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 20%), no benefit of ICD treatment was seen. Projected over each patient's predicted lifespan, ICD treatment added 6.3, 4.1, 3.0, 1.9, and 0.2 additional years of life in the lowest- to highest-risk groups, respectively. CONCLUSIONS: A clinical risk prediction model identified subsets of moderately symptomatic heart failure patients in SCD-HeFT in whom single-lead ICD therapy was of no benefit and other subsets in which benefit was substantial.

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. Numbers-needed-to-treat and YNT were calculated in the COMET trial, in which carvedilol vs. metoprolol tartrate resulted in 17% lower mortality over 4.8 years. A multivariate Cox model was used to predict survival. Remaining years of life were estimated using the mortality-life-table method. At trial-end, survival was 9% higher in the carvedilol arm. Assuming that patients remained on the same therapy post-trial, the average total years of life for carvedilol vs. metoprolol were 10.63 +/- 0.19 vs. 9.48 +/- 0.18 (P < 0.0001) or 1.15 (95% confidence interval 0.64-1.66) additional years of life. The YNT was 9.2, indicating that 9.2 person-years of treatment added 1 person-year of life, compared with NNT of 59. CONCLUSION: Compared with NNT, the YNT method more accurately accounts for potential long-term benefits of interventions in randomized trials.

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. During 16,735 person-years of follow-up, 2014 deaths occurred, which included 1014 sudden deaths and 684 pump-failure deaths. Compared with a SHFM score of 0, patients with a score of 1 had a 50% higher risk of sudden death, patients with a score of 2 had a nearly 3-fold higher risk, and patients with a score of 3 or 4 had a nearly 7-fold higher risk (P<0.001 for all comparisons; 1-year area under the receiver operating curve, 0.68). Stratification of risk of pump-failure death was even more pronounced, with a 4-fold higher risk with a score of 1, a 15-fold higher risk with a score of 2, a 38-fold higher risk with a score of 3, and an 88-fold higher risk with a score of 4 (P<0.001 for all comparisons; 1-year area under the receiver operating curve, 0.85). The proportion of deaths caused by sudden death versus pump-failure death decreased from a ratio of 7:1 with a SHFM score of 0 to a ratio of 1:2 with a SHFM score of 4 (P trend <0.001). CONCLUSIONS: The SHFM score provides information about the likely mode of death among ambulatory heart failure patients. Investigation is warranted to determine whether such information might predict responses to or cost-effectiveness of specific medications or devices in heart failure patients.

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. We discuss systolic heart failure risk models to estimate short- (30-day to 1-year) and longer-term (1- to 5-year) mortality in hospitalized and ambulatory heart failure patients.

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. METHODS: Nine atrial fibrillation risk models were compared using the Atherosclerosis Risk in Communities study (11 373 subjects, 60.0±5.7 years old). A new risk model (Screening for Asymptomatic Atrial Fibrillation Events-SAAFE) was created using data collected in the Monitoring Disparities in Chronic Conditions study (3790 subjects, 58.9±15.3 years old). The primary measure was the fraction of incident atrial fibrillation subjects who should receive treatment due to a high CHA2DS2-VASc score identified when screening a fixed number equivalent to the age criterion. Secondary measures were the C statistic and net benefit. RESULTS: Five risk models were significantly better than age. Age identified 71 (61%) of the subjects at risk for stroke who subsequently developed atrial fibrillation, while the best risk model identified 96 (82%). The newly developed SAAFE model identified 95 (81%), primarily based on age, congestive heart failure and coronary artery disease. CONCLUSIONS: Use of a risk model increases identification of subjects at risk for atrial fibrillation. One of the best performing models (SAAFE) does not require an ECG for its application, so that it could be used instead of age as a screening criterion without adding to the cost.

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. BACKGROUND: Recent data show that <50% of nominally eligible subjects receive guideline- recommended primary prevention ICDs. METHODS: In the SCD-HeFT (Sudden Cardiac Death in Heart Failure Trial), a placebo-controlled ICD trial in 2,521 patients with an ejection fraction ≤35% and symptomatic heart failure, we tested the use of patient-level SPRM-predicted probability of sudden death (relative to that of non-sudden death) as a summary measurement of the potential for ICD benefit. A Cox proportional hazards model was used to estimate variations in the relationship between patient-level SPRM predictions and ICD benefit. RESULTS: Relative to use of mortality predictions with the Seattle Heart Failure Model, the SPRM was much better at partitioning treatment benefit from ICD therapy (effect size was 2- to 3.6-fold larger for the ICD×SPRM interaction). ICD benefit varied significantly across SPRM-predicted risk quartiles: for all-cause mortality, a +10% increase with ICD therapy in the first quartile (highest risk of death, lowest proportion of sudden death) to a decrease of 66% in the fourth quartile (lowest risk of death, highest proportion of sudden death; p = 0.0013); for sudden death mortality, a 19% reduction in SPRM quartile 1 to 95% reduction in SPRM quartile 4 (p < 0.0001). CONCLUSIONS: In symptomatic systolic heart failure patients with a Class I recommendation for primary prevention ICD therapy, the SPRM offers a useful patient-centric tool for guiding shared decision making.

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. For medications and devices not available in the derivation database, hazard ratios were estimated from published literature. The model was prospectively validated in 5 additional cohorts totaling 9942 heart failure patients and 17,307 person-years of follow-up. The accuracy of the model was excellent, with predicted versus actual 1-year survival rates of 73.4% versus 74.3% in the derivation cohort and 90.5% versus 88.5%, 86.5% versus 86.5%, 83.8% versus 83.3%, 90.9% versus 91.0%, and 89.6% versus 86.7% in the 5 validation cohorts. For the lowest score, the 2-year survival was 92.8% compared with 88.7%, 77.8%, 58.1%, 29.5%, and 10.8% for scores of 0, 1, 2, 3, and 4, respectively. The overall receiver operating characteristic area under the curve was 0.729 (95% CI, 0.714 to 0.744). The model also allowed estimation of the benefit of adding medications or devices to an individual patient's therapeutic regimen. CONCLUSIONS: The Seattle Heart Failure Model provides an accurate estimate of 1-, 2-, and 3-year survival with the use of easily obtained clinical, pharmacological, device, and laboratory characteristics.

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. Patients (n = 4,077) from the hospital-based Intermountain Heart Collaborative Study registry with a diagnosis of HF were evaluated using prospectively collected data (mean +/- SD follow-up 4.4 +/- 3.1 years). The SHFM was used to calculate a risk score for each patient. Receiver-operating characteristic area under the curve provided SHFM predictive ability for a composite end point of survival free from death, transplantation, or left ventricular assist device implantation. Addition of creatinine, serum urea nitrogen, diabetes status, and B-type natriuretic peptide (BNP) to the SHFM was also evaluated. Patient age averaged 67 +/- 13 years and 61% were men. Area under the curves were 0.70 (95% confidence interval 0.66 to 0.70), 0.67 (95% confidence interval 0.66 to 0.69), 0.67 (95% confidence interval 0.065 to 0.68), and 0.66 (95% confidence interval 0.63 to 0.67) for 1-, 2-, 3-, and 5-year survivals, respectively. Area under the curves were slightly attenuated in patients >75 years of age (n = 1,339), implantable cardioverter-defibrillator recipients (n = 693), and patients with an ejection fraction >40% (n = 1,634). BNP added significantly to the model (area under the curve +0.06). BNP was found to add additional predictive ability at 1 year (area under the curve change +0.05) and nominally at 5 years (area under the curve change +0.02). In conclusion, the SHFM predicts survival in patients with HF in a hospital-based population, with areas under the curve similar to those from data on which models were initially fit.

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. When analyzing segments of 21 RR intervals or less, the algorithm had a significantly higher area under the receiver operating characteristic curve (AUC) than the other algorithms tested. At analysis segment sizes of up to 101 RR intervals, the algorithm continued to have a higher AUC than any of the other methods tested, although the difference from the second best other algorithm was no longer significant, with an AUC of 0.9992 with a 95% confidence interval (CI) of 0.9986-0.9998, vs. 0.9986 (CI 0.9978-0.9994). With identical per-subject sensitivity, per-subject specificity of the current algorithm was superior to the other tested algorithms even at 101 RR intervals, with no false positives (CI 0.0-0.8%) vs. 5.3% false positives for the second best algorithm (CI 3.4-7.9%). The described algorithm shows great promise for automated screening for atrial fibrillation by reducing false positives requiring manual review, while maintaining high sensitivity.

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). During playback, rest and stress images may be displayed at different frame rates, so that systole will occupy approximately the same time interval. Currently available commercial systems are compared with regard to these features, as well as hardware architecture and operating system.

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. Cox proportional hazards modeling was used to develop a multivariate risk score predictive of all-cause mortality. The SPIM risk score integrated lab and vital parameters, Killip class, reperfusion or revascularization, the number of cardiac evidence-based medicines (aspirin, statin, ß blocker, ACEI/ARB, aldosterone blocker), and the number of cardiac risk factors. The model was predictive of all-cause mortality after myocardial infarction, with an AUC of 0.75 at 6 months and 0.75 at 2 years in the derivation cohort and 0.77 and 0.78 for the same time points in the validation cohort. Model predicted versus Kaplan-Meier observed survival was excellent in the derivation cohort. It remained so in the validation cohort--84.9% versus 85.0% at 2 years. The 10% of subjects with the highest predicted risk had approximately 25 times higher mortality at 2 years than the 10% of subjects with the lowest predicted risk. CONCLUSION: The SPIM score was a powerful predictor of outcomes after myocardial infarction with left ventricular dysfunction. Its highly accurate predictions should improve patient and physician understanding of survival and may prove a useful tool in post-infarct risk stratification.

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. Patients were followed for death. The independent prediction of adding SCr or CrCl to the SHFM was assessed using multivariable Cox proportional hazards and the incremental value for prediction by changes in the ROC curves for 1- and 2-year event prediction. RESULTS: Among 7,146 patients (mean age 63 ± 11 years), 1,511 deaths occurred during a mean follow-up of 2.04 years. SCr and CrCl had a modest positive correlation with SHFM (r = 0.30, p = 0.002). In combination with SHFM, SCr (hazard ratio [HR] per mg/dl 1.25, 95% CI 1.13 to 1.38, p < 0.0001) and CrCl (HR per 10 ml/min 0.95, 95% CI 0.93 to 0.97, p < 0.0001) were both multivariable predictors of events. When stratified by absolute risk based on the SHFM, SCr or CrCl provided more additional information in lower risk patients and less or no additional information in higher risk patients. The addition of SCr and the SHFM*SCr, or CrCl and the SHFM*CrCl interaction to the SHFM was associated with almost no change in the 1- and 2-year area under ROC curves for the SHFM score. CONCLUSIONS: Compared with the SHFM alone, RD is independently predictive of mortality only in lower risk patients. Overall discrimination is only minimally improved with addition of SCr or CrCl to 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. A major difference between these two groups was in how fast online requests from patients were handled, which significantly influenced patients' satisfaction. Clinic's preparedness for handling the temporarily increased workload due to the introduction of a new health information system played a role as well. Also, we noticed that the physician leadership/championship made a difference in the acceptance of our system.

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. METHODS: The SHFM was applied to REMATCH patients with the prospective addition of inotropic agents and intra-aortic balloon pump (IABP) +/- ventilator. RESULTS: The SHFM was highly predictive of survival (p = 0.0004). One-year SHFM-predicted survival was similar to actual survival for both the REMATCH medical (30% vs 28%) and LVAD (49% vs 52%) groups. The estimated 1-year survival with medical therapy for patients in REMATCH was 30 +/- 21%, but with a range of 0% to 74%. The 1- and 2-year estimated survival was