Non-invasive assessment of Pulse Wave Transit Time (PWTT) is a poor predictor for intraoperative fluid responsiveness: a prospective observational trial (best-PWTT study)

BMC Anesthesiology

Research

Johannes M. Wirkus¹  Erik K. Hartmann¹  Eva-Verena Griemert¹  Gunther J. Pestel¹  Kimiko Fukui¹  Irene Schmidtmann² 

[1] Department of Anesthesiology, University Medical Center of the Johannes Gutenberg-University, Langenbeckstraße 1, 55131, Mainz, Germany;Institute for Medical Biostatistics, Epidemiology and Informatics Medical (IMBEI), University Medical Center of the Johannes Gutenberg-University, Mainz, Germany;

关键词: Pulse wave transit time;    Fluid responsiveness;    Hemodynamic monitoring;    Fluid resuscitation;    Pulse pressure variation;   

DOI  :  10.1186/s12871-023-02016-0

 received in 2022-10-31, accepted in 2023-02-09,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundAim of this study is to test the predictive value of Pulse Wave Transit Time (PWTT) for fluid responsiveness in comparison to the established fluid responsiveness parameters pulse pressure (ΔPP) and corrected flow time (FTc) during major abdominal surgery.MethodsForty patients undergoing major abdominal surgery were enrolled with continuous monitoring of PWTT (LifeScope® Modell J BSM-9101 Nihon Kohden Europe GmbH, Rosbach, Germany) and stroke volume (Esophageal Doppler Monitoring CardioQ-ODM®, Deltex Medical Ltd, Chichester, UK). In case of hypovolemia (difference in pulse pressure [∆PP] ≥ 9%, corrected flow time [FTc] ≤ 350 ms) a fluid bolus of 7 ml/kg ideal body weight was administered. Receiver operating characteristics (ROC) curves and corresponding areas under the curve (AUCs) were used to compare different methods of determining PWTT. A Wilcoxon test was used to discriminate fluid responders (increase in stroke volume of ≥ 10%) from non-responders. The predictive value of PWTT for fluid responsiveness was compared by testing for differences between ROC curves of PWTT, ΔPP and FTc using the methods by DeLong.ResultsAUCs (area under the ROC-curve) to predict fluid responsiveness for PWTT-parameters were 0.61 (raw c finger Q), 0.61 (raw c finger R), 0.57 (raw c ear Q), 0.53 (raw c ear R), 0.54 (raw non-c finger Q), 0.52 (raw non-c finger R), 0.50 (raw non-c ear Q), 0.55 (raw non-c ear R), 0.63 (∆ c finger Q), 0.61 (∆ c finger R), 0.64 (∆ c ear Q), 0.66 (∆ c ear R), 0.59 (∆ non-c finger Q), 0.57 (∆ non-c finger R), 0.57 (∆ non-c ear Q), 0.61 (∆ non-c ear R) [raw measurements vs. ∆ = respiratory variation; c = corrected measurements according to Bazett’s formula vs. non-c = uncorrected measurements; Q vs. R = start of PWTT-measurements with Q- or R-wave in ECG; finger vs. ear = pulse oximetry probe location]. Hence, the highest AUC to predict fluid responsiveness by PWTT was achieved by calculating its respiratory variation (∆PWTT), with a pulse oximeter attached to the earlobe, using the R-wave in ECG, and correction by Bazett’s formula (AUC best-PWTT 0.66, 95% CI 0.54–0.79). ∆PWTT was sufficient to discriminate fluid responders from non-responders (p = 0.029). No difference in predicting fluid responsiveness was found between best-PWTT and ∆PP (AUC 0.65, 95% CI 0.51–0.79; p = 0.88), or best-PWTT and FTc (AUC 0.62, 95% CI 0.49–0.75; p = 0.68).ConclusionΔPWTT shows poor ability to predict fluid responsiveness intraoperatively. Moreover, established alternatives ΔPP and FTc did not perform better.Trial registrationPrior to enrolement on clinicaltrials.gov (NC T03280953; date of registration 13/09/2017).

【 授权许可】

CC BY   
© The Author(s) 2023

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