Fingertip photoplethysmographic waveform variability and systemic vascular resistance in intensive care unit patients

Abstract

Low frequency variability in the fingertip photoplethysmogram (PPG) waveform has been utilized for inferring sympathetic vascular control, but its relationship with a quantitative measure of vascular tone has not been established. In this study, we examined the association between fingertip PPG waveform variability (PPGV) and systemic vascular resistance (SVR) obtained from thermodilution cardiac output (CO) and intra-arterial pressure measurements in 48 post cardiac surgery intensive care unit patients. Among the hemodynamic measurements, both CO (P < 0.05) and SVR (P < 0.0001) had statistically significant relationships with the normalized low frequency power (LF(nu)) of PPGV. The LF(nu) of baseline PPGV had moderate but significant positive correlation with SVR (r = 0.54, P < 0.0001), and a value below 52.5 nu was able to identify SVR < 900 dyn s cm⁻⁵ with sensitivity of 59% and specificity of 95%. The results have provided quantitative evidence to confirm the link between fingertip PPGV and sympathetic vascular control. Suppression of LF vasomotor waves leading to dominance of respiration-related HF fluctuations in the fingertip circulation was a specific (though not sensitive) marker of systemic vasodilatation, which could be potentially utilized for the assessment of critical care patients.

Investigation of photoplethysmographic signals and blood oxygen saturation values on healthy volunteers during cuff-induced hypoperfusion using a multimode PPG/SpO2 sensor

“…In a previous study [6], a moderate correlation (r = 0.54) was found between fingertip PPG waveform variability (PPGV) and systemic vascular resistance (SVR). The use of a multimode finger PPG probe in such a study may help establish a better link between the PPGV and SVR.…”

Method of estimation of synchronization strength between low-frequency oscillations in heart rate variability and photoplethysmographic waveform variability

“…The most popular opinion is that the low-frequency oscillations in PPGV characterize sympathetic regulation of peripheral vessels tone [26][27][28]. However, it is important to consider that PPG signal contains information about both peripheral blood flow (including microcirculatory bloodstream) and distal arterial bed [29].…”

Estimation of cardiac output and systemic vascular resistance using a multivariate regression model with features selected from the finger photoplethysmogram and routine cardiovascular measurements

“…The analysis was performed on data obtained from a heterogeneous group ( N =48) of post cardiac surgery patients from the intensive care unit (ICU) at the Prince of Wales Hospital, Sydney, Australia, as described previously in [17]. The patients had the following physiological characteristics (mean ±SEM): 33 males and 15 females, age (69 ±1.5 yr), heart rate (HR, 84 ±2 bpm), CO (5.7 ±0.2 L/min), mean arterial pressure (MAP, 78 ±3 mmHg), central venous pressure (CVP, 14.5 ±0.7 mmHg) and SVR (926 ±36 dyn.s.cm -5 ).…”

“…The signal was not subjected to any high-pass filtering prior to sampling to preserve the baseline variation. As described in a previous publication [17], the 48 sets of data used in the analysis were obtained after the exclusion of 16 sets of poor quality PPG signals (from a larger pool of 64 subjects) which contained severe motion artifact or baseline drift, frequent ectopic (abnormal) beats, and barely recognizable cardiac pulses which prohibited reliable pulse detection.…”

“…Specifically, unique features extracted from the PPG variability (PPGV), which were anticipated to improve the estimation accuracy, were used in the regression model. PPGV is the beat-to-beat fluctuation in the PPG waveform and the spectral power distribution of the PPGV has been found to be correlated with SVR [17] and to be potentially useful for clinical diagnosis (e.g., sepsis [18,19]). In addition to the PPGV features, the multivariate model in this study incorporated pulse wave morphological features from the PPG waveform (the pulse width) to help estimate CO and SVR.…”