Literature DB >> 33630220

Non-invasive measurement of pulse pressure variation using a finger-cuff method (CNAP system): a validation study in patients having neurosurgery.

Moritz Flick1, Phillip Hoppe1, Jasmin Matin Mehr1, Luisa Briesenick1, Karim Kouz1, Gillis Greiwe1, Jürgen Fortin2, Bernd Saugel3,4.   

Abstract

The finger-cuff system CNAP (CNSystems Medizintechnik, Graz, Austria) allows non-invasive automated measurement of pulse pressure variation (PPVCNAP). We sought to validate the PPVCNAP-algorithm and investigate the agreement between PPVCNAP and arterial catheter-derived manually calculated pulse pressure variation (PPVINV). This was a prospective method comparison study in patients having neurosurgery. PPVINV was the reference method. We applied the PPVCNAP-algorithm to arterial catheter-derived blood pressure waveforms (PPVINV-CNAP) and to CNAP finger-cuff-derived blood pressure waveforms (PPVCNAP). To validate the PPVCNAP-algorithm, we compared PPVINV-CNAP to PPVINV. To investigate the clinical performance of PPVCNAP, we compared PPVCNAP to PPVINV. We used Bland-Altman analysis (absolute agreement), Deming regression, concordance, and Cohen's kappa (predictive agreement for three pulse pressure variation categories). We analyzed 360 measurements from 36 patients. The mean of the differences between PPVINV-CNAP and PPVINV was -0.1% (95% limits of agreement (95%-LoA) -2.5 to 2.3%). Deming regression showed a slope of 0.99 (95% confidence interval (95%-CI) 0.91 to 1.06) and intercept of -0.02 (95%-CI -0.52 to 0.47). The predictive agreement between PPVINV-CNAP and PPVINV was 92% and Cohen's kappa was 0.79. The mean of the differences between PPVCNAP and PPVINV was -1.0% (95%-LoA-6.3 to 4.3%). Deming regression showed a slope of 0.85 (95%-CI 0.78 to 0.91) and intercept of 0.10 (95%-CI -0.34 to 0.55). The predictive agreement between PPVCNAP and PPVINV was 82% and Cohen's kappa was 0.48. The PPVCNAP-algorithm reliably calculates pulse pressure variation compared to manual offline pulse pressure variation calculation when applied on the same arterial blood pressure waveform. The absolute and predictive agreement between PPVCNAP and PPVINV are moderate.
© 2021. The Author(s).

Entities:  

Keywords:  Cardiac preload; Dynamic preload variable; Fluid responsiveness; Hemodynamic monitoring; Vascular unloading technology; Volume clamp method

Mesh:

Year:  2021        PMID: 33630220      PMCID: PMC7905968          DOI: 10.1007/s10877-021-00669-1

Source DB:  PubMed          Journal:  J Clin Monit Comput        ISSN: 1387-1307            Impact factor:   1.977


  24 in total

1.  Prediction of fluid responsiveness by a continuous non-invasive assessment of arterial pressure in critically ill patients: comparison with four other dynamic indices.

Authors:  X Monnet; M Dres; A Ferré; G Le Teuff; M Jozwiak; A Bleibtreu; M-C Le Deley; D Chemla; C Richard; J-L Teboul
Journal:  Br J Anaesth       Date:  2012-06-26       Impact factor: 9.166

2.  Assessing the diagnostic accuracy of pulse pressure variations for the prediction of fluid responsiveness: a "gray zone" approach.

Authors:  Maxime Cannesson; Yannick Le Manach; Christoph K Hofer; Jean Pierre Goarin; Jean-Jacques Lehot; Benoît Vallet; Benoît Tavernier
Journal:  Anesthesiology       Date:  2011-08       Impact factor: 7.892

3.  Arterial Pulse Pressure Variation with Mechanical Ventilation.

Authors:  Jean-Louis Teboul; Xavier Monnet; Denis Chemla; Frédéric Michard
Journal:  Am J Respir Crit Care Med       Date:  2019-01-01       Impact factor: 21.405

4.  Non-invasive arterial pressure monitoring revisited.

Authors:  Frederic Michard; Daniel I Sessler; Bernd Saugel
Journal:  Intensive Care Med       Date:  2018-03-07       Impact factor: 17.440

Review 5.  Continuous noninvasive pulse wave analysis using finger cuff technologies for arterial blood pressure and cardiac output monitoring in perioperative and intensive care medicine: a systematic review and meta-analysis.

Authors:  Bernd Saugel; Phillip Hoppe; Julia Y Nicklas; Karim Kouz; Annmarie Körner; Julia C Hempel; Jaap J Vos; Gerhard Schön; Thomas W L Scheeren
Journal:  Br J Anaesth       Date:  2020-05-29       Impact factor: 9.166

6.  Evaluation of regression procedures for methods comparison studies.

Authors:  K Linnet
Journal:  Clin Chem       Date:  1993-03       Impact factor: 8.327

Review 7.  Prediction of fluid responsiveness: an update.

Authors:  Xavier Monnet; Paul E Marik; Jean-Louis Teboul
Journal:  Ann Intensive Care       Date:  2016-11-17       Impact factor: 6.925

Review 8.  Metrology part 1: definition of quality criteria.

Authors:  Pierre Squara; Thomas W L Scheeren; Hollmann D Aya; Jan Bakker; Maurizio Cecconi; Sharon Einav; Manu L N G Malbrain; Xavier Monnet; Daniel A Reuter; Iwan C C van der Horst; Bernd Saugel
Journal:  J Clin Monit Comput       Date:  2020-03-17       Impact factor: 2.502

Review 9.  Metrology part 2: Procedures for the validation of major measurement quality criteria and measuring instrument properties.

Authors:  Pierre Squara; Thomas W L Scheeren; Hollmann D Aya; Jan Bakker; Maurizio Cecconi; Sharon Einav; Manu L N G Malbrain; Xavier Monnet; Daniel A Reuter; Iwan C C van der Horst; Bernd Saugel
Journal:  J Clin Monit Comput       Date:  2020-03-18       Impact factor: 2.502

10.  Non-invasive measurement of pulse pressure variation using a finger-cuff method in obese patients having laparoscopic bariatric surgery.

Authors:  Moritz Flick; Roman Schumann; Phillip Hoppe; Iwona Bonney; Wilbert Wesselink; Bernd Saugel
Journal:  J Clin Monit Comput       Date:  2020-11-10       Impact factor: 1.977

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