Eighty percent of annual costs for heart failure come from hospitalization for the condition, which more than 23 million persons suffer from worldwide, 6.2 million (27%) in the United States alone. These costs are estimated to increase almost 58% between 2012 and 2030, from $30.7 billion to $53 billion.
Eighty percent of annual costs for heart failure (HF) come from hospitalization for the condition, which more than 23 million persons suffer from worldwide, 6.2 million (27%) in the United States alone. These costs are estimated to increase almost 58% between 2012 and 2030, from $30.7 billion to $53 billion, so identifying the highest-risk patients is essential.
Surgical solutions to reduce these costs have shown promise, but the effectiveness of nonsurgical methods remains unknown. Study results published this month in Circulation: Heart Failure demonstrate that a nonsurgical, adhesive, wearable sensor and its algorithm were 85% successful at predicting potential rehospitalization from HF.
“If we can identify patients before heart failure worsens and if doctors have the opportunity to change therapy based on this novel prediction, we could avoid or reduce hospitalizations, improve patients’ lives and greatly reduce health care costs,” stated lead study author Josef Stehlik, MD, MPH, medical director of the Heart Transplant Program and co-chief of the Advance Heart Failure Program at the University of Utah Hospital and the Salt Lake City Veterans Affairs Medical Center.
There were 100 patients in the phase 2 LINK-HF (Multisensor Non-invasive Remote Monitoring for Prediction of Heart Failure Exacerbation) study; their median (SD) age was 68.4 (10.2) years, and 98% were men. They all had histories of HF and New York Heart Association class II to IV symptoms and were enrolled at Veterans Affairs medical centers in Salt Lake City, Utah; Palo Alto, California; Houston, Texas; and Gainesville, Florida. Seventy-four percent had HF with reduced ejection fraction (<50%), and 26% had HF with preserved ejection fraction (>50%). The most common comorbid conditions were diabetes, atrial fibrillation, anemia, and chronic obstructive pulmonary disease.
After study participants were instructed on their use, the sensors had to be worn all day and between 30 and 90 days after discharge for a baseline HF exacerbation hospitalization. Data were collected from August 2015 through December 2016. At the baseline visit, the median (SD) blood pressure readings were 130 (27) mm Hg for systolic and 74 (16) mm Hg for diastolic. These values varied slightly during the study follow-up, coming in at 132 (26) and 75 (16) mm Hg for SBP and DBP, respectively, in the 75 patients who did not require subsequent hospitalization for HF and 124 (28) and 71 (17) mm Hg for the 25 patients who did.
The cloud-based data collected were analyzed via similarity-based modeling, and personalized models were developed for each study participant by 72 hours after their baseline visit discharge. At 30 days of follow-up, 87 patients remained enrolled in the study, and of these, 74 made it to the 90-day mark and 93.5% of their data were analyzed. The median time from a clinical alert (the difference between a patient’s baseline visit values and sensor-collected data) to subsequent hospitalization ranged from 6.5 (interquartile range [IQR], 4.2-13.7) to 8.5 (IQR, 3.8-13.0) days.
“The platform was able to detect the risk of hospitalization for worsening of HF with 76.0% to 87.5% sensitivity and 85% specificity,” the authors noted. In addition, they believe that the 6.5-to-8.5-day range is a valuable window in which to introduce an intervention to prevent the hospitalization.
However, they say additional investigations are needed to evaluate the accuracy of predicting risk of HF using nonsurgical methods. They also hope to utilize the same algorithm from this study to assess the effects of changing treatment based on its alerts.
A team at Cedars-Sinai recently tested monitoring HF in outpatients using a digital necklace, and they have a current study investigating an arm sensor, with the goal to effectively manage patients in the home.
Stehlik J, Schmalfuss C, Bozkurt B, et al. Continuous wearable monitoring analytics predict heart failure hospitalization. Circ Heart Fail. 2020;13(3):e006513. doi: 10.1161/CIRCHEARTFAILURE.119.006513.