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 < 40% (30 ± 8%). The SHFM score was obtained at enrollment, and the cardiac MIBG washout rate (WR) was calculated from anterior chest images obtained at 20 and 200 minutes after isotope injection. During a mean follow-up of 6.8 ± 3.5 years (range 0 to 13), 32 of 106 patients died from cardiac causes. A multivariate Cox analysis revealed that the WR (p = 0.0002) and SHFM score (p = 0.0091) were independent predictors of cardiac death. Kaplan-Meier analysis showed that patients with an abnormal WR (> 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.
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http://dx.doi.org/10.1016/j.amjcard.2010.12.019 | DOI Listing |
Heart Rhythm
September 2024
Department of Cardiology, Angiology, Haemostaseology and Medical Intensive Care, University Medical Centre Mannheim (UMM), Medical Faculty Mannheim, Heidelberg University, European Center for AngioScience (ECAS), and German Center for Cardiovascular Research (DZHK) Partner Site Heidelberg/Mannheim, Mannheim, Germany. Electronic address:
J Clin Med
September 2024
Jesselson Integrated Heart Center Share Zedek Medical Center, Jerusalem 9103102, Israel.
Pol Arch Intern Med
August 2024
Silesian Center for Heart Diseases, Zabrze, Poland; Third Department of Cardiology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland.
Introduction: Accurate risk assessment in patients with heart failure (HF) is crucial. Developing new models that combine biochemical and clinical variables with novel biomarkers is the best approach to improving the management and prognostic evaluation in this population.
Objectives: We aimed to assess and compare the predictive utility of a new prognostic scale, the Barcelona Bio‑Heart Failure (BCN Bio‑HF) risk calculator, as well as traditional risk scores, the Heart Failure Survival Score (HFSS) and the Seattle Heart Failure Model (SHFM), in patients with end‑stage HF.
Clin Res Cardiol
September 2024
Physics Department, UC Santa Barbara, Santa Barbara, CA, USA.
Background: Referral of patients with heart failure (HF) who are at high mortality risk for specialist evaluation is recommended. Yet, most tools for identifying such patients are difficult to implement in electronic health record (EHR) systems.
Objective: To assess the performance and ease of implementation of Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF), a machine-learning model that uses structured data that is readily available in the EHR, and compare it with two commonly used risk scores: the Seattle Heart Failure Model (SHFM) and Meta-Analysis Global Group in Chronic (MAGGIC) Heart Failure Risk Score.
J Clin Med
December 2023
Department of Surgery, Transplant Institute, University of Chicago, Chicago, IL 60637, USA.
Cardiovascular disease is the leading cause of mortality following kidney transplantation. Heart failure affects 17-21% of patients with chronic kidney disease and increases along with time receiving dialysis. The Seattle Heart Failure Model (SHFM) is a validated mortality risk model for heart failure patients that incorporates clinical, therapeutic, and laboratory parameters but does not include measures of kidney function.
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