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Respiratory variations in the photoplethysmographic waveform amplitude depend on type of pulse oximetry device

  • Lars Øivind HøisethEmail author
  • Ingrid Elise Hoff
  • Ove Andreas Hagen
  • Knut Arvid Kirkebøen
  • Svein Aslak Landsverk
Original Research

Abstract

Respiratory variations in the photoplethysmographic waveform amplitude predict fluid responsiveness under certain conditions. Processing of the photoplethysmographic signal may vary between different devices, and may affect respiratory amplitude variations calculated by the standard formula. The aim of the present analysis was to explore agreement between respiratory amplitude variations calculated using photoplethysmographic waveforms available from two different pulse oximeters. Analysis of registrations before and after fluid loads performed before and after open-heart surgery (aortic valve replacement and/or coronary artery bypass grafting) with patients on controlled mechanical ventilation. Photoplethysmographic (Nellcor and Masimo pulse oximeters) and arterial pressure waveforms were recorded. Amplitude variations induced by ventilation were calculated and averaged over ten respiratory cycles. Agreements for absolute values are presented in scatterplots (with least median square regression through the origin, LMSO) and Bland–Altman plots. Agreement for trending presented in a four-quadrant plot. Agreement between respiratory photoplethysmographic amplitude variations from the two pulse oximeters was poor with LMSO ΔPOPNellc = 1.5 × ΔPOPMas and bias ± limits of agreement 7.4 ± 23 %. Concordance rate with a fluid load was 91 %. Agreement between respiratory variations in the photoplethysmographic waveform amplitude calculated from the available signals output by two different pulse oximeters was poor, both evaluated by LMSO and Bland–Altman plot. Respiratory amplitude variations from the available signals output by these two pulse oximeters are not interchangeable.

Keywords

Aortic valve stenosis Blood volume Coronary artery bypass Monitoring Physiologic Photoplethysmography 

Notes

Acknowledgments

The study was funded by departmental resources only.

Conflict of interest

Pulse oximeter Masimo Radical 7 provided by Masimo Corp., which had no influence on the planning or conduction of the study. Otherwise, the authors declare no conflicts of interest. The authors declare no financial disclosures.

Ethical standard

All procedures performed in the study were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments.

Informed consent

Written informed consent was given by all the patients participating in the study.

References

  1. 1.
    Marik PE, Cavallazzi R, Vasu T, Hirani A. Dynamic changes in arterial waveform derived variables and fluid responsiveness in mechanically ventilated patients: a systematic review of the literature. Crit Care Med. 2009;37(9):2642–7. doi: 10.1097/CCM.0b013e3181a590da.CrossRefPubMedGoogle Scholar
  2. 2.
    Maguire S, Rinehart J, Vakharia S, Cannesson M. Technical communication: respiratory variation in pulse pressure and plethysmographic waveforms: intraoperative applicability in a North American academic center. Anesth Analg. 2011;112(1):94–6. doi: 10.1213/ANE.0b013e318200366b.CrossRefPubMedGoogle Scholar
  3. 3.
    Antonsen LP, Kirkeboen KA. Evaluation of fluid responsiveness: Is photoplethysmography a noninvasive alternative? Anesthesiol Res Pract. 2012;2012:617380. doi: 10.1155/2012/617380.PubMedPubMedCentralGoogle Scholar
  4. 4.
    Guerin L, Monnet X, Teboul JL. Monitoring volume and fluid responsiveness: from static to dynamic indicators. Best Pract Res Clin Anaesthesiol. 2013;27(2):177–85. doi: 10.1016/j.bpa.2013.06.002.CrossRefPubMedGoogle Scholar
  5. 5.
    Mannheimer PD. The light-tissue interaction of pulse oximetry. Anesth Analg. 2007;105(6 Suppl):S10–7. doi: 10.1213/01.ane.0000269522.84942.54.CrossRefPubMedGoogle Scholar
  6. 6.
    Shelley KH. Photoplethysmography: beyond the calculation of arterial oxygen saturation and heart rate. Anesth Analg. 2007;105(6 Suppl):S31–6. doi: 10.1213/01.ane.0000269512.82836.c9.CrossRefPubMedGoogle Scholar
  7. 7.
    Shelley KH, Alian AA, Shelley AJ. Role of the photoplethysmographic waveform in the care of high-risk surgical patients. Anesthesiology. 2013;118(6):1479–80. doi: 10.1097/ALN.0b013e31829101fa.CrossRefPubMedGoogle Scholar
  8. 8.
    Awad AA, Ghobashy MA, Ouda W, Stout RG, Silverman DG, Shelley KH. Different responses of ear and finger pulse oximeter wave form to cold pressor test. Anesth Analg. 2001;92(6):1483–6.CrossRefPubMedGoogle Scholar
  9. 9.
    Jablonka DH, Awad AA, Stout RG, Silverman DG, Shelley KH. Comparing the effect of arginine vasopressin on ear and finger photoplethysmography. J Clin Anesth. 2008;20(2):90–3. doi: 10.1016/j.jclinane.2007.09.008.CrossRefPubMedGoogle Scholar
  10. 10.
    Phillips JP, Belhaj A, Shafqat K, Langford RM, Shelley KH, Kyriacou PA. Modulation of finger photoplethysmographic traces during forced respiration: venous blood in motion? Conf Proc IEEE Eng Med Biol Soc. 2012;2012:3644–7. doi: 10.1109/EMBC.2012.6346756.PubMedGoogle Scholar
  11. 11.
    Pleth variability index: a dynamic measurement to help assess physiology and fluid responsiveness. http://www.masimo.com/pdf/pvi/LAB4583B_Technical_Bulletin_Pleth_Variability_Index.pdf. Accessed 27 March 2015.
  12. 12.
    Sandroni C, Cavallaro F, Marano C, Falcone C, De Santis P, Antonelli M. Accuracy of plethysmographic indices as predictors of fluid responsiveness in mechanically ventilated adults: a systematic review and meta-analysis. Intensive Care Med. 2012;38(9):1429–37. doi: 10.1007/s00134-012-2621-1.CrossRefPubMedGoogle Scholar
  13. 13.
    Addison PS. A review of signal processing used in the implementation of the pulse oximetry photoplethysmographic fluid responsiveness parameter. Anesth Analg. 2014;119(6):1293–306. doi: 10.1213/ANE.0000000000000392.CrossRefPubMedGoogle Scholar
  14. 14.
    Hoiseth LO, Hoff IE, Hagen OA, Landsverk SA, Kirkeboen KA. Dynamic variables and fluid responsiveness in patients for aortic stenosis surgery. Acta Anaesthesiol Scand. 2014;58(7):826–34. doi: 10.1111/aas.12328.CrossRefPubMedGoogle Scholar
  15. 15.
    Bland JM, Altman DG. Agreement between methods of measurement with multiple observations per individual. J Biopharm Stat. 2007;17(4):571–82. doi: 10.1080/10543400701329422.CrossRefPubMedGoogle Scholar
  16. 16.
    Le Manach Y, Hofer CK, Lehot JJ, Vallet B, Goarin JP, Tavernier B, Cannesson M. Can changes in arterial pressure be used to detect changes in cardiac output during volume expansion in the perioperative period? Anesthesiology. 2012;117(6):1165–74. doi: 10.1097/ALN.0b013e318275561d.CrossRefPubMedGoogle Scholar
  17. 17.
    Addison PS, Wang R, Uribe AA, Bergese SD. On better estimating and normalizing the relationship between clinical parameters: comparing respiratory modulations in the photoplethysmogram and blood pressure signal (DPOP versus PPV). Comput Math Methods Med. 2015;2015:576340. doi: 10.1155/2015/576340.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Nilsson LM. Respiration signals from photoplethysmography. Anesth Analg. 2013;117(4):859–65. doi: 10.1213/ANE.0b013e31828098b2.CrossRefPubMedGoogle Scholar
  19. 19.
    Landsverk SA, Hoiseth LO, Kvandal P, Hisdal J, Skare O, Kirkeboen KA. Poor agreement between respiratory variations in pulse oximetry photoplethysmographic waveform amplitude and pulse pressure in intensive care unit patients. Anesthesiology. 2008;109(5):849–55. doi: 10.1097/ALN.0b013e3181895f9f.CrossRefPubMedGoogle Scholar
  20. 20.
    Alian AA, Shelley KH. Photoplethysmography. Best Pract Res Clin Anaesthesiol. 2014;28(4):395–406. doi: 10.1016/j.bpa.2014.08.006.CrossRefPubMedGoogle Scholar
  21. 21.
    Feldman JM. Can clinical monitors be used as scientific instruments? Anesth Analg. 2006;103(5):1071–2. doi: 10.1213/01.ane.0000247882.20257.b6.CrossRefPubMedGoogle Scholar
  22. 22.
    Alian AA, Galante NJ, Stachenfeld NS, Silverman DG, Shelley KH. Impact of central hypovolemia on photoplethysmographic waveform parameters in healthy volunteers part 2: frequency domain analysis. J Clin Monit Comput. 2011;25(6):387–96. doi: 10.1007/s10877-011-9317-x.CrossRefPubMedGoogle Scholar
  23. 23.
    Cannesson M, Le MY. Noninvasive hemodynamic monitoring: no high heels on the farm; no clogs to the opera. Anesthesiology. 2012;. doi: 10.1097/ALN.0b013e3182700ad6.Google Scholar
  24. 24.
    Nilsson L, Johansson A, Kalman S. Respiratory variations in the reflection mode photoplethysmographic signal. Relationships to peripheral venous pressure. Med Biol Eng Comput. 2003;41(3):249–54.CrossRefPubMedGoogle Scholar
  25. 25.
    Hoiseth LO, Hoff IE, Skare O, Kirkeboen KA, Landsverk SA. Photoplethysmographic and pulse pressure variations during abdominal surgery. Acta Anaesthesiol Scand. 2011;55(10):1221–30. doi: 10.1111/j.1399-6576.2011.02527.x.CrossRefPubMedGoogle Scholar
  26. 26.
    Hoiseth LO, Hoff IE, Myre K, Landsverk SA, Kirkeboen KA. Dynamic variables of fluid responsiveness during pneumoperitoneum and laparoscopic surgery. Acta Anaesthesiol Scand. 2012;56(6):777–86. doi: 10.1111/j.1399-6576.2011.02641.x.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Faculty of MedicineUniversity of OsloOsloNorway
  2. 2.Department of AnesthesiologyOslo University HospitalOsloNorway
  3. 3.Norwegian Air Ambulance FoundationDrøbakNorway

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