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Wayne C Levy - One of the best experts on this subject based on the ideXlab platform.
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abstract 12090 seattle heart Failure Model and seattle proportional risk Model together identify patients most likely to benefit from primary prevention icds an ncdr analysis
Circulation, 2016Co-Authors: Kenneth C Bilchick, Alan Cheng, Yongfei Wang, Jeptha P Curtis, Kumar Dharmarajan, Wayne C LevyAbstract:Introduction: Risk Models are greatly needed to target appropriate patients for implantable cardioverter defibrillators (ICDs) in clinical practice for primary prevention of sudden death. Hypothesis: We hypothesized that the Seattle Heart Failure Model (SHFM) for overall survival and the Seattle Proportional Risk Model (SPRM) for proportional risk of arrhythmic death (AD) would identify National Cardiovascular Data Registry (NCDR) ICD Registry patients most likely to have improved survival with versus without an ICD. Methods: SHFM and SPRM scores were determined for patients with and without ICDs (ICD Registry versus a control group derived from the University of Washington Registry, Italian Heart Failure Registry, Swedish Heart Failure Registry, COMET, Val-HeFT, and PRAISE trials). Multivariable Cox proportional hazards regression was used to evaluate adjusted associations of SPRM and SHFM with survival over 5 years from the Social Security Death Index. Results: Among 98,846 patients (87,914 with ICDs and 10,932 without ICDs), increasing SFHM risk was strongly associated with decreased survival (P mean, SPRM > mean) was greatly improved versus controls (HR 0.599, 95% CI 0.530-0.677, P Conclusions: The SHFM and SPRM scores together identify real-world patients most likely to benefit from primary prevention ICD implantation.
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from statistical significance to clinical relevance a simple algorithm to integrate brain natriuretic peptide and the seattle heart Failure Model for risk stratification in heart Failure
Journal of Heart and Lung Transplantation, 2016Co-Authors: Omar F Abouezzeddine, Wayne C Levy, Benjamin French, Sultan A Mirzoyev, Allan S Jaffe, James C Fang, Nancy K Sweitzer, Thomas P Cappola, Margaret M RedfieldAbstract:Background Heart Failure (HF) guidelines recommend brain natriuretic peptide (BNP) and multivariable risk scores, such as the Seattle Heart Failure Model (SHFM), to predict risk in HF with reduced ejection fraction (HFrEF). A practical way to integrate information from these 2 prognostic tools is lacking. We sought to establish a SHFM+BNP risk-stratification algorithm. Methods The retrospective derivation cohort included consecutive patients with HFrEF at the Mayo Clinic. One-year outcome (death, transplantation or ventricular assist device) was assessed. The SHFM+BNP algorithm was derived by stratifying patients within SHFM-predicted risk categories (≤2.5%, 2.6% to ≤10%, >10%) according to BNP above or below 700 pg/ml and comparing SHFM-predicted and observed event rates within each SHFM+BNP category. The algorithm was validated in a prospective, multicenter HFrEF registry (Penn HF Study). Results Derivation ( n = 441; 1-year event rate 17%) and validation ( n = 1,513; 1-year event rate 12%) cohorts differed with the former being older and more likely ischemic with worse symptoms, lower EF, worse renal function and higher BNP and SHFM scores. In both cohorts, across the 3 SHFM-predicted risk strata, a BNP >700 pg/ml consistently identified patients with approximately 3-fold the risk that the SHFM would have otherwise estimated, regardless of stage of HF, intensity and duration of HF therapy and comorbidities. Conversely, the SHFM was appropriately calibrated in patients with a BNP Conclusion The simple SHFM+BNP algorithm displays stable performance across diverse HFrEF cohorts and may enhance risk stratification to enable appropriate decision-making regarding HF therapeutic or palliative strategies.
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incremental and independent value of cardiopulmonary exercise test measures and the seattle heart Failure Model for prediction of risk in patients with heart Failure
Journal of Heart and Lung Transplantation, 2015Co-Authors: Todd F Dardas, Shelby D Reed, Christopher M Oconnor, David J Whellan, Stephen J Ellis, Kevin A Schulman, William E Kraus, Daniel E Forman, Wayne C LevyAbstract:Background Multivariable risk scores and exercise measures are well-validated risk prediction methods. Combining information from a functional evaluation and a risk Model may improve accuracy of risk predictions. We analyzed whether adding exercise measures to the Seattle Heart Failure Model (SHFM) improves risk prediction accuracy in systolic heart Failure. Methods We used a sample of patients from the Heart Failure and A Controlled Trial Investigating Outcomes of Exercise TraiNing (HF-ACTION) study (http://www.clinicaltrials.gov; unique identifier: NCT00047437) to examine the addition of peak oxygen consumption, expired volume per unit time/volume of carbon dioxide slope, 6-minute walk distance, or cardiopulmonary exercise duration to the SHFM. Multivariable Cox proportional hazards Models were used to test the association between the combined end point (death, left ventricular assist device, or cardiac transplantation) and the addition of exercise variables to the SHFM. Results The sample included 2,152 patients. The SHFM and all exercise measures were associated with events (all p Conclusions Exercise performance measures and the SHFM are independently useful for predicting risk in systolic heart Failure. Adding cardiopulmonary exercise testing measures and 6MWD to the SHFM offers only minimal improvement in risk reassignment at clinically meaningful cut points.
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comparison of the seattle heart Failure Model and cardiopulmonary exercise capacity for prediction of death in patients with chronic ischemic heart Failure and intracoronary progenitor cell application
Clinical Cardiology, 2013Co-Authors: Joerg Honold, Wayne C Levy, Salvatore Derosa, Ioakim Spyridopoulos, Ulrich Fischerrasokat, Florian Seeger, David M Leistner, Saskia Lotz, Andreas M Zeiher, Birgit AssmusAbstract:Background: Despite many therapeutic advances, the prognosis of patients with chronic heart Failure (CHF) remains poor. Therefore, reliable identification of high-risk patients with poor prognosis is of utmost importance. Cardiopulmonary exercise testing (CPET) provides important prognostic information by peak O2 uptake (peak VO2), maximal oxygen pulse (O2 Pmax), O2 uptake efficiency slope (OUES), and VE/VCO2 slope (VE/VCO2). A different approach for prognostic assessment is the Seattle Heart Failure Model (SHFM), which is based on clinical data and calculates the estimated annual mortality. Hypothesis: Comparison of the prognostic value of the Seattle Heart Failure Score and cardiopulmonary excercis testing in patients with chronic heart Failure. Methods: One hundred fifty-seven patients with ischemic heart Failure and recent intracoronary progenitor cell application were analyzed for mortality during a follow-up of 4 years. CPET (peak VO2, O2 Pmax, OUES, VE/VCO2) was performed in all patients at baseline. The SHFM score was calculated for every patient, with data obtained at the time of CPET. Results: During follow-up, 24 patients died (15.2%). Nonsurvivors had significantly worse initial CPET results and a higher SHFM score compared to survivors. Receiver operating characteristics curve analysis of sensitivity and specificity revealed the largest area under the curve value for the SHFM score, followed by VE/VCO2 slope. Kaplan Meier analysis using cutoff points of SHFM and VE/VCO2 slope with highest sensitivity and specificity resulted in significant discrimination of survivors and nonsurvivors. By multivariate analysis, only the SHFM score persisted as independent predictor of mortality in these patients. Conclusions: These data indicate superior prognostic power of the SHFM score compared to CPET in patients with chronic ischemic heart Failure. The authors have no funding, financial relationships, or conflicts of interest to disclose.
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selective improvement in seattle heart Failure Model risk stratification using iodine 123 meta iodobenzylguanidine imaging
Journal of Nuclear Cardiology, 2012Co-Authors: Eric S Ketchum, Arnold F Jacobson, James H Caldwell, Manuel D Cerqueira, Gregory S Thomas, Denis Agostini, Jagat Narula, Wayne C LevyAbstract:Background The Seattle Heart Failure Model (SHFM) is a multivariable Model that uses demographic and clinical markers to predict survival in patients with heart Failure. Inappropriate activation of the sympathetic nervous system, which contributes to the progression of heart Failure and increased mortality, can be assessed using iodine-123 meta-iodobenzylguanidine (MIBG) cardiac imaging. This study investigated the incremental value of MIBG cardiac imaging when added to the SHFM for prediction of all-cause mortality.
Eric S Ketchum - One of the best experts on this subject based on the ideXlab platform.
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selective improvement in seattle heart Failure Model risk stratification using iodine 123 meta iodobenzylguanidine imaging
Journal of Nuclear Cardiology, 2012Co-Authors: Eric S Ketchum, Arnold F Jacobson, James H Caldwell, Manuel D Cerqueira, Gregory S Thomas, Denis Agostini, Jagat Narula, Wayne C LevyAbstract:Background The Seattle Heart Failure Model (SHFM) is a multivariable Model that uses demographic and clinical markers to predict survival in patients with heart Failure. Inappropriate activation of the sympathetic nervous system, which contributes to the progression of heart Failure and increased mortality, can be assessed using iodine-123 meta-iodobenzylguanidine (MIBG) cardiac imaging. This study investigated the incremental value of MIBG cardiac imaging when added to the SHFM for prediction of all-cause mortality.
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predictive value of the seattle heart Failure Model in patients undergoing left ventricular assist device placement
Journal of Heart and Lung Transplantation, 2010Co-Authors: Eric S Ketchum, Alec J Moorman, Daniel P Fishbein, Nahush A Mokadam, Edward D Verrier, Gabriel S Aldea, Shauna Andrus, Kenneth W Kenyon, Wayne C LevyAbstract:BACKGROUND: Left ventricular assist devices (LVADs) are increasingly used in advanced heart Failure patients. Despite proven efficacy, optimal timing of LVAD implantation is not well defined. METHODS: Patients receiving an LVAD were prospectively recorded. Laboratory and clinical data were extracted and used to calculate the predicted survival with medical therapy using the Seattle Heart Failure Model (SHFM). This was compared with observed survival, hospital length of stay and timeliness of discharge. RESULTS: We identified 104 patients. Survival with an LVAD vs SHFM predicted survival was 69% vs 11% at 1 year, corresponding to a hazard ratio of 0.17 (p 0.0001). SHFM-estimated 1-year survival with medical therapy increased from 4% in 1997 to 2004 to 25% in 2007‐2008 (p 0.0001). Subgroup analysis of higher vs lower risk LVAD patients showed observed 1-year survival of 83% vs 57% (p 0.04). The lower risk group had a shorter length of stay (46 vs 75 days, p 0.03), along with higher rates of discharge prior to transplant (88% vs 61%, p 0.01) and discharge within 60 days of LVAD placement (77% vs 52%, p 0.03). CONCLUSIONS: The SHFM allows prediction of important features of a patient’s hospital course post-operatively, including length of stay and 1-year survival. Given evidence of improved survival and shorter hospital stay in lower risk patients, earlier LVAD placement based on a prediction Model like the SHFM should be considered in advanced heart Failure patients. The SHFM may have utility as a virtual control arm for single-arm LVAD trials.
A. Banerjee - One of the best experts on this subject based on the ideXlab platform.
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determination of johnson cook material and Failure Model constants and numerical Modelling of charpy impact test of armour steel
Materials Science and Engineering A-structural Materials Properties Microstructure and Processing, 2015Co-Authors: S Dhar, Sanjib Kumar Acharyya, Debasis Datta, A. Banerjee, N. NayakAbstract:Abstract The behaviour of typical armour steel material under large strains, high strain rates and elevated temperatures needs to be investigated to analyse and reliably predict its response to various types of dynamic loading like impact. An empirical constitutive relation developed by Johnson and Cook (J–C) is widely used to capture strain rate sensitivity of the metals. A Failure Model proposed by Johnson and Cook is used to Model the damage evolution and predict Failure in many engineering materials. In this work, Model constants of J–C constitutive relation and damage parameters of J–C Failure Model for a typical armour steel material have been determined experimentally from four types of uniaxial tensile test. Some modifications in the J–C damage Model have been suggested and Finite Element simulation of three different tensile tests on armour steel specimens under dynamic strain rate (10 −1 s −1 ), high triaxiality and elevated temperature respectively has been done in ABAQUS platform using the modified J–C Failure Model as user material sub-routine. The simulation results are validated by the experimental data. Thereafter, a moderately high strain rate event viz. Charpy impact test on armour steel specimen has been simulated using J–C material and Failure Models with the same material parameters. Reasonable agreement between the simulation and experimental results has been achieved.
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determination of johnson cook material and Failure Model constants and numerical Modelling of charpy impact test of armour steel
Materials Science and Engineering A-structural Materials Properties Microstructure and Processing, 2015Co-Authors: S Dhar, Sanjib Kumar Acharyya, Debasis Datta, A. Banerjee, N. NayakAbstract:Abstract The behaviour of typical armour steel material under large strains, high strain rates and elevated temperatures needs to be investigated to analyse and reliably predict its response to various types of dynamic loading like impact. An empirical constitutive relation developed by Johnson and Cook (J–C) is widely used to capture strain rate sensitivity of the metals. A Failure Model proposed by Johnson and Cook is used to Model the damage evolution and predict Failure in many engineering materials. In this work, Model constants of J–C constitutive relation and damage parameters of J–C Failure Model for a typical armour steel material have been determined experimentally from four types of uniaxial tensile test. Some modifications in the J–C damage Model have been suggested and Finite Element simulation of three different tensile tests on armour steel specimens under dynamic strain rate (10 −1 s −1 ), high triaxiality and elevated temperature respectively has been done in ABAQUS platform using the modified J–C Failure Model as user material sub-routine. The simulation results are validated by the experimental data. Thereafter, a moderately high strain rate event viz. Charpy impact test on armour steel specimen has been simulated using J–C material and Failure Models with the same material parameters. Reasonable agreement between the simulation and experimental results has been achieved.
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An Experimental Determination of Johnson Cook Material and Failure Model Constants for Armour Steel
Applied Mechanics and Materials, 2014Co-Authors: A. Banerjee, S Dhar, Sanjib Kumar Acharyya, Debasis Datta, N. NayakAbstract:An experimental programme for determination of the Johnson Cook material and Failure Model constants for a typical armour steel material is reported. Tensile tests on specimens made from the armour material have been conducted at quasi-static and dynamic strain rates and at ambient and elevated temperatures. The analysis of the experimental data generates the Model constants that are required as inputs during numerical simulation of dynamic events like armour impact using Johnson Cook constitutive relation and Failure Model implemented in most of the commercially available Finite Element codes.
N. Nayak - One of the best experts on this subject based on the ideXlab platform.
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determination of johnson cook material and Failure Model constants and numerical Modelling of charpy impact test of armour steel
Materials Science and Engineering A-structural Materials Properties Microstructure and Processing, 2015Co-Authors: S Dhar, Sanjib Kumar Acharyya, Debasis Datta, A. Banerjee, N. NayakAbstract:Abstract The behaviour of typical armour steel material under large strains, high strain rates and elevated temperatures needs to be investigated to analyse and reliably predict its response to various types of dynamic loading like impact. An empirical constitutive relation developed by Johnson and Cook (J–C) is widely used to capture strain rate sensitivity of the metals. A Failure Model proposed by Johnson and Cook is used to Model the damage evolution and predict Failure in many engineering materials. In this work, Model constants of J–C constitutive relation and damage parameters of J–C Failure Model for a typical armour steel material have been determined experimentally from four types of uniaxial tensile test. Some modifications in the J–C damage Model have been suggested and Finite Element simulation of three different tensile tests on armour steel specimens under dynamic strain rate (10 −1 s −1 ), high triaxiality and elevated temperature respectively has been done in ABAQUS platform using the modified J–C Failure Model as user material sub-routine. The simulation results are validated by the experimental data. Thereafter, a moderately high strain rate event viz. Charpy impact test on armour steel specimen has been simulated using J–C material and Failure Models with the same material parameters. Reasonable agreement between the simulation and experimental results has been achieved.
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determination of johnson cook material and Failure Model constants and numerical Modelling of charpy impact test of armour steel
Materials Science and Engineering A-structural Materials Properties Microstructure and Processing, 2015Co-Authors: S Dhar, Sanjib Kumar Acharyya, Debasis Datta, A. Banerjee, N. NayakAbstract:Abstract The behaviour of typical armour steel material under large strains, high strain rates and elevated temperatures needs to be investigated to analyse and reliably predict its response to various types of dynamic loading like impact. An empirical constitutive relation developed by Johnson and Cook (J–C) is widely used to capture strain rate sensitivity of the metals. A Failure Model proposed by Johnson and Cook is used to Model the damage evolution and predict Failure in many engineering materials. In this work, Model constants of J–C constitutive relation and damage parameters of J–C Failure Model for a typical armour steel material have been determined experimentally from four types of uniaxial tensile test. Some modifications in the J–C damage Model have been suggested and Finite Element simulation of three different tensile tests on armour steel specimens under dynamic strain rate (10 −1 s −1 ), high triaxiality and elevated temperature respectively has been done in ABAQUS platform using the modified J–C Failure Model as user material sub-routine. The simulation results are validated by the experimental data. Thereafter, a moderately high strain rate event viz. Charpy impact test on armour steel specimen has been simulated using J–C material and Failure Models with the same material parameters. Reasonable agreement between the simulation and experimental results has been achieved.
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An Experimental Determination of Johnson Cook Material and Failure Model Constants for Armour Steel
Applied Mechanics and Materials, 2014Co-Authors: A. Banerjee, S Dhar, Sanjib Kumar Acharyya, Debasis Datta, N. NayakAbstract:An experimental programme for determination of the Johnson Cook material and Failure Model constants for a typical armour steel material is reported. Tensile tests on specimens made from the armour material have been conducted at quasi-static and dynamic strain rates and at ambient and elevated temperatures. The analysis of the experimental data generates the Model constants that are required as inputs during numerical simulation of dynamic events like armour impact using Johnson Cook constitutive relation and Failure Model implemented in most of the commercially available Finite Element codes.
Hiromichi Ueda - One of the best experts on this subject based on the ideXlab platform.
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usefulness of cardiac iodine 123 meta iodobenzylguanidine imaging to improve prognostic power of seattle heart Failure Model in patients with chronic heart Failure
American Journal of Cardiology, 2011Co-Authors: Yuki Kuramoto, Shunsuke Tamaki, Takashi Morita, Yoshio Furukawa, Yusuke Iwasaki, T Yamada, Yuji Okuyama, Koji Tanaka, Taku Yasui, Hiromichi UedaAbstract:The Seattle Heart Failure Model (SHFM) is a validated prediction Model that estimates the mortality in patients with chronic heart Failure (CHF) using commonly obtained information, including clinical data, laboratory test results, medication use, and device implantation. In addition, cardiac iodine-123 meta-iodobenzylguanidine (MIBG) imaging provides prognostic information for patients with CHF. However, the long-term predictive value of combining the SHFM and cardiac MIBG imaging in patients with CHF has not been elucidated. To prospectively investigate whether cardiac iodine-123 MIBG imaging provides additional prognostic value to the SHFM in patients with CHF, we studied 106 outpatients with CHF who had radionuclide left ventricular ejection fraction 27%) had a significantly greater risk of cardiac death than did those with a normal WR for both those with a SHFM score of ≥ 1 (relative risk 3.3, 95% confidence interval 1.2 to 9.7, p = 0.01) and a SHFM score of ≤ 0 (relative risk 3.4, 95% confidence interval 1.2 to 9.6, p = 0.004). In conclusion, the cardiac MIBG WR provided additional prognostic information to the SHFM score for patients with CHF.
