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Heart failure post-myocardial infarction in the new millennium: how often does it occur, can we predict it, and what are the consequences?
The 8th Annual Scientific Meeting
307 An Individualized Prediction of Heart Failure Survival – Derivation from PRAISE1 and Validation in ELITE2 Wayne C. Levy,1 Dariush Mozaffarian,2 David T. Linker,1 Santosh C. Sutradhar,3 Bertram Pitt,4 Philip A. Poole-Wilson,5 Milton Packer6; 1Cardiology, University of Washington, Seattle, WA; 2Medicine, Harvard School of Public Health, Boston, MA; 3 Merck Research Laboratories, Blue Bell, PA; 4Cardiology, University of Michigan, Ann Arbor, MI; 5Cardiology, Imperial College London London, United Kingdom; 6 Cardiology, College of Physicians and Surgeons, Columbia University, New York, NY Previous HF risk models divide patients into 3 risk groups using a peak VO2. An individual estimate of survival in HF using easily obtained clinical variables, laboratory values, medications, and devices has not been reported. We used data from 1,125 patients with complete data in PRAISE1 (NYHA 3B-4, EF ⬍ 30%, ACEI, diuretics, 403 deaths) to derive a multivariate risk model. A Cox proportional hazard model was developed using age, gender, ischemic etiology, NYHA, EF, SBP, statins, allopurinol, hemoglobin, % lymphocyte, uric acid, sodium, cholesterol and diuretic dose/kg. Hazard ratios of medications (ACEI, ARB, beta blockers, aldosterone blockers) and devices (ICDs, CRT, CRT-ICD) that were not in the PRAISE1 database were estimated from the literature and added to the interactive model. The model was prospectively applied to the ELITE2 database for validation. The model predicts the annual mortality and median survival, and is applicable to the individual patient. The model allows you to alter the estimated survival by adding medications and/or devices. The predicted 1 and 2 year survival by estimated deciles of survival vs the actual survival in the derivation database (PRAISE1), and the validation database (ELITE2), are shown below. The correlation was excellent 0.98, with a standard error of the estimate of ⫾ 4%. The 1 year ROC AUC was 0.72 (95% CI 0.69–0.76) in PRAISE1 (derivation) and 0.68 (95% CI 0.64–0.71) in ELITE2 (validation). Conclusions: We have developed and validated an interactive model to estimate an individual HF patient’s survival using easily obtained clinical variables, laboratory values, medications, and devices. The model allows the user to re-calculate the estimated survival after adding known life saving medications and devices. A Java interactive program based on the model will be demonstrated.
•
S101
HFSA
would be $143,142,416 ($80.76 * 6 * 295,407). Six-month net savings in Medicare hospital spending would thus be $149,785,883. Savings would be roughly 50% higher using the upper bound of the range for the eligible cohort. Conclusions: Routine application of disease management programs for eligible Medicare patients with chronic HF could reduce hospital spending substantially through reduced readmissions across a broader cohort of beneficiaries.
309 Effect of Statin Use and Smoking Status on Two-Year Mortality in an Unselected Heart Failure Population Jonathan G. Howlett, David E. Johnstone, Jafna L. Cox; Medicine (Cardiology), Queen Elizabeth II Health Sciences Centre, Halifax, NS, Canada Background: Cigarette smoking and dyslipidemia are associated with atherosclerosis and possibly congestive heart failure (CHF). It is not known whether control of these factors influences outcome once CHF occurs. We sought to determine the impact of serum lipid levels, statin use and smoking status on mortality in an unselected patient population hospitalized with CHF. Methods: A prospective registry-based disease management database, the Improving Cardiovascular Outcomes in Nova Scotia (ICONS) study, was used to identify all individuals surviving to discharge with a diagnosis of CHF (excluding acute myocardial infarction) from any Nova Scotia hospital between October 15, 1997 and July 1, 2000. Demographic and clinical data were prospectively recorded, including smoking status, total cholesterol and LDL-C, and statin use at discharge. Follow up was censored at 2 years. Results: There were 4888 unique patients. Mean age was 76 years and 2 year mortality 38%. Mean LDL was 130 ⫹/- 46 mg/dl and 14.6% of patients were discharged on a statin. Data on smoking status was available in 86%; 16.2% were current, and 27.2% former smokers. After adjustment for 70 other variables, hazard ratios for mortality at two years were determined. Conclusion: Statin prescription at discharge was associated with marked mortality reduction in an unselected CHF population, whereas serum LDL was not. Smoking history was associated with increased mortality in an incremental fashion. Further study is required to determine whether continued control of these factors in CHF patients following hospital discharge is of benefit. Hazard Ratios for 2 Year Mortality with 95% Confidence Intervals Variable Discharge Statin Serum LDL Past vs. Never Smoker Current vs. Never Smoker
Hazard Ratio
95% CI
p Value
0.65 1.00 1.29 1.70
0.54–0.78 0.97–1.02 1.07–1.55 1.36–2.12
⬍ 0.0001 0.15 0.03 ⬍ 0.0001
310 Heart Failure Post-Myocardial Infarction in the New Millennium: How Often Does It Occur, Can We Predict It, and What Are the Consequences? E. F. Lewis,1 E. J. Velazquez,2 J. J. V. McMurray,3 J. L. Rouleau,4 S. D. Solomon,1 A. P. Maggioni,5 K. Swedberg,6 L. Kober,7 H. White,8 A. J. Dalby,9 G.S. Francis,10 K. S. Pieper,2 F. Zannad,11 R. M. Califf,2 M. A. Pfeffer1; 1Cardiovascular Medicine, Brigham and Women’s Hospital, Boston, MA; 2Duke University Medical Center, Duke Clinical Research Institute, Durham, NC; 3Western Infirmary, Glasgow, Scotland; 4Montreal Heart Institute, Montreal, QC, Canada; 5ANMCO Research Center, Florence, Italy; 6Sahlgrenska University Hospital-Ostra, Goteborg, Sweden; 7 Rigshospitalet, Copenhagen, Denmark; 8Auckland City Hospital, Auckland, New Zealand; 9Milpark Hospital, Johannesburg, South Africa; 10Cleveland Clinic Foundation, Cleveland, OH; 11Hoˆpital Jeanne d’Arc, Dommartin-Les-Toul, France
308 Heart Failure Disease Management and Hospital Costs among U.S. Medicare Beneficiaries Julie Sochaski,1 Victoria V. Dickson,1 Simon Stewart,2 Harlan M. Krumholz,3 Christopher O. Phillips,4 J. Sanford Schwartz,5 Barbara Riegel1; 1School of Nursing, University of Pennsylvania, Philadelphia, PA; 2School of Nursing and Midwifery, University of South Australia, Adelaide, Australia; 3Department of Medicine, Yale University School of Medicine, New Haven, CT; 4Quality of Care Research and General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD; 5 Department of Medicine, Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA Heart failure (HF) is the primary reason for nearly 800,000 hospitalizations annually in the elderly U.S. Medicare population at an average cost of $6,431 per discharge. Hospital spending may be substantially reduced by HF disease management, but the eligible population for and the cost savings with such programs have not been precisely estimated. Purpose: To estimate the proportion of U.S. Medicare beneficiaries with HF who are potentially eligible and would participate in HF disease management, and to derive more precise estimates for associated annual hospital cost savings for this cohort. Methods: The proportion of Medicare patients who were eligible and willing to enroll in U.S. randomized clinical trials of HF disease management from 1990–2003 were applied to the total population of hospitalized HF Medicare beneficiaries in the U.S. to determine the potentially eligible cohort. This method produced an eligible cohort ranging from 37.8% to 58% of hospitalized Medicare HF beneficiaries. For this cohort we applied estimates of the cost of these programs, their impact on hospital readmission rates, and estimated the potential 6-month hospital savings to Medicare if these programs were routinely adopted. Results: Using the lower bound of the range for the eligible cohort (n ⫽ 295,407), we estimate 130,142 readmissions occurring within six months of the index hospitalization. A recent meta-analysis of HF studies shows that disease management programs reduce hospital readmissions on average by 35% at an average cost in the U.S. of $80.76 per patient per month. Applying these figures to this simulation, an estimated 45,550 readmissions would be avoided in 6-months at a savings of $292,928,299 ($6,431 * 45,550). The total cost of implementing disease management programs for that cohort over that period
Purpose: Patients (pts) with acute myocardial infarction (MI) complicated by pulmonary edema and/or depressed ejection fraction (EF) are at heightened risk for chronic heart failure (HF) and/or death. We utilized the VALsartan In Acute myocardial iNfarcTion (VALIANT) trial experience to determine predictors of cardiovascular (CV) death and/or HF in high-risk MI pts undergoing modern therapy. Methods: VALIANT enrolled 14,703 pts with MI complicated by Killip class ⱖ2 and/or reduced EF between 0.5 and 10 days post-MI randomized to valsartan, captopril, or combination (median follow-up 24.7 months). Cox proportional hazards model was used to determine predictors of CV death and/or hospitalization for HF requiring intravenous therapy. Results: CV death occurred in 2484 (16.9%) pts, HF hospitalization in 2388 (16.2%), and 3992 (27%) died from CV cause and/or developed HF. Of the 2388 with HF hospitalization, 880 (36.9%) subsequently died. The most powerful independent predictors of HF and/or CV death were older age, higher baseline heart rate and diabetes mellitus (Table). There were no differences in outcomes between pts randomized to valsartan, captopril, or combination. Also, 73% (n ⫽ 10,711) neither developed HF nor died from CV causes. Conclusion: Even among high-risk MI pts, the majority did not develop HF or die from CV causes. Age, diabetes, and rapid heart rate remain significant predictors of CV death and/or HF hospitalization. Mortality is twice as high among pts hospitalized for HF. Variable
Hazard Ratio Chi(95% CI) square
Variable
Age (risk/10 years) 1.35(1.30-1.40) 256.9 Prior MI Heart rate (1 beat/ 1.02(1.01-1.02) 172.8 HF History min increase) Diabetes 1.48(1.37-1.59) 112.9 Pulse pressure mellitus (1 mm Hg increase) BMI (1 kg/m2 1.03(1.01-1.04) 98.1 Current smoker increase) Creatinine 1.05(1.04-1.07) 95.2 Hypertension (0.1 mg/dL History increase) Killip class 1.31(1.22-1.41) 55.2 Angioplasty (3 or 4) Post-MI
Hazard Ratio Chi(95% CI) square 1.33(1.24-1.46) 54.8 1.34(1.24-1.46) 49.6 0.79(0.72-0.87) 44.0 1.30(1.20-1.42) 39.5 1.24(1.16-1.34) 35.5 0.77(0.69-0.85) 24.9
307 An Individualized Prediction of Heart Failure Survival – Derivation from PRAISE1 and Validation in ELITE2 Wayne C. Levy,1 Dariush Mozaffarian,2 David T. Linker,1 Santosh C. Sutradhar,3 Bertram Pitt,4 Philip A. Poole-Wilson,5 Milton Packer6; 1Cardiology, University of Washington, Seattle, WA; 2Medicine, Harvard School of Public Health, Boston, MA; 3 Merck Research Laboratories, Blue Bell, PA; 4Cardiology, University of Michigan, Ann Arbor, MI; 5Cardiology, Imperial College London London, United Kingdom; 6 Cardiology, College of Physicians and Surgeons, Columbia University, New York, NY Previous HF risk models divide patients into 3 risk groups using a peak VO2. An individual estimate of survival in HF using easily obtained clinical variables, laboratory values, medications, and devices has not been reported. We used data from 1,125 patients with complete data in PRAISE1 (NYHA 3B-4, EF ⬍ 30%, ACEI, diuretics, 403 deaths) to derive a multivariate risk model. A Cox proportional hazard model was developed using age, gender, ischemic etiology, NYHA, EF, SBP, statins, allopurinol, hemoglobin, % lymphocyte, uric acid, sodium, cholesterol and diuretic dose/kg. Hazard ratios of medications (ACEI, ARB, beta blockers, aldosterone blockers) and devices (ICDs, CRT, CRT-ICD) that were not in the PRAISE1 database were estimated from the literature and added to the interactive model. The model was prospectively applied to the ELITE2 database for validation. The model predicts the annual mortality and median survival, and is applicable to the individual patient. The model allows you to alter the estimated survival by adding medications and/or devices. The predicted 1 and 2 year survival by estimated deciles of survival vs the actual survival in the derivation database (PRAISE1), and the validation database (ELITE2), are shown below. The correlation was excellent 0.98, with a standard error of the estimate of ⫾ 4%. The 1 year ROC AUC was 0.72 (95% CI 0.69–0.76) in PRAISE1 (derivation) and 0.68 (95% CI 0.64–0.71) in ELITE2 (validation). Conclusions: We have developed and validated an interactive model to estimate an individual HF patient’s survival using easily obtained clinical variables, laboratory values, medications, and devices. The model allows the user to re-calculate the estimated survival after adding known life saving medications and devices. A Java interactive program based on the model will be demonstrated.
•
S101
HFSA
would be $143,142,416 ($80.76 * 6 * 295,407). Six-month net savings in Medicare hospital spending would thus be $149,785,883. Savings would be roughly 50% higher using the upper bound of the range for the eligible cohort. Conclusions: Routine application of disease management programs for eligible Medicare patients with chronic HF could reduce hospital spending substantially through reduced readmissions across a broader cohort of beneficiaries.
309 Effect of Statin Use and Smoking Status on Two-Year Mortality in an Unselected Heart Failure Population Jonathan G. Howlett, David E. Johnstone, Jafna L. Cox; Medicine (Cardiology), Queen Elizabeth II Health Sciences Centre, Halifax, NS, Canada Background: Cigarette smoking and dyslipidemia are associated with atherosclerosis and possibly congestive heart failure (CHF). It is not known whether control of these factors influences outcome once CHF occurs. We sought to determine the impact of serum lipid levels, statin use and smoking status on mortality in an unselected patient population hospitalized with CHF. Methods: A prospective registry-based disease management database, the Improving Cardiovascular Outcomes in Nova Scotia (ICONS) study, was used to identify all individuals surviving to discharge with a diagnosis of CHF (excluding acute myocardial infarction) from any Nova Scotia hospital between October 15, 1997 and July 1, 2000. Demographic and clinical data were prospectively recorded, including smoking status, total cholesterol and LDL-C, and statin use at discharge. Follow up was censored at 2 years. Results: There were 4888 unique patients. Mean age was 76 years and 2 year mortality 38%. Mean LDL was 130 ⫹/- 46 mg/dl and 14.6% of patients were discharged on a statin. Data on smoking status was available in 86%; 16.2% were current, and 27.2% former smokers. After adjustment for 70 other variables, hazard ratios for mortality at two years were determined. Conclusion: Statin prescription at discharge was associated with marked mortality reduction in an unselected CHF population, whereas serum LDL was not. Smoking history was associated with increased mortality in an incremental fashion. Further study is required to determine whether continued control of these factors in CHF patients following hospital discharge is of benefit. Hazard Ratios for 2 Year Mortality with 95% Confidence Intervals Variable Discharge Statin Serum LDL Past vs. Never Smoker Current vs. Never Smoker
Hazard Ratio
95% CI
p Value
0.65 1.00 1.29 1.70
0.54–0.78 0.97–1.02 1.07–1.55 1.36–2.12
⬍ 0.0001 0.15 0.03 ⬍ 0.0001
310 Heart Failure Post-Myocardial Infarction in the New Millennium: How Often Does It Occur, Can We Predict It, and What Are the Consequences? E. F. Lewis,1 E. J. Velazquez,2 J. J. V. McMurray,3 J. L. Rouleau,4 S. D. Solomon,1 A. P. Maggioni,5 K. Swedberg,6 L. Kober,7 H. White,8 A. J. Dalby,9 G.S. Francis,10 K. S. Pieper,2 F. Zannad,11 R. M. Califf,2 M. A. Pfeffer1; 1Cardiovascular Medicine, Brigham and Women’s Hospital, Boston, MA; 2Duke University Medical Center, Duke Clinical Research Institute, Durham, NC; 3Western Infirmary, Glasgow, Scotland; 4Montreal Heart Institute, Montreal, QC, Canada; 5ANMCO Research Center, Florence, Italy; 6Sahlgrenska University Hospital-Ostra, Goteborg, Sweden; 7 Rigshospitalet, Copenhagen, Denmark; 8Auckland City Hospital, Auckland, New Zealand; 9Milpark Hospital, Johannesburg, South Africa; 10Cleveland Clinic Foundation, Cleveland, OH; 11Hoˆpital Jeanne d’Arc, Dommartin-Les-Toul, France
308 Heart Failure Disease Management and Hospital Costs among U.S. Medicare Beneficiaries Julie Sochaski,1 Victoria V. Dickson,1 Simon Stewart,2 Harlan M. Krumholz,3 Christopher O. Phillips,4 J. Sanford Schwartz,5 Barbara Riegel1; 1School of Nursing, University of Pennsylvania, Philadelphia, PA; 2School of Nursing and Midwifery, University of South Australia, Adelaide, Australia; 3Department of Medicine, Yale University School of Medicine, New Haven, CT; 4Quality of Care Research and General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD; 5 Department of Medicine, Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA Heart failure (HF) is the primary reason for nearly 800,000 hospitalizations annually in the elderly U.S. Medicare population at an average cost of $6,431 per discharge. Hospital spending may be substantially reduced by HF disease management, but the eligible population for and the cost savings with such programs have not been precisely estimated. Purpose: To estimate the proportion of U.S. Medicare beneficiaries with HF who are potentially eligible and would participate in HF disease management, and to derive more precise estimates for associated annual hospital cost savings for this cohort. Methods: The proportion of Medicare patients who were eligible and willing to enroll in U.S. randomized clinical trials of HF disease management from 1990–2003 were applied to the total population of hospitalized HF Medicare beneficiaries in the U.S. to determine the potentially eligible cohort. This method produced an eligible cohort ranging from 37.8% to 58% of hospitalized Medicare HF beneficiaries. For this cohort we applied estimates of the cost of these programs, their impact on hospital readmission rates, and estimated the potential 6-month hospital savings to Medicare if these programs were routinely adopted. Results: Using the lower bound of the range for the eligible cohort (n ⫽ 295,407), we estimate 130,142 readmissions occurring within six months of the index hospitalization. A recent meta-analysis of HF studies shows that disease management programs reduce hospital readmissions on average by 35% at an average cost in the U.S. of $80.76 per patient per month. Applying these figures to this simulation, an estimated 45,550 readmissions would be avoided in 6-months at a savings of $292,928,299 ($6,431 * 45,550). The total cost of implementing disease management programs for that cohort over that period
Purpose: Patients (pts) with acute myocardial infarction (MI) complicated by pulmonary edema and/or depressed ejection fraction (EF) are at heightened risk for chronic heart failure (HF) and/or death. We utilized the VALsartan In Acute myocardial iNfarcTion (VALIANT) trial experience to determine predictors of cardiovascular (CV) death and/or HF in high-risk MI pts undergoing modern therapy. Methods: VALIANT enrolled 14,703 pts with MI complicated by Killip class ⱖ2 and/or reduced EF between 0.5 and 10 days post-MI randomized to valsartan, captopril, or combination (median follow-up 24.7 months). Cox proportional hazards model was used to determine predictors of CV death and/or hospitalization for HF requiring intravenous therapy. Results: CV death occurred in 2484 (16.9%) pts, HF hospitalization in 2388 (16.2%), and 3992 (27%) died from CV cause and/or developed HF. Of the 2388 with HF hospitalization, 880 (36.9%) subsequently died. The most powerful independent predictors of HF and/or CV death were older age, higher baseline heart rate and diabetes mellitus (Table). There were no differences in outcomes between pts randomized to valsartan, captopril, or combination. Also, 73% (n ⫽ 10,711) neither developed HF nor died from CV causes. Conclusion: Even among high-risk MI pts, the majority did not develop HF or die from CV causes. Age, diabetes, and rapid heart rate remain significant predictors of CV death and/or HF hospitalization. Mortality is twice as high among pts hospitalized for HF. Variable
Hazard Ratio Chi(95% CI) square
Variable
Age (risk/10 years) 1.35(1.30-1.40) 256.9 Prior MI Heart rate (1 beat/ 1.02(1.01-1.02) 172.8 HF History min increase) Diabetes 1.48(1.37-1.59) 112.9 Pulse pressure mellitus (1 mm Hg increase) BMI (1 kg/m2 1.03(1.01-1.04) 98.1 Current smoker increase) Creatinine 1.05(1.04-1.07) 95.2 Hypertension (0.1 mg/dL History increase) Killip class 1.31(1.22-1.41) 55.2 Angioplasty (3 or 4) Post-MI
Hazard Ratio Chi(95% CI) square 1.33(1.24-1.46) 54.8 1.34(1.24-1.46) 49.6 0.79(0.72-0.87) 44.0 1.30(1.20-1.42) 39.5 1.24(1.16-1.34) 35.5 0.77(0.69-0.85) 24.9