Comparison between Respiratory Variations in Pulse Oximetry Plethysmographic Waveform Amplitude and Arterial Pulse Pressure during Major Abdominal Surgery

* Associate Professor, † Professor, Department of Anesthesiology and Critical Care, Hôpital de la Croix Rousse, Lyon, France.
Anesthesiology (Impact Factor: 6.17). 10/2012; 117(5):973-80. DOI: 10.1097/ALN.0b013e3182700901
Source: PubMed

ABSTRACT : To assess preload dependence, the variation of the plethysmographic waveform of pulse oximetry (ΔPOP) has been proposed as a surrogate of the pulse pressure variation (ΔPP). The aim of the study was to assess the ability of the pulse oximeter-derived plethysmographic analysis to accurately trend ΔPP in patients undergoing major abdominal surgery by using standard monitors.
: A continuous recording of arterial and plethysmographic waveform was performed in 43 patients undergoing abdominal surgery. ΔPP and ΔPOP were calculated on validated respiratory cycles.
: For analysis, 92,467 respiratory cycles were kept (73.5% of cycles recorded in 40 patients). The mean of intrapatient coefficients of correlation was low (r = 0.22). The Bland and Altman analysis showed a systematic bias of 5.21; the ΔPOP being greater than the ΔPP, this bias increased with the mean value of the two indices and the limits of agreement were wide (upper 21.7% and lower -11.3%). Considering a ΔPP threshold at 12% to classify respiratory cycles as responders and nonresponders, the corresponding best cutoff value of ΔPOP was 13.6 ± 4.3%. Using these threshold values, the observed classification agreement was moderate (κ = 0.50 ± 0.09).
: The wide limits of agreement between ΔPP and ΔPOP and the weak correlation between both values cast doubt regarding the ability of ΔPOP to substitute ΔPP to follow trend in preload dependence and classify respiratory cycles as responders or nonresponders using standard monitor during anesthesia for major abdominal surgery.

1 Follower
  • Source
    • "Whereas the same group in a later article 34 stated that the reported ∆POP was computed over 10 respiratory cycles and that these were calculated in a custom-made program in LabVIEW (National Instruments, Austin, TX). Hengy et al. 28 2012 " "
    [Show abstract] [Hide abstract]
    ABSTRACT: ΔPOP is a physiological parameter derived from the respiration-induced change in the pulse oximetry plethysmographic (POP) waveform or "pleth." It has been proposed as a proxy for pulse pressure variation used in the determination of the response to intravascular volume expansion in hypovolemic patients. Many studies have now reported on the parameter, and many research groups have constructed algorithms for its computation from the first principles where the implementation details have been described. This review focuses on the signal processing aspects of ΔPOP, as reported in the literature, and aims to provide a comprehensive summary of the wide-ranging algorithmic strategies that have been attempted in its computation. A search was conducted for articles concerning the use of ΔPOP as a fluid responsiveness parameter. In particular, articles concerning the correlation between ΔPOP and pulse pressure variation were targeted. Comments and replies to comments by the authors in which signal processing aspects were discussed were also included in the review. The parameter is first defined, and a history of the early work surrounding pleth-based fluid responsiveness parameters is presented. This is followed by an overview of the signal processing methods used in the reported studies, including details of exclusion criteria, manual filtering (preprocessing), gain change issues, acquisition details, selection of registration periods, averaging methods, physiological influences on the pleth, and comments by the investigators themselves. It is concluded that to develop a robust, fully automated ΔPOP algorithm for use in the clinical environment, more rigorous signal processing is required. Specifically, signals should be evaluated over significant periods of time, with emphasis on the quality and temporal relevance of the information.
    Anesthesia & Analgesia 12/2014; 119(6):1293-306. DOI:10.1213/ANE.0000000000000392 · 3.42 Impact Factor
  • Anesthesiology 10/2012; 117(5):937-9. DOI:10.1097/ALN.0b013e3182700ad6 · 6.17 Impact Factor
  • Source
    Anesthesiology 06/2013; 118(6):1479-80. DOI:10.1097/ALN.0b013e31829101fa · 6.17 Impact Factor
Show more