Venous Waveform Analysis Correlates With Echocardiography in Detecting Hypovolemia in a Rat Hemorrhage Model

Lefevre, Ryan J.; Balzer, Claudius; Baudenbacher, Franz; Riess, Matthias L.; Hernandez, Antonio; Eagle, Susan

doi:10.1177/1089253220960894

Cited by 6 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Significance for all ANOVA analysis was set at P < .05 (2-tailed), while significance for the pairwise difference in R 2 was set as the 95% CI not crossing Sample size analysis was calculated to detect a significant change in F1 with hemorrhage of 2% of the EBV. Based on study by Lefevre et al 20 who saw a reduction in F1 mean of 0.28 with hemorrhage of 6% of the EBV, we assumed a linear response when detecting 2% hemorrhage and thus anticipated a change in mean F1 of 0.93 and standard deviation of 0.1. 1 To allow adequate power of 0.8, allowing type 1 error of 0.05, in a continuous variable from a matched pairs study using a t test, would require 11 subjects to reject the null hypothesis.…”

Section: Discussionmentioning

confidence: 99%

Peripheral Intravenous Waveform Analysis Responsiveness to Subclinical Hemorrhage in a Rat Model

Barajas

Riess

Hampton

et al. 2023

Anesthesia &Amp; Analgesia

Self Cite

View full text Add to dashboard Cite

BACKGROUND:Early detection and quantification of perioperative hemorrhage remains challenging. Peripheral intravenous waveform analysis (PIVA) is a novel method that uses a standard intravenous catheter to detect interval hemorrhage. We hypothesize that subclinical blood loss of 2% of the estimated blood volume (EBV) in a rat model of hemorrhage is associated with significant changes in PIVA. Secondarily, we will compare PIVA association with volume loss to other static, invasive, and dynamic markers. METHODS: Eleven male Sprague Dawley rats were anesthetized and mechanically ventilated. A total of 20% of the EBV was removed over ten 5 minute-intervals. The peripheral intravenous pressure waveform was continuously transduced via a 22-G angiocatheter in the saphenous vein and analyzed using MATLAB. Mean arterial pressure (MAP) and central venous pressure (CVP) were continuously monitored. Cardiac output (CO), right ventricular diameter (RVd), and left ventricular end-diastolic area (LVEDA) were evaluated via transthoracic echocardiogram using the short axis left ventricular view. Dynamic markers such as pulse pressure variation (PPV) were calculated from the arterial waveform. The primary outcome was change in the first fundamental frequency (F1) of the venous waveform, which was assessed using analysis of variance (ANOVA). Mean F1 at each blood loss interval was compared to the mean at the subsequent interval. Additionally, the strength of the association between blood loss and F1 and each other marker was quantified using the marginal R 2 in a linear mixed-effects model. RESULTS: PIVA derived mean F1 decreased significantly after hemorrhage of only 2% of the EBV, from 0.17 to 0.11 mm Hg, P = .001, 95% confidence interval (CI) of difference in means 0.02 to 0.10, and decreased significantly from the prior hemorrhage interval at 4%, 6%, 8%, 10%, and 12%. Log F1 demonstrated a marginal R 2 value of 0.57 (95% CI 0.40-0.73), followed by PPV 0.41 (0.28-0.56) and CO 0.39 (0.26-0.58). MAP , LVEDA, and systolic pressure variation displayed R 2 values of 0.31, and the remaining predictors had R 2 values ≤0.2. The difference in log F1 R 2 was not significant when compared to PPV 0.16 (95% CI −0.07 to 0.38), CO 0.18 (−0.06 to 0.04), or MAP 0.25 (−0.01 to 0.49) but was significant for the remaining markers. CONCLUSIONS: The mean F1 amplitude of PIVA was significantly associated with subclinical blood loss and most strongly associated with blood volume among the markers considered. This study demonstrates feasibility of a minimally invasive, low-cost method for monitoring perioperative blood loss. (Anesth Analg 2023;136:941-8) KEY POINTS• Question: Is peripheral intravenous waveform analysis (PIVA) significantly associated with subclinical blood loss in a rat model of hemorrhage? • Findings: Mean PIVA was significantly associated with subclinical blood loss of 2% of the estimated blood volume and PIVA displayed a stronger association with blood loss than all static, dynamic, and echocardiographic predictors including cent...

show abstract

Section: Discussionmentioning

confidence: 99%

Peripheral Intravenous Waveform Analysis Responsiveness to Subclinical Hemorrhage in a Rat Model

Barajas

Riess

Hampton

et al. 2023

Anesthesia &Amp; Analgesia

Self Cite

View full text Add to dashboard Cite

show abstract

“…More recently, notable advances in signal processing and machine learning (ML) have fostered the application of these techniques in hypovolemia diagnosis. Representative examples include discrete Fourier transform-based analysis of arterial blood pressure waveform [ 13 ], fast Fourier transform-based analysis of central venous blood pressure waveform [ 14 ], ML analysis of arterial blood pressure waveform in its entirety (known as “Compensatory Reserve Index”) [ 15 ], support vector machine analysis of compressed arterial blood pressure waveform via principal components analysis [ 16 ], ML analysis of photoplethysmogram (PPG) signal features derived from time-frequency analysis [ 17 ], deep learning analysis of electronic medical records, vital signs, and laboratory values [ 18 , 19 ], and natural language processing-aided voting ensemble ML analysis of pulse pressure and unstructured clinical notes [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of Blood Volume Decompensation State via Machine Learning Analysis of Multi-Modal Wearable-Compatible Physiological Signals

Chalumuri

Kimball

Mousavi

et al. 2022

Sensors

View full text Add to dashboard Cite

This paper presents a novel computational algorithm to estimate blood volume decompensation state based on machine learning (ML) analysis of multi-modal wearable-compatible physiological signals. To the best of our knowledge, our algorithm may be the first of its kind which can not only discriminate normovolemia from hypovolemia but also classify hypovolemia into absolute hypovolemia and relative hypovolemia. We realized our blood volume classification algorithm by (i) extracting a multitude of features from multi-modal physiological signals including the electrocardiogram (ECG), the seismocardiogram (SCG), the ballistocardiogram (BCG), and the photoplethysmogram (PPG), (ii) constructing two ML classifiers using the features, one to classify normovolemia vs. hypovolemia and the other to classify hypovolemia into absolute hypovolemia and relative hypovolemia, and (iii) sequentially integrating the two to enable multi-class classification (normovolemia, absolute hypovolemia, and relative hypovolemia). We developed the blood volume decompensation state classification algorithm using the experimental data collected from six animals undergoing normovolemia, relative hypovolemia, and absolute hypovolemia challenges. Leave-one-subject-out analysis showed that our classification algorithm achieved an F1 score and accuracy of (i) 0.93 and 0.89 in classifying normovolemia vs. hypovolemia, (ii) 0.88 and 0.89 in classifying hypovolemia into absolute hypovolemia and relative hypovolemia, and (iii) 0.77 and 0.81 in classifying the overall blood volume decompensation state. The analysis of the features embedded in the ML classifiers indicated that many features are physiologically plausible, and that multi-modal SCG-BCG fusion may play an important role in achieving good blood volume classification efficacy. Our work may complement existing computational algorithms to estimate blood volume compensatory reserve as a potential decision-support tool to provide guidance on context-sensitive hypovolemia therapeutic strategy.

show abstract

“…Lefevre et al 1 in their basic science study of hemorrhagic shock in rat model demonstrated that intravenous waveform analysis obtained from fast Fourier transform analysis and fundamental frequency amplitude was superior to central venous pressure measurement, and also correlated well with left ventricular end-diastolic area and mean arterial pressure. Hemorrhagic shock experimental models are mainly 4 types, a fixed-pressure, a fixed-volume, a controlled hemorrhage, and an uncontrolled hemorrhage models.…”

mentioning

confidence: 99%

A Change of Tide or the Beginning of the End: COVID-19

Pal

Weitzel

Kertai

2021

Semin Cardiothorac Vasc Anesth

View full text Add to dashboard Cite

Venous Waveform Analysis Correlates With Echocardiography in Detecting Hypovolemia in a Rat Hemorrhage Model

Cited by 6 publications

References 21 publications

Peripheral Intravenous Waveform Analysis Responsiveness to Subclinical Hemorrhage in a Rat Model

Peripheral Intravenous Waveform Analysis Responsiveness to Subclinical Hemorrhage in a Rat Model

Classification of Blood Volume Decompensation State via Machine Learning Analysis of Multi-Modal Wearable-Compatible Physiological Signals

A Change of Tide or the Beginning of the End: COVID-19

Contact Info

Product

Resources

About