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J Card Fail ; 26(2): 136-141, 2020 Feb.
Article in English | MEDLINE | ID: mdl-31574315

ABSTRACT

BACKGROUND: Outpatient monitoring and management of patients with heart failure (HF) reduces hospitalizations and health care costs. However, the availability of noninvasive approaches to assess congestion is limited. Noninvasive venous waveform analysis (NIVA) uses a unique physiologic signal, the morphology of the venous waveform, to assess intracardiac filling pressures. This study is a proof of concept analysis of the correlation between NIVA value and pulmonary capillary wedge pressure (PCWP) and the ability of the NIVA value to predict PCWP > 18 mmHg in subjects undergoing elective right heart catheterization (RHC). PCWP was also compared across common clinical correlates of congestion. METHODS AND RESULTS: A prototype NIVA device, which consists of a piezoelectric sensor placed over the skin on the volar aspect of the wrist, connected to a data-capture control box, was used to collect venous waveforms in 96 patients during RHC. PCWP was collected at end-expiration by an experienced cardiologist. The venous waveform signal was transformed to the frequency domain (Fourier transform), where a ratiometric algorithm of the frequencies of the pulse rate and its harmonics was used to derive a NIVA value. NIVA values were successfully captured in 83 of 96 enrolled patients. PCWP ranged from 4-40 mmHg with a median of 13 mmHg. NIVA values demonstrated a linear correlation with PCWP (r = 0.69, P < 0.05). CONCLUSIONS: This observational proof-of-concept study using a prototype NIVA device demonstrates a moderate correlation between NIVA value and PCWP in patients undergoing RHC. NIVA, thus, represents a promising developing technology for noninvasive assessment of congestion in spontaneously breathing patients.


Subject(s)
Cardiac Catheterization/methods , Heart Failure/diagnosis , Pulmonary Wedge Pressure/physiology , Pulse Wave Analysis/methods , Stroke Volume/physiology , Adult , Aged , Female , Heart Failure/physiopathology , Heart Failure/surgery , Humans , Male , Middle Aged , Principal Component Analysis/methods
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