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Novel Dynamic Heart Failure Risk Score Incorporating Implanted Device Diagnostic Parameters
S42 Journal of Cardiac Failure Vol. 16 No. 8S August 2010
Surgery, Transplantation, Devices 129
131
Predictive Value of the Seattle Heart Failure Model in Patients Undergoing Left Ventricular Assist Device Placement Eric S. Ketchum1, Daniel P. Fishbein1, Nahush A. Mokadam2, Edward D. Verrier2, Gabriel S. Aldea2, Shauna Andrus2, Kenneth W. Kenyon1, Wayne C. Levy1; 1 Cardiology/Internal Medicine, University of Washington, Seattle, WA; 2 Cardiothoracic Surgery, University of Washington, Seattle, WA
Impact of Shock Energy and Ventricular Rhythm on the Success of First Shock Therapy e The ALTITUDE Study Group Yong-Mei Cha1, David L. Hayes1, Samuel J. Asirvatham1, Brian D. Powell1, David A. Cesario2, Michael Cao2, F. Roosevelt Gilliam III3, Paul Jones4, Milan Seth4, Leslie A. Saxon5; 1Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN; 2Cardiovascular Medicine, Keck School of Medicine, Los Angeles, CA; 3 Cardiology Associates of Northeast Arkansas, Jonesboro, AR; 4Boston Scientific, St. Paul, MN; 5University of Southern California, Los Angeles, CA
Background: Left ventricular assist devices (LVADs) are increasingly used in advanced heart failure patients as a life-prolonging bridge to transplantation or as definitive destination therapy. Despite proven efficacy, optimal timing of LVAD implantation is not well defined. We sought to test the Seattle Heart Failure Model (SHFM) as a predictor of outcomes in patients undergoing LVAD placement. Methods: Patients receiving an LVAD between 1997 and 2008 at a single institution were prospectively recorded. Labs and clinical data were extracted from medical records and used to calculate a risk score and predicted survival using the SHFM. This was compared to actual survival, length of stay in the hospital, and timeliness of discharge from the hospital. Patients who underwent transplantation were censored as alive. Results: We identified 104 patients who had a history of at least 30 days of refractory heart failure. Ejection fraction was 18 þ /8% (þ /SD). 77% were dependent on a balloon pump, and 88% required intravenous inotropic support. Survival with an LVAD versus SHFM predicted survival with medical therapy was 69% vs. 11% at one year, corresponding to a hazard ratio of 0.17 (p ! 0.0001). SHFM estimated one-year survival with medical therapy increased from 4% in 1997-2004 to 25% in 2007-2008 (p ! 0.0001). Subgroup analysis of higher vs. lower risk patients showed observed one year survival of 83% vs. 57% (p 5 0.04). The lower risk group had a shorter length of stay (46 vs. 75 days, p 5 0.03), along with higher rates of discharge prior to transplant (88% vs. 61%, p 5 0.01) and discharge within 60 days of LVAD placement (77% vs. 52%, p 5 0.03). Conclusions: The SHFM allows prediction of important features of a patient’s hospital course post-operatively, including length of stay and one 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.
Introduction: The efficacy of first shock therapy in converting different types of ventricular tachyarrhythmia has not been well characterized. The objective of the study was to determine the first shock success rate by shock energy and ventricular rhythm from a large cohort of patients with implantable cardiovertor defibrillation (ICD). Methods: From 81,081 ICD patients in the Boston Scientific LATITUDE remote monitoring system, we randomly sampled 2000 patients with 5279 shock episodes for analysis. Results: Of the 3,462 episodes with a shock as initial therapy, 2,679 were classified as ventricular arrhythmia, including 1,544 episodes of monomorphic (MVT, 58%), 371 polymorphic (PVT, 14%) ventricular tachycardia, and 764 ventricular fibrillation (VF, 28%). The first shock success rate was 90.9%, 88.9% and 89.7% for MVT, PVT and VF (p 5 .430). The first shock success rate by energy and rhythm is depicted in Figure 1. By fitting a logistic regression (GEE) model, energy, but not adjudicated rhythm, was a significant predictor of first shock success (OR 5 1.16, 95% CI 1.03-1.30, p 5 .013) for a 5J increase. However, among episodes with energy level #20J, the success rate for MVT (89.2%) was significantly higher than VF (80.8%, p 5 .04). Conclusions: MVT is the most common ventricular tachyarrhythmia triggering device therapy. Despite high shock energy achieves greater success across any type of ventricular rhythm, the first shock failure rate is approximate 10%. Programming the highest energy output for the first shock is recommended to achieve maximal efficacy of ICD therapy.
130 Novel Dynamic Heart Failure Risk Score Incorporating Implanted Device Diagnostic Parameters Shantanu Sarkar, Jodi Koehler, Holly Vitense, Doug Hettrick; Cardiac Rhythm and Disease Management, Medtronic Inc., Moundsview, MN Introduction: Besides providing therapy, implantable cardioverter defibrillators (ICDs) and cardiac resynchronization therapy defibrillators (CRT-D) devices also monitor valuable heart failure (HF) diagnostics. Whether the clinical utility of implantable device-derived HF diagnostic parameters can be improved by combining all the parameters into a single HF risk score is unknown. We developed and validated a novel dynamic HF risk score incorporating daily measurements of device-derived HF diagnostics parameters. Methods: The development set included 921 HF subjects with 9790 follow-up months. A separate validation set included 784 subjects with 8,149 months. Clinical trial datasets consisted of device derived intrathoracic impedance, night heart rate, heart rate variability, patient activity, ventricular and atrial tachyarrhythmias (AT), ventricular rate during ATand % CRT pacing. A Bayesian directed acyclic graph model was constructed using the development dataset to estimate the HF risk score, which is the probability of a HF event in the next 6 months given each of the device diagnostic meets certain criteria. Performance evaluation of the HF risk score was performed by simulating a monthly follow-up which consisted of looking back at the maximum risk score (MRS) in the last 30 days and evaluating the occurrence of HF events (HF hospitalization or ER visit with IV diuretic administration) in the following 30 days using a generalized linear model. A Cox Proportional Hazards model was used to compare survival free from HF events based on MRS. Results: In the validation dataset, follow up months preceding a HF event within the next 30 days had significantly higher MRS than months not preceding a HF event (p ! 0.001, adjusted for multiple observations per subject). Risk of a HF event was significantly higher for the MRS scores in the highest vs lowest distribution quartile (Hazard ratio 9.83 [4.31, 22.42; 95%CI]). Conclusion: A novel integrated HF risk score including all device derived HF related diagnostics prospectively identified patients at significantly higher risk of a subsequent HF event within the next 30 days. The potential for remote patient monitoring of HF risk to improve patient management requires prospective investigation.
Validation data set results (n 5 784)
Monthly Risk Score (MRS)
No pending HF event (8027 months)
Pending HF event (122 months)
P
5.5% [3.7%e11.0%]
15.9% [7.0%e26.5%]
!0.001
Data are Median [25-75 percentile]
132 Acute Left Ventricular Reconstruction With Circumferential Mid-Ventricular Intramyocardial Injections of Alginate Hydrogel in Dogs with Chronic Heart Failure Itamar Ilsar1, Mengjun Wang1, Michael S. Sabbah1, Ramesh C. Gupta1, Sharad Rastogi1, Sam Helgerson2, Norman Tarazona2, Randall J. Lee3, Hani N. Sabbah1; 1 Henry Ford Hospital, Detroit, MI; 2Cardio Polymers, Inc., Laguna Hills, CA; 3 UCSF, San Francisco, CA Background: LV chamber dilation and sphericity are features of heart failure (HF) associated with increased mortality and morbidity. Surgical LV reconstruction and passive LV containment are approaches that address this maladaptive geometry. We examined the acute effects of a novel approach for improving LV function and geometry in dogs with HF, namely injections of Alginate hydrogel implants (AHIs) (AlgisylLVR) into the LV free wall. Methods: 12 microembolization-induced HF dogs (LV ejection fraction, EF!30%) were studied; 6 received 7 injections of 0.25-0.35 ml of AHI during open-chest procedure and 6 received saline (Sham Controls). Injections were made 1 to 1.5 cm apart along an LV wall circumference from the anteroseptal to the posteroseptal groove halfway between the apex and base. LV end-diastolic (ED) volume (EDV), end-systolic (ES) volume (ESV), EF, ES sphericity index (ESSI), ES thicknesses of the anterior (AWT) and posterior (PWT) LV wall, and slopes of the ES and ED pressure-volume relationships (PVR) quantified from pressurevolume loops, were measured before (Pre) and 3 hours after therapy. Results: Compared to controls, AHI reduced both EDV and ESV and increased EF, ESSI, mid-ventricular AWT and PWT.
Surgery, Transplantation, Devices 129
131
Predictive Value of the Seattle Heart Failure Model in Patients Undergoing Left Ventricular Assist Device Placement Eric S. Ketchum1, Daniel P. Fishbein1, Nahush A. Mokadam2, Edward D. Verrier2, Gabriel S. Aldea2, Shauna Andrus2, Kenneth W. Kenyon1, Wayne C. Levy1; 1 Cardiology/Internal Medicine, University of Washington, Seattle, WA; 2 Cardiothoracic Surgery, University of Washington, Seattle, WA
Impact of Shock Energy and Ventricular Rhythm on the Success of First Shock Therapy e The ALTITUDE Study Group Yong-Mei Cha1, David L. Hayes1, Samuel J. Asirvatham1, Brian D. Powell1, David A. Cesario2, Michael Cao2, F. Roosevelt Gilliam III3, Paul Jones4, Milan Seth4, Leslie A. Saxon5; 1Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN; 2Cardiovascular Medicine, Keck School of Medicine, Los Angeles, CA; 3 Cardiology Associates of Northeast Arkansas, Jonesboro, AR; 4Boston Scientific, St. Paul, MN; 5University of Southern California, Los Angeles, CA
Background: Left ventricular assist devices (LVADs) are increasingly used in advanced heart failure patients as a life-prolonging bridge to transplantation or as definitive destination therapy. Despite proven efficacy, optimal timing of LVAD implantation is not well defined. We sought to test the Seattle Heart Failure Model (SHFM) as a predictor of outcomes in patients undergoing LVAD placement. Methods: Patients receiving an LVAD between 1997 and 2008 at a single institution were prospectively recorded. Labs and clinical data were extracted from medical records and used to calculate a risk score and predicted survival using the SHFM. This was compared to actual survival, length of stay in the hospital, and timeliness of discharge from the hospital. Patients who underwent transplantation were censored as alive. Results: We identified 104 patients who had a history of at least 30 days of refractory heart failure. Ejection fraction was 18 þ /8% (þ /SD). 77% were dependent on a balloon pump, and 88% required intravenous inotropic support. Survival with an LVAD versus SHFM predicted survival with medical therapy was 69% vs. 11% at one year, corresponding to a hazard ratio of 0.17 (p ! 0.0001). SHFM estimated one-year survival with medical therapy increased from 4% in 1997-2004 to 25% in 2007-2008 (p ! 0.0001). Subgroup analysis of higher vs. lower risk patients showed observed one year survival of 83% vs. 57% (p 5 0.04). The lower risk group had a shorter length of stay (46 vs. 75 days, p 5 0.03), along with higher rates of discharge prior to transplant (88% vs. 61%, p 5 0.01) and discharge within 60 days of LVAD placement (77% vs. 52%, p 5 0.03). Conclusions: The SHFM allows prediction of important features of a patient’s hospital course post-operatively, including length of stay and one 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.
Introduction: The efficacy of first shock therapy in converting different types of ventricular tachyarrhythmia has not been well characterized. The objective of the study was to determine the first shock success rate by shock energy and ventricular rhythm from a large cohort of patients with implantable cardiovertor defibrillation (ICD). Methods: From 81,081 ICD patients in the Boston Scientific LATITUDE remote monitoring system, we randomly sampled 2000 patients with 5279 shock episodes for analysis. Results: Of the 3,462 episodes with a shock as initial therapy, 2,679 were classified as ventricular arrhythmia, including 1,544 episodes of monomorphic (MVT, 58%), 371 polymorphic (PVT, 14%) ventricular tachycardia, and 764 ventricular fibrillation (VF, 28%). The first shock success rate was 90.9%, 88.9% and 89.7% for MVT, PVT and VF (p 5 .430). The first shock success rate by energy and rhythm is depicted in Figure 1. By fitting a logistic regression (GEE) model, energy, but not adjudicated rhythm, was a significant predictor of first shock success (OR 5 1.16, 95% CI 1.03-1.30, p 5 .013) for a 5J increase. However, among episodes with energy level #20J, the success rate for MVT (89.2%) was significantly higher than VF (80.8%, p 5 .04). Conclusions: MVT is the most common ventricular tachyarrhythmia triggering device therapy. Despite high shock energy achieves greater success across any type of ventricular rhythm, the first shock failure rate is approximate 10%. Programming the highest energy output for the first shock is recommended to achieve maximal efficacy of ICD therapy.
130 Novel Dynamic Heart Failure Risk Score Incorporating Implanted Device Diagnostic Parameters Shantanu Sarkar, Jodi Koehler, Holly Vitense, Doug Hettrick; Cardiac Rhythm and Disease Management, Medtronic Inc., Moundsview, MN Introduction: Besides providing therapy, implantable cardioverter defibrillators (ICDs) and cardiac resynchronization therapy defibrillators (CRT-D) devices also monitor valuable heart failure (HF) diagnostics. Whether the clinical utility of implantable device-derived HF diagnostic parameters can be improved by combining all the parameters into a single HF risk score is unknown. We developed and validated a novel dynamic HF risk score incorporating daily measurements of device-derived HF diagnostics parameters. Methods: The development set included 921 HF subjects with 9790 follow-up months. A separate validation set included 784 subjects with 8,149 months. Clinical trial datasets consisted of device derived intrathoracic impedance, night heart rate, heart rate variability, patient activity, ventricular and atrial tachyarrhythmias (AT), ventricular rate during ATand % CRT pacing. A Bayesian directed acyclic graph model was constructed using the development dataset to estimate the HF risk score, which is the probability of a HF event in the next 6 months given each of the device diagnostic meets certain criteria. Performance evaluation of the HF risk score was performed by simulating a monthly follow-up which consisted of looking back at the maximum risk score (MRS) in the last 30 days and evaluating the occurrence of HF events (HF hospitalization or ER visit with IV diuretic administration) in the following 30 days using a generalized linear model. A Cox Proportional Hazards model was used to compare survival free from HF events based on MRS. Results: In the validation dataset, follow up months preceding a HF event within the next 30 days had significantly higher MRS than months not preceding a HF event (p ! 0.001, adjusted for multiple observations per subject). Risk of a HF event was significantly higher for the MRS scores in the highest vs lowest distribution quartile (Hazard ratio 9.83 [4.31, 22.42; 95%CI]). Conclusion: A novel integrated HF risk score including all device derived HF related diagnostics prospectively identified patients at significantly higher risk of a subsequent HF event within the next 30 days. The potential for remote patient monitoring of HF risk to improve patient management requires prospective investigation.
Validation data set results (n 5 784)
Monthly Risk Score (MRS)
No pending HF event (8027 months)
Pending HF event (122 months)
P
5.5% [3.7%e11.0%]
15.9% [7.0%e26.5%]
!0.001
Data are Median [25-75 percentile]
132 Acute Left Ventricular Reconstruction With Circumferential Mid-Ventricular Intramyocardial Injections of Alginate Hydrogel in Dogs with Chronic Heart Failure Itamar Ilsar1, Mengjun Wang1, Michael S. Sabbah1, Ramesh C. Gupta1, Sharad Rastogi1, Sam Helgerson2, Norman Tarazona2, Randall J. Lee3, Hani N. Sabbah1; 1 Henry Ford Hospital, Detroit, MI; 2Cardio Polymers, Inc., Laguna Hills, CA; 3 UCSF, San Francisco, CA Background: LV chamber dilation and sphericity are features of heart failure (HF) associated with increased mortality and morbidity. Surgical LV reconstruction and passive LV containment are approaches that address this maladaptive geometry. We examined the acute effects of a novel approach for improving LV function and geometry in dogs with HF, namely injections of Alginate hydrogel implants (AHIs) (AlgisylLVR) into the LV free wall. Methods: 12 microembolization-induced HF dogs (LV ejection fraction, EF!30%) were studied; 6 received 7 injections of 0.25-0.35 ml of AHI during open-chest procedure and 6 received saline (Sham Controls). Injections were made 1 to 1.5 cm apart along an LV wall circumference from the anteroseptal to the posteroseptal groove halfway between the apex and base. LV end-diastolic (ED) volume (EDV), end-systolic (ES) volume (ESV), EF, ES sphericity index (ESSI), ES thicknesses of the anterior (AWT) and posterior (PWT) LV wall, and slopes of the ES and ED pressure-volume relationships (PVR) quantified from pressurevolume loops, were measured before (Pre) and 3 hours after therapy. Results: Compared to controls, AHI reduced both EDV and ESV and increased EF, ESSI, mid-ventricular AWT and PWT.