An enhanced automatic algorithm for estimation of respiratory variations in arterial pulse pressure during regions of abrupt hemodynamic changes

Vital Recorder-a free research tool for automatic recording of high-resolution time-synchronised physiological data from multiple anaesthesia devices

The current anaesthesia information management system (AIMS) has limited capability for the acquisition of high-quality vital signs data. We have developed a Vital Recorder program to overcome the disadvantages of AIMS and to support research. Physiological data of surgical patients were collected from 10 operating rooms using the Vital Recorder. The basic equipment used were a patient monitor, the anaesthesia machine, and the bispectral index (BIS) monitor. Infusion pumps, cardiac output monitors, regional oximeter, and rapid infusion device were added as required. The automatic recording option was used exclusively and the status of recording was frequently checked through web monitoring. Automatic recording was successful in 98.5% (4,272/4,335) cases during eight months of operation. The total recorded time was 13,489 h (3.2 ± 1.9 h/case). The Vital Recorder's automatic recording and remote monitoring capabilities enabled us to record physiological big data with minimal effort. The Vital Recorder also provided time-synchronised data captured from a variety of devices to facilitate an integrated analysis of vital signs data. The free distribution of the Vital Recorder is expected to improve data access for researchers attempting physiological data studies and to eliminate inequalities in research opportunities due to differences in data collection capabilities.

On algorithms for calculating arterial pulse pressure variation during major surgery

OBJECTIVE

Arterial pulse pressure variation (PPV) is widely used for predicting fluid responsiveness and supporting fluid management in the operating room and intensive care unit. Available PPV algorithms have been typically validated for fluid responsiveness during episodes of hemodynamic stability. Yet, little is known about the performance of PPV algorithms during surgery, where fast changes of the blood pressure may affect the robustness of the presented PPV value. This work provides a comprehensive understanding of how various existing algorithmic designs affect the robustness of the presented PPV value during surgery, and proposes additional processing for the pulse pressure signal before calculating PPV.

APPROACH

We recorded arterial blood pressure waveforms from 23 patients undergoing major abdominal surgery. To evaluate the performance, we designed three clinically relevant metrics. Main results and Significance: The results show that all algorithms performed well during episodes of hemodynamic stability. Moreover, it is demonstrated that the proposed processing helps improve the robustness of PPV during the entire course of surgery.

A novel particle filtering method for estimation of pulse pressure variation during spontaneous breathing

Background

We describe the first automatic algorithm designed to estimate the pulse pressure variation (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {PPV}$$\end{document}PPV) from arterial blood pressure (ABP) signals under spontaneous breathing conditions. While currently there are a few publicly available algorithms to automatically estimate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {PPV}$$\end{document}PPV accurately and reliably in mechanically ventilated subjects, at the moment there is no automatic algorithm for estimating \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {PPV}$$\end{document}PPV on spontaneously breathing subjects. The algorithm utilizes our recently developed sequential Monte Carlo method (SMCM), which is called a maximum a-posteriori adaptive marginalized particle filter (MAM-PF). We report the performance assessment results of the proposed algorithm on real ABP signals from spontaneously breathing subjects.

Results

Our assessment results indicate good agreement between the automatically estimated \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {PPV}$$\end{document}PPV and the gold standard \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {PPV}$$\end{document}PPV obtained with manual annotations. All of the automatically estimated \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {PPV}$$\end{document}PPV index measurements (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {PPV}_{\text {auto}}$$\end{document}PPVauto) were in agreement with manual gold standard measurements (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {PPV}_{\text {manu}}$$\end{document}PPVmanu) within ±4 % accuracy.

Conclusion

The proposed automatic algorithm is able to give reliable estimations of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {PPV}$$\end{document}PPV given ABP signals alone during spontaneous breathing.

Visual estimation of pulse pressure variation is not reliable: a randomized simulation study

Pulse pressure variation (PPV) can be monitored several ways, but according to recent survey data it is most often visually estimated ("eyeballed") by practitioners. It is not known how accurate visual estimation of PPV is, or whether eyeballing of PPV in goal-directed fluid therapy studies may limit the ability to blind the control group to PPV value. The goal of this study was to test the accuracy of visual estimation of PPV. Using a simulator program designed by the authors that runs on a PC, 20 residents and 19 attendings were shown five arterial pressure waveforms each with different PPV values (range 1-30 %) moving at one of three sweep speeds (6.25, 12.5, or 25 mm/s) and asked to determine the PPV. There was a weak but significant relationship between true PPV and eyeball PPV (r (2) = 0.22; p < 0.01). The agreement between true PPV and eyeball PPV was 3.3 ± 8.7 %. The mean percent error was 122 %. The rate of correct response group classification was 65 %. Mean percent error was higher the faster the waveform sweep speed (130 % at 25 mm/s vs. 117 % at 6.25 mm/s), and correct responsiveness classification lower (58 % at 25 mm/s vs. 69 % at 6.25 mm/s). The results from this study show that eyeballing the arterial pressure waveform in order to evaluate pulse pressure variation is not accurate.

The ability of anesthesia providers to visually estimate systolic pressure variability using the "eyeball" technique

BACKGROUND

Systemic arterial respiratory variation has been shown to be a reliable predictor of changes in cardiac output after fluid administration. Arterial respiratory variation is often estimated from visual examination of the arterial waveform tracing. Our goal in this study was to assess the ability of anesthesia providers to visually estimate systolic pressure variation (SPV) as a percentage of systolic blood pressure (SPV).

METHODS

Fifty anesthesia providers were recruited and asked to visually examine 10 recorded arterial waveform tracings (played in real time), to estimate SPV, and to state whether or not a fluid bolus was indicated. After completion of the examination, the participants were shown the original tracings, the true value for SPV, and their estimate. The percentage of incorrect physician decisions to administer or not administer additional fluid was analyzed using a binomial proportion confidence interval. Clinical utility was also assessed using clinical significance analysis. Limits of agreement were analyzed using the nonparametric approach recommended by Bland and Altman.

RESULTS

The mean bias was +1.2%. The nonparametric limits of agreement were -5.1 and 7.5%, and contained 82% of values. Actual physician decisions were incorrect 4.4% of the time (95% confidence interval [CI] 2.8% to 6.6%). On the basis of the clinical significance analysis, only 1% of treatments based on the visual estimation would have been incorrect.

CONCLUSION

Visual estimates of respiratory variation are within clinically reasonable limits 82% of the time and lead to erroneous management decisions in 4.4% of measurements.

Pulse pressure variation: where are we today?

In the present review we will describe and discuss the physiological and technological background necessary in understanding the dynamic parameters of fluid responsiveness and how they relate to recent softwares and algorithms' applications. We will also discuss the potential clinical applications of these parameters in the management of patients under general anesthesia and mechanical ventilation along with the potential improvements in the computational algorithms.