Sudden cardiac deaths account for 350,000 to 380,000 deaths in the United States annually. Implantable cardioverter-defibrillators have improved sudden death outcomes in patients with heart failure, but only a minority of patients with defibrillators receives appropriate therapy for ventricular arrhythmias. The risk prediction for sudden death and selection of patients for defibrillators is based largely on left ventricular ejection fraction and heart failure symptoms because there are no other risk stratification tools that can determine the individual patients who will derive the greatest benefit. There are several other pharmacologic strategies designed to prevent sudden death in patients with heart failure.

Each year, there are over one million hospitalizations for heart failure in the United States, with a similar number in Western Europe. Although these patients respond to initial therapies, they have very high short and intermediate term (2-6 months) mortality and readmission rates, while the healthcare system incurs substantial costs. Several risk prediction models that can accurately identify high-risk patients have been developed using data from clinical trials, large registries or administrative databases. Use of multi-variable risk models at the time of hospital admission or discharge offers better risk stratification and should be encouraged, as it allows for appropriate allocation of existing resources and development of clinical trials testing new treatment strategies for patients admitted with heart failure.

Aims

Heart failure (HF) treatment guided by physicians with access to real-time pressure measurement from a wireless implantable pulmonary artery pressure (PAP) sensor (CardioMEMS), has previously been shown to reduce HF-related hospital admissions in the CHAMPION trial. However, uncertainty remains regarding the value of CardioMEMS in European health systems where healthcare costs are significantly lower than in the USA.

Methods and results

A Markov model was developed to estimate the cost-effectiveness of PAP-guided treatment of HF using the CardioMEMS™ HF system compared with usual care. Cost-effectiveness was measured as the incremental cost per quality-adjusted life year (QALY) gained. In the base case analysis over a time horizon of 10 years, PAP-guided HF therapy increased cost compared with usual care by £10 916 (€14 030). QALYs per patient for usual care and PAP-guided patients were 2.57 and 3.14, respectively, reflecting an increase of 0.57 QALYs with PAP-guided treatment. The resultant incremental cost-effectiveness ratio (ICER) is £19 274 (€24 772) per QALY gained. The base case analysis did not include staff time, due to a lack of data concerning this variable. Running the model with estimated staff time included resulted in an increased ICER of between £22 342 and £25 464 per QALY gained (€28 709-32 721).

Conclusion

The analysis indicates that integrating wireless PAP monitoring into the management of UK HF patients is likely to be a cost-effective addition to the HF treatment pathway for appropriate patients.

OBJECTIVE: Tracking changes in hemodynamic congestion and the consequent proactive readjustment of treatment has shown efficacy in reducing hospitalizations for patients with heart failure (HF). However, the cost-prohibitive nature of these invasive sensing systems precludes their usage in the large patient population affected by HF. The objective of this research is to estimate the changes in pulmonary artery mean pressure (PAM) and pulmonary capillary wedge pressure (PCWP) following vasodilator infusion during right heart catheterization (RHC), using changes in simultaneously recorded wearable seismocardiogram (SCG) signals captured with a small wearable patch. METHODS: A total of 20 patients with HF (20% women, median age 55 (interquartile range (IQR), 44-64) years, ejection fraction 24 (IQR, 16-43)) were fitted with a wearable sensing patch and underwent RHC with vasodilator challenge. We divided the dataset randomly into a training-testing set (n = 15) and a separate validation set (n = 5). We developed globalized (population) regression models to estimate changes in PAM and PCWP from the changes in simultaneously recorded SCG. RESULTS: The regression model estimated both pressures with good accuracies: root-mean-square-error (RMSE) of 2.5 mmHg and R2 of 0.83 for estimating changes in PAM, and RMSE of 1.9 mmHg and R2 of 0.93 for estimating changes in PCWP for the training-testing set, and RMSE of 2.7 mmHg and R2 of 0.81 for estimating changes in PAM, and RMSE of 2.9 mmHg and R2 of 0.95 for estimating changes in PCWP for the validation set respectively. CONCLUSION: Changes in wearable SCG signals may be used to track acute changes in intracardiac hemodynamics in patients with HF. SIGNIFICANCE: This method holds promise in tracking longitudinal changes in hemodynamic congestion in hemodynamically-guided remote home monitoring and treatment for patients with HF.

We present a low power multi-modal patch designed for measuring activity, altitude (based on high-resolution barometric pressure), a single-lead electrocardiogram, and a tri-axial seismocardiogram (SCG). Enabled by a novel embedded systems design methodology, this patch offers a powerful means of monitoring the physiology for both patients with chronic cardiovascular diseases, and the general population interested in personal health and fitness measures. Specifically, to the best of our knowledge, this patch represents the first demonstration of combined activity, environmental context, and hemodynamics monitoring, all on the same hardware, capable of operating for longer than 48 hours at a time with continuous recording. The three-channels of SCG and one-lead ECG are all sampled at 500 Hz with high signal-to-noise ratio, the pressure sensor is sampled at 10 Hz, and all signals are stored to a microSD card with an average current consumption of less than 2 mA from a 3.7 V coin cell (LIR2450) battery. In addition to electronic characterization, proof-of-concept exercise recovery studies were performed with this patch, suggesting the ability to discriminate between hemodynamic and electrophysiology response to light, moderate, and heavy exercise.

Ballistocardiography, the measurement of the reaction forces of the body to cardiac ejection of blood, is one of the few techniques available for unobtrusively assessing the mechanical aspects of cardiovascular health outside clinical settings. Recently, multiple experimental studies involving healthy subjects and subjects with various cardiovascular diseases have demonstrated that the ballistocardiogram (BCG) signal can be used to trend cardiac output, contractility, and beat-by-beat ventricular function for arrhythmias. The majority of these studies has been performed with "fixed" BCG instrumentation-such as weighing scales or chairs-rather than wearable measurements. Enabling wearable, and thus continuous, recording of BCG signals would greatly expand the capabilities of the technique; however, BCG signals measured using wearable devices are morphologically dissimilar to measurements from "fixed" instruments, precluding the analysis and interpretation techniques from one domain to be applied to the other. In particular, the time intervals between the electrocardiogram (ECG) and BCG-namely, the R-J interval, a surrogate for measuring contractility changes-are significantly different for the accelerometer compared to a "fixed" BCG measurement. This paper addresses this need for quantitatively normalizing wearable BCG measurement to "fixed" measurements with a systematic experimental approach. With these methods, the same analysis and interpretation techniques developed over the past decade for "fixed" BCG measurement can be successfully translated to wearable measurements.

AIMS: Prognostic enrichment strategies can make trials more efficient, although potentially at the cost of diminishing external validity. Whether using a risk score to identify a population at increased mortality risk could improve trial efficiency is uncertain. We aimed to assess whether Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF), a previously validated risk score, could improve clinical trial efficiency. METHODS AND RESULTS: Mortality rates and association of MARKER-HF with all-cause death by 1 year were evaluated in four community-based heart failure (HF) and five HF clinical trial cohorts. Sample size required to assess effects of an investigational therapy on mortality was calculated assuming varying underlying MARKER-HF risk and proposed treatment effect profiles. Patients from community-based HF cohorts (n = 11 297) had higher observed mortality and MARKER-HF scores than did clinical trial patients (n = 13 165) with HF with either reduced ejection fraction (HFrEF) or preserved ejection fraction (HFpEF). MARKER-HF score was strongly associated with risk of 1-year mortality both in the community (hazard ratio [HR] 1.48, 95% confidence interval [CI] 1.44-1.52) and clinical trial cohorts with HFrEF (HR 1.41, 95% CI 1.30-1.54), and HFpEF (HR 1.74, 95% CI 1.53-1.98), per 0.1 increase in MARKER-HF. Using MARKER-HF to identify patients for a hypothetical clinical trial assessing mortality reduction with an intervention, enabled a reduction in sample size required to show benefit. CONCLUSION: Using a reliable predictor of mortality such as MARKER-HF to enrich clinical trial populations provides a potential strategy to improve efficiency by requiring a smaller sample size to demonstrate a clinical benefit.

Background

In the current era, where advanced heart failure (AHF) has become an American Board of Internal Medicine-certified subspecialty, new data are needed to benchmark and value levels of clinical effort performed by AHF specialists (AHFMDs).

Methods and results

A 36-question survey was sent to 728 AHFMDs, members of the Heart Failure Society of America, and 224 (31%) responded. Overall, 56% worked in academic medical centers (AMCs) and were younger (48 ± 9 y vs 52 ± 10 y; P < .01) and were represented by a higher proportion of women (34% vs 21%, P < .01) compared with non-AMCs. The percentage of time in clinical care was lower in AMCs (64 ± 19% vs 78 ± 18%; P = .002), with similar concentration on evaluation and management services (79 ± 18% in AMCs vs 72 ± 18 % in non-AMCs; P = NS). The majority of nonclinical time was spent in program administration (10% in both AMCs and non-AMCs) and education/research (15% in AMC vs 5% in non-AMCs). Although 69% of respondents were compensated by work-relative value units (wRVUs), only a small percentage knew their target or the amount of RVUs generated. The mean annual wRVUs generated were lower in AMCs compared to non-AMCs (5,452 ± 1,961 vs 9,071 ± 3,484; P < .001). The annual compensation in AMCs was lower than in non-AMCs (45% vs 10% <$250,000 and 17% vs 61% >$350,000; P < .001) and the satisfaction with compensation was higher in non-AMCs.

Conclusions

AHFMDs' compensation is largely dependent by practice type (AMC vs non-AMC) and clinical productivity as measured by wRVUs. These data provide an opportunity for benchmarking work effort and compensation for AHFMDs, allowing distinction from segments of cardiologists with greater opportunity to accrue procedural wRVUs. They also show several differences between AMCs and non-AMCs that should be considered when formulating work assignment and compensation for AHFMDs.