Evaluation of New Calibrated Pulse-Wave Analysis (VolumeViewTM/EV1000TM) for Cardiac Output Monitoring Undergoing Living Donor Liver Transplantation

Effect of Hemodynamic Monitoring Systems on Short-Term Outcomes after Living Donor Liver Transplantation

Background and Objectives: To evaluate the effects of the pulse index continuous cardiac output and MostCare Pressure Recording Analytical Method hemodynamic monitoring systems on short-term graft and patient outcomes during living donor liver transplantation in adult patients. Materials and Methods: Overall, 163 adult patients who underwent living donor liver transplantation between January 2018 and March 2022 and met the study inclusion criteria were divided into two groups based on the hemodynamic monitoring systems used during surgery: the MostCare Pressure Recording Analytical Method group (n = 73) and the pulse index continuous cardiac output group (n = 90). The groups were compared with respect to preoperative clinicodemographic features (age, sex, body mass index, graft-to-recipient weight ratio, and Model for End-stage Liver Disease score), intraoperative clinical characteristics, and postoperative biochemical parameters (aspartate aminotransferase, alanine aminotransferase, total bilirubin, direct bilirubin, prothrombin time, international normalized ratio, and platelet count). Results: There were no significant between-group differences with respect to recipient age, sex, body mass index, graft-to-recipient weight ratio, Child, Model for End-stage Liver Disease score, ejection fraction, systolic pulmonary artery pressure, surgery time, anhepatic phase, cold ischemia time, warm ischemia time, erythrocyte suspension use, human albumin use, crystalloid use, urine output, hospital stay, and intensive care unit stay. However, there was a significant difference in fresh frozen plasma use (p < 0.001) and platelet use (p = 0.037). Conclusions: The clinical and biochemical outcomes are not significantly different between pulse index continuous cardiac output and MostCare Pressure Recording Analytical Method as hemodynamic monitoring systems in living donor liver transplantation. However, the MostCare Pressure Recording Analytical Method is more economical and minimally invasive.

Improved U-Net Model to Estimate Cardiac Output Based on Photoplethysmography and Arterial Pressure Waveform

We aimed to estimate cardiac output (CO) from photoplethysmography (PPG) and the arterial pressure waveform (ART) using a deep learning approach, which is minimally invasive, does not require patient demographic information, and is operator-independent, eliminating the need to artificially extract a feature of the waveform by implementing a traditional formula. We aimed to present an alternative to measuring cardiac output with greater accuracy for a wider range of patients. Using a publicly available dataset, we selected 543 eligible patients and divided them into test and training sets after preprocessing. The data consisted of PPG and ART waveforms containing 2048 points with the corresponding CO. We achieved an improvement based on the U-Net modeling framework and built a two-channel deep learning model to automatically extract the waveform features to estimate the CO in the dataset as the reference, acquired using the EV1000, a commercially available instrument. The model demonstrated strong consistency with the reference values on the test dataset. The mean CO was 5.01 ± 1.60 L/min and 4.98 ± 1.59 L/min for the reference value and the predicted value, respectively. The average bias was -0.04 L/min with a -1.025 and 0.944 L/min 95% limit of agreement (LOA). The bias was 0.79% with a 95% LOA between -20.4% and 18.8% when calculating the percentage of the difference from the reference. The normalized root-mean-squared error (RMSNE) was 10.0%. The Pearson correlation coefficient (r) was 0.951. The percentage error (PE) was 19.5%, being below 30%. These results surpassed the performance of traditional formula-based calculation methods, meeting clinical acceptability standards. We propose a dual-channel, improved U-Net deep learning model for estimating cardiac output, demonstrating excellent and consistent results. This method offers a superior reference method for assessing cardiac output in cases where it is unnecessary to employ specialized cardiac output measurement devices or when patients are not suitable for pulmonary-artery-catheter-based measurements, providing a viable alternative solution.

Estimation of Stroke Volume Variance from Arterial Blood Pressure: Using a 1-D Convolutional Neural Network

BACKGROUND

We aimed to create a novel model using a deep learning method to estimate stroke volume variation (SVV), a widely used predictor of fluid responsiveness, from arterial blood pressure waveform (ABPW).

METHODS

In total, 557 patients and 8,512,564 SVV datasets were collected and were divided into three groups: training, validation, and test. Data was composed of 10 s of ABPW and corresponding SVV data recorded every 2 s. We built a convolutional neural network (CNN) model to estimate SVV from the ABPW with pre-existing commercialized model (EV1000) as a reference. We applied pre-processing, multichannel, and dimension reduction to improve the CNN model with diversified inputs.

RESULTS

Our CNN model showed an acceptable performance with sample data (r = 0.91, MSE = 6.92). Diversification of inputs, such as normalization, frequency, and slope of ABPW significantly improved the model correlation (r = 0.95), lowered mean squared error (MSE = 2.13), and resulted in a high concordance rate (96.26%) with the SVV from the commercialized model.

CONCLUSIONS

We developed a new CNN deep-learning model to estimate SVV. Our CNN model seems to be a viable alternative when the necessary medical device is not available, thereby allowing a wider range of application and resulting in optimal patient management.

Deep Learning-Based Stroke Volume Estimation Outperforms Conventional Arterial Contour Method in Patients with Hemodynamic Instability

Although the stroke volume (SV) estimation by arterial blood pressure has been widely used in clinical practice, its accuracy is questionable, especially during periods of hemodynamic instability. We aimed to create novel SV estimating model based on deep-learning (DL) method. A convolutional neural network was applied to estimate SV from arterial blood pressure waveform data recorded from liver transplantation (LT) surgeries. The model was trained using a gold standard referential SV measured via pulmonary artery thermodilution method. Merging a gold standard SV and corresponding 10.24 seconds of arterial blood pressure waveform as an input/output data set with 2-senconds of sliding overlap, 484,384 data sets from 34 LT surgeries were used for training and validation of DL model. The performance of DL model was evaluated by correlation and concordance analyses in another 491,353 data sets from 31 LT surgeries. We also evaluated the performance of pre-existing commercialized model (EV1000), and the performance results of DL model and EV1000 were compared. The DL model provided an acceptable performance throughout the surgery (r = 0.813, concordance rate = 74.15%). During the reperfusion phase, where the most severe hemodynamic instability occurred, DL model showed superior correlation (0.861; 95% Confidence Interval, (CI), 0.855-0.866 vs. 0.570; 95% CI, 0.556-0.584, P < 0.001) and higher concordance rate (90.6% vs. 75.8%) over EV1000. In conclusion, the DL-based model was superior for estimating intraoperative SV and thus might guide physicians to precise intraoperative hemodynamic management. Moreover, the DL model seems to be particularly promising because it outperformed EV1000 in circumstance of rapid hemodynamic changes where physicians need most help.

Non-invasive cardiac output measurement with electrical velocimetry in patients undergoing liver transplantation: comparison of an invasive method with pulmonary thermodilution

BACKGROUND

The goal of this study was to evaluate the accuracy and interchangeability between continuous cardiac output (CO) measured by electrical velocimetry (CO_Ev) and continuous cardiac output obtained using the pulmonary thermodilution method (CO_PAC) during living donor liver transplantation (LDLT).

METHOD

Twenty-three patients were enrolled in this prospective observational study. CO was recorded by both two methods and compared at nine specific time points. The data were analyzed using correlation coefficients, Bland-Altman analysis for the percentage errors, and the concordance rate for trend analysis using a four-quadrant plot.

RESULTS

In total, 207 paired datasets were recorded during LDLT. CO data were in the range of 2.8-12.7 L/min measured by PAC and 3.4-14.9 L/min derived from the EV machine. The correction coefficient between CO_PAC and CO_Ev was 0.415 with p < 0.01. The 95% limitation agreement was - 5.9 to 3.4 L/min and the percentage error was 60%. The concordance rate was 56.5%.

CONCLUSIONS

The Aesculon™ monitor is not yet interchangeable with continuous thermodilution CO monitoring during LDLT.

TRIAL REGISTRATION

The study was approved by the Institutional Review Board of Chang Gung Medical Foundation in Taiwan (registration number: 201600264B0 ).

Cardiac output measurement in liver transplantation patients using pulmonary and transpulmonary thermodilution: a comparative study

During liver transplantation surgery, the pulmonary artery catheter-despite its invasiveness-remains the gold standard for measuring cardiac output. However, the new EV1000 transpulmonary thermodilution calibration technique was recently introduced into the market by Edwards LifeSciences. We designed a single-center prospective observational study to determine if these two techniques for measuring cardiac output are interchangeable in this group of patients. Patients were monitored with both pulmonary artery catheter and the EV1000 system. Simultaneous intermittent cardiac output measurements were collected at predefined steps: after induction of anesthesia (T1), during the anhepatic phase (T2), after liver reperfusion (T3), and at the end of the surgery (T4). The 4-quadrant and polar plot techniques were used to assess trending ability between the two methods. We enrolled 49 patients who underwent orthotopic liver transplantation surgery. We analyzed a total of 588 paired measurements. The mean bias between pulmonary artery catheter and the EV1000 system was 0.35 L/min with 95% limits of agreement of - 2.30 to 3.01 L/min, and an overall percentage error of 35%. The concordance rate between the two techniques in 4-quadrant plot analysis was 65% overall. The concordance rate of the polar plot showed an overall value of 83% for all pairs. In the present study, in liver transplantation patients we found that intermittent cardiac output monitoring with EV1000 system showed a percentage error compared with pulmonary artery catheter in the acceptable threshold of 45%. On the others hand, our results showed a questionable trending ability between the two techniques.

Cardiac output Optimisation following Liver Transplant (COLT) trial: study protocol for a feasibility randomised controlled trial

Background

Patients with liver cirrhosis undergoing liver transplantation have a hyperdynamic circulation which persists into the early postoperative period making accurate assessment of fluid requirements challenging. Goal-directed fluid therapy (GDFT) has been shown to reduce morbidity and mortality in a number of surgery settings. The impact of GDFT in patients undergoing liver transplantation is unknown. A feasibility trial was designed to determine patient and clinician support for recruitment into a randomised controlled trial of GDFT following liver transplantation, adherence to a GDFT protocol, participant withdrawal, and to determine appropriate endpoints for a subsequent larger trial to evaluate the efficacy of GDFT in patients undergoing liver transplantation.

Methods

The Cardiac output Optimisation following Liver Transplant (COLT) trial is designed as a prospective, single-centre, randomised controlled study to assess the feasibility and safety of GDFT in liver transplantation for patients with cirrhosis. Consenting adults (aged between 18 and 80 years) with biopsy-proven liver cirrhosis who have been selected to undergo a first liver transplantation will be included in the trial and randomised into GDFT or standard care starting immediately after surgery and continuing for the first 12 h thereafter. Both groups will have cardiac output and stroke volume monitored using the FloTrac (EV1000) device. The intervention will consist of a protocolised GDFT approach to patient management, using stroke volume optimisation. The control group will receive standard care, without stroke volume and cardiac output measurement. After 12 h the patient’s fluid management will revert to standard of care. The primary endpoint of this study is feasibility. Secondary endpoints will include a safety assessment of the intervention, graft and patient survival, liver function, postoperative complications graded by Clavien-Dindo criteria, length of intensive care and hospital stay and quality of life across the intervention and control groups.

Discussion

There is a growing body of evidence that the use of perioperative GDFT in surgical patients can improve outcomes; however, signals of harm have also been detected. Patients with liver cirrhosis undergoing liver transplantation have markedly different cardiovascular physiology than general surgical patients. If GDFT is proven to be feasible and safe in this patient group, then a multicentre trial to demonstrate efficacy and cost-effectiveness will be required.

Trial registration

International Standard Randomised Controlled Trial Registry, ID: ISRCTN10329248. Registered on 4 April 2016.

Electronic supplementary material

The online version of this article (10.1186/s13063-018-2488-8) contains supplementary material, which is available to authorized users.