Improving the Use of Primary Prevention Implantable Cardioverter-Defibrillators Therapy With Validated Patient-Centric Risk Estimates.

Objectives: The authors previously developed the Seattle Proportional Risk Model (SPRM) in systolic heart failure patients without implantable cardioverter-defibrillators (ICDs)to predict the proportion of deaths that were sudden. They subsequently validated the SPRM in 2 observational ICD data sets. The objectives in the present study were to determine whether this validated model could improve identification of clinically important variations in the expected magnitude of ICD survival benefit by using a pivotal randomized trial of primary prevention ICD therapy.

Selective screening for atrial fibrillation using multivariable risk models.

Objective: Atrial fibrillation can lead to stroke if untreated, and identifying those at higher risk is necessary for cost-effective screening for asymptomatic, paroxysmal atrial fibrillation. Age has been proposed to identify those at risk, but risk models may provide better discrimination. This study compares atrial fibrillation risk models with age for screening for atrial fibrillation.

Accurate, Automated Detection of Atrial Fibrillation in Ambulatory Recordings.

A highly accurate, automated algorithm would facilitate cost-effective screening for asymptomatic atrial fibrillation. This study analyzed a new algorithm and compared it to existing techniques. The incremental benefit of each step in refinement of the algorithm was measured, and the algorithm was compared to other methods using the Physionet atrial fibrillation and normal sinus rhythm databases.

The Seattle Post Myocardial Infarction Model (SPIM): prediction of mortality after acute myocardial infarction with left ventricular dysfunction.

Aims: Ischemic heart disease is a leading worldwide cause of death. The Seattle Post Myocardial Infarction Model (SPIM) was developed to predict survival 6 months to 2 years after an acute myocardial infarction with evidence of left ventricular dysfunction.

Methods And Results: A total of 6632 subjects from the EPHESUS trial were used to derive the predictive model, while 5477 subjects from the OPTIMAAL trial were used to validate the model.

Impact of renal dysfunction on the Seattle Heart Failure Model.

Background: Renal dysfunction (RD) is a strong predictor of mortality in patients with heart failure (HF). However, its impact on the discrimination of the Seattle Heart Failure Model (SHFM) is poorly understood.

Methods: Serum creatinine (SCr) and creatinine clearance (CrCl) were reviewed for patients from four of the six cohorts originally used to derive and validate the SHFM.

Factors affecting acceptance of a Web-based self-referral system.

With the growing availability of health information on the Web, people are becoming more knowledgeable on their health conditions and treatment options, and more patients seek specialists by themselves. To aid patients in requesting self-referrals, we have developed and evaluated a web-based self-referral system in three specialty clinics at the University of Washington. Two clinics adopted the system for routine clinical use, while the third clinic decided not to.

Maximizing survival benefit with primary prevention implantable cardioverter-defibrillator therapy in a heart failure population.

Background: Although implantable cardioverter-defibrillator (ICD) therapy reduces mortality in moderately symptomatic heart failure patients with an ejection fraction
Methods And Results: Using a modification of a previously validated risk prediction model based on routine clinical variables, we examined the relationship between baseline predicted mortality risk and the relative and absolute survival benefits of ICD treatment in the primary prevention Sudden Cardiac Death in Heart Failure Trial (SCD-HeFT). In the placebo arm, predicted 4-year mortality grouped into 5 equal-sized risk groups varied from 12% to 50% (c statistic=0.

Can the Seattle heart failure model be used to risk-stratify heart failure patients for potential left ventricular assist device therapy?

Background: According to results of the REMATCH trial, left ventricular assist device therapy in patients with severe heart failure has resulted in a 48% reduction in mortality. A decision tool will be necessary to aid in the selection of patients for destination left ventricular assist devices (LVADs) as the technology progresses for implantation in ambulatory Stage D heart failure patients. The purpose of this analysis was to determine whether the Seattle Heart Failure Model (SHFM) can be used to risk-stratify heart failure patients for potential LVAD therapy.

Years-needed-to-treat to add 1 year of life: a new metric to estimate treatment effects in randomized trials.

Aims: A standard metric to estimate absolute treatment effects is numbers-needed-to-treat (NNT), which implicitly assumes that all benefits reverse at trial-end. However, in-trial survival benefits typically do not reverse until long after trial-end, so that NNT will substantially underestimate lifetime benefits.

Methods And Results: We developed a new concept, years-needed-to-treat (YNT) to add 1 year of life, that quantifies the expected average life expectancy for two treatments including the estimated years of life remaining post-trial.

Prediction of mortality in patients with heart failure and systolic dysfunction.

Systolic heart failure has a highly variable mortality that can be altered with medications and cardiac devices. This review focuses on recently published predictive models in heart failure. These models may help with difficult decisions such as listing for cardiac transplantation, selecting cardiac devices, and making end-of-life decisions.

Validation of the Seattle Heart Failure Model in a community-based heart failure population and enhancement by adding B-type natriuretic peptide.

Management of heart failure (HF) remains complex with low 5-year survival. The Seattle Heart Failure Model (SHFM) is a recently described risk score derived predominantly from clinical trial populations that may enable the prediction of survival in patients with HF. This study sought to validate the SHFM in an independent, nonclinical trial-based HF population.

Prediction of mode of death in heart failure: the Seattle Heart Failure Model.

Background: Prognosis and mode of death in heart failure patients are highly variable in that some patients die suddenly (often from ventricular arrhythmia) and others die of progressive failure of cardiac function (pump failure). Prediction of mode of death may facilitate decisions about specific medications or devices.

Methods And Results: We used the Seattle Heart Failure Model (SHFM), a validated prediction model for total mortality in heart failure, to assess the mode of death in 10,538 ambulatory patients with New York Heart Association class II to IV heart failure and predominantly systolic dysfunction enrolled in 6 randomized trials or registries.

The Seattle Heart Failure Model: prediction of survival in heart failure.

Background: Heart failure has an annual mortality rate ranging from 5% to 75%. The purpose of the study was to develop and validate a multivariate risk model to predict 1-, 2-, and 3-year survival in heart failure patients with the use of easily obtainable characteristics relating to clinical status, therapy (pharmacological as well as devices), and laboratory parameters.

Methods And Results: The Seattle Heart Failure Model was derived in a cohort of 1125 heart failure patients with the use of a multivariate Cox model.

Technical Issues Related to Digital Stress Echocardiography Analysis and Acquisition Stations.

Digital acquisition and display of echocardiographic images has facilitated the development of stress echocardiography. This review will outline technical issues involved with digital capture and manipulation of echocardiographic data, referring to currently available commercial equipment. Among the acquisition items to be discussed are the source of the digitized data (direct digital transfer from the echo machine vs video capture); image resolution (spatial and temporal spacing of the digitized data); and EKG triggering options (direct triggering of the QRS complex vs detecting the QRS on the video transfer).