Remote Monitoring in Heart Failure: Are We There Yet?

From 2013-2016, about 6.2 million American adults had heart failure and its prevalence continues to rise¹. The challenging nature of heart failure management is represented by the high potential for hospital admissions, poor quality of life and economic burden¹.

Bui and Fonarow captured the need for more effective and scalable strategies to assess, monitor, and treat heart failure in their 2012 paper “Home Monitoring for Heart Failure Management”². They note that it is increasingly apparent that interventions geared towards identifying and monitoring sub-clinical congestion would be of value in home management of chronic heart failure. Earlier identification and treatment of congestion, together with improved care coordination, management of comorbid conditions, and enhanced patient self-care may help prevent hospitalizations in patients with chronic heart failure².

While self-care is a main stay of heart failure management, it does have its limitations. First, recognition of impending congestion occurs late in the compensation/decompensation process. A patient’s intracardiac and pulmonary artery pressures (PAP) increase several days to weeks before onset of symptoms that typically trigger hospital admission^3-5. Therefore, self-recognition of symptoms provides a very narrow window for intervention.

An additional challenge to self-care is the limitations of instruments to assess congestion. For example: although checking daily weight is an important part of heart failure self-management⁶, fewer than half of patients with heart failure weigh themselves daily⁷. Even if they did, an increase of greater than two kilograms over 24–72 hours only has a 9% sensitivity for detecting clinical deterioration⁸. And finally, patients often delay seeking care for heart failure symptoms or fail to bring new symptoms to the attention of their providers when they are seen⁹.

Recent advances in implanted and wearable biosensors, streaming physiological data, and personalized analytics are enabling a new frontier of available insight by which to manage these patients. For example, CardioMEMS^TM is a wireless device that monitors PAP and is implanted in the distal pulmonary artery via right heart catheterization. The CHAMPION trial showed a promising result with use of CardioMEMS^TM achieving 28% reduction of heart failure hospitalization in six months and 37% in 15 months without increasing other causes of hospitalization¹⁰. In a latter study, improvement was also seen in Kansas City cardiomyopathy questionnaire score and 6-minute walk test¹¹.

While this implanted device has strong efficacy evidence, its on-label use is limited to patients with heart failure NYHA class III on optimal medical therapy and a history of heart failure hospitalization within the last year; only a fraction of the existing heart failure population. This implant comes at a cost of around $19,000¹². However, studies have shown an overall cost savings over the average patient’s life of about $44,800¹².

Assuming early identification of pulmonary congestion is as powerful of a decompensation sign as it appears, an alternative and less invasive method of identifying increased PAP would have tremendous value. physIQ’s pinpointIQ^® is a system that is being developed to identify compensation and decompensation days before symptoms manifest¹³. This system avoids the typical pitfalls of relying solely on self-care, while giving healthcare professionals unprecedented insight into patient status, without the patient having to recognize or prompt a call for attention.

physIQ’s FDA 510(k)-cleared machine learning analytics address these challenges by applying proprietary artificial intelligence to “learn” the unique patterns of an individual’s vital signs across the full spectrum of activities in an ambulatory environment. From there a personalized baseline for each patient is automatically generated that, when compared to incoming monitored data, can indicate subtle changes that may be a precursor to clinical deterioration. In the context of the clinician’s workflow, pinpointIQ^® is a personalized anomaly-detection tool specifically designed to pinpoint—pun intended— within a large population the handful of patients who, at any given time, are indicating physiological change and may be at risk of decompensation. Patient-level drill-down functionality then allows the clinician to explore specialized analytics to further characterize potential health issues. The result is a disease-agnostic solution that enables proactive care delivery based on personalized insight, with the intent of ultimately supporting exception-based care across a large population of patients.

Early observational work supports pinpointIQ^®’s analytics by demonstrating high levels of sensitivity and specificity. In the LINK HF study, researchers examined the performance of our personalized analytics platform using continuous multivariate data streams to predict rehospitalization after heart failure admission¹³. We concluded that use of pinpointIQ^® could accurately detect impending rehospitalization as much as 10 days before hospitalization (a predictive accuracy comparable to implanted devices)¹³.

Endotronix’s, Cordella™ PA Pressure Sensor System is launching its pivotal trial with similar end points as the CHAMPION trial. While a new competitor to the market could impact pricing, it is still an implanted device and will likely have similar labeling restrictions and certainly a higher price point than a non-invasive system like pinpointIQ^®.

Early detection of compensation/decompensation signs in heart failure allows clinicians to make more timely and informed treatment decisions. With an emerging buffet of systems that can provide this insight, the question now becomes, ‘which system is the most cost-effective?’ Stay tuned, this market is picking up the pace!

¹ Benjamin, E. et al. Heart Disease and Stroke Statistics—2018 Update: A Report From the American Heart Association. Circulation. 2018; 137:e67–e492. doi: 10.1161/CIR.0000000000000558

² Bui, AL and Fonarow, GC Home Monitoring for Heart Failure. J Am Coll Cardiol. 2012; 59(2): 97–104.

doi: 10.1016/j.jacc.2011.09.044

³ Zile MR, Bennett TD, St John Sutton M, et al. Transition from chronic compensated to acute decompensated heart failure: pathophysiological insights obtained from continuous monitoring of intracardiac pressures. Circulation. 2008;118:1433–1441.

⁴ Ritzema J, Troughton R, Melton I, et al. Physician-directed patient self-management of left atrial pressure in advanced chronic heart failure. Circulation. 2010;121:1086–1095.

⁵ Adamson PB, Magalski A, Braunschweig F, et al. Ongoing right ventricular hemodynamics in heart failure: clinical value of measurements derived from an implantable monitoring system. J Am Coll Cardiol. 2003;41:565–571.

⁶ Chaudhry SI, Wang Y, Concato J, Gill TM, Krumholz HM. Patterns of weight change preceding hospitalization for heart failure. Circulation. 2007;116:1549–1554.

⁷ Moser DK, Doering LV, Chung ML. Vulnerabilities of patients recovering from an exacerbation of chronic heart failure. Am Heart J. 2005;150:984.

⁸ Lewin J, Ledwidge M, O'Loughlin C, McNally C, McDonald K. Clinical deterioration in established heart failure: what is the value of BNP and weight gain in aiding diagnosis? Eur J Heart Fail. 2005;7:953–957.

⁹ Evangelista LS, Dracup K, Doering LV. Treatment-seeking delays in heart failure patients. J Heart Lung Transplant. 2000;19:932–938.

¹⁰ Abraham, W. T., Adamson, P. B., Bourge, R. C., Aaron, M., Costanzo, M. R., Stevenson, L. W., … Yadav, J. S., for the CHAMPION Trial Study Group. The Wireless pulmonary artery haemodynamic monitoring in chronic heart failure: A randomised controlled trial. 2011; 377(9766), 658-666. doi.org/10.1016/S0140-6736(11)60101-3

¹¹ Alam, A; Jermyn, R; Joseph, M; Patel, S; Jorde, U; and Saeed, O. Improved quality of life scores and exercise capacity with remote pulmonary artery pressure monitoring in patients with chronic heart failure. JACC. 67, 13 S 2016 DOI: 10.1016/S0735-1097(16)31300-6

¹² Schmier JK, Ong KL, Fonarow GC. Cost-Effectiveness of Remote Cardiac Monitoring With the CardioMEMS Heart Failure System Clin Cardiol. 2017; 40(7):430-436. doi: 10.1002/clc.22696. 

¹³ Stehlik, J; et al. Continuous wearable monitoring analytics predict heart failure decompensation: the link-hf multi-center study. JACC. 2018; 71(11):646.