial. All patients were admitted to the hospital for decompensated heart failure and had severe heart failure requiring intravenous vasoactive therapy. Results: Of 498 patients, 263 (47.3%) had the previous diagnosis of diabetes mellitus. During a mean follow-up of 6 months, 113 patients (22.7%) died. A Cox proportional-hazards model, adjusted for age, gender, weight, baseline creatinine, primary etiology of heart failure stratified as ischemic or nonischemic, left ventricular ejection fraction, type of vasoactive therapy (nesiritide or nitroglycerin) and use of beta-blockers, digoxin, and amiodarone, showed a significant association between diabetes mellitus and worse survival following hospital discharge (RR ⫽ 1.8, 95% CI 1.2-2.7,p ⫽ 0.007). The Figure shows adjusted survival curves for patient with and without diabetes mellitus. Conclusion: Diabetes mellitus is common among patients admitted with decompensated heart failure. Patients with diabetes mellitus admitted with decompensated heart failure appear to have a worse survival compared with patients without diabetes mellitus. These results suggest that diabetes-related biological differences in the progression of heart failure are present even with advanced disease.
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Risk Stratification for In-Hospital Mortality in Heart Failure Using Classification and Regression Tree (CART) Methodology: Analysis of 33,046 Patients in the ADHERE Registry Gregg C. Fonarow,1 Kirkwood F. Adams,2 William T. Abraham,3 ADHERE Investigators4—1Ahmanson-UCLA Cardiomyopathy Center, UCLA Medical Center, Los Angeles, CA; 2Division of Cardiology, University of North Carolina at Chapel Hill, Chapel Hill, NC; 3Division of Cardiology, Ohio State University Medical Center, Columbus, OH; 4ADHERE Registry, Scios, Inc, Sunnyvale, CA
Trends in Survival for the U.S. Elderly Following a First Admission for Heart Failure Paul A. Heidenreich1—1Medicine, VA Palo Alto Health Care System, Palo Alto, CA
Background: Acute decompensated heart failure (AHF) is a leading cause of hospital admission and is associated with significant in-hospital mortality risk. Although a number of individual clinical variables have been identified as being associated with increased in-hospital mortality, no clinically practical method of risk stratification for AHF patients has previously been described. Methods: ADHERE (Acute Decompensated HEart Failure National REgistry) is a national prospective, observation registry of patients hospitalized with AHF. Over 250 hospitals participated, including community, tertiary, and academic medical centers. Data from the first 33,046 patients enrolled in the ADHERE registry were analyzed. Details of medical history, clinical presentation, laboratories, medical management, and health outcomes were collected through hospital discharge medical record review. Classification and regression tree (CART) methodology using recursive partitioning techniques for in-hospital mortality to assess individual variables as potential branch points on a risk assessment decision tree was applied using 45 variables of interest. Results: Patients had a mean age of 72.5 (±13.9), 52% female, and 59% with CAD. In-hospital mortality was 4.19%. CART identified variables providing the greatest discrimination between survivors and nonsurvivors. Based on available data, recursive partitioning indicated that the best single predictor for mortality in this population was high admission blood urea nitrogen (BUN ⬎ 43 mg/dL). The next most predicative factor was low admission systolic blood pressure (SBP ⬍ 115 mmHg) followed by high creatinine (Cr ⬎ 2.75 mg/dL). These branch points allowed identification of patients with mortality rates as low as 2.14% (BUN ⬍ 43, SBP ⬎ 115; n ⫽ 20,834) and as high as 21.94% (BUN ⬎ 43, SBP ⬍ 115, Cr ⬎ 2.75; n ⫽ 620). The odds ratio for mortality between patients identified as low andhigh risk was 12.9 (95% CI 10.4-15.9). Intermediate risk-patients could also be identified (see figure). Conclusions: These results suggest that heart failure patients at low, medium and high risk for inhospital mortality can be easily identified using vital sign and laboratory data obtained on hospital admission. This ADHERE risk tree provides clinicians with a practical bedside tool for mortality risk stratification.
Background: Several life-prolonging therapies for patients with heart failure have been introduced during the last 15 years. However, the impact on survival for patients with heart failure in the community is unclear. Methods: We examined Medicare hospitalization claims for a 20% random sample of all patients admitted with heart failure between 1987 and 1999. Only patients with a new diagnosis of heart failure (none in at least the prior three years) were included. Comorbidity diagnoses during the index admission were recorded and combined into a general (Charleson) and a heart failure specific score. Patient’s records including mortality data were linked to create a longitudinal record for each patient. The primary outcome was death at 1 year following discharge. Results: From 1987 to 1999 the mean age of the patients (total 910,915) increased from 79.9% to 81.6%, the mean length of stay decreased from 11.3 to 6.5 days, the mean Charelson comorbidity score increased from 1.82 to 2.24 (all p ⬍ 0.0001). Despite the increase in age and comorbidiity, survival to one year improved from 59% in 1987 to 66% by 1999. However, 1-year survival peaked at 1994 at 72%. After adjustment for patient demographics and comorbidities, survival improved until 1994 and then declined (Figure). Similar trends in survival (initial decline followed by increase in the mid 1990s) were observed for women and men, andthose youngerand older than 80 years of age. Heart failiure redmission rates at90days following dischargedecreased only slightly from 12.7% in 1995 to 12.5% in 1999 (P fortrend ⫽ 0.11). Conclusion: Survival for the U.S. elderly following a first admission for heart failure improved steadily from 1987 to 1994 despite increasing age and comorbidity. However, beginning in 1995 survival adjusted for comorbidities began to decrease to early 1990 levels. Additional studies are needed to determine the cause of this concerning trend. Trends for New Heart Failure Patients (Medicare Population) Variable
1987
1991
1995
1999
Age Female (%) Diabetes (%) Hypertension (%) Myocardial Infarction (%) Ventricular Arrhythmia (%) Malignancy (%) Comorbidity (Charleson) Score Mortality 1-Year (%)
79.9 56.7 13 13 5.2 2.2 3.4 1.82 36.5
80.2 60.3 14 17 3.8 1.2 3.2 1.87 31.6
80.5 57.8 22 32 10 0.7 7.8 2.20 23.9
81.6 57.2 24 38 11 0.7 9.1 2.24 29.1
P ⬍ 0.0001 except for Male% (p ⫽ 0.3)
a
•
HFSA
S79
288
289
Superiority of Big Endothelin-1 and Endothelin-1 over Natriuretic Peptides To Predict Survival in Severe Congestive Heart Failure Michel F. Rousseau,1 Ronald Van Beneden,2 Olivier Gurne,1 Philippe L. Selvais,1 Annie R. Robert,3 Sylvie A. Ahn,1 Hubert G. Pouleur,1 Jean Marie Ketelslegers2—1Division of Cardiology, University of Louvain, Brussels, Belgium; 2Diabetes and Nutrition Unit, University of Louvain, Brussels, Belgium; 3School of Public Health, University of Louvain, Brussels, Belgium
Prognostic Importance of Diabetes Mellitus in Patients with Acute Decompensated Heart Failure Andrew J. Burger,1 Darlene P. Horton,2 Doron Aronson3—1Cardiology Department, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA; 2, Scios Inc, Sunnyvale, CA; 3Cardiology Department, Rambam Medical Center, Haifa, Israel
Plasma concentrations of atrial and brain natriuretic peptides (ANP, BNP), of their N-terminal pro-peptides, of endothelin-1 (ET-1) and big ET-1 have prognostic significance in congestive heart failure (CHF) but their respective predictive value remains controversial and had never been directly compared in a group of patients with severe CHF. Methods: We analyzed the predictive value of plasma N-ANP 1-25, N-ANP 68-98, BNP, N-BNP, ET-1 and big ET-1 in 47 patients with severe CHF (NYHA III-IV, mean ejection fraction: 20 ⫾ 6%, mean age: 66 ⫾ 8 years, etiology:ischemic n ⫽ 38, non ischemic n ⫽ 9). Thirty healthy subjects served as controls. Thirty-four patients (72%) died during the first 34 months of follow-up and 1 had heart transplant. The mean follow-up for the 12 remaining alive patients was 81 ⫾ 15 months. Results: Geometric mean (and range) baseline values were: N-ANP 1-25:1903 pg/mL (6237148), N-ANP 68-98:2507 pg/mL (709-9075), BNP by radioimmunoassay (RIA):75 pg/mL (15-192), N-BNP:1521 fMol/mL (598-5491), ET-1:10.0 pg/mL (8.7-11.6) and big ET-1 by RIA:12.0 pg/mL (5.8-22.9). In patients with severe CHF, all neurohormones were significantly higher than in controls (p ⬍ 0.001). There were no significant differences in ejection fraction (20% vs 20%), in incidence of ischemic etiology (83% vs 79%) and in age (64 years vs 68 years) between survivors and non survivors. Using Cox univariate analysis, all neurohormones but BNP (p ⫽ 0.14) significantly predicted survival. By multivariate analysis, big ET-1 and ET-1 independently predicted survival (improvement χ2: 7.5 and 4.6, p ⬍ 0.01 and ⬍0.05). Using upper tertiles as cutpoints (big ET-1 ⱖ 12 pg/mL and/or ET-1 ⬎ 9 pg/mL), we defined 2 subgroups of CHF patients with a different prognosis (5 years survival : 55% vs 18%, p ⬍ 0.01). Conclusions: Big ET-1 and ET-1 are strong independent predictors of survival in patients with severe CHF. Further, their predicting value is better than natriuretic peptides or their pro-peptides. These markers identify a subgroup of patients with a very high mortality risk, which could improve the selection process for more aggressive therapies.
Background: The risk of congestive heart failure (CHF) and idiopathic cardiomyopathy is strongly increased in diabetes mellitus. Natural history studies on patients with CHF have suggested that the presence of diabetes mellitus adversely affects prognosis. However, it is not known whether the effect of diabetes mellitus extends to patients with severe decompensated heart failure. Methods: We studied 498 patients (mean age 62 ⫾ 14 years) enrolled in the VMAC (Vasodilation in the Management of Acute CHF) trial. All patients were admitted to the hospital for decompensated heart failure and had severe heart failure requiring intravenous vasoactive therapy. Results: Of 498 patients, 263 (47.3%) had the previous diagnosis of diabetes mellitus. During a mean follow-up of 6 months, 113 patients (22.7%) died. A Cox proportional-hazards model, adjusted for age, gender, weight, baseline creatinine, primary etiology of heart failure stratified as ischemic or nonischemic, left ventricular ejection fraction, type of vasoactive therapy (nesiritide or nitroglycerin) and use of beta-blockers, digoxin, and amiodarone, showed a significant association between diabetes mellitus and worse survival following hospital discharge (RR ⫽ 1.8, 95% CI 1.2-2.7,p ⫽ 0.007). The Figure shows adjusted survival curves for patient with and without diabetes mellitus. Conclusion: Diabetes mellitus is common among patients admitted with decompensated heart failure. Patients with diabetes mellitus admitted with decompensated heart failure appear to have a worse survival compared with patients without diabetes mellitus. These results suggest that diabetes-related biological differences in the progression of heart failure are present even with advanced disease.
290
291
Risk Stratification for In-Hospital Mortality in Heart Failure Using Classification and Regression Tree (CART) Methodology: Analysis of 33,046 Patients in the ADHERE Registry Gregg C. Fonarow,1 Kirkwood F. Adams,2 William T. Abraham,3 ADHERE Investigators4—1Ahmanson-UCLA Cardiomyopathy Center, UCLA Medical Center, Los Angeles, CA; 2Division of Cardiology, University of North Carolina at Chapel Hill, Chapel Hill, NC; 3Division of Cardiology, Ohio State University Medical Center, Columbus, OH; 4ADHERE Registry, Scios, Inc, Sunnyvale, CA
Trends in Survival for the U.S. Elderly Following a First Admission for Heart Failure Paul A. Heidenreich1—1Medicine, VA Palo Alto Health Care System, Palo Alto, CA
Background: Acute decompensated heart failure (AHF) is a leading cause of hospital admission and is associated with significant in-hospital mortality risk. Although a number of individual clinical variables have been identified as being associated with increased in-hospital mortality, no clinically practical method of risk stratification for AHF patients has previously been described. Methods: ADHERE (Acute Decompensated HEart Failure National REgistry) is a national prospective, observation registry of patients hospitalized with AHF. Over 250 hospitals participated, including community, tertiary, and academic medical centers. Data from the first 33,046 patients enrolled in the ADHERE registry were analyzed. Details of medical history, clinical presentation, laboratories, medical management, and health outcomes were collected through hospital discharge medical record review. Classification and regression tree (CART) methodology using recursive partitioning techniques for in-hospital mortality to assess individual variables as potential branch points on a risk assessment decision tree was applied using 45 variables of interest. Results: Patients had a mean age of 72.5 (±13.9), 52% female, and 59% with CAD. In-hospital mortality was 4.19%. CART identified variables providing the greatest discrimination between survivors and nonsurvivors. Based on available data, recursive partitioning indicated that the best single predictor for mortality in this population was high admission blood urea nitrogen (BUN ⬎ 43 mg/dL). The next most predicative factor was low admission systolic blood pressure (SBP ⬍ 115 mmHg) followed by high creatinine (Cr ⬎ 2.75 mg/dL). These branch points allowed identification of patients with mortality rates as low as 2.14% (BUN ⬍ 43, SBP ⬎ 115; n ⫽ 20,834) and as high as 21.94% (BUN ⬎ 43, SBP ⬍ 115, Cr ⬎ 2.75; n ⫽ 620). The odds ratio for mortality between patients identified as low andhigh risk was 12.9 (95% CI 10.4-15.9). Intermediate risk-patients could also be identified (see figure). Conclusions: These results suggest that heart failure patients at low, medium and high risk for inhospital mortality can be easily identified using vital sign and laboratory data obtained on hospital admission. This ADHERE risk tree provides clinicians with a practical bedside tool for mortality risk stratification.
Background: Several life-prolonging therapies for patients with heart failure have been introduced during the last 15 years. However, the impact on survival for patients with heart failure in the community is unclear. Methods: We examined Medicare hospitalization claims for a 20% random sample of all patients admitted with heart failure between 1987 and 1999. Only patients with a new diagnosis of heart failure (none in at least the prior three years) were included. Comorbidity diagnoses during the index admission were recorded and combined into a general (Charleson) and a heart failure specific score. Patient’s records including mortality data were linked to create a longitudinal record for each patient. The primary outcome was death at 1 year following discharge. Results: From 1987 to 1999 the mean age of the patients (total 910,915) increased from 79.9% to 81.6%, the mean length of stay decreased from 11.3 to 6.5 days, the mean Charelson comorbidity score increased from 1.82 to 2.24 (all p ⬍ 0.0001). Despite the increase in age and comorbidiity, survival to one year improved from 59% in 1987 to 66% by 1999. However, 1-year survival peaked at 1994 at 72%. After adjustment for patient demographics and comorbidities, survival improved until 1994 and then declined (Figure). Similar trends in survival (initial decline followed by increase in the mid 1990s) were observed for women and men, andthose youngerand older than 80 years of age. Heart failiure redmission rates at90days following dischargedecreased only slightly from 12.7% in 1995 to 12.5% in 1999 (P fortrend ⫽ 0.11). Conclusion: Survival for the U.S. elderly following a first admission for heart failure improved steadily from 1987 to 1994 despite increasing age and comorbidity. However, beginning in 1995 survival adjusted for comorbidities began to decrease to early 1990 levels. Additional studies are needed to determine the cause of this concerning trend. Trends for New Heart Failure Patients (Medicare Population) Variable
1987
1991
1995
1999
Age Female (%) Diabetes (%) Hypertension (%) Myocardial Infarction (%) Ventricular Arrhythmia (%) Malignancy (%) Comorbidity (Charleson) Score Mortality 1-Year (%)
79.9 56.7 13 13 5.2 2.2 3.4 1.82 36.5
80.2 60.3 14 17 3.8 1.2 3.2 1.87 31.6
80.5 57.8 22 32 10 0.7 7.8 2.20 23.9
81.6 57.2 24 38 11 0.7 9.1 2.24 29.1
P ⬍ 0.0001 except for Male% (p ⫽ 0.3)
a