Monitoring tissue perfusion: a pilot clinical feasibility and safety study of a urethral photoplethysmography-derived perfusion device in high-risk patients

Abstract

Continuous monitoring of tissue perfusion in patients with hemodynamic instability remains challenging because of the lack of tools available. Through using urethral photoplethysmography, the urethral perfusion index (uPI) could allow tissue perfusion monitoring through a modified urinary catheter. The first objective of our study was to evaluate the feasibility and safety of the IKORUS UP (Advanced Perfusion Diagnostics, Villeurbanne, France), a new device in the field. The secondary objectives were to evaluate the performance (duration and signal quality) of the IKORUS UP probe and to assess the uPI variations during hemodynamic events during major abdominal surgery. “STEP UP” was a prospective, multicenter, observational study. The inclusion criteria were age 18 years or older with signed informed consent and admitted to intensive care unit (ICU) with hemodynamic instability or high-risk surgical patient. Thirty patients were included in the study, 26 in the operating room, and four in the ICU. Of these patients, 28 were analyzed. For the primary outcome, six (21%) patients had pain scores assessed at insertion of and 22 (79%) at withdrawal of the catheter. The mean EVA score was 1.5 (IQ 1–2) and 0.7 (IQ 0–1), respectively, with the highest score being 3. One (4%) minor urethral bleeding and one (4%) catheter-associated urinary tract infection were reported. The IKORUS UP probe remained in the urethra for an average of 172 h (IQ40–328). The median signal measurement time was 33 h (IQ 5.2–46). The signal quality was recorded as good or excellent for 99% (IQ 82–100) of the insertion duration. The signal quality index was 93% (IQ 87–96) with a signal-to-noise ratio of 26 (IQ 21–36). We observed clinically relevant variations in the uPI over time during hemodynamic events or therapeutic interventions, with a strong cross-correlation with macrohemodynamic variables in some patients, while others did not display macrohemodynamic changes. The IKORUS UP probe was well tolerated and allowed urethral perfusion monitoring. Clinically relevant changes in tissue perfusion could be recorded during the observational period. Trial Registration: (www.clinicaltrials.gov NCT03410069) registered January 25, 2018.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

ICU:

Intensive care unit

BMI:

Body mass index

SV:

Stroke volume

IQ:

Interquartile

cvAUC:

Area under the receiver operating characteristics curve

RRT:

Renal replacement therapy

PI:

Perfusion index

uPI:

Urethral perfusion index

References

  1. 1.

    Vincent J-L, De Backer D. Circulatory shock. N Engl J Med. 2013;369:1726–34.

    CAS  Article  Google Scholar 

  2. 2.

    Hernández G, Ospina-Tascón GA, Damiani LP, Estenssoro E, Dubin A, Hurtado J, et al. Effect of a resuscitation strategy targeting peripheral perfusion status vs serum lactate levels on 28-day mortality among patients with septic shock: the ANDROMEDA-SHOCK Randomized Clinical Trial. JAMA. 2019;321:654.

    Article  Google Scholar 

  3. 3.

    Dubin A, Henriquez E, Hernández G. Monitoring peripheral perfusion and microcirculation. Curr Opin Crit Care. 2018;24:173–80.

    Article  Google Scholar 

  4. 4.

    Robert B, Poon MW, Stewart SC. Effect of cardiopulmonary bypass on urethral blood flow as measured by laser doppler flowmetry. J Urol. 1998;160(6):2030–2.

    Google Scholar 

  5. 5.

    Talja M, Schröder T, Lehtola A, Nuutinen P, Ruutu M, Alfthan O. Blood circulation in the urethra during hypovolemia ? An experimental study. Urol Res. 1986;14(5):267–70. https://doi.org/10.1007/BF00256572.

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Toulouse E, Masseguin C, Lafont B, McGurk G, Harbonn A, Roberts JA, et al. French legal approach to clinical research. Anaesth Crit Care Pain Med. 2018;37:607–14.

    Article  Google Scholar 

  7. 7.

    Lima AP, Beelen P, Bakker J. Use of a peripheral perfusion index derived from the pulse oximetry signal as a noninvasive indicator of perfusion. Crit Care Med. 2002;30:1210–3.

    Article  Google Scholar 

  8. 8.

    Chen T, Clifford G, Mark R. The effect of signal quality on six cardiac output estimators. Comput Cardiol. 2009;2009:197–200.

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Ono M, Arnaoutakis GJ, Fine DM, Brady K, Easley RB, Zheng Y, et al. Blood pressure excursions below the cerebral autoregulation threshold during cardiac surgery are associated with acute kidney injury. Crit Care Med. 2013;41:464–71.

    Article  Google Scholar 

  10. 10.

    Brown CH, Neufeld KJ, Tian J, Probert J, LaFlam A, Max L, et al. Effect of targeting mean arterial pressure during cardiopulmonary bypass by monitoring cerebral autoregulation on postsurgical delirium among older patients: a nested randomized clinical trial. JAMA Surg. 2019. https://doi.org/10.1001/jamasurg.2019.1163.

    Article  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Pasanen L, Holmström L. Scale space multiresolution correlation analysis for time series data. Comput Stat. 2017;32:197–218.

    Article  Google Scholar 

  12. 12.

    Hollingsworth JM, Rogers MAM, Krein SL, Hickner A, Kuhn L, Cheng A, et al. Determining the noninfectious complications of indwelling urethral catheters: a systematic review and meta-analysis. Ann Intern Med. 2013;159:401.

    Article  Google Scholar 

  13. 13.

    Saint S, Trautner BW, Fowler KE, Colozzi J, Ratz D, Lescinskas E, et al. A multicenter study of patient-reported infectious and noninfectious complications associated with indwelling urethral catheters. JAMA Intern Med. 2018;178:1078.

    Article  Google Scholar 

  14. 14.

    Zugail AS, Pinar U, Irani J. Evaluation of pain and catheter-related bladder discomfort relative to balloon volumes of indwelling urinary catheters: a prospective study. Investig Clin Urol. 2019;60:35–9.

    Article  Google Scholar 

  15. 15.

    Hariri G, Joffre J, Leblanc G, Bonsey M, Lavillegrand J-R, Urbina T, et al. Narrative review: clinical assessment of peripheral tissue perfusion in septic shock. Ann Intensive Care. 2019;9(1):37. https://doi.org/10.1186/s13613-019-0511-1.

    Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Thooft A, Favory R, Salgado D, Taccone FS, Donadello K, De Backer D, et al. Effects of changes in arterial pressure on organ perfusion during septic shock. Crit Care. 2011;15:R222.

    Article  Google Scholar 

  17. 17.

    Bouattour K, Teboul J-L, Varin L, Vicaut E, Duranteau J. Preload dependence is associated with reduced sublingual microcirculation during major abdominal surgery. Anesthesiology. 2019;130:541–9.

    Article  Google Scholar 

  18. 18.

    Asfar P, Meziani F, Hamel J-F, Grelon F, Megarbane B, Anguel N, et al. High versus low blood-pressure target in patients with septic shock. N Engl J Med. 2014;370:1583–93.

    CAS  Article  Google Scholar 

  19. 19.

    LeDoux D, Astiz ME, Carpati CM, Rackow EC. Effects of perfusion pressure on tissue perfusion in septic shock. Crit Care Med. 2000;28:2729–32.

    CAS  Article  Google Scholar 

  20. 20.

    Bourgoin A, Leone M, Delmas A, Garnier F, Albanèse J, Martin C. Increasing mean arterial pressure in patients with septic shock: effects on oxygen variables and renal function. Crit Care Med. 2005;33:780–6.

    CAS  Article  Google Scholar 

  21. 21.

    Mayevsky A, Blum Y, Dekel N, Deutsch A, Halfon R, Kremer S, et al. The CritiView: a new fiber optic based optical device for the assessment of tissue vitality. In: Gannot I, editor. Optical fibers and sensors for medical diagnostics and treatment applications. San Jose: SPIE; 2006. p. 60830Z.

    Google Scholar 

  22. 22.

    Mayevsky A, Tolmasov M, Mandelbaum M. Perioperative cardiovascular evaluation of patients oxygen balance and tissue metabolic score (TMS). Am J Cardiovasc Thorac Surg. 2018;3:1–17.

    Article  Google Scholar 

  23. 23.

    Galvin EM, Niehof S, Verbrugge SJ, Maissan I, Jahn A, Klein J, et al. Peripheral flow index is a reliable and early indicator of regional block success. Anesth Analg. 2006;103:239–43.

    Article  Google Scholar 

  24. 24.

    Ginosar Y, Weiniger CF, Meroz Y, Kurz V, Bdolah-Abram T, Babchenko A, et al. Pulse oximeter perfusion index as an early indicator of sympathectomy after epidural anesthesia. Acta Anaesthesiol Scand. 2009;53:1018–26.

    CAS  Article  Google Scholar 

  25. 25.

    van Genderen ME, Bartels SA, Lima A, Bezemer R, Ince C, Bakker J, et al. Peripheral perfusion index as an early predictor for central hypovolemia in awake healthy volunteers. Anesth Analg. 2013;116:351–6.

    Article  Google Scholar 

  26. 26.

    De Backer D, Foulon P. Minimizing catecholamines and optimizing perfusion. Crit Care Lond Engl. 2019;23:149.

    Article  Google Scholar 

  27. 27.

    Ince C. Hemodynamic coherence and the rationale for monitoring the microcirculation. Crit Care Lond Engl. 2015;19(Suppl 3):S8.

    Article  Google Scholar 

  28. 28.

    Trzeciak S, McCoy JV, Phillip Dellinger R, Arnold RC, Rizzuto M, Abate NL, et al. Early increases in microcirculatory perfusion during protocol-directed resuscitation are associated with reduced multi-organ failure at 24 h in patients with sepsis. Intensive Care Med. 2008;34:2210–7.

    Article  Google Scholar 

  29. 29.

    Leone M, Blidi S, Antonini F, Meyssignac B, Bordon S, Garcin F, et al. Oxygen tissue saturation is lower in nonsurvivors than in survivors after early resuscitation of septic shock. Anesthesiology. 2009;111:366–71.

    CAS  Article  Google Scholar 

  30. 30.

    Coutrot M, Joachim J, Dépret F, Millasseau S, Nougué H, Matéo J, et al. Non-invasive continuous detection of arterial hypotension during induction of anaesthesia using a photoplethysmographic signal: proof of concept. Br J Anaesth. 2019;122(5):605–12.

    Article  Google Scholar 

  31. 31.

    Edul VSK, Ince C, Navarro N, Previgliano L, Risso-Vazquez A, Rubatto PN, et al. Dissociation between sublingual and gut microcirculation in the response to a fluid challenge in postoperative patients with abdominal sepsis. Ann Intensive Care. 2014;4(1):39.

    Article  Google Scholar 

  32. 32.

    On behalf of the Cardiovascular Dynamics Section of the ESICM, Ince C, Boerma EC, Cecconi M, De Backer D, Shapiro NI, et al. Second consensus on the assessment of sublingual microcirculation in critically ill patients: results from a task force of the European Society of Intensive Care Medicine. Intensive Care Med. 2018;44(3):281–99. https://doi.org/10.1007/s00134-018-5070-7.

    Article  Google Scholar 

  33. 33.

    Clavijo JA, van Bastelaar J, Pinsky MR, Puyana JC, Mayevsky A. Minimally invasive real time monitoring of mitochondrial NADH and tissue blood flow in the urethral wall during hemorrhage and resuscitation. Med Sci Monit Int Med J Exp Clin Res. 2008;14:BR175–82.

    Google Scholar 

  34. 34.

    Evans RG, Smith JA, Wright C, Gardiner BS, Smith DW, Cochrane AD. Urinary oxygen tension: a clinical window on the health of the renal medulla? Am J Physiol-Regul Integr Comp Physiol. 2014;306:R45–50.

    CAS  Article  Google Scholar 

  35. 35.

    Clavijo-Alvarez JA, Sims CA, Menconi M, Shim I, Ochoa C, Puyana JC. Bladder mucosa pH and Pco2 as a minimally invasive monitor of hemorrhagic shock and resuscitation. J Trauma Inj Infect Crit Care. 2004;57:1199–210.

    Article  Google Scholar 

  36. 36.

    Dubin A, Pozo MO, Edul VSK, Murias G, Canales HS, Barán M, et al. Urinary bladder partial carbon dioxide tension during hemorrhagic shock and reperfusion: an observational study. Crit Care Lond Engl. 2005;9:R556–61.

    Article  Google Scholar 

  37. 37.

    Morgaz J, Espigares-Rodríguez L, Muñoz-Rascón P, Navarrete R, Fernández-Sarmiento JA, del Granados Machuca MD. Evaluation of gastric and bladder tonometry as indicators of tissue perfusion in induced hypotension in dogs: gastric and vesical tonometry in dogs. J Vet Emerg Crit Care. 2017;27:532–8.

    Article  Google Scholar 

Download references

Funding

The project was carried out with the support of the Société Française d’Anesthésie Réanimation research contract SFAR 2018.

Author information

Affiliations

Authors

Contributions

FD collected data, performed the analysis and interpretation of the data, and drafted the manuscript. ML collected data, performed the analysis and interpretation of the data, and drafted the manuscript. GD collected data, performed the analysis and interpretation of the data, and drafted the manuscript. EF collected data, performed the analysis and interpretation of the data, and drafted the manuscript. MM collected data, performed the analysis and interpretation of the data, and drafted the manuscript. ML collected data, performed the analysis and interpretation of the data, and drafted the manuscript.BA designed the study, collected data, performed the analysis and interpretation of the data, and drafted the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to François Dépret.

Ethics declarations

Conflict of interest

FDépret: research grants from the French Ministry of Health and SFAR (société française d’anesthésie réanimation). M Leone: MSD, Pfizer, Octapharma, Aspen, Orion (Lecturer) Aguettant, Amomed (Consultant). Gary Duclos: nothing to declare. Emmanuel Futier: research grants from the French Ministry of Health, consulting fees from Drager Medical, General Electric Healthcare, Edwards Lifesciences, and Orion Pharma, along with lecture fees from Fesenius Kabi, Fisher and Paykel Healthcare, and Getinge. Maxime Montagne: nothing to declare. Matthieu Legrand: research grants from the French Ministry of Health, research support from Sphingotec, lecture fees from Baxter and Fresenius, consulting fees from Novartis. Bernard Allaouchiche: Consulting fees from APD. Consultant for APPD.

Ethical approval

All patients signed an informed consent, and the study was approved by favorable opinion from an ethics committee (CPP Ouest V); the study was registered on the website www.clinicaltrials.gov under the identifier NCT03410069 and recorded under the ID-RCB number: 2017-A03466-47.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (JPEG 24 kb). Supplementary data Fig. 1: Patient repartition depending on the proportion correlation time between the SV and uPI. SV: stroke volume

Supplementary material 2 (JPEG 65 kb). Supplementary data Fig. 2: Panel A (upper panel): IKORUS Perfusion Monitor. Panel B (middle panel): UP-distal probe variant, Panel C (lower panel): UP-proximal probe variant, The body of the probe (1), PPG sensor (2) and connector (3)

Supplementary material 3 (PNG 152 kb). Supplementary data Fig. 3: Example of a patient with a high correlation between the uPI and SV. uPI: urethral perfusion index; SVI: stroke volume indexed. Patient no 205 was a 60-year-old female with history of caustic ingestion one month before surgery. She was admitted to the OR for coloplasty because of esophagus stenosis. Invasive arterial pressure and cardiac output were recorded with EV1000 Flotrac system (Edwards Life Science, Irvine, California, United States). We observed a positive correlation between the uPI and SV (uPI = -4.4 + 0.3 × SVI, r2 = 0.84)

Supplementary material 4 (PNG 114 kb). Supplementary data Fig. 4: Examples of a patient with a high positive correlation between the uPI and MAP. Patient no 105 was a 61-year-old female with no noticeable medical history except an ovarian carcinoma with peritoneal carcinosis. She was admitted to the operative room for a cytoreduction plus intraperitoneal chemotherapy. Invasive arterial pressure and cardiac output were recorded with EV1000 Flotrac system (Edwards Life Science, Irvine, California, United States). In this case, we observed a positive correlation between the MAP and uPI (uPI = -0.22 + 0.044 x MAP, r2 = 0.966). uPI: urethral perfusion index; MAP: mean arterial pressure; SVI: stroke volume indexed

Supplementary material 5 (PNG 106 kb). Supplementary data Fig. 5: Example of a patient with a high negative correlation between the uPI and MAP. Patient no 403 was a 63-year-old female with a history of hypertension, obesity, and peritoneal pseudomyxoma. She was admitted in the OR for a surgery of cytoreduction plus intraperitoneal chemotherapy. In this case, we observed a negative correlation between the MAP and uPI (PI = 35 + -0.28 x MAP, r2 = 0.77). uPI: urethral perfusion index; MAP: mean arterial pressure; SVI: stroke volume indexed

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Dépret, F., Leone, M., Duclos, G. et al. Monitoring tissue perfusion: a pilot clinical feasibility and safety study of a urethral photoplethysmography-derived perfusion device in high-risk patients. J Clin Monit Comput 34, 961–969 (2020). https://doi.org/10.1007/s10877-019-00414-9

Download citation

Keywords

  • Microcirculation
  • Intensive care
  • Urethral perfusion
  • Perfusion index
  • Hemodynamics
  • Cardiac index