Should we continue trusting bodyweight for HF volume telemonitoring?

Abstract

Abstract Background Telemonitoring of body weight, blood pressure and heart rate has extensively been used to facilitate early recognition of heart failure (HF) decompensations in order to prevent hospital admissions. Mixed results have been shown by different clinical trials regarding the effectiveness of telemonitoring in HF management. It is recognized that HF decompensation starts with an increase in intra cardiac filling pressure. The relationship between daily pulmonary artery pressure (PAP) values measurements and body weight is not completely established. Purpose To study the relationship between daily changes in body weight and invasive daily measurements such as diastolic PAP assessed by a pulmonary artery pressure sensor (CardioMEMS). Methods Eleven patients with left sided chronic HF implanted with a CardioMEMS device were included in the study (age 75±9 years, ejection fraction 52±10%). Daily measured body weight and pulmonary artery pressures were recorded during 6 months. The primary endpoint was to assess the correlation between the “PAP deviation” – defined as the difference between daily diastolic PAP and mean diastolic PAP over the study period – and the “weight deviation” – defined as the difference between daily body weight determinations and mean body weight. Results During the 6-months study period, 1766 body weight and diastolic PAP measurements were recorded (158±47 daily measurements per patient). We found a weak although significant correlation between changes in body weight and changes in diastolic PAP (Figure 1). When analyzing data individually, no patient presented a strong correlation between the two variables. Conclusions Our results suggest that repeated measurements of body weight have limited utility in detecting left sided heart failure decompensations. More complex and patient-specific thresholds for the biometric measurements are needed to improve early detection of decompensation in HF patients. For the development of early detection algorithms, we may learn from invasive methods, such as the CardioMEMS device. Funding Acknowledgement Type of funding sources: None.