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. METHODS: Survival data from 961 NYHA II-III subjects in the ADMIRE-HFX trial were included in this analysis. The predictive value of the SHFM alone and in combination with MIBG heart-to-mediastinum ratio (H/M) was compared for all-cause mortality (101 deaths during a median follow-up of 2 years). RESULTS: The addition of H/M to the SHFM in a Cox model significantly improved risk prediction (P < .0001), with a greater utility in higher risk SHFM patients. The observed 2-year mortality in the highest-risk SHFM subjects (rounded SHFM score of 1) was 24%, but varied from 46% with H/M <1.2 to 0% with H/M >1.8. Net reclassification improvement was 22.7% (P < .001), with 14.9% of subjects who died reclassified into a higher risk category than suggested by SHFM score alone (P = .01) and 7.9% of subjects who survived reclassified into a lower risk category (P < .0001). The 2-year integrated discrimination improvement (+4.14%, P < .0001) and the 1-year area under the receiver-operator characteristic curve (+0.04, P = .026) both showed significant improvement for the combined model with H/M compared to the SHFM alone. CONCLUSION: The addition of MIBG imaging to the SHFM improves risk stratification, especially in higher risk patients. MIBG may have clinical utility in higher risk patients who are being considered for devices such as ICD, CRT-D, LVAD, and cardiac transplantation.
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. METHODS: Survival data from 961 NYHA II-III subjects in the ADMIRE-HFX trial were included in this analysis. The predictive value of the SHFM alone and in combination with MIBG heart-to-mediastinum ratio (H/M) was compared for all-cause mortality (101 deaths during a median follow-up of 2 years). RESULTS: The addition of H/M to the SHFM in a Cox model significantly improved risk prediction (P < .0001), with a greater utility in higher risk SHFM patients. The observed 2-year mortality in the highest-risk SHFM subjects (rounded SHFM score of 1) was 24%, but varied from 46% with H/M <1.2 to 0% with H/M >1.8. Net reclassification improvement was 22.7% (P < .001), with 14.9% of subjects who died reclassified into a higher risk category than suggested by SHFM score alone (P = .01) and 7.9% of subjects who survived reclassified into a lower risk category (P < .0001). The 2-year integrated discrimination improvement (+4.14%, P < .0001) and the 1-year area under the receiver-operator characteristic curve (+0.04, P = .026) both showed significant improvement for the combined model with H/M compared to the SHFM alone. CONCLUSION: The addition of MIBG imaging to the SHFM improves risk stratification, especially in higher risk patients. MIBG may have clinical utility in higher risk patients who are being considered for devices such as ICD, CRT-D, LVAD, and cardiac transplantation.
Authors: Ilan Goldenberg; Anant K Vyas; W Jackson Hall; Arthur J Moss; Hongyue Wang; Hua He; Wojciech Zareba; Scott McNitt; Mark L Andrews Journal: J Am Coll Cardiol Date: 2008-01-22 Impact factor: 24.094
Authors: Ewout W Steyerberg; Andrew J Vickers; Nancy R Cook; Thomas Gerds; Mithat Gonen; Nancy Obuchowski; Michael J Pencina; Michael W Kattan Journal: Epidemiology Date: 2010-01 Impact factor: 4.822
Authors: D M Wieland; L E Brown; W L Rogers; K C Worthington; J L Wu; N H Clinthorne; C A Otto; D P Swanson; W H Beierwaltes Journal: J Nucl Med Date: 1981-01 Impact factor: 10.057
Authors: Wayne C Levy; Dariush Mozaffarian; David T Linker; Santosh C Sutradhar; Stefan D Anker; Anne B Cropp; Inder Anand; Aldo Maggioni; Paul Burton; Mark D Sullivan; Bertram Pitt; Philip A Poole-Wilson; Douglas L Mann; Milton Packer Journal: Circulation Date: 2006-03-13 Impact factor: 29.690
Authors: M R Bristow; R Ginsburg; W Minobe; R S Cubicciotti; W S Sageman; K Lurie; M E Billingham; D C Harrison; E B Stinson Journal: N Engl J Med Date: 1982-07-22 Impact factor: 91.245